International journal of women's health · 2026
Novel Perspective for Prognostic Stratification and Personalized Therapy in Breast Cancer Patients: Development of Cancer Stem Cells and Metabolism-Associated Prognostic Model.
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Li Wanjun, Li Shuo, Quan Shaomin, Han Tuo, Chen Jiaqi, Zhai Chao, Xiao Dong, Fan Zhigang
Аннотация
BACKGROUND: Breast cancer is a common malignant tumor in the female population, and cancer stem cells (CSCs) and metabolic reprogramming are key factors for tumor progression. This study aimed to construct a CSCs and metabolism-associated prognostic model for breast cancer patients.
METHODS: Differentially expressed genes (DEGs) were identified from the GSE42568 dataset and intersected with CSCs-associated genes (from BCSCdb) and metabolism-associated genes (from KEGG). A prognostic model was established via univariate and LASSO Cox regression, validated in GSE7390 and brca_metabric datasets. In addition, functional annotations, immune cell infiltration analysis, drug sensitivity analysis, and immunohistochemical assay were also conducted.
RESULTS: A risk score model established from 12 CSCs and metabolism-associated DEGs (ETFDH, PLA2G4A, ABCA1, ALDH2, ADRA2A, TRIB3, CYB5A, STARD3, UGCG, CACNA1D, ASS1, and GSTP1) showed favorable prognostic predictive value. Immunohistochemical results showed that the expression trends of proteins encoded by these model genes were consistent with those of gene expression in public databases. Multivariate Cox regression analysis revealed that lymph and risk score were independent prognostic factors for breast cancer patients. Functional annotation results clearly revealed significant biological differences between the high- and low-risk groups. In addition, there were differences in immune cell infiltration levels between the two groups, and the expression levels of immune checkpoints were significantly higher in the high-risk group. The results of drug sensitivity prediction showed that there may be different drug responses between high and low risk groups.
CONCLUSION: The CSCs and metabolism-associated model provides a potential tool for prognostic stratification and personalized treatment guidance in breast cancer.
METHODS: Differentially expressed genes (DEGs) were identified from the GSE42568 dataset and intersected with CSCs-associated genes (from BCSCdb) and metabolism-associated genes (from KEGG). A prognostic model was established via univariate and LASSO Cox regression, validated in GSE7390 and brca_metabric datasets. In addition, functional annotations, immune cell infiltration analysis, drug sensitivity analysis, and immunohistochemical assay were also conducted.
RESULTS: A risk score model established from 12 CSCs and metabolism-associated DEGs (ETFDH, PLA2G4A, ABCA1, ALDH2, ADRA2A, TRIB3, CYB5A, STARD3, UGCG, CACNA1D, ASS1, and GSTP1) showed favorable prognostic predictive value. Immunohistochemical results showed that the expression trends of proteins encoded by these model genes were consistent with those of gene expression in public databases. Multivariate Cox regression analysis revealed that lymph and risk score were independent prognostic factors for breast cancer patients. Functional annotation results clearly revealed significant biological differences between the high- and low-risk groups. In addition, there were differences in immune cell infiltration levels between the two groups, and the expression levels of immune checkpoints were significantly higher in the high-risk group. The results of drug sensitivity prediction showed that there may be different drug responses between high and low risk groups.
CONCLUSION: The CSCs and metabolism-associated model provides a potential tool for prognostic stratification and personalized treatment guidance in breast cancer.