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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月18日

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  • Kezhen Shen,
  • Xiaozhe Li,
  • Sihan Zhao,
  • Xinyu Zhang,
  • Le Yu,
  • Jun Li,
  • Lei Cai

摘要

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.