Unravelling hub genes as potential therapeutic targets in lung cancer using integrated transcriptomic meta-analysis and in silico approach

Discovering Promising Biomarkers and Therapeutic Targets for Duchenne Muscular Dystrophy: a Multiomics Meta-Analysis Approach

Duchenne muscular dystrophy (DMD) is a genetic disorder that causes muscle weakness and degeneration. In this study, we identified potential biomarkers and drug targets for DMD through a comprehensive meta-analysis of mRNA profiles. We conducted an in-depth analysis of three microarray datasets from the GEO database, utilizing the Affymetrix platform. A rigorous data pre-processing pipeline encompassed background correction, normalization, log2 transformation and probe-to-gene symbol mapping. Robust multi-array average method followed by Limma package in R was employed to ensure differential expression analysis within individual datasets, yielding gene-specific p-values. We identified 63 genes exhibiting statistically significant differential expression across the three datasets (p < 0.05) and an absolute log fold change > 1.5. Functional enrichment analyses of these differentially expressed genes were done, followed by pathway analyses. Our results suggested pertinent biological processes, molecular functions and cellular components associated with DMD. Finally, eight hub genes-COL6A3, COL1A1, COL3A1, COL1A2, POSTN, TIMP1, THBS2 and SPP1-were pinpointed as central players in the network. Two differentially expressed genes with substantial absolute log-fold changes, namely, DMD, downregulated and MYH3, upregulated, were identified as potential therapeutic candidates. In light of these findings, our work contributes not only to understanding DMD at the molecular level but also presents potential targets for therapeutic strategies. Finally, our study facilitates the development of therapeutic interventions that can effectively control and mitigate the impact of DMD.