Screening of novel biomarkers for breast cancer based on WGCNA and multiple machine learning algorithms.

ABSTRACT:

Background

Breast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prognosis.

Methods

We analyzed gene expression data from 99 normal and 1,081 BC tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using "limma" R package, and relevant modules were chosen through Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were obtained by matching DEGs to WGCNA module genes. Functional enrichment studies were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were screened via

SUBMITTER: Jin X 

PROVIDER: S-EPMC10331707 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature