Bioinformatics-based identification of key genes for Olaparib resistance in breast cancer: prognostic implications and therapeutic relevance

Breast cancer is the most common malignancy among women worldwide, with drug therapy playing a crucial role in its treatment. In recent years, poly ADP-ribose polymerase (PARP) inhibitors, such as Olaparib, have shown significant efficacy in the management of BReast CAncer gene (BRCA)-mutated breast cancers. However, the emergence of resistance has become a major clinical challenge, limiting their long-term effectiveness. This in-silico study aimed to identify key genes associated with Olaparib resistance through comprehensive bioinformatics analysis. Differential expression and drug sensitivity prediction were performed to identify resistance-associated genes, followed by pathway enrichment and protein-protein interaction (PPI) network construction. Kaplan-Meier survival analysis and Cox regression were conducted to evaluate the prognostic value of candidate genes. Four immune-related genes-CD19, CXCL9, ICOS, and CXCL13-were identified as being closely associated with Olaparib resistance and relapse-free survival. Additionally, comparative drug sensitivity analysis revealed that high-risk subgroups may exhibit differential response patterns to specific chemotherapeutic agents. These findings provide a theoretical framework for understanding the molecular basis of Olaparib resistance in breast cancer and offer insights for future experimental and translational research.