Table13_Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization.XLSX

Our research aimed to identify new therapeutic targets for Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer known for its low 5-year survival rate of 22%. By employing a comprehensive methodological approach, we analyzed bulk RNA sequencing data from 513 LUAD and 59 non-tumorous tissues, identifying 2,688 differentially expressed genes. Using Mendelian randomization (MR), we identified 74 genes with strong evidence for a causal effect on risk of LUAD. Survival analysis on these genes revealed significant differences in survival rates for 13 of them. Our pathway enrichment analysis highlighted their roles in immune response and cell communication, deepening our understanding. We also utilized single-cell RNA sequencing (scRNA-seq) to uncover cell type-specific gene expression patterns within LUAD, emphasizing the tumor microenvironment’s heterogeneity. Pseudotime analysis further assisted in assessing the heterogeneity of tumor cell populations. Additionally, protein-protein interaction (PPI) network analysis was conducted to evaluate the potential druggability of these identified genes. The culmination of our efforts led to the identification of five genes (tier 1) with the most compelling evidence, including SECISBP2L, PRCD, SMAD9, C2orf91, and HSD17B13, and eight genes (tier 2) with convincing evidence for their potential as therapeutic targets.

本研究旨在为肺腺癌（LUAD，Lung adenocarcinoma）发掘全新治疗靶点。肺腺癌作为非小细胞肺癌的主要亚型，其5年生存率仅为22%。本研究采用系统化研究策略，对513份肺腺癌组织与59份非肿瘤组织的批量RNA测序（bulk RNA sequencing）数据进行整合分析，共筛选得到2688个差异表达基因。通过孟德尔随机化（Mendelian randomization, MR）分析，本研究鉴定出74个对肺腺癌发病风险具有显著因果关联的基因。对上述74个基因的生存分析显示，其中13个基因与患者总生存率存在显著相关性。通路富集分析结果表明，这些基因主要参与免疫应答与细胞通讯过程，深化了我们对肺腺癌发病机制的理解。本研究还采用单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）技术，解析了肺腺癌组织内的细胞类型特异性基因表达模式，重点揭示了肿瘤微环境的异质性。拟时序分析（Pseudotime analysis）进一步辅助评估了肿瘤细胞群体的异质性。此外，本研究开展了蛋白质相互作用（protein-protein interaction, PPI）网络分析，以评估上述候选基因的潜在成药性。本研究最终筛选出5个证据等级为Tier 1的最优候选基因，包括SECISBP2L、PRCD、SMAD9、C2orf91及HSD17B13，同时确定了8个证据等级为Tier 2的具有说服力的潜在治疗靶点基因。