Proposing a novel molecular subtyping scheme for predicting distant recurrence-free survival in breast cancer post-neoadjuvant chemotherapy with close correlation to metabolism and senescence.

ABSTRACT:

Background

High relapse rates remain a clinical challenge in the management of breast cancer (BC), with distant recurrence being a major driver of patient deterioration. To optimize the surveillance regimen for distant recurrence after neoadjuvant chemotherapy (NAC), we conducted a comprehensive analysis using bioinformatics and machine learning approaches.

Materials and methods

Microarray data were retrieved from the GEO database, and differential expression analysis was performed with the R package 'Limma'. We used the Metascape tool for enrichment analyses, and 'WGCNA' was utilized to establish co-expression networks, selecting the soft threshold power with the 'pickSoftThreshold' algorithm. We integrated ten machine learning algorithms and 101 algorithm combinations to i

SUBMITTER: Huang J 

PROVIDER: S-EPMC10602753 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature