Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer.

ABSTRACT: Cancer stem cells (CSCs) have the potential to self-renew and induce cancer, which may contribute to a poor prognosis by enabling metastasis, recurrence, and therapy resistance. Hence, this study was performed to identify the association between CSC-related genes and triple-negative breast cancer (TNBC) development. Stemness gene sets were downloaded from StemChecker. Based on the online databases, a consensus clustering algorithm was conducted for unsupervised classification of TNBC samples. The variations between subtypes were assessed with regard to prognosis, tumor immune microenvironment (TIME), and chemotherapeutic sensitivity. The stemness-related gene signature was established and random survival forest analysis was employed to identify the core gene for validation experiments and

SUBMITTER: Guo Q 

PROVIDER: S-EPMC11399402 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature