Integrated bioinformatics analysis reveals ISG15, IFIH1, OAS2, MX1, and CXCL10 as predictive biomarkers of neoadjuvant chemotherapy response in triple-negative breast cancer

Background: The prognosis for triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer, is challenging, and there are few available treatments. Despite neoadjuvant chemotherapy (NAC) being the prevalent treatment, it has a wide range of side effects. Hence, identifying predictive indicators of NAC response could enhance treatment selection and outcomes. Methods: We employed a standard bioinformatics technique to analyze the RNA-seq data from GSE260989 on a Linux system. After trimming and quality assessment, the reads were aligned to the GRCh38 reference genome, yielding gene-level counts. We employed DESeq2 to analyze expression differences and utilized WGCNA to identify co-expression modules associated with the NAC response. Functional enrichment analyses (KEGG, Reactome) and protein–protein interaction studies were performed to identify key pathways. Hub genes were ranked based on their topological scores within the PPI network. Results: The pre- and post-NAC TNBC samples exhibited 1023 genes that were either up- or down-regulated (padj < 0.05). The strongest association between treatment response and the turquoise module was observed. Five hub genes—ISG15, IFIH1, OAS2, MX1, and CXCL10—linked to interferon signaling, immune modulation, and chemotherapy resistance, were identified through combined network and enrichment analyses. After NAC treatment, all five genes showed consistent downregulation, suggesting increased chemosensitivity and a shift toward a less aggressive tumor phenotype. Conclusion: ISG15 , IFIH1 , OAS2 , MX1 , and CXCL10 are identified as putative predictive biomarkers of NAC response in TNBC by this integrative bioinformatics research. Better treatment sensitivity may be reflected in their coordinated downregulation, warranting additional verification in larger clinical cohorts.