| Description |
Lung cancer is characterized by high mortality and encompasses various subtypes. Treatment options and outcomes vary considerably across these subtypes. Although molecular-level investigations of individual lung cancer subtypes have been previously reported, genome-wide and fine-scale DNA methylation analyses across multiple subtypes remain largely unexplored. In the present study, subtype-specific CpG sites in lung cancer were identified, and a novel methylation vector-based algorithm was proposed to assess the methylation status across continuous CpG regions. By employing this approach, subtype-specific methylation vector clusters (SMVCs) were detected across various tissues, which were further validated using data from diverse sources. The aberrant methylation signals observed in each lung cancer subtype appear to be influenced by the cell of origin and the immune environment within the blood. Evidence is provided that small cell lung cancer (SCLC) exhibits abnormal methylation intervals within neural cells and pancreatic islet cells, with NeuroD1 binding specifically to these regions. Furthermore, neurological disease drugs that interact with NeuroD1 are proposed as potential therapeutic candidates for SCLC. The investigation of abnormal methylation signals across distinct lung cancer subtypes offers novel insights into the diagnosis and therapeutic strategies for the disease. |
