Identifying Multi-Omics Interactions for Lung Cancer Drug Targets Discovery Using Kernel Machine Regression

Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. This study investigates multi-omics lung cancer data obtained from The Cancer Genome Atlas (TCGA) repository. Differentially expressed genes were identified using four statistical approaches: LIMMA, T-test, Canonical Correlation Analysis (CCA), and the Wilcoxon test applied across gene expression (GE), DNA methylation, and microRNA (miRNA) datasets. Kernel Machine Regression (KMR) was subsequently employed to perform data fusion across the multi-modal datasets. The empirical results highlight notable interactions among GE, miRNA expression, and DNA methylation in lung cancer. Our analysis identified 38 genes that show significant associations with lung cancer. Among these, 8 genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, and ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that may offer promising therapeutic potential against lung cancer. The effectiveness of these candidate drugs is further reinforced by evidence from independent research studies, which emphasize their potential in lung cancer treatment.