A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer

Pan, Xin and Ma, Xiaoxin (2020) A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer. Frontiers in Genetics, 11. ISSN 1664-8021

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Abstract

Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of OC patients. In this study, samples from 367 patients in The Cancer Genome Atlas (TCGA) database were subjected to mRNA expression profiling. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with epithelial–mesenchymal transition (EMT) and assess their prognostic power with a Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, after which the patients were divided into high-risk and low-risk groups. The six-gene signature was an independent prognostic biomarker of OS for OC patients based on the multivariate Cox regression analysis. In addition, the six-gene model was validated with samples from the Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of OC, which might become a therapeutic tool with clinical applications in the future.

Item Type: Article
Subjects: Open Article Repository > Medical Science
Depositing User: Unnamed user with email support@openarticledepository.com
Date Deposited: 27 Jan 2023 06:32
Last Modified: 24 Jun 2024 04:20
URI: http://journal.251news.co.in/id/eprint/346

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