This study offers a detailed exploration of lung adenocarcinoma (LUAD), addressing its heterogeneity and treatment challenges through a multi‐faceted analysis that includes gene expression, genetic subtyping, pathway analysis, immune assessment, and drug sensitivity. It identifies 165 genes with significant expression differences and 46 genes associated with survival, revealing insights into oxidative stress and autophagy. LUAD samples were divided into three subtypes using consensus clustering on these 46 genes, with distinct survival outcomes. Gene Set Enrichment Analysis (GSEA) on HALLMARK gene sets indicated pathway variations with survival implications. The immune landscape, analyzed using the CIBERSORT algorithm, showed different immune cell distributions across subtypes, with the first subtype exhibiting a better immune environment and survival prospects. Advanced machine learning techniques developed a risk model from a set of four genes, effectively categorizing patients into high and low‐risk groups, validated through external datasets and analyses. This model linked lower risk scores to better clinical stages, with a higher mutation rate and potential immunotherapy benefits observed in the high‐risk group. Drug sensitivity assessments highlighted varied treatment responses between risk groups, suggesting avenues for personalized therapy. This comprehensive analysis enhances the understanding of LUAD's molecular and clinical nuances, offering valuable insights for tailored treatment approaches.

中文翻译：

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肺腺癌的综合分析：揭示差异基因表达、生存相关基因、亚型分层和免疫景观影响

本研究对肺腺癌 (LUAD) 进行了详细探索，通过包括基因表达、遗传亚型、通路分析、免疫评估和药物敏感性在内的多方面分析解决其异质性和治疗挑战。它鉴定了 165 个具有显着表达差异的基因和 46 个与生存相关的基因，揭示了对氧化应激和自噬的见解。使用这 46 个基因的共识聚类将 LUAD 样本分为三个亚型，具有不同的生存结果。 HALLMARK 基因集的基因集富集分析 (GSEA) 表明了对生存有影响的途径变化。使用 CIBERSORT 算法分析的免疫景观显示，不同亚型的免疫细胞分布不同，第一个亚型表现出更好的免疫环境和生存前景。先进的机器学习技术根据四个基因开发了一个风险模型，有效地将患者分为高风险组和低风险组，并通过外部数据集和分析进行验证。该模型将较低的风险评分与更好的临床阶段联系起来，在高风险组中观察到较高的突变率和潜在的免疫治疗益处。药物敏感性评估强调了风险组之间不同的治疗反应，这提出了个性化治疗的途径。这种全面的分析增强了对 LUAD 分子和临床细微差别的理解，为定制治疗方法提供了宝贵的见解。