Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients.

ABSTRACT:

Background

Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications for breast cancer prognosis remain unclear. The work aimed to identify prognostic factors linked to palmitoylation in breast cancer and assess its effectiveness in predicting responses to chemotherapy and immunotherapy.

Methods

We utilized the "limma" package to analyze the differential expression of palmitoylation-related genes between breast cancer and normal tissues. Hub genes were identified using the "WGCNA" package. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a prog

SUBMITTER: Zhu H 

PROVIDER: S-EPMC11363247 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature