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Machine Learning-Based Identification of Tamoxifen Resistance-Associated Genes and Their Application in Prognostic
Chaoxu Gao1, Guojian Shi2, Lili Hou2
1School of Life Sciences, North China University of Science and Technology; Jiangsu Province Engineering Research Center of Molecular Target Therapy and Companion Diagnostics in Oncology, Suzhou Vocational Health College.
None:
Tamoxifen is a key endocrine therapy for estrogen receptor-positive (ER+) breast cancer, but acquired resistance limits long-term efficacy. The molecular mechanisms remain complex, and predictive biomarkers are lacking. Gene expression data related to tamoxifen resistance were obtained from GEO (GSE67916), and differentially expressed genes (DEGs) were identified using the limma algorithm. Functional enrichment analyses (GO and KEGG) revealed involvement in immune processes, antiviral responses, endocytosis, lysosome pathways, and estrogen signaling. Three machine learning algorithms (LASSO, SVM-RFE, and RF) identified six hub genes (CAMK1D, CHAC1, KIAA0513, MED13, NDRG1, STXBP5). A prognostic risk model based on these genes was constructed using TCGA-BRCA data, effectively stratifying patients into high- and low-risk groups with significantly different overall survival. The model demonstrated good predictive accuracy (AUC = 0.70) and stable performance in time-dependent ROC analyses, validated in an independent cohort. This study provides a robust tamoxifen resistance-related gene signature and a multigene prognostic model, offering novel insights into resistance mechanisms and potential guidance for individualized prognosis and therapy in ER+ breast cancer.
