Wang Ning, Li Yinan, Wang Yaoyao, Wang Wenting
Department of Respiratory and Critical Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
Department of Oncology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China.
Front Pharmacol. 2025 Feb 19;16:1540477. doi: 10.3389/fphar.2025.1540477. eCollection 2025.
Lung adenocarcinoma (LUAD) remains a major cause of cancer-related mortality worldwide, with high heterogeneity and poor prognosis. Epigenetic dysregulation plays a crucial role in LUAD progression, yet its potential in molecular classification and therapeutic prediction remains largely unexplored.
We performed an integrated multi-omics analysis of 432 LUAD patients from TCGA and 398 patients from GEO datasets. Using consensus clustering and random survival forest (RSF) algorithms, we established an epigenetic-based molecular classification system and constructed a prognostic model. The model's performance was validated in multiple independent cohorts, and its biological implications were investigated through comprehensive functional analyses.
We identified two distinct molecular subtypes (CS1 and CS2) with significant differences in epigenetic modification patterns, immune microenvironment, and clinical outcomes (P = 0.005). The RSF-based prognostic model demonstrated robust performance in both training (TCGA-LUAD) and validation (GSE72094) cohorts, with time-dependent AUC values ranging from 0.625 to 0.694. Low-risk patients exhibited enhanced immune cell infiltration, particularly CD8 T cells and M1 macrophages, and showed better responses to immune checkpoint inhibitors. Drug sensitivity analysis revealed subtype-specific therapeutic vulnerabilities, with low-risk patients showing higher sensitivity to conventional chemotherapy and targeted therapy.
Our study establishes a novel epigenetic-based classification system and predictive model for LUAD, providing valuable insights into patient stratification and personalized treatment selection. The model's ability to predict immunotherapy response and drug sensitivity offers practical guidance for clinical decision-making, potentially improving patient outcomes through precision medicine approaches.
肺腺癌（LUAD）仍是全球癌症相关死亡的主要原因，具有高度异质性和不良预后。表观遗传失调在LUAD进展中起关键作用，但其在分子分类和治疗预测方面的潜力仍 largely未被探索。
我们对来自TCGA的432例LUAD患者和来自GEO数据集的398例患者进行了综合多组学分析。使用共识聚类和随机生存森林（RSF）算法，我们建立了基于表观遗传的分子分类系统并构建了预后模型。该模型的性能在多个独立队列中得到验证，并通过全面的功能分析研究了其生物学意义。
我们鉴定出两种不同的分子亚型（CS1和CS2），它们在表观遗传修饰模式、免疫微环境和临床结果方面存在显著差异（P = 0.005）。基于RSF的预后模型在训练（TCGA-LUAD）和验证（GSE72094）队列中均表现出强大的性能，时间依赖性AUC值范围为0.625至0.694。低风险患者表现出增强的免疫细胞浸润，尤其是CD8 T细胞和M1巨噬细胞，并且对免疫检查点抑制剂表现出更好的反应。药物敏感性分析揭示了亚型特异性的治疗脆弱性，低风险患者对传统化疗和靶向治疗表现出更高的敏感性。
我们的研究建立了一种新颖的基于表观遗传的LUAD分类系统和预测模型，为患者分层和个性化治疗选择提供了有价值的见解。该模型预测免疫治疗反应和药物敏感性的能力为临床决策提供了实用指导，有可能通过精准医学方法改善患者预后。
