肺腺癌（LUAD）是原发性肺癌最常见的组织学亚型，其治疗效果仍然不足，准确的预后评估构成了重大挑战。本研究试图通过综合多组学方法阐明 LUAD 中线粒体相关基因的预后意义，旨在制定个性化治疗策略。利用转录组和单细胞 RNA 测序 (scRNA-seq) 数据以及来自公开数据库的临床信息，我们首先将降维和聚类技术应用于 LUAD 单细胞数据集，重点关注成纤维细胞、上皮细胞和T细胞。随后使用 TCGA-LUAD 数据识别线粒体相关的预后基因，并通过共识聚类将 LUAD 病例分层为不同的分子亚型，从而可以探索不同亚型的基因表达谱和临床特征分布。通过利用机器学习算法的集合，我们开发了基于线粒体相关基因的人工智能衍生的预后特征（AIDPS）模型，并在多个独立数据集中验证了其预后准确性。 AIDPS 模型对 LUAD 患者的结局表现出强大的预测能力，揭示了免疫疗法和化疗反应的显着差异，以及风险组之间生存结局的显着差异。此外，我们对肿瘤突变负荷（TMB）、免疫微环境特征和全基因组关联研究（GWAS）数据进行了全面分析，为线粒体相关基因在 LUAD 发病机制中的机制作用提供了更多见解。这项研究不仅提供了一种改善 LUAD 预后评估的新方法，而且还为个性化治疗干预措施的发展奠定了坚实的基础。© 2024。作者。

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.© 2024. The Author(s).