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• Published: 17 May 2023

Global burden of hematologic malignancies and evolution patterns over the past 30 years

• Nan Zhang 1   na1 ,
• Jinxian Wu 1   na1 ,
• Qian Wang 1   na1 ,
• Yuxing Liang 1 ,
• Xinqi Li 1 ,
• Guopeng Chen 1 ,
• Linlu Ma 1 ,
• Xiaoyan Liu 1 &
• Fuling Zhou 1 , 2  

Blood Cancer Journal volume  13 , Article number:  82 ( 2023 ) Cite this article

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• Epidemiology
• Haematological cancer
• Risk factors

Hematologic malignancies are among the most common cancers, and understanding their incidence and death is crucial for targeting prevention, clinical practice improvement, and research resources appropriately. Here, we investigated detailed information on hematological malignancies for the period 1990–2019 from the Global Burden of Disease study. The age-standardized incidence rate (ASIR), the age-standardized death rate (ASDR), and the corresponding estimated annual percentage changes (EAPC) were calculated to assess temporal trends in 204 countries and territories over the past 30 years. Globally, incident cases of hematologic malignancies have been increasing since 1990, reaching 1343.85 thousand in 2019, but the ASDR for all types of hematologic malignancies has been declining. The ASDR for leukemia, multiple myeloma, non-Hodgkin lymphoma, and Hodgkin lymphoma were 4.26, 1.42, 3.19, and 0.34 per 100,000 population in 2019, respectively, with Hodgkin lymphoma showing the most significant decline. However, the trend varies by gender, age, region, and the country’s economic situation. The burden of hematologic malignancies is generally higher in men, and this gender gap decreases after peaking at a given age. The regions with the largest increasing trend in the ASIR of leukemia, multiple myeloma, non-Hodgkin lymphoma, and Hodgkin lymphoma were Central Europe, Eastern Europe, East Asia, and Caribbean, respectively. In addition, the proportion of deaths attributed to high body-mass index continued to rise across regions, especially in regions with high socio-demographic indices (SDI). Meanwhile, the burden of leukemia from occupational exposure to benzene and formaldehyde was more widespread in areas with low SDI. Thus, hematologic malignancies remain the leading cause of the global tumor burden, with growing absolute numbers but sharp among several age-standardized measures over the past three decades. The results of the study will inform analysis of trends in the global burden of disease for specific hematologic malignancies and develop appropriate policies for these modifiable risks.

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Introduction.

Health systems are challenged by a rapidly aging population, which is causing an increase in the burden of hematologic malignancies [ 1 , 2 ]. The epidemiological transitions and demographic changes have led to increased prioritization of malignant tumors around the world [ 3 ]. Evidence regarding the disease incidence, disease prevalence, and disease burden associated with hematologic malignancies worldwide is limited. Only several local studies have reported the burden of individual diseases [ 4 , 5 ]. We have previously reported on the clinical and basic mechanisms of hematologic malignancies [ 6 , 7 ]. However, a more comprehensive and accurate understanding of the magnitude and trends of all hematological malignancies is not yet available, and evidence-based epidemiological studies are needed to become essential for healthcare decision-making and planning.

Hematological malignancies are myeloid and lymphatic tumors caused by disruption of normal hematopoietic function [ 8 ]. They are classified into several common subtypes, generally consisting of leukemia, multiple myeloma (MM), non-Hodgkin lymphoma (NHL), and Hodgkin lymphoma (HL) [ 9 ]. As the number of cancer cases increases, the spectrum of hematologic malignancies is also changing. For example, the incidence of leukemia is declining globally, but it is still rising in developed regions (such as France, Spain, Slovenia, and Cyprus). Countries and regions differ in the types of hematologic malignancies on account of differences resulting from different socioeconomic development stages [ 10 ]. Although survival rates for patients with hematological malignancies have improved dramatically over the past few decades, knowing the specific patterns and time trends in morbidity and mortality from hematologic malignancies remains a priority, which can help to develop more targeted prevention strategies.

The Global Burden of Disease (GBD) project, providing the best possible comparable estimates of ill health, injury, and risk factors, is an important achievement of the long-term collaboration between governments around the world [ 11 ]. A key benefit of this resource is that it provides insights into the epidemiological dynamics of hematological malignancies. In this study, we extracted epidemiological data on the incidence and death of hematologic malignancies based on subtype, sex, and age groups from GBD. The disease burden of hematological malignancies was further assessed by determining time trends of hematologic malignancies generated by specific aetiologies at global, regional, and country levels between 1990 and 2019. The results of our study can be an important extension of previous studies and contribute to the development of hematological malignancies prevention strategies in different countries.

Data sources

The GBD study provides comprehensive estimates of the incidence, deaths, and prevalence of diseases for each country and territory. The detailed procedures for collecting, processing, and generating this dataset have been extensively reported in GBD Study 2019 [ 12 , 13 ]. As a whole, all available information is utilized in the GBD study, including survey data, surveillance data, published literature, as well as hospital and clinical information. The Bayesian meta-regression tool DisMod-MR 2.1 was utilized to synthesize available resources of data and generate internally consistent projections of incidence and deaths, providing a 95% uncertainty interval (95% UI). Studies on the epidemiology of hematological malignancies benefited from regional statistical analyses of morbidity and mortality. The complete datasets of hematological malignancies are accessible from the Global Health Data Exchange ( http://ghdx.healthdata.org/ ).

Data collection

We collected information from 204 countries and territories, including incidence, death, sex, age, and age-standardized rate (ASR) of hematological malignancies from 1990 to 2019. Specifically, we obtained information on the morbidity and mortality of four major hematologic malignancies, namely leukemia, MM, NHL, and HL, among which leukemia includes acute myeloid leukemia (AML), chronic myeloid leukemia (CML), acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other leukemia. The socio-demographic indices (SDI) are a comprehensive predictor that describes the socio-demographic level of a country and is strongly correlated with the outcome of human health development [ 14 ]. Following the classification of these countries and territories by their SDI, five distinct regions were defined: high, high-middle, middle, low-middle, and low. Furthermore, 21 geographic regions in 204 countries were defined around the world based on similar geography to facilitate data follow-up at the regional level (Table S1 ) [ 14 , 15 ].

Case definition

The GBD 2019 study adopted the International Classification of Diseases (ICD) definition of hematologic malignancies by the World Health Organization (WHO), which refers to cancers originating from blood-forming cells. Incidence included causes coded as AML (C92.0–C92.02, C92.3–C92.62, C93.0–C93.02, C94.0–C94.02, C94.2–C94.22), ALL (C91.0–C91.02), CML (C92.1–C92.12), CLL (C91.1–C91.12), MM (C88–C90.32), NHL (C82–C85.29, C85.7–C86.6, C96–C96.9), HL (C81–C81.49, C81.7–C81.79, C81.9–C81.99, Z85.71–Z85.72). Death included causes coded as AML (C92.0, C92.3–C92.6, C93.0, C94.0, C94.2, C94.4–C94.5), ALL (C91.0), CML (C92.1), CLL (C91.1), other leukemia (C91.2–C91.9, C92.2, C92.7–C92.9, C93.1–C93.9, C94.1, C94.3, C94.6–C95.9), MM (C88–C90.9), NHL (C82–C86.6, C96–C96.9), HL (C81–C81.9). Unspecified hematologic malignancies were not taken into account as the ICD-10 classification lacks the ability to differentiate between disease subtypes within each hematologic malignancy category.

Risk factor

GBD risk factors are estimated using a comparative risk assessment framework that involves six steps. The first step involves identifying risk-outcome pairs that have convincing or plausible evidence according to World Cancer Research Fund criteria. Only these pairs are included in the estimation of GBD risk factors. The second step is estimating relative risk (RR) as a function of exposure for each risk-outcome pair. The third step is determining the distribution of exposure for each risk factor by age, sex, location, and year. The fourth step is determining the theoretical minimum risk exposure level (TMREL). The fifth step involves estimating the population attributable fraction (PAF) and attributable burden, using the RR for each risk-outcome pair, exposure levels, and TMREL. This information is used to model the PAF and then multiplied by cancer deaths to generate the deaths attributable to that risk factor. The sixth step involves estimating the PAF and attributable burden for the combination of risk factors. The details of each of these steps and the underlying methodology are published elsewhere [ 16 ]. Eighty-seven risk factors were included in this GBD iteration, in which the contribution of the risk attributable to occupational exposure to formaldehyde and benzene to leukemia mortality was nonzero. Data on occupational exposure to carcinogens were collected from the International Labour Organization (ILO), which provided information on the proportion of the population ever exposed to benzene/formaldehyde at work or through their occupation based on population distributions across 17 economic activities.

Where individual-level survey data were available, the mean body-mass index (BMI) was calculated using weight and height [ 17 ]. High BMI for adults (aged over 20 years) was defined as a BMI greater than or equal to 25 kg/m 2 . High BMI for children (aged 1–19 years) was defined as being overweight or obese based on International Obesity Task Force (IOTF) standards. For individuals aged over 20 years, we considered them to be overweight if their BMI was greater than or equal to 25 kg/m 2 , and obese if their BMI was greater than or equal to 30 kg/m 2 . For individuals aged 1 to 19 years, we used monthly IOTF cutoffs [ 2 ] to determine overweight and obese status when age in months was available [ 18 ]. The individual and combined age-standardized summary exposure values (SEVs) for each area-associated risk factor are provided in Table S2 .

Statistical analysis

To quantify trends associated with the incidence and death of hematologic malignancies in various regions, we used the ASR and estimated annual percentage change (EAPC) [ 19 , 20 , 21 ]. The standardization of data is necessary when comparing several populations with varying age structures or when comparing the same population over time, as the age profile changes [ 13 ]. The age-standardized incidence rate (ASIR) and age-standardized death rate (ASDR) per one hundred thousand population were computed by summing the products of age-specific rates (where α k = distribution of the selected reference standard population in the k age groups, and β k = age-specific rate). Based on the age distribution, a weighted average of rates was calculated, the formulae are described below:

The EAPC and their 95% confidence intervals (CIs) are a comprehensive measure of ASR trends over a specific period, with lower bounds above 0 points to an upward trend, whereas upper bounds below 0 points to a downward trend. We use a log-linear regression to calculate (where y = ln[ASR], and x = calendar year), as follows:

In addition, correlations between EAPC and ASR in 1990, EAPC and SDI in 2019, and ASR and SDI were analyzed for all hematologic malignancies and their subtypes. The global incidence and death of leukemia, MM, NHL, and HL were mapped by the country, including the ASR (in 2019), the percentage change in cases, and the EAPC from 1990 to 2019. Pearson correlation analysis was performed to estimate the ρ indices and p-value of the correlation. To clarify the combined effect of the GBD risk factors, we calculated a scalar describing the proportion of prevalence attributable to high BMI, occupational exposure to benzene and formaldehyde. All data visualization and statistical analysis were performed using GraphPad Prism (version 8.02) and R software (version 3.5.2).

Patient and public involvement

GBD Study is an international scientific collaboration. In designing the study, we did not consider involving patients since we used secondary data from the GBD Study 2019, and the research question did not relate directly to the management of patients with hematologic malignancies. Researchers did not involve patients in designing the study, collecting and analyzing data, interpreting results, or drafting the manuscript.

Global burden of hematologic malignancies

Globally, the incident cases, deaths, and their change trends with hematologic malignancies from 1990 and 2019 are presented in Table 1 . In 2019, the incident cases of leukemia, MM, NHL, and HL increased to 643.58 thousand, 155.69 thousand, 457.08 thousand, and 87.51 thousand, respectively, while the number of deaths increased to 334.59 thousand, 113.47 thousand, 254.61 thousand, and 27.55 thousand, respectively (Fig. 1A, B ). Although the number was almost two to three times when since 1990, the ASDR for all hematologic malignancies is on a declining trend, while the ASIR is relatively stable. According to 2019 data, the hematologic malignancies with the highest incidence and mortality rate is leukemia (Fig. 1C, D ). The global incidence trends of four major hematologic malignancies were essentially different during 1990–2019. The ASIR of leukemia and HL were 8.22 and 1.1 per 100,000 population in 2019, respectively, and the EAPC exhibited a decreasing tendency (−0.68 and −0.47). The ASIR of MM and NHL was 1.92 and 5.73 per 100,000 population in 2019, respectively, and the EAPC illustrated an increasing trend (0.25 and 0.56). However, the ASDR of leukemia, MM, NHL, and HL were 4.26, 1.42, 3.19, and 0.34 per 100,000 population in 2019, respectively, with a significant downward trend in EAPC (−1.15, −0.07, −0.09 and −2.08).

A The new cases of hematological malignancies from 1990 to 2019. B The number of deaths due to hematological malignancies from 1990 to 2019. C ASIR and EAPC in hematological malignancies over the last 30 years. D ASDR and EAPC in hematological malignancies over the last 30 years. E The proportion of new cases of hematological malignancies from 1990 to 2019. F The proportion of deaths of hematological malignancies from 1990 to 2019. ASIR age-standardized incidence rate, ASDR age-standardized death rate, EAPC estimated annual percentage change, FISH fluorescence in situ hybridization, mPCR multiplex polymerase chain reaction, ELISA enzyme-linked immunosorbent assay, FCM flow cytometry, IHC immunohistochemical, NGS Next-generation sequencing, MS mass spectrum, ctDNA circulating tumor DNA, WHO World Health Organization, IMWG International Myeloma Working Group, REAL Revised European American Lymphoma, CHOP cyclophosphamide, doxorubicin, vincristine, and prednisone.

Moreover, we analyzed global trends in the proportion of new cases and deaths associated with all hematologic malignancies. Leukemia has always accounted for the largest proportion of new cases, but from 1990 to 2019 the proportion has been declining (47.9% in 2019), and leukemia has been less than half of the hematological malignancies since 2012. Compared to that, the proportion of new cases of MM and lymphoma increased (Fig. 1E ). In terms of deaths, the trend in the proportion is similar to that of new cases (45.8% in 2019), but since 2007 leukemia has accounted for less than half of the deaths of hematological malignancies (Fig. 1F ). For specific leukemia subtypes, the incident cases of AML, ALL, CML, and CLL increased to 124.33 thousand, 153.32 thousand, 65.8 thousand, and 103.47 thousand in 2019, respectively, while the proportion of other subtypes of leukemia decreased significantly due to the precision of classification in the past three decades (Fig. S1A, B ). The number of deaths from AML, ALL, and CLL increased except for CML (Fig. S1C, D ). From 1990 to 2019, CML was the type of leukemia with a decreased AISR (EAPC = −1.04) (Fig. S1E ), while AML was the type of leukemia with an increased ADSR (EAPC = 0.35) (Fig. S1F ).

Global burden of hematologic malignancies by gender and age

The age distribution of the population can provide evidence for primary prevention by reflecting the health burden of different age groups. People over the age of 70 are at high risk of developing hematologic malignancies, about 80% of all age groups (Fig. 2A, B ). Among all age groups, the highest incidence rates of leukemia, MM, NHL, and HL were 95 plus group, 85 to 89 group, 95 plus group, and 90 to 94 group, respectively, with rates of 116.18, 23.55, 103.62, and 10.29 per 100,000 population in 2019 (Fig. 2A ). The highest death rates of leukemia, MM, NHL, and HL were 95 plus group, 90 to 94 group, 95 plus group, and 85 to 89 group, respectively, with rates of 75.81, 24.71, 62.18, and 1.96 per 100,000 population in 2019 (Fig. 2B ). In terms of gender, the incidence and death of hematologic malignancies are generally higher in males than in females globally (Fig. S2 ). The male-to-female ratio for leukemia, MM, and NHL continued to increase from 1990 to 2019 (Fig. 2C, D ). The increasing male-to-female ratio may be caused by hormonal, genetic, and environmental factors and requires further research. We show the contribution of different age groups and different genders to hematologic malignancies in 1990 and 2019, not all age groups are more male than female. In most age groups of 1 to 4 and above 75, the proportion of females is larger than males (Fig. 2E, F ).

A Global hematological malignancies incidence rates by age for both sexes combined in 1990 and 2019. B Global hematological malignancies deaths by age for both sexes combined in 1990 and 2019. For each group, the below column shows case data in 1990 and the above column shows data in 2019. C The sex ratio of hematological malignancies incident cases from 1990 to 2019 by four causes. D The sex ratio of hematological malignancies deaths from 1990 to 2019 by four causes. E The ratio of male to female in global hematological malignancies incident cases by age in 1990 and 2019. F The ratio of male to female in global hematological malignancies deaths by age in 1990 and 2019.

For specific leukemia subtypes, the highest incidence rates of AML, ALL, CML, and CLL in 2019 were 95 plus group, 70 to 74 group, 90 to 94 group, and 95 plus group, respectively, with rates of 56.31, 4.80, 14.69, and 25.76 per 100,000 population (Fig. S3A ). The highest death rates of AML, ALL, CML, and CLL were 90 to 94 group, 85 to 89 group, 95 plus group, and 95 plus group, respectively, with rates of 13.24, 2.03, 5.19, and 24.61 per 100,000 population in 2019 (Fig. S3B ). Regarding gender, all subtypes of leukemia were male with higher morbidity and mortality (Fig. S3C, D ), but there were slight differences between different age groups (Fig. S4 ).

Leukemia burden in different regions and countries

With regard to the SDI regions, incident cases of leukemia increased across the five SDI regions. However, the ASIR decreased in the low, low-middle, middle, and high-middle SDI regions from 1990 to 2019. The highest ASIR was in the high SDI regions and was on the rise, with an EAPC of 0.05 (Fig. S5A and Table S3 ). Of the leukemia subtypes, AML, ALL, CML, and CLL have the highest ASIR in the high SDI regions (Fig. S5B–F ). For geographical regions, the highest ASIR of leukemia in 2019 appeared in Western Europe (16.87 per 100,000 population), North America (10.69 per 100,000 population), and Australasia (10.46 per 100,000 population). The regions with the lowest ASIR were South Asia (3.81 per 100,000 population), Central Sub-Saharan Africa (3.89 per 100,000 population), and Western Sub-Saharan Africa (3.9 per 100,000 population) (Fig. 3A and Table S3 ). The regions with the largest increases in the ASIR were Central Europe (EAPC = 0.79), Western Europe (EAPC = 0.71), and Asia Pacific (EAPC = 0.5). In contrast, the regions with the largest declines were Central Asia (EAPC = −1.53), Eastern Sub-Saharan Africa (EAPC = −1.11), and Central Sub-Saharan Africa (EAPC = −1.09) (Fig. 3C and Table S3 ). Among the 204 countries, Qatar, United Arab Emirates, and Cyprus had the most dramatic increases in leukemia incident cases (percent change: 300% to 500%), while the Republic of Moldova, Georgia, and the Democratic People’s Republic of Korea had the most significant declines (percent change: −60% to −40%) (Fig. S7A and Table S7 ). Extremely high levels of ASIR were observed in San Marino, far higher than in any other country (35.14 per 100,000 population) (Fig. 4A ). On the other hand, the lowest ASIR was found in Palau, with 2.97 per 100,000 population in 2019 (Table S7 ). In addition, the Cyprus (EAPC = 3.01) and Republic of Moldova (EAPC = −2.37) have the most significant increasing and decreasing trends respectively in the ASIR of leukemia (Fig. 4B ).

A The ASIR of leukemia at a regional level in 1990 and 2019. B The ASDR of leukemia at a regional level in 1990 and 2019. C The EAPC in ASIR of leukemia from 1990 to 2019, by subtypes and by regions, for both sexes, combined. D The EAPC in ASDR of leukemia from 1990 to 2019, by subtypes and by regions, for both sexes, combined. Blue indicates a downward trend and Red indicates an upward trend. AML acute myeloid leukemia, ALL acute lymphoid leukemia, CML chronic myeloid leukemia, CLL chronic lymphoid leukemia, ASIR age-standardized incidence rate, ASDR age-standardized death rate, EAPC estimated annual percentage change.

A The ASIR of leukemia in 2019. B The EAPC in ASIR of leukemia from 1990 to 2019. C The ASDR of leukemia in 2019. D The EAPC in ASDR of leukemia from 1990 to 2019. ASIR age-standardized incidence rate, ASDR age-standardized death rate, EAPC estimated annual percentage change.

Notably, the leukemia of ASDR showed a significant decline over three decades in all five SDI regions, most notably in the high-middle SDI regions, with an EAPC of −1.42 (Fig. S6A and Table S3 ). Of the leukemia subtypes, the ASDR of AML and CLL was the highest in the high SDI regions, the ASDR of CML was the highest in the low SDI regions, and the ASDR of ALL changed from the highest in high-middle SDI to middle SDI from 1990 to 2019 (Fig. S6B–F ). For geographical regions, the highest ASDR of leukemia in 2019 was observed in North America (5.65 per 100,000 population), North Africa and the Middle East (5.41 per 100,000 population), and Andean Latin America (5.15 per 100,000 population). The regions with the lowest ASDR were South Asia (2.98 per 100,000 population), Central Sub-Saharan Africa (2.99 per 100,000 population), and Asia Pacific (3.02 per 100,000 population) (Fig. 3B and Table S3 ). Almost all regions showed a downward trend in ASDR, with the largest declines in East Asia (EAPC = −1.99), Eastern Europe (EAPC = −1.55), and the Asia Pacific (EAPC = −1.48) (Fig. 3D and Table S3 ). Among the 204 countries, the United Arab Emirates and Qatar had the most pronounced increases in leukemia deaths (percent change: 350% to 500%), while the Republic of Moldova, Ukraine, and Georgia had the most significant declines (percent change: −50% to −30%) (Fig. S7B and Table S7 ). The two countries with higher ASDR were found to be the Syrian Arab Republic and Afghanistan, with rates of 10 to 20 per 100,000 population in 2019 (Fig. 4C ). Lesotho (EAPC = 2.01) and Ukraine (EAPC = −2.48) have the most significant increasing and decreasing trends respectively in the ASDR of leukemia (Fig. 4D ).

For the correlation analysis of EAPC influencing factors in leukemia, we found that EAPC (in ASIR) was not correlated with ASIR (in 1990) and SDI (in 2019), but EAPC (in ASDR) was significantly negatively connected with ASDR in 1990 ( P  < 0.001, ρ  = −0.457). It is also significantly negatively correlated with SDI (in 2019) ( P  < 0.001, ρ = −0.368). Interestingly, ASIR demonstrated a clear positive correlation with SDI levels ( P  < 0.001, ρ  = 0.781) (Fig. S8 ). Although the data show a trend towards a higher incidence of leukemia and lower mortality in regions with higher economic levels, it is also possible that this is due to improved reporting or earlier diagnosis.

Multiple myeloma burden in different regions and countries

During the period from 1990 to 2019, the number of MM cases increased across the five SDI regions. As for ASIR, ASIR in high SDI regions remained the highest, while that in low-middle SDI and low SDI regions were consistently low (Figs. 5A and S9A ). The ASIR of MM increased across all SDI regions with the largest increase in middle SDI regions (EAPC = 0.83), while the ASIR was a slow increase in high SDI regions (EAPC = 0.33) (Fig. 5B and Table S4 ). For geographical regions, the highest ASIR of MM in 2019 was observed in Australasia (5.33 per 100,000 population), North America (4.8 per 100,000 population), and Western Europe (4.24 per 100,000 population). The regions with the lowest ASIR were Central Asia (0.8 per 100,000 population), Southeast Asia (0.82 per 100,000 population), and Western Sub-Saharan Africa (0.91 per 100,000 population) (Fig. 5A and Table S4 ). The regions with the largest increases in the ASIR were Eastern Europe (EAPC = 1.4), Tropical Latin America (EAPC = 1.3), and Central Latin America (EAPC = 1.17). In contrast, the region with the largest decline was Oceania (EAPC = −0.08) (Fig. 5B and Table S4 ). Among the 204 countries, Qatar and the United Arab Emirates had the most significant increases in MM incident cases (percent change: 800% to 1000%), while Tokelau and Niue had the most significant declines (percent change: −1% to −10%) (Fig. S10A and Table S7 ). Extremely high levels of ASIR were observed in Monaco, far higher than in other countries (14.95 per 100,000 population) (Fig. 6A ). On the other hand, the lowest ASIR was found in Kyrgyzstan, with 0.62 per 100,000 population in 2019 (Table S7 ). In addition, Jamaica (EAPC = 4.15) and Northern Mariana Islands (EAPC = −1.29) have the most significant increasing and decreasing trends respectively in the ASIR of MM (Fig. 6B ).

A The ASIR of multiple myeloma at a regional level in 1990 and 2019. B The EAPC in ASIR of multiple myeloma from 1990 to 2019 by region, for both sexes, combined. C The ASDR of multiple myeloma at a regional level in 1990 and 2019. D The EAPC in ASDR of multiple myeloma from 1990 to 2019 by region, for both sexes, combined. ASIR age-standardized incidence rate, ASDR age-standardized death rate, EAPC estimated annual percentage change.

A The ASIR of multiple myeloma in 2019. B The EAPC in ASIR of multiple myeloma from 1990 to 2019. C The ASDR of multiple myeloma in 2019. D The EAPC in ASDR of multiple myeloma from 1990 to 2019. ASIR age-standardized incidence rate, ASDR age-standardized death rate, EAPC estimated annual percentage change.

Although regions with high SDI also consistently had the highest ASDR from 1990 to 2019, the overall trend was downward, with an EAPC of −0.17 (Figs. 5C, D and S9B ). And the increasing trend in ASDR was primarily concentrated in low SDI (EAPC = 0.2), low-middle SDI (EAPC = 0.44), middle SDI (EAPC = 0.39), and high-middle SDI (EAPC = 0.3) regions over this period (Fig. 5D ). For geographical regions, the highest ASDR of MM in 2019 was observed in North America (3.07 per 100,000 population), Australasia (2.97 per 100,000 population), and Western Europe (2.64 per 100,000 population). The regions with the lowest ASDR were East Asia (0.68 per 100,000 population), Central Asia (0.69 per 100,000 population), and Southeast Asia (0.71 per 100,000 population) (Fig. 5C and Table S4 ). Almost all regions demonstrated an increasing tendency in ASDR, with the largest increases in Tropical Latin America (EAPC = 1.07), Eastern Europe (EAPC = 1.05), and Central Asia (EAPC = 0.99) (Fig. 5D and Table S4 ). Among the 204 countries, the United Arab Emirates and Qatar had the most pronounced increases in MM deaths (percent change: 650% to 900%), while Tokelau and Niue had the most significant declines (percent change: −15% to −9%) (Fig. S10B and Table S7 ). The two countries with higher ASDR were found to be Monaco and Barbados, with rates of 6 to 10 per 100,000 population in 2019 (Fig. 6C ). Jamaica (EAPC = 3.96) and Jordan (EAPC = −1.61) have the most significant increasing and decreasing trends respectively in the ASDR of MM (Fig. 6D ).

For the correlation analysis of EAPC influencing factors in MM, the EAPC (in ASIR) was positively correlated with the SDI (in 2019) ( P  = 0.007, ρ  = 0.19), but not the ASIR (in 1990) ( P  = 0.273, ρ  = −0.077). Although EAPC (in ASDR) was not significantly associated with ASDR (in 1990) and SDI (in 2019), we found that both ASIR and ASDR were higher in regions with higher SDI levels (Fig. S11 ), suggesting that areas with higher SDI levels should better perform preventive healthcare and promotion for MM.

Lymphoma burden in different regions and countries

Lymphoma mainly consists of NHL and HL, with NHL being consistently 3–5 times more common than HL. For SDI regions, ASIR in high SDI and high-middle SDI regions remained the highest in NHL and HL (Fig. S12A, B ). The ASIR in high SDI regions was 9.93 per 100,000 population for NHL and 2.51 per 100,000 population for HL (Fig. 7A ). For geographical regions, the highest and lowest ASIR of NHL in 2019 were observed in Australasia (11.31 per 100,000 population) and Oceania (1.71 per 100,000 population) (Fig. 7A and Table S5 ), the highest and lowest ASIR of HL in 2019 were observed in Western Europe (2.96 per 100,000 population) and Oceania (0.39 per 100,000 population) (Fig. 7A and Table S6 ). Except for North America, Australasia, and Western Europe, the ASIR of NHL showed an increasing trend in all regions, with the largest increases in East Asia (EAPC = 3.57), Andean Latin America (EAPC = 2.41), and Eastern Europe (EAPC = 1.77) (Fig. 7C ). The regions with the largest increasing trend in the ASIR of HL were the Caribbean (EAPC = 2.4), Asia Pacific (EAPC = 1.21), and Australasia (EAPC = 1.11) (Fig. 7C ). Among the 204 countries, Qatar and the United Arab Emirates had the most pronounced increases in NHL incident cases (percent change: 700% to 1000%), while Malawi and Zimbabwe had the most modest increases (percent change: 5% to 10%) (Fig. S13A and Table S7 ). The Qatar and Republic of Korea had the most pronounced increases in HL incident cases (percent change: 700% to 1000%), while Georgia and Hungary had the most significant declines (percent change: −50% to −30%) (Fig. S14A and Table S7 ). The two countries with higher ASIR of all lymphoma types were found to be Monaco and San Marino (Figs. 8A and S14B ). In addition, Georgia (EAPC = 4.7) and Zimbabwe (EAPC = −2.0) have the most significant increasing and decreasing trends respectively in the ASIR of NHL (Fig. 8B ), Cuba (EAPC = 5.86) and Equatorial Guinea (EAPC = −2.42) has the most significant increasing and decreasing trends respectively in the ASIR of HL (Fig. S14C ).

A The ASIR of non-Hodgkin lymphoma and Hodgkin lymphoma at a regional level in 1990 and 2019. B The ASDR of non-Hodgkin lymphoma and Hodgkin lymphoma at a regional level in 1990 and 2019. C The EAPC in ASIR of non-Hodgkin lymphoma and Hodgkin lymphoma from 1990 to 2019 by regions, for both sexes, combined. D The EAPC in ASDR of non-Hodgkin lymphoma and Hodgkin lymphoma from 1990 to 2019 by regions, for both sexes, combined. ASIR age-standardized incidence rate, ASDR age-standardized death rate, EAPC estimated annual percentage change.

A The ASIR of Non-Hodgkin lymphoma in 2019. B The EAPC in ASIR of Non-Hodgkin lymphoma from 1990 to 2019. C The ASDR of Non-Hodgkin lymphoma in 2019. D The EAPC in ASDR of Non-Hodgkin lymphoma from 1990 to 2019. ASIR age-standardized incidence rate, ASDR age-standardized death rate, EAPC estimated annual percentage change.

Regarding the death status of lymphoma by SDI regions, high SDI regions consistently have the highest ASDR of NHL from 1990 to 2019, and low SDI regions consistently have the highest ASDR of HL (Fig. S12C, D ). For geographical regions, the highest and lowest ASDR of NHL in 2019 was observed in North America (5.27 per 100,000 population) and Oceania (1.7 per 100,000 population) (Fig. 7B and Table S5 ), the highest and lowest ASDR of HL in 2019 were appeared in Western Sub-Saharan Africa (0.66 per 100,000 population) and Asia Pacific (0.08 per 100,000 population) (Fig. 7B and Table S6 ). The regions with the largest increasing trend in the ASDR of NHL were South Asia (EAPC = 1.25), East Asia (EAPC = 1.16), and Andean Latin America (EAPC = 1.14) (Fig. 7D ). Except for the Caribbean (EAPC = 0.88), the ASDR of HL demonstrated a decreasing tendency in all regions, with the largest decreases in East Asia (EAPC = −4.68), Central Europe (EAPC = −3.4), and Western Europe (EAPC = −2.42) (Fig. 7D ). Among the 204 countries, Qatar and the United Arab Emirates had the most significant increases in all lymphoma types deaths (Figs. S13B , S14D , and Table S7 ). The two countries with higher ASDR of NHL were found to be Monaco and Afghanistan, with rates of 14 to 16 per 100,000 population in 2019 (Fig. 8C ). The two countries with higher ASDR of HL were found to be Pakistan and Nigeria, with rates of 1.1 to 1.3 per 100,000 population in 2019 (Fig. S14E ). In addition, Georgia (EAPC = 4.54) and Syrian Arab Republic (EAPC = −2.46) has the most significant increasing and decreasing trends respectively in the ASDR of NHL (Fig. 8D ), Cuba (EAPC = 3.96) and China (EAPC = −4.77) has the most significant increasing and decreasing trends respectively in the ASDR of HL (Fig. S14F ). For Qatar and the United Arab Emirates, which had the largest fluctuations in the number of cases, further analysis showed that the huge changes in population structure were an important reason for the fluctuations in the number of cases (Fig. S21 ).

For the correlation analysis of EAPC influencing factors in lymphoma, the EAPC was negatively connected with ASIR ( P  < 0.001, ρ  = −0.342) and ASDR ( P  < 0.001, ρ  = −0.444) in NHL (Fig. S15 ), the EAPC was negatively associated with ASDR ( P  < 0.001, ρ  = −0.417) and SDI (in 2019) ( P  < 0.001, ρ  = −0.431) in HL (Fig. S16 ). In addition, we found that in areas with higher SDI levels, ASIR and ASDR in NHL were higher, ASIR in HL was higher, and only ASDR in HL showed a decrease, suggesting that areas with higher SDI levels should better perform preventive healthcare and promotion for NHL.

Contribution of high BMI burden to death from hematologic malignancies

Leukemia was the leading cause of death for hematologic malignancies at the global and regional scales, followed by NHL, MM, and HL, accordingly for 45.8%, 34.9%, 15.5%, and 3.8% of total deaths in 2019, respectively (Fig. S17A ). High BMI is the main cause of diverse metabolic disorders, the public health issues associated with high BMI have been recognized for a long time and are becoming increasingly important [ 22 , 23 , 24 , 25 ]. Our analysis found that the proportion of mortality risk attributable to high BMI in 2019 was significantly higher in all regions than in 1990. The proportion of deaths attributed to high BMI had a strong positive correlation with SDI levels, and the proportion of leukemia, MM, and lymphoma in low SDI regions was 2.4%, 3.7%, and 2.9%, respectively, in 2019, lower than in other SDI regions (Fig. S17B ). Meanwhile, the region with high SDI consistently had the highest risk of death attributable to high BMI from 1990 to 2019 (Fig. S17C, E ). Regarding the effect of gender and age, the proportion of deaths attributed to high BMI was higher in females with leukemia and MM in all regions (Fig. S19A ). Globally, the highest risk of death from hematologic malignancies (including leukemia, MM, and NHL) attributed to high BMI in 2019 was in the 50 to 69 age group (Fig. S19B ). Table S8 shows the details of the risk of death attributed to high BMI at the national level, Congo males (15.9%), Canadian males (15.2%), and Colombia males (17.6%) account for the highest percentages of leukemia, MM, and NHL respectively, in 2019.

Contribution of occupational carcinogens burden to death from leukemia

Exposure to benzene and formaldehyde in occupational carcinogens is a known risk factor for leukemia [ 26 ]. Globally, the proportion of leukemia deaths attributable to occupational exposure to benzene and formaldehyde in 2019 was 0.56% and 0.18%, respectively. The proportion of deaths attributed to occupational carcinogens was always the highest in middle SDI regions, and the proportion of deaths from occupational benzene exposure changed from the lowest in the low SDI regions to high SDI regions from 1990 to 2019 (Fig. S18A, B ). For all leukemia subtypes, the largest risk of death attributed to occupational exposure to benzene and formaldehyde globally in 2019 was CML. The regions with the highest proportion of leukemia deaths attributable to occupational carcinogens in 2019 were Andean Latin America and Central Latin America (Fig. S18C ). Regarding the effect of gender, the proportion of deaths attributable to occupational exposure to benzene in CML and CLL was predominant in males, while AML and ALL were predominant in females, and occupational exposure to formaldehyde was more common in males with all types of leukemia (Fig. S20 ). Ireland and Dominica are at high risk of occupational exposure to benzene and formaldehyde, the detailed death risk caused by occupational carcinogens for all types of leukemia at the national level is shown in Table S9 .

The findings presented in this report provide a systematic understanding of the global burden of hematologic malignancies by region and country from 1990 to 2019. As predicted by previous studies, we found that hematologic malignancies are one of the major causes of the global tumor burden, with leukemia having the highest burden of all types [ 27 ]. Globally, the incident cases of hematologic malignancies have been increasing since 1990, reaching 1343.85 thousand in 2019, but the mortality rate for all types of hematologic malignancies has been declining, reflecting years of relentless endeavors for prevention, early detection, and treatment of hematologic malignancies.

The predominance of males in hematologic malignancies has been observed throughout the world, with male-to-female ASIR ratios of 1.3:1 for leukemia, 1.4:1 for MM, 1.6:1 for NHL, and 1.5:1 for HL. Notably, the gender differences varied by age. The male-to-female ratios of hematologic malignancies continue to decline from certain ages until females are dominant, with the peak age for leukemia approaching 70 to 74 years, MM at 25 to 29 years, and lymphoma at 55 to 59 years. Consistent with findings in the literature, the gender difference was evident in incident cases and ASIR for leukemia, MM, and lymphoma [ 28 , 29 , 30 ]. During the period from 1990 to 2019, the male-to-female ratio for leukemia, MM, and NHL continued to increase (Fig. 2C, D ). This trend may be attributed to various factors, including differences in hormonal and genetic factors between males and females, as well as differences in environmental exposures and lifestyle factors that may increase the risk of developing these malignancies in males compared to females [ 31 ]. However, the burden and trend of hematologic malignancies varied among regions. For overall hematologic malignancies, the trends in ASDR remained or decreased stable in high and high-middle SDI regions, although other SDI regions had an increasing trend. This is mainly due to imbalances in socio-economic development, while inadequate investments in healthcare and poor health awareness exacerbate the burden in low SDI regions, and can be improved through global collaborative partnerships [ 32 , 33 ]. However, ASIR for all types of hematologic malignancies in the high SDI regions has remained the highest over the past three decades.

During the past study period, advances in laboratory techniques have had a significant impact on the diagnosis and management of hematologic malignancies [ 34 , 35 ]. With the development of new technologies, such as next-generation sequencing, flow cytometry, and molecular genetics, it is now possible to identify specific genetic mutations and biomarkers that can be used to classify and prognosticate hematologic malignancies [ 36 ]. This 30-year time series shows significant hematologic malignancies and specific temporal features, and below we consider possible contributing factors to changes in morbidity and mortality. Changes in diagnostic criteria or definitions have a certain impact on the increase or decrease of incidence (Fig. 1C ). For example, before 1999, the diagnosis of CML was based on clinical features and the presence of the Philadelphia chromosome. The discovery of the BCR-ABL1 fusion gene provided a more specific diagnostic marker for CML [ 37 ]. More cases previously classified as leukemia solely based on morphological diagnosis were excluded without immunophenotypic typing and genetic testing. The new criteria require confirmation of the diagnosis with specific biomarkers. For example, cytoplasmic CD3 or TdT for T-cell ALL, and cytoplasmic CD10 or CD19 for B-cell ALL [ 38 ]. This makes the diagnosis of ALL more accurate and specific, reducing the inclusion of cases with overlapping features, such as Burkitt’s lymphoma previously classified as ALL. In 2008, the WHO introduced the concept of recurrent genetic abnormalities as the main diagnostic criteria, leading to the emergence of new subtypes and the reclassification of some previously defined subtypes [ 39 ]. This change resulted in an increase in the diagnosis of certain subtypes (such as AML with NPM1 and FLT3-ITD mutations), while other subtypes (such as AML with bone marrow proliferative abnormalities and treatment-related myeloid neoplasms) gained new recognition [ 40 ]. Similarly, in 2016, WHO further improved the diagnostic criteria for other subtypes, which have more or less impact on the incidence of leukemia [ 41 ]. For MM, the International Myeloma Working Group (IMWG) updated the diagnostic criteria for MM in 2003 to include the use of new laboratory tests such as serum-free light chain assay, and imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) to better detect bone lesions [ 42 ]. In 2014, IMWG updated the diagnostic criteria for MM again. Before this, patients had to exhibit end-organ damage to be diagnosed [ 43 ]. They also introduced a new category called “smoldering MM” for patients with malignant tumor biomarkers but no end-organ damage [ 44 ]. The updated criteria allow for earlier detection and identification of patients who may benefit from early treatment, but may also overestimate the incidence and mortality rates of this disease (with over 140,000 cases of MM diagnosed annually since 2015) [ 45 , 46 ]. Similarly, for lymphoma, the inclusion of immunophenotypic and molecular criteria in the Revised European American Lymphoma (REAL) and WHO classifications may lead to the identification of previously unrecognized lymphoma subtypes, resulting in an overestimation of incidence rates [ 47 , 48 ].

According to the disease-specific survival rates reported in the Nordic countries [ 49 ], the 5-year survival rate for MM patients increased from 30% in 1990 to 60% in 2019, while the 5-year survival rate for AML increased from 10% in 1990 to 35% in 2019, which is due to significant advances in the treatment of hematological malignancies, including the development of new targeted therapies, immunotherapies, and more effective chemotherapy regimens (Fig. 1D ). However, in our study, except for leukemia, the mortality rates of other hematologic tumors appear to be stable, which may be due to changes in the world’s population structure over the past 30 years. Unlike survival rates, the population mortality rate is an important indicator for measuring population health status and can help determine trends and patterns in mortality rates.

The age-standardized is a useful measure for comparing disease burden across populations of different sizes, as it adjusts for differences in age distribution [ 50 ]. However, in countries with very small populations, the reliability of age-standardized morbidity rates may be limited due to small sample sizes and the potential for random variation. As we found consistently high rates in small countries such as Monaco and San Marino, we again used crude incidence rates to reduce the size effect. We found that the exclusion of population age stratification differences did not significantly affect the overall results (Fig. S22 ). Nonetheless, available data on the incidence of hematological malignancies in small countries are still limited, and GBD provides the reference available. Therefore, caution should be exercised when comparing the incidence of hematologic malignancies in different populations (the number of cases can be referenced). Additionally, the incidence of a small country can be estimated by extrapolating data from neighboring countries with similar demographics and population characteristics, such as Monaco and San Marino, which belong to the geographical region of Western Europe, a region with a higher incidence of hematologic malignancies. Potential prevention strategies for these countries may include raising awareness and education of risk factors associated with hematologic malignancies, such as exposure to certain chemicals or radiation, as well as lifestyle factors such as smoking and drinking. Our study provides a starting point for further research and discussion, and we hope that our findings will contribute to the development of effective prevention and management strategies for hematologic malignancies.

The unique feature of this study is that we provided a comprehensive overview of the burden of hematologic malignancies according to the most recent national statistics worldwide, but there are some limitations. Similar to issues with many of the diseases from the GBD study, the accuracy of hematological malignancy models depends largely on the quality and quantity of input data. These common deficiencies have been explained in detail in previously published GBD studies [ 12 , 51 , 52 , 53 ]. In brief, data collected from different regions and countries may vary considerably in terms of quality, comparability, accuracy, and the degree of data missing, which will inevitably lead to some deviation in the estimates, even if the data is adjusted as much as possible using multiple statistical methods. Better reporting quality and earlier diagnosis in developed countries may result in an overestimation of certain disease data. Additionally, underreporting and failure to diagnose can be sources of bias in the registration of hematologic malignancies, particularly in less developed countries with limited clinical hematology, so some estimates of hematologic malignancies may be understated. Despite these limitations, this study is the first comprehensive assessment of global trends in hematological malignancies over three decades at global, regional, and national levels. The results provide a foundation for future research in this area and help identify resources and efforts to improve the quality and comparability of data on hematological malignancies worldwide.

In summary, hematologic malignancies remain a major public health concern globally, and while ASDR is declining globally, they are still on the rise in many countries. The burden of hematologic malignancies is generally higher in men, and this gender gap decreases after peaking at a given age. In addition, the proportion of deaths attributed to high BMI continued to rise across regions over the past three decades, especially in regions with high SDI. In relatively low SDI regions, occupational exposure to benzene and formaldehyde remained a risk factor for leukemia. The demographics of different regions and countries, social and economic factors, and lifestyles all contribute to these differences. This study provides information for analyzing trends in the global burden of disease for specific hematologic malignancies and for developing appropriate policies for these modifiable risk factors.

Data availability

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Number 81770179); the Zhongnan Hospital of Wuhan University Science, Discipline and Platform Construction Fund [Grant Number PDJH202217]; and the Zhongnan Hospital of Wuhan University Science, Technology and Innovation Cultivation Fund [Grant Number ZNLH201902].

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Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China

Nan Zhang, Jinxian Wu, Qian Wang, Yuxing Liang, Xinqi Li, Guopeng Chen, Linlu Ma, Xiaoyan Liu & Fuling Zhou

School of Nursing, Wuhan University, Wuhan, Hubei, China

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NZ and FZ contributed to the study conception and design. JW and QW collected and verified the accuracy of the data. NZ, JW, and QW performed the analyses. The first draft of the manuscript was written by NZ. XL, YL, GC, and LM contributed to data interpretation. FZ and XL contributed to the evaluation of the methods and revision of the manuscript. All authors have read and approved the final manuscript for submission.

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Zhang, N., Wu, J., Wang, Q. et al. Global burden of hematologic malignancies and evolution patterns over the past 30 years. Blood Cancer J. 13 , 82 (2023). https://doi.org/10.1038/s41408-023-00853-3

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Received : 22 January 2023

Revised : 26 April 2023

Accepted : 03 May 2023

Published : 17 May 2023

DOI : https://doi.org/10.1038/s41408-023-00853-3

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Home > Eppley Institute > Theses & Dissertations

Theses & Dissertations: Cancer Research

Theses/dissertations from 2024 2024.

Novel Spirocyclic Dimer (SpiD3) Displays Potent Preclinical Effects in Hematological Malignancies , Alexandria Eiken

Chemotherapy-Induced Modulation of Tumor Antigen Presentation , Alaina C. Larson

Understanding the role of MASTL in colon homeostasis and colitis-associated cancer development , Kristina Pravoverov

Dying Right: Supporting Anti-Cancer Therapy Through Immunogenic Cell Death , Elizabeth Schmitz

Therapeutic Effects of BET Protein Inhibition in B-cell Malignancies and Beyond , Audrey L. Smith

Targeting KSR1 to inhibit stemness and therapy resistance , Heidi M. Vieira

Identifying the Molecular Determinants of Lung Metastatic Adaptation in Prostate Cancer , Grace M. Waldron

Identification of Mitotic Phosphatases and Cyclin K as Novel Molecular Targets in Pancreatic Cancer , Yi Xiao

Theses/Dissertations from 2023 2023

Development of Combination Therapy Strategies to Treat Cancer Using Dihydroorotate Dehydrogenase Inhibitors , Nicholas Mullen

Overcoming Resistance Mechanisms to CDK4/6 Inhibitor Treatment Using CDK6-Selective PROTAC , Sarah Truong

Theses/Dissertations from 2022 2022

Omics Analysis in Cancer and Development , Emalie J. Clement

Investigating the Role of Splenic Macrophages in Pancreatic Cancer , Daisy V. Gonzalez

Polymeric Chloroquine in Metastatic Pancreatic Cancer Therapy , Rubayat Islam Khan

Evaluating Targets and Therapeutics for the Treatment of Pancreatic Cancer , Shelby M. Knoche

Characterization of 1,1-Diarylethylene FOXM1 Inhibitors Against High-Grade Serous Ovarian Carcinoma Cells , Cassie Liu

Novel Mechanisms of Protein Kinase C α Regulation and Function , Xinyue Li

SOX2 Dosage Governs Tumor Cell Identity and Proliferation , Ethan P. Metz

Post-Transcriptional Control of the Epithelial-to-Mesenchymal Transition (EMT) in Ras-Driven Colorectal Cancers , Chaitra Rao

Use of Machine Learning Algorithms and Highly Multiplexed Immunohistochemistry to Perform In-Depth Characterization of Primary Pancreatic Tumors and Metastatic Sites , Krysten Vance

Characterization of Metastatic Cutaneous Squamous Cell Carcinoma in the Immunosuppressed Patient , Megan E. Wackel

Visceral adipose tissue remodeling in pancreatic ductal adenocarcinoma cachexia: the role of activin A signaling , Pauline Xu

Phos-Tag-Based Screens Identify Novel Therapeutic Targets in Ovarian Cancer and Pancreatic Cancer , Renya Zeng

Theses/Dissertations from 2021 2021

Functional Characterization of Cancer-Associated DNA Polymerase ε Variants , Stephanie R. Barbari

Pancreatic Cancer: Novel Therapy, Research Tools, and Educational Outreach , Ayrianne J. Crawford

Apixaban to Prevent Thrombosis in Adult Patients Treated With Asparaginase , Krishna Gundabolu

Molecular Investigation into the Biologic and Prognostic Elements of Peripheral T-cell Lymphoma with Regulators of Tumor Microenvironment Signaling Explored in Model Systems , Tyler Herek

Utilizing Proteolysis-Targeting Chimeras to Target the Transcriptional Cyclin-Dependent Kinases 9 and 12 , Hannah King

Insights into Cutaneous Squamous Cell Carcinoma Pathogenesis and Metastasis Using a Bedside-to-Bench Approach , Marissa Lobl

Development of a MUC16-Targeted Near-Infrared Antibody Probe for Fluorescence-Guided Surgery of Pancreatic Cancer , Madeline T. Olson

FGFR4 glycosylation and processing in cholangiocarcinoma promote cancer signaling , Andrew J. Phillips

Theses/Dissertations from 2020 2020

Cooperativity of CCNE1 and FOXM1 in High-Grade Serous Ovarian Cancer , Lucy Elge

Characterizing the critical role of metabolic and redox homeostasis in colorectal cancer , Danielle Frodyma

Genomic and Transcriptomic Alterations in Metabolic Regulators and Implications for Anti-tumoral Immune Response , Ryan J. King

Dimers of Isatin Derived Spirocyclic NF-κB Inhibitor Exhibit Potent Anticancer Activity by Inducing UPR Mediated Apoptosis , Smit Kour

From Development to Therapy: A Panoramic Approach to Further Our Understanding of Cancer , Brittany Poelaert

The Cellular Origin and Molecular Drivers of Claudin-Low Mammary Cancer , Patrick D. Raedler

Mitochondrial Metabolism as a Therapeutic Target for Pancreatic Cancer , Simon Shin

Development of Fluorescent Hyaluronic Acid Nanoparticles for Intraoperative Tumor Detection , Nicholas E. Wojtynek

Theses/Dissertations from 2019 2019

The role of E3 ubiquitin ligase FBXO9 in normal and malignant hematopoiesis , R. Willow Hynes-Smith

BRCA1 & CTDP1 BRCT Domainomics in the DNA Damage Response , Kimiko L. Krieger

Targeted Inhibition of Histone Deacetyltransferases for Pancreatic Cancer Therapy , Richard Laschanzky

Human Leukocyte Antigen (HLA) Class I Molecule Components and Amyloid Precursor-Like Protein 2 (APLP2): Roles in Pancreatic Cancer Cell Migration , Bailee Sliker

Theses/Dissertations from 2018 2018

FOXM1 Expression and Contribution to Genomic Instability and Chemoresistance in High-Grade Serous Ovarian Cancer , Carter J. Barger

Overcoming TCF4-Driven BCR Signaling in Diffuse Large B-Cell Lymphoma , Keenan Hartert

Functional Role of Protein Kinase C Alpha in Endometrial Carcinogenesis , Alice Hsu

Functional Signature Ontology-Based Identification and Validation of Novel Therapeutic Targets and Natural Products for the Treatment of Cancer , Beth Neilsen

Elucidating the Roles of Lunatic Fringe in Pancreatic Ductal Adenocarcinoma , Prathamesh Patil

Theses/Dissertations from 2017 2017

Metabolic Reprogramming of Pancreatic Ductal Adenocarcinoma Cells in Response to Chronic Low pH Stress , Jaime Abrego

Understanding the Relationship between TGF-Beta and IGF-1R Signaling in Colorectal Cancer , Katie L. Bailey

The Role of EHD2 in Triple-Negative Breast Cancer Tumorigenesis and Progression , Timothy A. Bielecki

Perturbing anti-apoptotic proteins to develop novel cancer therapies , Jacob Contreras

Role of Ezrin in Colorectal Cancer Cell Survival Regulation , Premila Leiphrakpam

Evaluation of Aminopyrazole Analogs as Cyclin-Dependent Kinase Inhibitors for Colorectal Cancer Therapy , Caroline Robb

Identifying the Role of Janus Kinase 1 in Mammary Gland Development and Breast Cancer , Barbara Swenson

DNMT3A Haploinsufficiency Provokes Hematologic Malignancy of B-Lymphoid, T-Lymphoid, and Myeloid Lineage in Mice , Garland Michael Upchurch

Theses/Dissertations from 2016 2016

EHD1 As a Positive Regulator of Macrophage Colony-Stimulating Factor-1 Receptor , Luke R. Cypher

Inflammation- and Cancer-Associated Neurolymphatic Remodeling and Cachexia in Pancreatic Ductal Adenocarcinoma , Darci M. Fink

Role of CBL-family Ubiquitin Ligases as Critical Negative Regulators of T Cell Activation and Functions , Benjamin Goetz

Exploration into the Functional Impact of MUC1 on the Formation and Regulation of Transcriptional Complexes Containing AP-1 and p53 , Ryan L. Hanson

DNA Polymerase Zeta-Dependent Mutagenesis: Molecular Specificity, Extent of Error-Prone Synthesis, and the Role of dNTP Pools , Olga V. Kochenova

Defining the Role of Phosphorylation and Dephosphorylation in the Regulation of Gap Junction Proteins , Hanjun Li

Molecular Mechanisms Regulating MYC and PGC1β Expression in Colon Cancer , Jamie L. McCall

Pancreatic Cancer Invasion of the Lymphatic Vasculature and Contributions of the Tumor Microenvironment: Roles for E-selectin and CXCR4 , Maria M. Steele

Altered Levels of SOX2, and Its Associated Protein Musashi2, Disrupt Critical Cell Functions in Cancer and Embryonic Stem Cells , Erin L. Wuebben

Theses/Dissertations from 2015 2015

Characterization and target identification of non-toxic IKKβ inhibitors for anticancer therapy , Elizabeth Blowers

Effectors of Ras and KSR1 dependent colon tumorigenesis , Binita Das

Characterization of cancer-associated DNA polymerase delta variants , Tony M. Mertz

A Role for EHD Family Endocytic Regulators in Endothelial Biology , Alexandra E. J. Moffitt

Biochemical pathways regulating mammary epithelial cell homeostasis and differentiation , Chandrani Mukhopadhyay

EPACs: epigenetic regulators that affect cell survival in cancer. , Catherine Murari

Role of the C-terminus of the Catalytic Subunit of Translesion Synthesis Polymerase ζ (Zeta) in UV-induced Mutagensis , Hollie M. Siebler

LGR5 Activates TGFbeta Signaling and Suppresses Metastasis in Colon Cancer , Xiaolin Zhou

LGR5 Activates TGFβ Signaling and Suppresses Metastasis in Colon Cancer , Xiaolin Zhou

Theses/Dissertations from 2014 2014

Genetic dissection of the role of CBL-family ubiquitin ligases and their associated adapters in epidermal growth factor receptor endocytosis , Gulzar Ahmad

Strategies for the identification of chemical probes to study signaling pathways , Jamie Leigh Arnst

Defining the mechanism of signaling through the C-terminus of MUC1 , Roger B. Brown

Targeting telomerase in human pancreatic cancer cells , Katrina Burchett

The identification of KSR1-like molecules in ras-addicted colorectal cancer cells , Drew Gehring

Mechanisms of regulation of AID APOBEC deaminases activity and protection of the genome from promiscuous deamination , Artem Georgievich Lada

Characterization of the DNA-biding properties of human telomeric proteins , Amanda Lakamp-Hawley

Studies on MUC1, p120-catenin, Kaiso: coordinate role of mucins, cell adhesion molecules and cell cycle players in pancreatic cancer , Xiang Liu

Epac interaction with the TGFbeta PKA pathway to regulate cell survival in colon cancer , Meghan Lynn Mendick

Theses/Dissertations from 2013 2013

Deconvolution of the phosphorylation patterns of replication protein A by the DNA damage response to breaks , Kerry D. Brader

Modeling malignant breast cancer occurrence and survival in black and white women , Michael Gleason

The role of dna methyltransferases in myc-induced lymphomagenesis , Ryan A. Hlady

Design and development of inhibitors of CBL (TKB)-protein interactions , Eric A. Kumar

Pancreatic cancer-associated miRNAs : expression, regulation and function , Ashley M. Mohr

Mechanistic studies of mitochondrial outer membrane permeabilization (MOMP) , Xiaming Pang

Novel roles for JAK2/STAT5 signaling in mammary gland development, cancer, and immune dysregulation , Jeffrey Wayne Schmidt

Optimization of therapeutics against lethal pancreatic cancer , Joshua J. Souchek

Theses/Dissertations from 2012 2012

Immune-based novel diagnostic mechanisms for pancreatic cancer , Michael J. Baine

Sox2 associated proteins are essential for cell fate , Jesse Lee Cox

KSR2 regulates cellular proliferation, transformation, and metabolism , Mario R. Fernandez

Discovery of a novel signaling cross-talk between TPX2 and the aurora kinases during mitosis , Jyoti Iyer

Regulation of metabolism by KSR proteins , Paula Jean Klutho

The role of ERK 1/2 signaling in the dna damage-induced G2 , Ryan Kolb

Regulation of the Bcl-2 family network during apoptosis induced by different stimuli , Hernando Lopez

Studies on the role of cullin3 in mitosis , Saili Moghe

Characteristics of amyloid precursor-like protein 2 (APLP2) in pancreatic cancer and Ewing's sarcoma , Haley Louise Capek Peters

Structural and biophysical analysis of a human inosine triphosphate pyrophosphatase polymorphism , Peter David Simone

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Remove a method, edit datasets, a comprehensive study on blood cancer detection and classification using convolutional neural network.

10 Sep 2024  ·  Md Taimur Ahad , Sajib Bin Mamun , Sumaya Mustofa , Bo Song , Yan Li · Edit social preview

Over the years in object detection several efficient Convolutional Neural Networks (CNN) networks, such as DenseNet201, InceptionV3, ResNet152v2, SEresNet152, VGG19, Xception gained significant attention due to their performance. Moreover, CNN paradigms have expanded to transfer learning and ensemble models from original CNN architectures. Research studies suggest that transfer learning and ensemble models are capable of increasing the accuracy of deep learning (DL) models. However, very few studies have conducted comprehensive experiments utilizing these techniques in detecting and localizing blood malignancies. Realizing the gap, this study conducted three experiments; in the first experiment -- six original CNNs were used, in the second experiment -- transfer learning and, in the third experiment a novel ensemble model DIX (DenseNet201, InceptionV3, and Xception) was developed to detect and classify blood cancer. The statistical result suggests that DIX outperformed the original and transfer learning performance, providing an accuracy of 99.12%. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original CNNs. Like many other cancers, blood cancer diseases require timely identification for effective treatment plans and increased survival possibilities. The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection. This research is significant in the fields of biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

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Non-coding RNA in cancer

Affiliations.

• 1 Key Laboratory of RNA Biology, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
• 2 College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
• 3 Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China.
• PMID: 33860799
• PMCID: PMC8564738
• DOI: 10.1042/EBC20200032

Majority of the human genome is transcribed to RNAs that do not encode proteins. These non-coding RNAs (ncRNAs) play crucial roles in regulating the initiation and progression of various cancers. Given the importance of the ncRNAs, the roles of ncRNAs in cancers have been reviewed elsewhere. Thus, in this review, we mainly focus on the recent studies of the function, regulatory mechanism and therapeutic potential of the ncRNAs including microRNA (miRNA), long ncRNA (lncRNA), circular RNA (circRNA) and PIWI interacting RNA (piRNA), in different type of cancers.

Keywords: cancer; circular RNA; long non-coding RNA; microRNA; non-coding RNA; piwi RNA.

© 2021 The Author(s).

PubMed Disclaimer

Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figure 1. The biogenesis and effector machineries…

Figure 1. The biogenesis and effector machineries of miRNAs

miRNAs are transcribed as pri-miRNAs by…

Figure 2. The biogenesis and effector machineries…

Figure 2. The biogenesis and effector machineries of lncRNAs

LncRNAs are transcribed by RNA polymerase…

Figure 3. The biogenesis and effector machineries…

Figure 3. The biogenesis and effector machineries of circRNAs

circRNAs are transcribed by RNA polymerase…

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• v.5(4); 2015 Apr

Haematological cancer and quality of life: a systematic literature review

P allart-vorelli.

1 Laboratory of Psychology ‘Health and Quality of Life' EA 4139, University Bordeaux Segalen, Bordeaux, France

2 Department of Psychology, Epsylon EA 4556 Laboratory ‘Dynamics of Human Abilities and Health Behaviors', University Paul Valéry Montpellier 3, Montpellier, France

3 ICM, Institut Régional du Cancer de Montpellier, Epidaure Prevention Unit - Rue des Apothicaires, Montpellier Cedex 5, France

4 MIS, Montpellier Institut du Sein – 25 rue de Clémentville, Montpellier, France

F Cousson-Gélie

The aim of this study is to examine the impact of haematological cancers on quality of life (QoL). A review of the international literature was conducted from the databases ‘PsycInfo' and 'Medline' using the keywords: 'haematological cancer', 'quality of life', 'physical', 'psychological', 'social', 'vocational', 'professional', 'economic', 'cognitive', and 'sexual'. Twenty-one reliable studies were analysed. Among these studies, 12 showed that haematological cancer altered overall QoL, 8 papers found a deterioration of physical dimension, 8 papers reported on functional and role dimensions, 11 papers reported on the psychological component and 9 on the social component. Moreover, one study and two manuscripts, respectively, reported deteriorated sexual and cognitive dimensions. Our review demonstrates that the different dimensions of QoL are deteriorated by haematological malignancies and, probably, by the side effects of treatment.

Introduction

Haematological cancers include various diseases (Hodgkin's lymphoma, non-Hodgkin's lymphoma, leukaemia and multiple myeloma. The term leukaemia comprises acute myeloid leukaemia, chronic myelogenous leukaemia, acute lymphoblastic leukaemia and chronic lymphoblastic leukaemia); they can affect children, young adults and the elderly, and their incidence increases with age. 1 As liquid tumours moving in the blood or lymph, acute or chronic diseases, with side effects induced by different treatments, are unique. Just as these diseases are distinct entities showing many differences from solid tumours, so too is the manner in which they are managed. 2 In 1999, leukaemias and lymphomas accounted for approximately 8% of all cancers in adults. 3 The 5-year survival rates vary from 47% to 95% depending on the malignancy. 4

Quality of life (QoL) is usually impaired in the elderly: the biological nature and course of treatment of haematological cancer differ among children and adults, 5 , 6 , 7 with long-term survival outcomes favouring young people diagnosed and treated as children. 3

Our paper also focuses on health-related QoL (QoL), a factor reflecting the individual's assessment of his/her life at any one time relative to his/her previous state and prior experience. 8 Health-related QoL is multidimensional and temporal, relating to a state of functional, physical, psychological and social/family well-being. 9 Compared with the general population, the health-related QoL of cancer patients is worse in most dimensions. 10 , 11

This review describes the QoL and the different problems that patients with haematological malignancies encounter.

Materials and methods

Search strategy.

A review was conducted from databases ‘PsycInfo' and ‘Medline', searching for studies published between 1990 and 2011 with keywords: ‘haematological cancer', ‘quality of life', ‘physical', ‘psychological', ‘social', ‘vocational', ‘professional', ‘economic', ‘cognitive', and ‘sexual' appearing in the abstracts.

We used nine combinations for all databases: (1) ‘QoL and haematological cancer', (2) ‘haematological cancer and physical', (3) ‘haematological cancer and psychological', (4) ‘haematological cancer and social', (5) ‘haematological cancer and cognitive', (6) ‘haematological cancer and economic', (7) ‘haematological cancer and professional', (8) ‘haematological cancer and vocational', and (9) ‘haematological cancer and sexual'.

Criteria for inclusion/exclusion

Prospective, comparative, exploratory, longitudinal or cross-sectional studies, assessing the QoL or health-related QoL, were analysed. Papers focusing on lymphoma, leukaemia or myeloma patients with chemotherapy, radiotherapy or blood transfusion in periods of remission or relapse were included. However, retrospective studies with other forms of cancer and reviews of the literature were excluded.

Quality assessment and levels of evidence

The studies had to be based on reliable methodological procedure (large population study, standardized tools and relevant statistical methods) and meet the criteria of a table that describes five levels of evidence (Level I: high-quality prospective study (all patients were enrolled at the same point in their disease with 80% follow-up of patients); Level II: retrospective study, untreated controls from a randomized control trial, lesser prospective study (patients enrolled at different points in their disease or <80% follow-up); Level III: case control study; Level IV: case series; Level V: expert opinion) in prognostic studies (investigating the effect of a patient's characteristic on the outcome of the disease). 12 We considered only level I and II studies.

Data synthesis

Studies were analyzed by dimensions of QoL and symptoms (description of QoL in Table 1 ).

Abbreviations: AA, acute amyloid; ADL, Activities of Daily Living Scale (Katz et al. , 1970); AET, aerobic exercise training; AL, acute leukaemia; ALL, acute lymphoblastic leukaemia; AML, acute myeloid leukaemia; BFI, Brief Fatigue Inventory (Mendoza et al. , 1999); BMFQ, Brief Mental Fatigue Questionnaire (Bentall et al. , 1993); BMT, bone marrow transplantation; BPI, Brief Pain Inventory (Cleeland, 1989); CG, control group; CLL, chronic lymphocytic leukaemia; CML, chronic myelogenous leukaemia; ECR, Eindhoven Cancer Registry; FACT-An, Functional Assessment of Cancer Therapy—Anaemia (Cella, 1997); FACT-BMT, Functional Assessment of Cancer Therapy—Bone Marrow Transplant (McQuellon et al. , 1197); FACT-G, Functional Assessment of Cancer Therapy-General (Cella et al. , 2003); FACIT, Functional Assessment of Chronic Illness Therapy (Cella, 1997); GF, general fatigue; GH, general health; HADS, Hospital Anxiety and Depression Scale (Snaith et Zigmond, 1983); HRQoL, health-related quality of life; HL, Hodgkin's lymphoma; HML, highly malignant lymphoma; IFN, interferon; IOC, Impact Of Cancer (Zebrack et al. , 2008); LSQ, Life Situation Questionnaire (Joly et al. , 1996); LTS, long-term survivors; MA, mean age; MCS, Mental Component Summary; MDS, myelodysplastic syndrome; MF, mental fatigue; MFI-20, Multidimensional Fatigue Inventory (Smets et al. , 2000); MM, multiple myeloma; MGUS, monoclonal gammopathy of unknown significance; MIRT, Myeloma Institute for Research and Therapy; MOS SS, Medical Outcome Study Social Support (Sherbourne & Stewart, 1991); MP, Melphalan and Prednisone; NART, National Adult Reading Test (Nelson, 1991); NHL, Non-Hodgkin's Lymphoma; PASAT, Paced Auditory Serial Addition Task (Gronwall & Sampson, 1974); PF, Physical Fatigue; PBSCT, peripheral blood stem cell transplantation; PCS, Physical Component Summary; PG-SGA, Patient-Generated Subjective Global Assessment (Ottery, 1996); POMS, Profile of Mood States (McNair et al. , 1971); POMS-F, Profile of Mood States—Fatigue Scale (McNair et al. , 1992); PTSD Checklist, PostTraumatic Stress Disorder Checklist (Weathers et al. , 1993); QLQC30, Quality of Life Questionnaire C30 (Aaronson et al. , 1983); QLQ-S, Quality of Life Questionnaire—Survivors (Sprangers et al. , 1993); QoL, quality of life; QoL-CS, quality of life—cancer survivors; RA, reduced activity; RBMT, Rivermead Behavioural Memory Test (Wislon et al. , 1991); RM, reduced motivation; SEIQoL-DW, Schedule for the Evaluation of the Individual Quality of Life-Direct Weighting (Browne et al. , 1997); SF-12, Short Form 12 items (Ware, 1995); SF-36, Short-Form 36 items; SSQ6, Brief Measure of Social Support (Sarason et al. , 1987); STAI, Spielberger State Anxiety Inventory (Spielberger, 1993); STS, short-term survivors; UC, usual care. * P <0.05; ** P <0.01.

Article identification

In total, 82 studies emerged: 73 studies for ‘PsycInfo' and 9 studies for ‘Medline'. There were 21 studies for combination 1, 14 for combination 2, 14 for combination 3, 9 for combination 4, 11 for combination 5, 3 for the combination 6, 10 for the combination 7 and 0 for the combinations 8 and 9. By limiting inclusion to studies that provided evidence of the impact of cancer on QoL, we selected 21 studies.

Methodological characteristics

One paper presented the level of evidence I 13 and 20 level II. 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 Twelve were comparative, 14 , 15 , 16 , 17 , 18 , 20 , 23 , 24 , 26 , 28 , 30 , 32 , 34 5 were longitudinal, 14 , 19 , 20 , 22 , 33 5 were cross-sectional, 21 , 23 , 24 , 26 , 27 2 were descriptive, 16 , 17 2 were pilot, 25 , 29 1 was prospective, 23 1 was retrospective, 31 1 was international 28 and 1 was a web-based survey. 28

Patient population

In total, 7349 patients (3987 patients with lymphoma, 2303 with leukaemia, 711 with myeloma, 6 patients with amyloidosis and 1 with myelofibrosis; 341 no specified patients) were included in the studies (average age of 54.8 years).

One study focussed on the cognitive functioning of lymphoma patients by comparing two groups (test group and no-test group (diagnosis unknown). 15 Another paper examined the QoL, without specifying the number of patients per diagnosis. 33 The health-related QoL was studied in acute lymphoblastic leukaemia, myelofibrosis or unclassified leukaemia patients, but the authors did not specify the sample size of patients per diagnosis. 21 There were 1171 control groups with haematological patients and healthy subjects in 3 studies. 20 , 26 , 32

QoL and health-related QoL of haematological cancer patients

Overall qol.

Twelve papers showed that haematological cancer negatively affect overall QoL and health-related QoL. 13 , 14 , 16 , 18 , 20 , 22 , 24 , 25 , 27 , 28 , 30 , 31 We noted a strong association between anaemia and QoL in lymphoma patients before chemotherapy. 22 We found an impairment of QoL in multiple myeloma patients at diagnosis, 18 at the beginning of treatment 13 and during treatment, 27 in chronic lymphoblastic leukaemia patients with chemotherapy 16 and in multiple myeloma and acute lymphoblastic leukaemia patients at the start of chemotherapy. 25 The latter study found that QoL was more deteriorated in patients with relapses, in comparison to patients who had no relapse, even at the onset of treatment. Moreover, QoL was worse in patients with an advanced stage of disease. 28

Chronic lymphoblastic leukaemia patients had impaired health-related QoL compared with the general population. 16 Compared with healthy controls, chronic lymphoblastic leukaemia patients with chemotherapy reported a lower QoL. 20 Non-Hodgkin's lymphoma survivors with active disease presented a worse QoL compared with short- or long-term survivors. 30 Moreover, one paper found a better QoL in Hodgkin's lymphoma survivors diagnosed 10–15 years previously than patients diagnosed 5–9 years ago. 24

QoL improved after aerobic exercise training programme 14 and was better in non-Hodgkin's lymphoma patients meeting public health exercise guidelines, compared with those who did not. 31 Nevertheless, one study found that QoL of chronic lymphoblastic leukaemia patients was similar to or better than published population. 28 However, one study demonstrated that QoL improved during and after chemotherapy in aggressive non-Hodgkin's lymphoma patients. 22

General health

Five reports investigated the general health in haematological population. 14 , 17 , 22 , 23 , 24 For multiple myeloma and non-Hodgkin's lymphoma patients, their physical health and mobility were the most frequent domains affected by the disease. 17 Two studies noted that non-Hodgkin's lymphoma patients had a worse general health and that Hodgkin's lymphoma survivors presented lower general health compared with the population. 23 , 24 In another study comparing general health in patients treated with usual care or aerobic exercise training programme, aerobic exercise training patients had better general health than the other patients. 14 However, after chemotherapy, general health improved in non-Hodgkin's lymphoma patients in one study. 22

Physical dimension

Eight studies showed that haematological cancer deteriorates the physical component of QoL. 14 , 18 , 19 , 20 , 21 , 24 , 29 , 30 Some patients negatively perceived their physical well-being after bone marrow transplanation. 19 Four other studies showed that physical function was affected in multiple myeloma patients 17 , 18 , 21 , 29 and that older patients presented more reduced physical functioning than younger patients. 21 Non-Hodgkin's lymphoma survivors with active disease demonstrated worse physical functioning compared with disease-free survivors and population. 30 Aerobic exercise training programme patients had better cardiovascular fitness than usual care patients. 14 Long-term Hodgkin's lymphoma survivors diagnosed 10–15 years earlier reported better physical functioning than survivors diagnosed 5–9 years before. 24 For chronic lymphoblastic leukaemia patients, physical functioning was significantly deteriorated compared with the healthy controls. 20

Fatigue, lack of vitality and energy: Ten papers found that fatigue was one of the most prevalent symptoms experienced in haematological patients. 14 , 15 , 16 , 18 , 20 , 21 , 25 , 26 , 29 , 32 Compared with population, chronic lymphoblastic leukaemia patients had impaired health-related QoL for fatigue. 16 For 55% of haematological patients, fatigue was the main symptom with insomnia, 21 in particular in acute leukaemia and highly malignant lymphoma patients. 25 One paper reported that levels of fatigue in Hodgkin's lymphoma and chronic lymphoblastic patients were higher than patients in healthy controls, even for years after treatment. 20 Having severe illnesses in Hodgkin's lymphoma survivors was positively associated with fatigue. 26 Another study showed that lymphoma patients who reported concentration and memory difficulties demonstrated much fatigue; 15 symptom less pronounced in aerobic exercise training programme patients compared with usual care patients. 14 These findings are consistent with the results found in another report. 18 , 29

Three studies noted that patients with bone marrow transplanation, 19 Hodgkin's lymphoma survivors treated by radiotherapy or chemotherapy 32 and Hodgkin's lymphoma survivors 24 presented a lack of energy. For vitality, patients diagnosed 5–9 years before presented a greater lack of vitality than those diagnosed 10–15 years before. 24 Finally, non-Hodgkin's lymphoma patients reported less vitality compared with population. 23

Pain: Painful sensations were frequent in haematological patients for five studies. 18 , 21 , 22 , 23 , 29 Pain was the most distressing problem for multiple myeloma, 18 monoclonal gammopathy of unknown significance (MGUS) 29 and leukaemia and lymphoma patients. 21 In the latter study, older patients had more pain than younger patients. Similar results were found in non-Hodgkin's lymphoma patients who reported more bodily pain than the general population. 23 However, during chemotherapy, less pain was experienced by aggressive non-Hodgkin's lymphoma patients in only one study. 22

Sleep disorders: Four studies found that sleep was affected by haematological cancer. 16 , 19 , 22 , 25 Sleep disorders were prevalent in acute leukaemia and highly malignant lymphoma patients at the start of treatment. 25 Compared with the general population, chronic lymphoblastic leukaemia patients presented more sleep disorders, 16 related to functional well-being. 19 An improvement was found in sleep disturbances during and after chemotherapy in aggressive non-Hodgkin's lymphoma patients. 22

Digestive symptoms: Digestive symptoms may occur during haematological disease in four studies. 20 , 21 , 22 , 33 Among the most common problems in acute leukaemia patients, we found lack of appetite, weight loss, nausea and vomiting, 1 day after the end of chemotherapy or bone marrow transplanation. However, these symptoms had improved 10 and 21 days after the end of treatment. 33 Older and recently hospitalised patients had more constipation, nausea and loss of appetite than younger patients and outpatients. 21 Moreover, non-Hodgkin's lymphoma patients presented diarrhoea during chemotherapy but showed constipation 1 month after the end of treatment. 22 Finally, chronic lymphoblastic leukaemia patients showed more nausea and vomiting, constipation and appetite loss than healthy controls. 20

Dyspnoea: In three studies, dyspnoea, predominant with chemotherapy, was one of the most common symptoms in acute leukaemia and highly malignant lymphoma patients 25 and in chronic lymphoblastic leukaemia patients. 16 , 20

Nutrition: In one study, nutritional deficits predominated in multiple myeloma and MGUS patients, treated with transplant. 29

Fever: Only one study mentioned the problem of fever in acute leukaemia during chemotherapy. 33

Functional and role dimensions

Two studies focussed on the functional dimension, 19 , 28 negatively affected after a bone marrow transplanation. However, in one study could most patients carry on with their daily activities without any help 1 year after bone marrow transplanation. 19 Moreover, some authors found that daily functioning was similar or better than the population norms. 28

Concerning role, six studies focussed on this dimension. 16 , 18 , 20 , 21 , 22 , 25 One study analysed the deterioration of role function in leukaemia, multiple myeloma or lymphoma patients. 21 Role was affected in leukaemia and lymphoma 25 and multiple myeloma patients, 18 essentially in older patients. Compared with the general population, chronic lymphoblastic lymphoma patients had impaired role functioning for two studies. 16 , 20 However, improvement of role was observed 1 month after the end of chemotherapy in non-Hodgkin's lymphoma patients. 22

Psychological dimension

Eleven studies showed that haematological cancers affect psychological QoL. 14 , 15 , 19 , 20 , 23 , 24 , 28 , 29 , 30 , 32 , 33 One paper found that patients diagnosed 10–15 years earlier reported better psychological well-being than patients diagnosed 5–9 years ago. 23 Lymphoma patients with chemotherapy presented possible anxiety and depression 15 and we noted a high frequency of anxiety and depression in acute leukaemia patients, with a trend to improvement at the end of treatment. 33 One study suggested that patients experienced more anxiety and mood disturbance after bone marrow transplanation compared with those with a longer follow-up. 19 Hodgkin's lymphoma or non-Hodgkin's lymphoma patients receiving aerobic exercise training programme reported less depression and greater happiness compared with those who did not participate in the programme. 14 Individuals with active disease demonstrated worse mental health functioning compared with population and disease-free survivors. 30 Additional studies reported that emotional distress was present in multiple myeloma, MGUS, amyloid 29 and chronic lymphoblastic leukaemia patients. 20 Finally, chronic lymphoblastic leukaemia patients presented lower emotional well-being compared with the general population. 24 , 28 Moreover, Hodgkin's lymphoma survivors presented a different and positive vision of life after disease. 32

Cognitive dimension

Two papers focussed on the cognitive functioning. 15 , 20 The cognitive area was significantly deteriorated in chronic lymphoblastic leukaemia patients, compared with healthy controls, 20 and lymphoma patients with memory problems had a lower QoL. 15

Social, professional and economic dimensions

Nine papers showed that social, professional and financial QoL were affected by haematological cancer. 17 , 19 , 20 , 21 , 23 , 24 , 25 , 28 , 32 One report found a deterioration of social functioning in leukaemia and lymphoma patients with chemotherapy. 25 Chronic lymphoblastic leukaemia patients presented a lower social QoL, mainly women. 20 Another study found the same finding in patients diagnosed 5–9 years earlier compared with patients diagnosed 10–15 years before. 24 In one study, the availability of, and satisfaction with, social support declined after bone marrow transplanation. 19 The domain of family was affected in 89% of haematological patients. 17 However, in one paper, the social functioning of chronic lymphoblastic leukaemia patients was similar to or better than that of the general population. 28

Furthermore, one paper showed that Hodgkin's lymphoma survivors mentioned the topics of leisure and finance less frequently than controls. 32 Older patients had fewer financial difficulties than outpatients, and multiple myeloma patients had a worse social QoL compared with those with other haematological cancers. 21 Finally, most frequent domains mentioned were hobbies/pastimes, partnerships, profession and social life and friends 17 and difficulties to obtain health-care insurance and life insurance. 23

Sexual dimension

One study focussed on the sexual component 29 and found that multiple myeoloma or MGUS patients presented sexual difficulties associated with body image. 29 The problem of body image could be associated with hair loss mentioned in another paper. 33

The general findings show that the haematological disease negatively affects overall QoL. 13 , 14 , 16 , 18 , 20 , 22 , 24 , 25 , 27 , 28 , 30 , 31 Compared with the general population, fatigue, pain or vitality were the more exposed 35 , 36 aspects of QoL, which were specifically deteriorated during an advanced stage of haematological cancer. 28 Compared with the general population, haematological patients had an adverse general health. 23 These results confirm other findings concerning cancer populations. 37

Fatigue was the most prevalent physical symptom. 14 , 15 , 16 , 18 , 21 , 25 , 26 , 29 , 32 , 33 Most of the samples included elderly patients, and the progressive loss of autonomy in older people is not conducive to maintaining physical QoL. Haematological patients were more susceptible to fatigue than others because of the comorbidity and side effects due to treatment. 38 Moreover, the benefits of physical programme on physical well-being were demonstrated. 27 Similar data were found in Hodgkin's lymphoma survivors, an improvement in physical functioning and cardiovascular fitness being observed after exercise. 39 The other physical symptoms were common to patients with other forms of cancers 40 as well as breast cancer patients. 41 , 42

Only one study found that haematological patients can manage acts of daily life without the need for support after bone marrow transplanation. 19 However, older and multiple myeloma patients experienced more reduced role function than younger patients and subjects with other diagnoses. 21 Indeed, the multiple myeloma patients were older than patients with other diagnoses, and advanced age proved to be a predictor of symptoms. Role was more affected in haematological patients than in the general population. 16 Because of physical disabilities, it is plausible that familial or social missions were disturbed.

Mostly, psychological QoL was found to be worse. 14 , 15 , 19 , 20 , 23 , 24 , 28 , 29 , 30 , 32 , 33 One study noted that aerobic exercise training programme helped maintain good mental QoL. 27 This may be due to the involvement of social interaction and a process of being distracted from one's cancer and treatments, a finding already made in advanced cancer patients. 43 Moreover, emotional benefits occurred after patients with breast cancer followed a sports programme. 44

Furthermore, haematological cancer damaged social, professional and financial QoL. 17 , 19 , 20 , 21 , 23 , 24 , 25 , 28 , 32 Having cancer may improve social and familial relations by increasing the intensity of support and the availability of family caregivers. Conversely, emotional distress can affect the family sphere, and interpersonal relationships are likely to move towards the feeling of ambiguity or fear. Familial structure may be modified, leading to distress within the family. The deterioration of social well-being could be linked with self-image, including hair loss which increases after chemotherapy. 33 Otherwise, the time since diagnosis may also have an impact on social QoL: in two studies, patients diagnosed 10–15 years earlier presented a better social QoL than those diagnosed 5–9 years before. 23 , 24 These findings were similar among families of patients with a head and neck cancer. 45

Some studies found that professional life was negatively affected in patients. 23 , 39 , 41 , 46 Another study strengthened this finding by establishing that economic stress was negatively associated with QoL in gynaecological survivors. 47 Moreover, one paper showed an increase in disability days in patients with breast, lung and gastrointestinal cancers. 48 These consequences can lead to social isolation and frustration. 49

Sexual activity, related to body image, was also reduced. 29 Body image could be an important aspect of our criteria, with the fear of loss of masculinity or femininity and self-image. Other forms of cancer such as gynaecological malignancies also affected patients' sex life. 50

With regard to cognitive functioning, haematological patients presented several memory and concentration disturbances. 28 Similar results were found in cancer patients, in whom cognitive deficits were observed after chemotherapy. 51

Physical, psychological, social and professional problems may be associated with the effects of treatment modalities. QoL was particularly affected in multiple myeloma and chronic lymphoblastic leukaemia patients, treated by chemotherapy or transplantation, in older patients, and in patients with active disease or an advanced stage of disease. Therefore, it would be interesting to conduct a further review with a synthesis of articles that highlight the impact on QoL of treatments recommended for a haematological malignancy.

The potential limitations of this review concern the literature search. Others involve the complexity in interpreting and measuring QoL, the heterogeneity of samples and the loss of subjects during research due to poor medical conditions, death or refusal.

The major strength of our review is the reliability of the selected studies. It shows that haematological cancer patients have a poor QoL or health-related QoL compared with the general population. These findings hold regardless of the type of disease, the treatment modality and the stage of the disease. Generally, we found similar outcomes in other cancers, such as fatigue, which was greater in haematological patients. In theoretical terms, QoL is a complex concept that encompasses various aspects of life and is similar to well-being, so the very meaning of the notion is debatable. Clinically, it is important to analyse QoL early in the course of care. Some types of intervention may prove helpful such as physical programmes, which may be considered as a form of functional care intervention, and other supportive actions, such as psychotherapy which can improve physical and mental functioning.

The authors declare no conflict of interest.

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Causal Effect of Blood and Urine Metabolites on Prostate Cancer and Their Mediating Role in Tumor Immunity

43 Pages Posted: 12 Sep 2024

Capital Medical University - Beijing Tongren Hospital

Ruijing Yao

Capital Medical University - Beijing Chaoyang Hospital

Zhizhi Wang

Zhejiang University

Background: Metabolites play a crucial role in the development of prostate cancer (PCa) and may mediate the effects in tumor immunity. This study aims to explore the causal relationship between blood and urine metabolites and PCa, as well as their mediating role in tumor immunity, using data from the National Health and Nutrition Examination Survey (NHANES) 2007–2018 and Mendelian Randomization (MR) analysis. Materials and Methods: Our observational study evaluated the correlation between blood and urine metabolites and PCa using data from the NHANES 2007-2018. Feature selection was performed using the Boruta algorithm and random forest algorithm. Propensity score matching (PSM) and multivariable logistic regression analysis were employed to control for potential confounders. Non-linearity was assessed using restricted cubic spline plots. We utilized weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) to detect potential collinearity among metabolites. In the MR analyses, the primary method for evaluating the causal effects of exposure on outcomes was the inverse variance weighted (IVW) approach, which was further validated by employing various supplementary MR methods and analyses on another PCa data. Mediation analyses were conducted to identify metabolites that may mediate the effects of specific immune cells on PCa. Results: Data analysis from NHANES revealed significant associations of specific blood and urine metabolites, including alanine aminotransferase (ALT) to aspartate aminotransferase (AST) ratio (OR = 17.828 [95%CI, 1.849-171.915], P = 0.017) and vitamin D (OR = 0.980 [95%CI, 0.968-0.991], P = 0.001), with PCa. MR analysis indicated potential causal relationships between several metabolites and PCa, including ALT (OR = 0.943[95% CI, 0.899-0.988], P = 0.014), cholesterol (OR = 1.058 [95% CI, 1.030-1.086], P < 0.001), IGF-1 (OR = 1.086 [95% CI, 1.052-1.122], P < 0.001), LDL (OR = 1.055 [95% CI, 1.001-1.111], P = 0.045), lipoprotein A (OR = 1.130 [95% CI, 1.062-1.201], P < 0.001) and BUN (OR = 1.063 [95% CI, 1.017-1.112], P = 0.007). Furthermore, the study identified the mediating role of metabolites in the immune cell response to PCa, including cholesterol, lipoprotein A, and BUN. Conclusions: The study identified 8 blood and urine metabolites and 52 immune cell phenotypes significantly correlated with PCa, with BUN mediating, lipoprotein A masking, and cholesterol exhibiting both effects on immune responses to PCa. These findings offer novel insights into the pathophysiology of prostate cancer and suggest potential targets for diagnosis and therapeutic interventions. Funding: None. Declaration of Interest: Data analyzed in the observational study were sourced from NHANES. Approval of the survey protocol was granted by the National Center for Health Statistics (NCHS) Institutional Review Board, and written informed consent was obtained from all participants. Data used in the MR study were derived from existing studies, obviating the need for additional ethical approval. Ethical Approval: The NHANES study protocols were approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). All participants in the NHANES study provided informed consent. This study was not subject to institutional review board approval, as it employed de-identified, publicly accessible data.

Keywords: Metabolite, Immune Cell, Prostate Cancer, National Health and Nutrition Examination Survey, Mendelian Randomization, Causality

Suggested Citation: Suggested Citation

Capital Medical University - Beijing Tongren Hospital ( email )

Beijing China

Capital Medical University - Beijing Chaoyang Hospital ( email )

Zhejiang university ( email ).

38 Zheda Road Hangzhou, 310058 China

Wei Wang (Contact Author)

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Breast Cancer Screening Studies Evaluation Research Paper

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The Study’s Purpose, Research Design, and Methodology

Data collection, credibility and trustworthiness, results and clinical implications, the level of evidence and quality of evidence.

Agyemang et al.’s (2020) study aimed to understand why Ghanaian women with breast cancer were not given information about their diagnosis. It explored cultural and structural factors contributing to this issue and identified ways to improve women’s health in Ghana. The study used an ethnographic research design involving observing and interpreting people’s culture and behavior to understand how cultural and structural factors affect women’s access to information about breast cancer. The approach involved in-depth interviews with various respondents to understand their experiences with information access, diagnosis, and treatment. Secondly, the researchers observed interactions between healthcare professionals and patients to understand cultural behaviors and beliefs. The data were analyzed thematically, and a narrative framework was used to present the study’s findings.

Ozkan and Taylan’s (2021) study aimed to determine the obstacles hindering women from breast cancer screening in various nations. The study incorporated the meta-synthesis research design of multiple studies to find similar themes and trends across several countries. The authors searched several databases for research addressing barriers to breast cancer screening for women aged 40 and over, published between 2000 and 2019. The selected studies had to adhere to an inclusion criterion. The data collection and analysis processes used in this study’s methodology included a literature search to find papers that satisfied the inclusion criteria and a systematic approach to analyzing the findings for the common themes and sub-themes to be synthesized.

Agyemang et al.’s (2020) study collected data through semi-structured interviews and observations from different respondents. The participants were selected using purposive maximal variation sampling for diversity purposes. The observation focused on interactions and information exchange between healthcare providers, patients, and their families. Interview guides were used for all participant interviews. In total, 31 and 29 participants were observed and engaged in semi-structured interviews. The data were thematically analyzed, and three overarching themes were identified: unequal power relationships, language barriers, and structural constraints.

Ozkan and Taylan (2021) conducted a comprehensive search of electronic databases and identified 16 qualitative studies that met the inclusion criteria. The studies were conducted in different countries and used various qualitative research methods, such as focus groups, interviews, and ethnography. The researchers synthesized the common themes and sub-themes linked to the barriers to breast cancer screening after using a systematic strategy to assess the findings of the included studies.

The credibility and trustworthiness of these studies would be assured by their dependability, confirmability, and transferability. Dependability refers to how consistently and steadily the research findings can hold over time (Galaitsi et al., 2021). A study’s confirmability refers to the objectivity of the research findings and the degree to which the researcher’s prejudices and values impact them. A study’s transferability describes how well the study’s conclusions can be applied to various situations or settings (Kyngäs et al., 2020).

In Agyemang et al.’s (2020) study, dependability was ensured through multiple data collection techniques, including interviews and observations, and an iterative data analysis method. The use of diverse respondents enabled triangulation and a more thorough comprehension of the contextual elements impacting the adoption of breast cancer treatment. Its ethnographic approach and reflexivity ensured the researchers suspended their biases and allowed them to immerse themselves in the study, assuring its confirmability. The researchers provided a thorough description of the study’s context and participants to increase transferability, enabling other researchers to evaluate the applicability of the findings in their contexts. They also explain the structural and cultural elements affecting Ghanaian breast cancer patients’ access to information, which can guide research in other low- and middle-income nations.

Ozkan and Taylan’s (2021) study is also credible and trustworthy due to its precise and methodical data collection and analysis approach. To confirm the accuracy of their conclusions, the researchers conducted numerous iterations of coding and discussions. They also used direct quotations to support their main points. The study’s confirmability was evident in how the authors reduced their personal biases and values in the analysis and grounded their findings in the data. However, the study’s transferability is constrained because the chosen studies were carried out in particular nations and cultural contexts.

According to Agyemang et al. (2020), Ghanaian women would experience “hidden information” on their alternatives for treatment after receiving a breast cancer diagnosis due to cultural and structural issues, which would negatively affect their capacity for decision-making. The study emphasizes the significance of comprehending these factors that will affect these women’s access to health care and information in low-resource settings and the requirement for tailored interventions to remove these obstacles.

Ozkan and Taylan’s (2021) study identified common barriers to breast cancer screening in different countries, including lack of information, fear, cultural and religious prejudices, and logistical difficulties. The findings highlighted the need for healthcare professionals to prioritize educating women on the importance of breast cancer screening and addressing myths and concerns. Governments should consider implementing measures to make screening more accessible by reducing financial and practical barriers such as transportation costs.

An ethnographic research design is typically considered to have a low degree of evidence, especially for this research topic. Nonetheless, the quality of the evidence provided by Agyemang et al. (2020) is high due to this study’s proper planning and execution. Using various data collection methods, including different viewpoints, and applying rigorous data analysis procedures improve the quality of the evidence in this study. Notably, the results become applicable to the study’s particular environment or other environments with similar characteristics. This limits the transferability of the study’s findings to other contexts due to the study design’s constraints. However, the study offers insightful information about the structural and cultural elements that affect Ghanaian women with breast cancer’s access to information. The results can help Ghanaian authorities and healthcare professionals plan to increase breast cancer patients’ access to information.

Ozkan and Taylan’s (2021) use of the methodical approach to data collection, analysis, and interpretation gives its evidence high and high-quality levels. The study employed various techniques to reduce bias, including using numerous databases to find relevant studies written in various languages and reviewing study quality. To confirm the correctness of their conclusions, the writers also employed a detailed and iterative coding and discussion procedure, which included direct quotations to support their themes.

Agyemang, L. S., Foster, C., McLean, C., Fenlon, D., and Wagland, R. (2020). The cultural and structural influences hiding information from women diagnosed with breast cancer in Ghana: An ethnography . Health Expectations, 23 (2), 351-360. Web.

Galaitsi, S. E., Keisler, J. M., Trump, B. D., & Linkov, I. (2021). The need to reconcile concepts that characterize systems facing threats . Risk Analysis, 41 (1), 3-15. Web.

Kurnia, H., Jaqin, C., & Purba, H. H. (2022). Quality improvement with PDCA approach and experiment method design in Indonesia’s single socks industry . In AIP Conference Proceedings 2470 (1), p. 020007). Web.

Kyngäs, H., Kääriäinen, M., & Elo, S. (2020). The trustworthiness of content analysis . The application of content analysis in nursing science research , 41-48. Web.

Ozkan, I., and Taylan, S. (2021). Barriers to women’s breast cancer screening behaviors in several countries: A meta-synthesis study . European Journal of Oncology Nursing, 51 , 101942. Web.

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