Integrative bulk RNA analysis unveils immune evasion mechanisms and predictive biomarkers of osimertinib resistance in non-small cell lung cancer.

整合式批量 RNA 分析揭示了非小细胞肺癌的免疫逃逸机制和奥希替尼耐药性的预测生物标志物

阅读:11
作者:Shi Ling, Qiu Feng, Shi Chao, Zhang Guohua, Yu Feng
Non-small cell lung cancer (NSCLC) is one of the most prevalent and deadliest cancers worldwide, accounting for a significant global health burden. Targeted therapies such as osimertinib, a third-generation EGFR inhibitor, have transformed the treatment landscape for EGFR-mutant NSCLC by offering improved progression-free survival. However, the inevitable development of resistance remains a formidable challenge, necessitating deeper insights into its molecular underpinnings. In this study, we employed an integrative bioinformatics approach to analyze multi-cohort transcriptomic datasets, uncovering 126 resistance-associated genes, revealing 50 significant osimertinib resistance-related genes, and identifying eight key hub genes (KRT14, KRT16, KRT17, KRT5, KRT6A, KRT6B, TP63, and TRIM29) that contribute to immune evasion and tumor microenvironment remodeling. Integrated qPCR and Western blot analyses validated the significant upregulation of KRT14, KRT16, KRT6A, and TRIM29 in osimertinib-resistant cell lines (PC9 OR and HCC827 OR) at both transcriptional and translational levels, with KRT14 exhibiting the most pronounced upregulation. Functional assays demonstrated that KRT14 knockdown restored osimertinib sensitivity, suppressed proliferation, and impaired migration in resistant cells. Functional enrichment analyses revealed critical pathways, including p53 signaling and metabolic reprogramming, underlying resistance mechanisms. Batch effect analysis highlighted a marked reduction in effector immune cells, such as activated CD8 + T cells, alongside an increase in immunosuppressive populations, emphasizing the role of immune evasion in osimertinib resistance.We constructed a robust diagnostic model, nomoScore, based on the hub genes, achieving excellent predictive accuracy (AUC > 0.9) in training and validation datasets. These findings offer novel insights into resistance mechanisms and propose actionable strategies for integrating targeted and immunotherapies to improve outcomes for NSCLC patients. Future experimental and clinical studies are essential to validate and translate these findings into therapeutic advances.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。