The Effect of Statins in Cancer Risk Reduction in Patients on Dialysis: A Population-Based Case-Control Study

Background: To realize whether statins reduce the risk of cancer in susceptible dialysis populations, this study analyzed the relationship between statin use and cancer risk in patients on dialysis. Methods: Patients having a history of chronic kidney disease with hemodialysis or peritoneal dialysis and receiving statin prescriptions or not were enrolled. The main outcome was cancer diagnosis. This study used univariate and multivariate Cox regression analyses. Results: In total, 4236 individuals in the statin group and 8472 individuals in the statin nonuser group were included in the study. Multivariate Cox regression analysis revealed that statin users are significantly less likely to develop cancer than statin nonusers (adjusted hazard ratio (HR) 0.81, 95% confidence interval (CI) 0.78–0.90). Subgroup analyses revealed that statin cumulative defined daily doses >365 were associated with a significantly decreased risk of cancer incidence (adjusted HR 0.59, 95% CI 0.45–0.87), and statin users have a reduced risk of respiratory, soft tissue and connective tissue, breast, gynecological, prostate, central nervous system, and lymphatic and hematopoietic cancer than nonusers. Conclusions: Our population-based cohort study provides an association that statins reduce the risk of malignancy in patients on dialysis, especially with a longer treatment duration, and certain types of cancer.

Association between the Interleukin-1 Receptor Antagonist (IL1RN) Variable Number of Tandem Repeats (VNTR) Polymorphism and Lymphoma

Introduction: Lymphoma is a common hematopoietic cancer. Immunosuppression is one of the main risk factors for the development of lymphoma. The interleukin (IL)-1 receptor antagonist IL1RN, which binds to the IL-1 receptor, moderates a variety of immune responses related to IL-1. We aimed to assess the impact of IL1RN variable number of tandem repeats (VNTR) polymorphism on lymphoma risk in an Iranian population sample. Materials and Methods: DNA was extracted from peripheral blood of 120 subjects with non-Hodgkin Lymphoma (NHL), 50 subjects with Hodgkin’s lymphoma (HL), and 186 unrelated healthy individuals. IL1RN VNTR polymorphism was detected using polymerase chain reaction. Results: Our findings revealed that the IL1RN VNTR polymorphism was associated with protection against NHL (P≤0.001, OR: 0.30, 95% CI: 0.18-0.53). The IL1RN 2 allele significantly decreased the risk of NHL (p = 0.023, OR = 0.66, 95%CI = 0.46–0.93). In addition, we found that IL1RN 1/2 was associated with a lower risk of HL (p ≤0.001, OR = 0.24, 95%CI = 0.12–0.50). Conclusion: Our results suggest that the presence of IL1RN VNTR polymorphism is associated with a decreased risk of lymphoma in an Iranian subpopulation in southeast Iran.

Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification

Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, we propose not only to build a classification model for five major subtypes using two kinds of losses, namely reconstruction loss and classification loss, but also to extract suitable features using a deep autoencoder. Furthermore, for considering the data imbalance problem, we apply an oversampling algorithm, the synthetic minority oversampling technique (SMOTE). For validation of our proposed autoencoder-based feature extraction approach for hematopoietic cancer subtype classification, we compared other traditional feature selection algorithms (principal component analysis, non-negative matrix factorization) and classification algorithms with the SMOTE oversampling approach. Additionally, we used the Shapley Additive exPlanations (SHAP) interpretation technique in our model to explain the important gene/protein for hematopoietic cancer subtype classification. Furthermore, we compared five widely used classification algorithms, including logistic regression, random forest, k-nearest neighbor, artificial neural network and support vector machine. The results of autoencoder-based feature extraction approaches showed good performance, and the best result was the SMOTE oversampling-applied support vector machine algorithm consider both focal loss and reconstruction loss as the loss function for autoencoder (AE) feature selection approach, which produced 97.01% accuracy, 92.60% recall, 99.52% specificity, 93.54% F1-measure, 97.87% G-mean and 95.46% index of balanced accuracy as subtype classification performance measures.

Abstract 16828: Risk of Hematopoietic Cancer in Congenital Heart Diseased Children With or Without Genetic Syndromes

Background: Individuals with genetic syndromes can manifest both congenital heart disease (CHD) and cancer due to possible common underlying pathways. However, reliable risk estimates of hematopoietic cancer (HC) among children with CHD based on large population-based data are scant. Method: We conducted a population-based analysis to estimate the cumulative incidence of HC in a cohort of children (0-18) born between 1999 and 2017 with at least one hospitalization records of CHD diagnosis or CHD-related procedural code. The data source was the Canadian Institute for Health Informatics-Discharge Abstract Database which regularly collects hospitalization and day surgery records in all Canadian provinces except Quebec, comprising over 30 million people. Hematopoietic cancer and syndromes were both identified by hospitalization diagnoses. We used modified Kaplan-Meier curve analysis to estimate the cumulative incidences [with 95% confidence intervals (CI)] up to 18 years of age, with death as a competing risk and stratified by the binary indicator of genetic syndrome status. Result: We followed 92815 CHD children from birth for 921,866 person-years. In this study population,10.5% had genetic syndromes and 461 cases of HC were observed, yielding incidence rates of HC 22.5 (95% CI: 19.4-25.4) and 3.0 (2.6-3.3) per 10,000 person-years for children with and without genetic syndrome, respectively. Cumulative incidence of HC up to age 18 was 2.6% (95% CI: 2.2-2.9%) among children with a genetic syndrome, and 0.40% (0.34-0.46%) without the syndrome. It was higher in the first 6 years of life (2.1%) than the subsequent 6-years intervals up to adulthood (0.4% in 6-12 years and 0.2% in 12-18 years of age). Conclusion: This is the first population-based analysis documenting that genetic syndromes in CHD children are a powerful predictor of hematopoietic cancers. The finding is important in informing risk-stratified policy recommendations to protect CHD children from cancer.

Rotating Nightshift Work and Hematopoietic Cancer Risk in US Female Nurses

Background Nightshift work is a plausible risk factor for hematologic cancer, but epidemiological evidence remains sparse, especially for individual subtypes. We prospectively examined the association of rotating nightshift work with hematopoietic cancer risk. Methods This cohort study included US women from the Nurses’ Health Study (NHS: n = 76 846, 1988–2012) and Nurses’ Health Study II (NHSII: n = 113 087, 1989–2013). Rotating nightshift work duration was assessed at baseline (both cohorts) and cumulatively updated (NHSII). Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for overall hematopoietic cancer and specific histologic subtypes. All statistical tests were two-sided. Results We documented 1405 (NHS) and 505 (NHSII) incident hematopoietic cancer cases during follow-up. In NHS, compared with women who never worked rotating nightshifts, longer rotating nightshift work duration was associated with an increased risk of overall hematopoietic cancer (HR1–14y = 0.93, 95% CI = 0.83 to 1.04; HR≥15y = 1.28, 95% CI = 1.06 to 1.55; Ptrend = .009). In NHSII, results were similar though not statistically significant (HR1–14y = 0.99, 95% CI = 0.82 to 1.21; HR≥15y = 1.41, 95% CI = 0.88 to 2.26; Ptrend = .47). In the subtype analyses in the NHS, the association of history of rotating nightshift work with risk of diffuse large B-cell lymphoma varied by duration (HR1–14y = 0.71, 95% CI = 0.51 to 0.98; HR≥15y = 1.69, 95% CI = 1.07 to 2.67; Ptrend = .01) compared with those who never worked rotating nightshifts. Women reporting a longer history of rotating nightshifts also had suggestive (statistically nonsignificant) increased risks of overall non-Hodgkin lymphoma (HR≥15y = 1.19, 95% CI = 0.95 to 1.49), Hodgkin lymphoma (HR≥15y = 1.32, 95% CI = 0.43 to 4.06), and multiple myeloma (HR≥15y = 1.42, 95% CI = 0.85 to 2.39). Conclusions Longer duration (≥15 years) of rotating nightshift work was associated with increased risks of overall and several subtypes of hematopoietic cancer.

Cytotoxic and Proapoptotic Activity of Sanguinarine, Berberine, and Extracts of Chelidonium majus L. and Berberis thunbergii DC. toward Hematopoietic Cancer Cell Lines

Isoquinoline alkaloids belong to the toxic secondary metabolites occurring in plants of many families. The high biological activity makes these compounds promising agents for use in medicine, particularly as anticancer drugs. The aim of our study was to evaluate the cytotoxicity and proapoptotic activity of sanguinarine, berberine, and extracts of Chelidonium majus L. and Berberis thunbergii DC. IC10, IC50, and IC90 doses were established toward hematopoietic cancer cell lines using trypan blue staining. Alterations in the expression of 18 apoptosis-related genes in cells exposed to IC10, IC50, and IC90 were evaluated using real-time PCR. Sanguinarine and Chelidonium majus L. extract exhibit significant cytotoxicity against all studied cell lines. Lower cytotoxic activity was demonstrated for berberine. Berberis thunbergii DC. extract had no influence on cell viability. Berberine, sanguinarine, and Chelidonium majus L. extract altered the expression of apoptosis-related genes in all tested cell lines, indicating the induction of apoptosis. The presented study confirmed the substantial cytotoxicity and proapoptotic activity of sanguinarine, berberine, and Chelidonium majus L. extract toward the studied hematopoietic cell lines, which indicates the utility of these substances in anticancer therapy.