Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning - Zeng, Xianchang; Wei, Lingyun; Lv, Lu; Wu, Di; Shen, Yingying; Lu, Xinliang; Kong, Xianghui; Cai, Zhijian; Wang, Jianli (Frontiers in Pharmacology, 2025)
BackgroundThe molecular pathogenesis of lung adenocarcinoma (LUAD) involves genomic mutations, autophagy dysregulation, and signaling pathway disruptions. Autophagy, a key cellular process, is tightly linked to cancer development; genes like ATG5 and ATG10 influence lung cancer progression, and epigenetic regulators modulate autophagy-related carcinogenesis. However, the role of epigenetic-autophagy genes in LUAD’s tumor microenvironment is under-researched.MethodsWe used the “limma”” package to identify differential epigenetic-related genes associated with altered autophagy regulation (A-ERGs) in LUAD. Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. Cell experiments, real-time PCR, and Western blot were conducted to verify the expression of KDM6B and KANSL1 and their effects on T-cell differentiation.ResultsBased on single-cell and transcriptome analyses, we screened 19 A-ERGs that were significantly differentially expressed in lung cancer tissues. These genes were primarily enriched in exhausted T cells. Subsequently, through machine learning, KDM6B and KANSL1 were identified to have excellent diagnostic performance. Single-cell level and transcriptome correlation analyses revealed that the expression of these two genes was associated with exhausted T cells. Results from in vitro cell experiments showed that high expression of these two genes promoted the occurrence of T cell exhaustion.ConclusionIn this study, we utilized bulk and single-cell transcriptomic data to uncover the potential molecular mechanisms of A-ERGs in lung cancer. We explored the characteristic distribution of these genes in the tumor immune microenvironment and identified two A-ERGs, KDM6B and KANSL1, as potential diagnostic biomarkers for lung adenocarcinoma (LUAD). Our findings offer novel strategies for targeted therapeutic interventions in LUAD.