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<p>Lung cancer is the leading cause of cancer-related deaths globally. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases, and drug resistance severely undermines treatment efficacy. This review summarizes recent advances in elucidating NSCLC drug-resistance mechanisms using multi-omics integration. Multi-omics integration systematically reveals the molecular networks of drug resistance, identifies key biomarkers and targets, and facilitates the screening of high-priority candidates for drug development through experimental validation. Small-molecule inhibitors targeting drug-resistant proteins and multi-omics-guided combination therapies offer strategies to reverse resistance. Future directions involve developing simultaneous multi-omics detection technologies, leveraging artificial intelligence for intelligent data analysis, establishing standardized frameworks for data sharing, and implementing personalized medicine based on multi-omics to improve patient prognosis.</p>

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Multi-omics integration in deciphering non-small cell lung cancer drug resistance: current status, challenges, and future prospects

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Jidong Miao,
  • Wenqiang Guan,
  • Jing Wang,
  • Huiying Gong,
  • Qian Xie,
  • Yang Gao

摘要

Lung cancer is the leading cause of cancer-related deaths globally. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases, and drug resistance severely undermines treatment efficacy. This review summarizes recent advances in elucidating NSCLC drug-resistance mechanisms using multi-omics integration. Multi-omics integration systematically reveals the molecular networks of drug resistance, identifies key biomarkers and targets, and facilitates the screening of high-priority candidates for drug development through experimental validation. Small-molecule inhibitors targeting drug-resistant proteins and multi-omics-guided combination therapies offer strategies to reverse resistance. Future directions involve developing simultaneous multi-omics detection technologies, leveraging artificial intelligence for intelligent data analysis, establishing standardized frameworks for data sharing, and implementing personalized medicine based on multi-omics to improve patient prognosis.