Chen Zilu, Mei Kun, Tan Foxing, Zhou Yuheng, Du Haolin, Wang Min, Gu Renjun, Huang Yan
Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China.
Authors contributed equally.
Cancer Drug Resist. 2025 Jan 14;8:3. doi: 10.20517/cdr.2024.91. eCollection 2025.
Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung cancer (NSCLC), presents significant clinical challenges due to its high mortality and limited therapeutic options. The molecular heterogeneity and the development of therapeutic resistance further complicate treatment, underscoring the need for a more comprehensive understanding of its cellular and molecular characteristics. This study sought to delineate novel cellular subpopulations and molecular subtypes of LUAD, identify critical biomarkers, and explore potential therapeutic targets to enhance treatment efficacy and patient prognosis. An integrative multi-omics approach was employed to incorporate single-cell RNA sequencing (scRNA-seq), bulk transcriptomic analysis, and genome-wide association study (GWAS) data from multiple LUAD patient cohorts. Advanced computational approaches, including Bayesian deconvolution and machine learning algorithms, were used to comprehensively characterize the tumor microenvironment, classify LUAD subtypes, and develop a robust prognostic model. Our analysis identified eleven distinct cellular subpopulations within LUAD, with epithelial cells predominating and exhibiting high mutation frequencies in Tumor Protein 53 ( and Titin ( genes. Two molecular subtypes of LUAD [consensus subtype (CS)1 and CS2] were identified, each showing distinct immune landscapes and clinical outcomes. The CS2 subtype, characterized by increased immune cell infiltration, demonstrated a more favorable prognosis and higher sensitivity to immunotherapy. Furthermore, a multi-omics-driven machine learning signature (MOMLS) identified ribonucleotide reductase M1 (RRM1) as a critical biomarker associated with chemotherapy response. Based on this model, several potential therapeutic agents targeting different subtypes were proposed. This study presents a comprehensive multi-omics framework for understanding the molecular complexity of LUAD, providing insights into cellular heterogeneity, molecular subtypes, and potential therapeutic targets. Differential sensitivity to immunotherapy among various cellular subpopulations was identified, paving the way for future immunotherapy-focused research.
肺腺癌（LUAD）是非小细胞肺癌（NSCLC）最常见的亚型，因其高死亡率和有限的治疗选择而带来了重大的临床挑战。分子异质性和治疗耐药性的发展使治疗更加复杂，凸显了更全面了解其细胞和分子特征的必要性。本研究旨在描绘LUAD新的细胞亚群和分子亚型，识别关键生物标志物，并探索潜在的治疗靶点，以提高治疗效果和患者预后。采用整合多组学方法，纳入来自多个LUAD患者队列的单细胞RNA测序（scRNA-seq）、批量转录组分析和全基因组关联研究（GWAS）数据。使用先进的计算方法，包括贝叶斯反卷积和机器学习算法，全面表征肿瘤微环境，对LUAD亚型进行分类，并建立一个强大的预后模型。我们的分析在LUAD中确定了11个不同的细胞亚群，其中上皮细胞占主导，在肿瘤蛋白53（TP53）和肌联蛋白（TTN）基因中表现出高突变频率。确定了LUAD的两种分子亚型[共识亚型（CS）1和CS2]，每种亚型都显示出不同的免疫格局和临床结果。以免疫细胞浸润增加为特征的CS2亚型显示出更有利的预后和对免疫治疗更高的敏感性。此外，一个多组学驱动的机器学习特征（MOMLS）确定核糖核苷酸还原酶M1（RRM1）是与化疗反应相关的关键生物标志物。基于该模型，提出了几种针对不同亚型的潜在治疗药物。本研究提出了一个全面的多组学框架，用于理解LUAD的分子复杂性，为细胞异质性、分子亚型和潜在治疗靶点提供了见解。确定了不同细胞亚群对免疫治疗的差异敏感性，为未来以免疫治疗为重点的研究铺平了道路。
Cancer Discov. 2024-2-8
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