Cancer reports
Integrative Bioinformatics and Machine Learning Approaches for Biomarker and Pathway Discovery in Malignant Pleural Mesothelioma
By Rohman ML and Biswas S.
Abstract
Background
Malignant pleural mesothelioma (MPM) is a highly aggressive thoracic cancer with limited treatment options and poor prognosis.
Aims
This study employed an integrative transcriptomic and computational approach to uncover potential biomarkers for MPM.
Methods
Gene expression data from the GSE51024 dataset, comprising 41 normal and 55 MPM tumor samples, were preprocessed using the robust multiarray average (RMA) method. Differentially expressed genes (DEGs) were identified using the Limma package and Wilcoxon rank tests. Weighted gene co-expression network analysis (WGCNA) revealed significant gene modules using topological overlap and preservation statistics, followed by functional annotation through Gene Ontology (GO) and KEGG pathway analyses.