Machine Learning Model for the Identification of Lung Cancer Subtypes based on DNA Methylation

Lung Adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two main histology subtypes of non-small cell lung cancer (NSCLC) with 70% of total Lung Cancer. In this article we proposed an ensemble-based model for the identification of subtypes of NSCLC using methylation data. Proposed Random Forest-based model along with out of bag (OOB) error based feature selection technique identified the top ten most important CpG sites that are highly differentiator between LUSC and LUAD subtypes of NSCLC with an accuracy, precision and F1 Score of. The proposed model outperformed the other existing models for the same purpose with huge margin of 12%. Pathway analysis of the proposed 10 CpG sites revealed different pathways for LUAD and LUSC associated genes, LUAD-associated genes primarily participated in TP53, PTEN, GLP-1, Incretin regulation, and apoptosis. Conversely, LUSC-associated genes were predominantly involved in pathways for platelet degranulation, serine biosynthesis, and Nephrin family interaction.