Center for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India.
Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India.
Sci Rep. 2024 Oct 25;14(1):25327. doi: 10.1038/s41598-024-75861-9.
Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named "HNCDrugResDb" was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.
耐药性是头颈部癌症 (HNC) 抗癌治疗成功的重大障碍。我们旨在开发一个用于可视化和分析与 HNC 耐药性相关的分子表达改变的平台。通过数据挖掘,我们召集了涉及耐药性的差异表达分子和特定于上下文的信号事件。使用生物信息学方法探索了驱动基因、相互作用网络和转录调节。在 78 种针对 14 种抗癌药物的耐药细胞中,共鉴定出 2364 种差异表达分子,包括 1131 个 mRNAs、746 个蛋白质、62 个 lncRNAs、257 个 miRNAs、1 个 circRNA 和 166 个翻译后修饰。其中,有 255 种分子的差异表达水平显著,这些分子是 HNC 耐药性的特征性驱动基因。此外,我们还构建了一个信号通路景观,以及它们与耐药性相关的不同信号模块之间的相互作用。此外,还设计了一个名为“HNCDrugResDb”的公共数据库,具有浏览、查询和途径探索选项,用于获取和丰富耐药性中改变的分子改变和信号通路。HNCDrugResDb 还配备了一个耐药性分析工具,作为一个初始平台,根据驱动基因的表达模式推断耐药性的可能性。HNCDrugResDb 预计对头颈部癌症治疗和个性化医疗方法的药物发现和优化的未来进展具有重大意义。