Construction of a Macrophage Infiltration Regulatory Network and Related Prognostic Model of High-Grade Serous Ovarian Cancer.

ABSTRACT:

Background

High-grade serous ovarian cancer (HGSOC) carries the highest mortality in the gynecological cancers; however, therapeutic outcomes have not significantly improved in recent decades. Macrophages play an essential role in the occurrence and development of ovarian cancer, so the mechanisms of macrophage infiltration should be elucidated.

Method

We downloaded transcriptome data of ovarian cancers from the Gene Expression Omnibus and The Cancer Genome Atlas. After rigorous screening, 1566 HGSOC were used for data analysis. CIBERSORT was used to estimate the level of macrophage infiltration and WGCNA was used to identify macrophage-related modules. We constructed a macrophage-related prognostic model using machine learning LASSO algorithm and verified it using multiple

SUBMITTER: Chang H 

PROVIDER: S-EPMC8635947 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature