Development and Validation of an Immune-Related Prognostic Signature for Ovarian Cancer Based on Weighted Gene Coexpression Network Analysis

Overview

Journal Biomed Res Int

Publisher Wiley

Specialties Biomedical Engineering
Biotechnology
General Medicine

Date 2020 Dec 31

PMID 33381581

Citations 5

Authors

Yuanyuan An

Qing Yang

Affiliations

Soon will be listed here.

Abstract

Background: Ovarian cancer is one of the most lethal diseases of women. The prognosis of ovarian cancer patients was closely correlated with immune cell expression and immune responses. Therefore, it is important to identify a robust prognostic signature, which correlates not only with prognoses but also with immune responses in ovarian cancer, thus, providing immune-related patient therapies.

Methods: The weighted gene coexpression network analysis (WGCNA) was used to identify candidate genes correlated with ovarian cancer prognoses. Univariate and multivariate Cox regression analyses were used to construct the prognostic signature. The Kaplan-Meier method was used to predict survival, and the immune-related bioinformatics analysis was performed using the R software. The relationship between the signature and clinical parameters was analyzed with the GraphPad Prism 7 and SPSS software.

Results: Gene expression from The Cancer Genome Atlas dataset was used to perform the WGCNA analysis, and candidate prognostic-related genes in patients with ovarian cancer were identified. According to the Cox regression analysis, the prognostic signature was constructed, which divided patients into two groups. The high-risk group showed the least favorable prognosis. Three independent cohorts from the Gene Expression Omnibus (GEO) database were used for the validation studies. According to the immune analyses, the GEO database signatures were significantly correlated with the immune statuses of ovarian cancer patients. By analyzing the combination of the prognostic signature and total mutational burden (TMB), ovarian cancer patients were divided into four groups. In these groups, memory B cell, resting memory CD4 T cell, M2 macrophage, resting mast cell, and neutrophil were found with significant distinctions among these groups.

Conclusions: This novel signature predicted the prognosis of ovarian cancer patients precisely and independently and showed significant correlations with immune responses. Therefore, this prognostic signature could indicate targeted immunotherapies for ovarian cancer patients.

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