Su Yazhou, Huo Tingting, Wang Yanan, Li Jingyan
Department of Thoracic Surgery, The First Affiliated Hospital of Xinxiang Medical University, 88 Jiankang Road, Weihui, 453100, Henan province, China.
Department of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, Henan province, China.
Clin Transl Oncol. 2025 Apr;27(4):1539-1557. doi: 10.1007/s12094-024-03703-1. Epub 2024 Sep 18.
Cancer driver genes (CDGs) have been reported as key factors influencing the progression of lung adenocarcinoma (LUAD). However, the role of CDGs in LUAD prognosis has not been fully elucidated.
LUAD transcriptome data and CDG-related data were obtained from public databases and literature. Differentially expressed CDGs (DE-CDGs) greatly associated with LUAD survival (P < 0.05) were identified to establish a prognostic model. In addition, immune analysis of high-risk (HR) and low-risk (LR) groups was conducted by utilizing the CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms to assess immune differences. Subsequently, mutation analysis was conducted using maftools. Finally, candidate drugs were identified using the CellMiner database.
40 DE-CDGs significantly associated with LUAD survival and 11 DE-CDGs associated with prognosis were identified through screening. Regression analysis revealed that risk score can independently predict LUAD prognosis (P < 0.05). Immune landscape analysis revealed that compared to the HR group, the LR group had higher immune scores and high infiltration of various immune cells such as follicular helper B cells and T cells. Mutation landscape analysis demonstrated that missense mutation was the most common mutation type in both risk groups. Drug prediction analysis revealed strong correlations of fulvestrant, S-63845, sapacitabine, lomustine, BLU-667, SR16157, motesanib, AZD-9496, XK-469, dimethylfasudil, P-529, and imatinib with the model genes, suggesting their potential as candidate drugs targeting the model genes.
This study identified 11 effective biomarkers, DE-CDGs, which can predict LUAD prognosis and explored the biological significance of CDGs in LUAD prognosis, immunotherapy, and treatment.
癌症驱动基因（CDGs）已被报道为影响肺腺癌（LUAD）进展的关键因素。然而，CDGs在LUAD预后中的作用尚未完全阐明。
从公共数据库和文献中获取LUAD转录组数据和与CDG相关的数据。识别出与LUAD生存密切相关（P < 0.05）的差异表达CDGs（DE-CDGs），以建立预后模型。此外，利用CIBERSORT和单样本基因集富集分析（ssGSEA）算法对高风险（HR）和低风险（LR）组进行免疫分析，以评估免疫差异。随后，使用maftools进行突变分析。最后，使用CellMiner数据库识别候选药物。
通过筛选，确定了40个与LUAD生存显著相关的DE-CDGs和11个与预后相关的DE-CDGs。回归分析显示，风险评分可独立预测LUAD预后（P < 0.05）。免疫景观分析显示，与HR组相比，LR组具有更高的免疫评分以及滤泡辅助性B细胞和T细胞等多种免疫细胞的高浸润。突变景观分析表明，错义突变是两个风险组中最常见的突变类型。药物预测分析显示，氟维司群、S-63845、沙培他滨、洛莫司汀、BLU-667、SR16157、莫替沙尼、AZD-9496、XK-469、二甲磺酸法舒地尔、P-529和伊马替尼与模型基因有很强的相关性，表明它们作为靶向模型基因的候选药物的潜力。
本研究确定了11个有效的生物标志物，即DE-CDGs，它们可以预测LUAD预后，并探索了CDGs在LUAD预后、免疫治疗和治疗中的生物学意义。
