Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.

ABSTRACT: The impact of mitochondrial and lysosomal co-dysfunction on breast cancer patient outcomes is unclear. The objective of this study is to develop a predictive machine learning (ML) model utilizing mitochondrial and lysosomal co-regulators in order to provide a foundation for future studies focused on breast cancer (BC) patients' stratification and personalized interventions. Firstly, Differences and correlations of mitochondrial and lysosome related genes were screened and validated by differential analysis, copy number variation (CNV), single nucleotide polymorphism (SNPs) and correlation analysis. WGCNA and univariate Cox regression were employed to identify prognostic mitochondrial and lysosomal co-regulators. ML was utilized to further selected these regulators and then the coxboost + S

SUBMITTER: Chen H 

PROVIDER: S-EPMC11770110 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature