A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning.

ABSTRACT:

Background

Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy.

Methods

Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and

SUBMITTER: Zhao B 

PROVIDER: S-EPMC10544447 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature