全面的多摩学整合发现肺腺癌预后和个性化疗法的线粒体基因特征
Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma
影响因子:7.50000
分区:医学2区 Top / 医学：研究与实验2区
发表日期:2024 Oct 21
作者:
Wenjia Zhang, Lei Zhao, Tiansheng Zheng, Lihong Fan, Kai Wang, Guoshu Li
摘要
肺腺癌（LUAD）的治疗功效是原发性肺癌最普遍的组织学亚型，仍然不足，准确的预后评估带来了重大挑战。这项研究旨在通过一种旨在开发个性化的治疗策略的综合多媒体方法来阐明LUAD中线粒体相关基因的预后意义。我们首先利用转录组和单细胞RNA测序（SCRNA-SEQ）数据，以及来自公开可用数据库的临床信息，首先将降低性降低和聚类技术应用于LUAD单细胞数据集，重点放在成纤维细胞，上皮细胞和T细胞的亚分类上。随后使用TCGA-LUAD数据鉴定了与线粒体相关的预后基因，并通过共有的聚类将LUAD病例分为不同的分子亚型，从而允许探索基因表达谱和跨子类型的临床特征分布。通过利用机器学习算法的合奏，我们开发了基于线粒体相关基因的人工智能衍生的预后签名（AIDP）模型，并验证了多个独立数据集的预后准确性。 AIDPS模型显示出对LUAD患者结局的强大预测能力，揭示了对免疫疗法和化学疗法的反应显着差异，以及风险群体之间的生存结果。此外，我们对肿瘤突变负担（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，从而提供了对线粒体相关基因在LUAD病原体中的机械作用的更多见解。这项研究不仅提供了一种新的方法来改善LUAD的预后评估，而且还为开发个性化的治疗干预措施树立了坚实的基础。
Abstract
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.