多组学整合分析揭示线粒体基因特征用于肺腺癌的预后评估与个性化治疗
Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma
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影响因子:7.5
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
DOI:
10.1186/s12967-024-05754-y
摘要
肺腺癌（LUAD）作为原发性肺癌中最常见的组织学亚型，其治疗效果仍然有限，准确的预后评估面临重大挑战。本研究旨在通过多组学整合方法阐明线粒体相关基因在肺腺癌中的预后意义，旨在开发个性化治疗策略。我们利用公开数据库中的转录组和单细胞RNA测序（scRNA-seq）数据，以及临床信息，首先对LUAD单细胞数据集应用降维和聚类技术，重点对成纤维细胞、上皮细胞和T细胞进行亚分类。随后，利用TCGA-LUAD数据鉴定线粒体相关的预后基因，并通过共识聚类将LUAD病例划分为不同的分子亚型，探讨各亚型间基因表达谱和临床特征的差异。借助多机器学习算法的集成，我们建立了基于线粒体相关基因的人工智能预后签名（AIDPS）模型，并在多个独立数据集上验证其预后预测的准确性。结果显示，AIDPS模型在LUAD患者预后预测中具有稳健的表现，且不同风险组在免疫治疗和化疗反应以及生存结局方面存在显著差异。此外，我们还对肿瘤突变负荷（TMB）、免疫微环境特征以及全基因组关联研究（GWAS）数据进行了全面分析，为线粒体相关基因在LUAD发病机制中的作用提供了新的见解。本研究不仅为改善LUAD的预后评估提供了新思路，也为个性化治疗的开发奠定了坚实基础。
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.