The gene family and gene are inextricably linked to an elevated risk of cancer development. This systemic review and meta-analysis seeks to establish the relationship between (rs11064, rs1045241, rs1045242, and rs3813308), (rs1060555), and (rs710100 and rs8126) polymorphisms with the risk of cancer. A systematic search of multiple databases from January 2022 to April 2022 was used to identify relevant studies. Odds ratios (ORs) with corresponding 95% CI and -value were calculated to assess the association. Bonferroni correction was performed to correct -values. Trial sequential analysis (TSA) and messenger RNA expression were also performed. Review Manager 5.4 software was used for performing this meta-analysis. This study comprised 6909 cancer patients and 7087 healthy participants from 14 studies. Four genetic models of rs11064 (codominant 2 [COD2]: OR = 2.30, = 7.83 × 10; codominant 3 [COD3]: OR = 2.10, = .0006; recessive model [RM]: OR = 2.24, = .0001; AC: OR = 1.47, = .037), two genetic models of rs1045241 (codominant 1 [COD1]: OR = 1.27, = .009; overdominant model [ODM]: OR = 1.24, = .018), four genetic models of rs1045242 (COD1: OR = 1.52, = .005; dominant model (DM): OR = 1.56, = .002; OD: OR = 1.48, = .008; AC: OR = 1.48, = .002), and three genetic models of rs8126 (COD2: OR = 1.41, = .0005; COD3: OR = 1.44, = .0002; RM: OR = 1.43, = .0001) were statistically linked to cancer risk. Only one genetic model of rs1060555 polymorphism showed a significant protective association with cancer (COD2: OR = 0.80, = .048). The outcomes of TSA also validated the findings of the meta-analysis. This study summarizes that rs11064, rs1045241, and rs1045242 polymorphisms of gene and rs8126 polymorphism of gene are significantly linked with the risk of cancer development. This meta-analysis was registered at INPLASY (registration number: INPLASY202270073).
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http://dx.doi.org/10.1177/15330338221123109 | DOI Listing |
Genet Epidemiol
January 2025
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
Large-scale gene-environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C).
View Article and Find Full Text PDFPer Med
January 2025
Department of Clinical Pharmacy, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Efforts have been made to leverage technology to accurately identify tumor characteristics and predict how each cancer patient may respond to medications. This involves collecting data from various sources such as genomic data, histological information, functional drug profiling, and drug metabolism using techniques like polymerase chain reaction, sanger sequencing, next-generation sequencing, fluorescence in situ hybridization, immunohistochemistry staining, patient-derived tumor xenograft models, patient-derived organoid models, and therapeutic drug monitoring. The utilization of diverse detection technologies in clinical practice has made "individualized treatment" possible, but the desired level of accuracy has not been fully attained yet.
View Article and Find Full Text PDFHum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
J Trop Med
December 2024
Department of Infectious Disease, Faculty of Medicine, Aja University of Medical Sciences, Tehran, Iran.
After the global impact of the COVID-19 pandemic, concerns over virus transmission have risen. A state of health emergency was declared in 2022 due to Clade 2 of the monkeypox (MPOX) virus. In August 2024, another emergency was declared by the World Health Organization (WHO) because of the widespread Clade 1b, which caused a more severe and lethal disease.
View Article and Find Full Text PDFEClinicalMedicine
August 2024
Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China.
Background: Pulmonary embolism causes a substantial burden of morbidity and mortality. Although there are several well-established risk factors for pulmonary embolism, a substantial proportion of cases cannot be attributed to provoked or known risk factors. Accumulating evidence has suggested an association of clonal hematopoiesis of indeterminate potential (CHIP) with the risk of arterial thromboembolism.
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