CYP4F2 and CYP3A5 gene polymorphisms and lung cancer in Chinese Han population

Genetic Variants of CYP4F2 Associated with Ischemic Stroke Susceptibility in the Han Population from Southern China

Background

The pathophysiological mechanism of ischemic stroke is complex. Traditional risk factors cannot fully or only partially explain the occurrence and development of IS. Genetic factors are getting more and more attention. Our study aimed to explore the association between CYP4F2 gene polymorphism and susceptibility to IS.

Methods

A total of 1322 volunteers were enrolled to perform an association analysis through SNPStats online software. Using FPRP (false-positive report probability) to detect whether the result is a noteworthy finding. The interaction of SNP-SNP in IS risk was assessed by multi-factor dimensionality reduction. Statistical analysis of this study was mainly completed by SPSS 22.0 software.

Results

Mutant allele "A" (OR = 1.24) and genotype "AA" (OR = 1.49) or "GA" (OR = 1.26) of CYP4F2-rs2108622 are risk genetic factors for IS. Rs2108622 is significantly associated with an increased risk of IS among subjects who are females, aging >60 years old, with BMI ≥24 kg/m², and smoking or drinking volunteers. CYP4F2-rs3093106 and -rs3093105 are associated with susceptibility to IS among smoking, drinking subjects, or IS patients complicated with hypertension.

Conclusion

CYP4F2-rs2108622, -rs3093106, and -rs3093105 are associated with an increased risk of IS.

A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.