Unraveling the Intricate Molecular Landscape and Potential Biomarkers in Lung Adenocarcinoma through Integrative Epigenomic and Transcriptomic Data Analysis

Lung carcinoma is one of the most prevalent and life-threatening cancers globally, with tobacco smoking being the most significant cause of lung cancer deaths. Lung adenocarcinoma (LUAD) accounts for approximately 40% of reported lung cancer cases and unfolds in a sequential multistage pattern, gradually developing genetic and epigenetic alterations. Alterations in DNA methylation at CpG sites are associated with smoking-induced lung cancer. Smoking-related epigenetic alterations are involved in the modulation of multiple biological pathways. Numerous tumors exhibit atypical methylation patterns, which can involve either increased (hypermethylation) or decreased (hypomethylation) addition of a methyl group to the cytosine. Demethylation of CpG sites is associated with the upregulation of oncogenes and genomic instability observed in multiple solid tumors, including lung cancer. However, hypermethylation is linked to the downregulation of the genes and silencing of tumor suppressors. Enhancers govern gene expression across great distances by looping DNA and offering distant regulatory regions closer to their target gene promoters.Therefore, we employed Illumina HM450k DNA methylation data of patients from The Cancer Genome Atlas (TCGA) to determine enhancers and link enhancer status with the expression of target genes to discover transcriptional targets using The Enhancer Linking by Methylation/Expression Relationship (ELMER) package of Bioconductor. In this study, we investigated a technique for predicting enhancer-target interactions by combining epigenomic and transcriptomic data from a substantial collection of primary tumor samples. This approach allowed us to identify target genes specifically regulated by enhancers with differential methylation patterns in LUAD and revealed the target genes of the differentially methylated sites and the enriched motifs modulating their expression in LUAD progression. The network-based approach aided in determining the hub genes playing a key role as central regulators of ribosome biogenesis, RNA processing, and cell cycle regulation in LUAD pathogenesis.