Uncovering GSTK1 and PTGER3 as biomarkers for breast cancer prognosis through comprehensive analyses

Breast Cancer threatens women’s health globally, and identifying prognostic genes is therefore critical for optimizing diagnosis and therapy. In this study, we retrieved datasets from the GEO database, and differential analysis subsequently identified 66 DEGs. GO and KEGG enrichment analyses revealed these DEGs were not only enriched in biological processes like angiogenesis and granulocyte differentiation but also in key pathways including Th17 cell differentiation and Cancer transcriptional dysregulation. Subsequently, we evaluated 21 machine learning algorithms, among which RidgeCV and XGBoost significantly outperformed Others. Specifically, prognostic models constructed based on these two algorithms exhibited excellent performance in decision curve analysis, confusion matrix evaluation, and feature importance assessment. Consequently, COX regression analysis of the 29 overlapping genes identified by both models pinpointed GSTK1 and PTGER3 as key prognostic genes. Notably, GSTK1 and PTGER3 were underexpressed in breast Cancer tissues, with high diagnostic potential and a strong correlation with pathological staging. Moreover, high expression of these two genes correlated with favorable patient survival, and a nomogram integrating them could effectively predict patient survival probabilities. Additionally, CIBERSORT and ssGSEA analyses linked GSTK1/PTGER3 to immune cell infiltration in the tumor microenvironment, further indicating their crucial roles in regulating the breast Cancer immune landscape. In vitro experiments further confirmed that GSTK1 and PTGER3 are weakly expressed in breast Cancer cells, and their high expression could inhibit the proliferation of breast Cancer cells while predicting favorable patient survival outcomes. Collectively, this study identifies GSTK1 and PTGER3 as potential biomarkers for breast Cancer precision medicine, thereby providing valuable insights into disease pathogenesis and novel therapy development.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12672-025-04329-7.

Biomarker; Breast cancer; GSTK1; Machine learning; PTGER3.