Machine Learning-Based Identification of Tamoxifen Resistance-Associated Genes and Their Application in Prognostic Modeling of Breast Cancer

Summary

We developed a machine learning–derived six-gene tamoxifen resistance signature that stratifies breast cancer patients by survival risk and may support personalized prognostic assessment and therapeutic decision-making in clinical practice.

Abstract

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.

Introduction

Breast cancer is the most commonly diagnosed cancer among women globally and remains a major contributor to cancer-associated deaths¹. Based on molecular profiling, approximately 60-70% of patients present with the estrogen receptor-positive (ER⁺) subtype, which generally responds well to initial endocrine therapy. Among various endocrine agents, tamoxifen—a selective estrogen receptor modulator (SERM)—has long been the standard of care for adjuvant and metastatic treatment of ER⁺ breast cancer, significantly improving survival outcomes in large-scale randomized trials².

Protocol

All data used in this study were obtained from publicly accessible databases (TCGA, GEO, and METABRIC). No human participants or animals were involved; therefore, institutional review board approval and informed consent were not required.

Data acquisition and preprocessing

Gene expression data related to tamoxifen resistance were retrieved from the Gene Expression Omnibus (GEO) database¹⁰. The GSE67916 dataset (Affymetrix Human Genome U133 Plus 2.0 Array) includes 18 breast cancer cell samples: 8 untreated control samples and 10 tamoxifen-resistant samples generated through ....

Results

Identification of differentially expressed genes associated with tamoxifen resistance

A total of 854 differentially expressed genes (DEGs) were identified between tamoxifen-resistant and control samples, including 556 upregulated and 298 downregulated genes. The distribution of DEGs showed a predominance of upregulated genes in resistant samples, suggesting extensive transcriptional activation associated with tamoxifen resistance (Figure 1).

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Discussion

Tamoxifen resistance remains a pivotal obstacle in the treatment of ER⁺ breast cancer. While mechanisms such as ER mutations are well known, the systemic molecular adaptations to long-term therapy remain less well understood. In this study, we integrated transcriptomics and machine learning to identify a robust six-gene signature (CAMK1D, CHAC1, KIAA0513, MED13, NDRG1, STXBP5) that predicts both tamoxifen resistance and patient prognosis.

Our functional analysis revealed that resist.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Affy package	Bioconductor	-	Microarray data processing
clusterProfiler package	Bioconductor	v4.4.2	GO and KEGG enrichment analysis
CIBERSORT algorithm	Newman et al.	-	Immune cell infiltration estimation
e1071 package (SVM-RFE)	CRAN	-	Support vector machine recursive feature elimination
Gene Expression Omnibus (GEO)	NCBI	GSE67916	Tamoxifen resistance dataset
glmnet package	CRAN / Bioconductor	-	LASSO regression
limma package	Bioconductor	-	Differential expression analysis
METABRIC dataset	cBioPortal / Curtis et al.	-	Validation cohort
pROC package	CRAN	-	ROC curve analysis
R software	R Core Team	v4.4.2	Statistical computing environment
Random forest package	CRAN	-	Random Forest algorithm
rms package	CRAN	-	Nomogram construction
TCGA-BRCA cohort	The Cancer Genome Atlas (TCGA)	-	Training cohort RNA-seq data
timeROC package	CRAN	-	Time-dependent ROC analysis