Dataset Information

TNFAIP8 variants as potential epidemiological and predictive biomarkers in ovarian cancer.

ABSTRACT: Background:This research aimed to investigate the association between tumor necrosis factor-a-induced protein 8 (TNFAIP8) polymorphisms and ovarian cancer (OC) susceptibility. Methods:A case-control study of 210 patients with OC and 231 healthy controls was conducted to assess the association between TNFAIP8 polymorphisms (rs11064, rs1045241, and rs1045242) and OC risk in Heilongjiang Province of China. The SNaPshot SNP assay was conducted to detect SNP genotype. Logistic regression analysis was applied to illustrate the underlying association. Results:Our research found that TNFAIP8 rs11064 and rs1045242 were significantly connected with the susceptibility of OC. Additionally, rs1045242 increased the risk of OC, while rs11064 performed a protective role in the risk of OC. Data revealed that rs1045242 strongly related with advanced FIGO stage, larger residual tumor, and the presence of recurrence. Conclusions:TNFAIP8 genetic variants, which may play difference roles, were significantly associated with OC susceptibility. The underlying molecular mechanism needs be clarified with scientific evidence.

SUBMITTER: Gao H

PROVIDER: S-EPMC7433149 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

TNFAIP8 variants as potential epidemiological and predictive biomarkers in ovarian cancer.

Gao Hongyu H Zhang Zhiran Z Jiang Liangliang L Zhang Lei L Qin Ling L Liu Tianbo T Yang Shanshan S

Cancer cell international 20200817

<h4>Background</h4>This research aimed to investigate the association between tumor necrosis factor-a-induced protein 8 (TNFAIP8) polymorphisms and ovarian cancer (OC) susceptibility.<h4>Methods</h4>A case-control study of 210 patients with OC and 231 healthy controls was conducted to assess the association between TNFAIP8 polymorphisms (rs11064, rs1045241, and rs1045242) and OC risk in Heilongjiang Province of China. The SNaPshot SNP assay was conducted to detect SNP genotype. Logistic regressi ...[more]

PMID: 32821249

Similar Datasets

Project description:BackgroundOvarian cancer is a common gynecological malignant tumor that greatly threatens women's health, so we screened potential biomarkers of ovarian cancer and analyzed their prognostic value.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to analyze the ovarian cancer-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of ovarian cancer-related genes. The survival-related genes were screened out through the least absolute shrinkage and selection operator (LASSO) method. Multivariate Cox regression model and stepwise regression analysis were performed to construct the risk model. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were used to evaluate the prediction accuracy of risk score model. Finally, gene set enrichment analysis (GSEA) and immune cell infiltration analysis were performed to investigate the biological function and immune cell infiltration.ResultsA total of 111 genes were found to have common effects on survival. These genes were mainly involved in metabolism, protein phosphorylation and immune-related signaling pathways. Seven risk genes (AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1, ZNF426) were screened out. Among these genes, AP3D1 and LRFN4 are risk genes and DCAF10, FBXO16, PTPN2, SAYSD1, and ZNF426 are protective genes. These findings suggest that risk status may be an independent prognostic factor. The risk score had a high predictive value for the prognosis of ovarian cancer. In addition, GSEA revealed that the biological function of genes expressed in patients at a high risk was mostly related to immune-related function. The contents of CD4+ T cells, macrophages, myeloid dendritic cells (mDC) and neutrophils were high in samples at a high risk for ovarian cancer.ConclusionsThe abnormal expression of AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1 and ZNF426 is highly related to the progression of ovarian cancer. These seven genes can be used as independent prognostic markers of ovarian cancer. This study not only adds evidence to the pathogenesis of ovarian cancer but also provides scientific basis for judging the prognosis of ovarian cancer.

Dataset Information

TNFAIP8 variants as potential epidemiological and predictive biomarkers in ovarian cancer.

Publications

TNFAIP8 variants as potential epidemiological and predictive biomarkers in ovarian cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets