Identifying regulatory driver motifs in non-small cell lung carcinoma via a systematic approach
Abstract
Background: Lung cancer exhibits highest incidence among all cancer types worldwide and even after rigorous research and advanced treatment strategies, it constitutes a primary cause of cancer-related mortality. Non-small cell lung cancer is the predominant subtype, constituting the majority of lung cancer cases. Therefore, exploring novel biomarkers is crucial for betterment of diagnostic and therapeutic approaches.
Methods: The meta-analysis was performed using GEO datasets, to explore the differentially expressed genes (DEGs) and miRNAs (DEMs) in the non-small cell lung cancer (NSCLC) cases. We explored the ChEA database to extract the relevant transcription factors regulating the expression of our hub genes. Further, based on the highest degree of centrality, the feed-forward loop was identified with highest sub-network motif comprising of gene-TF-miRNA. We used pathway and GO term enrichment analysis to determine the importance of these DEGs in different biological processes.
Results: In NSCLC, we found 950 differentially expressed miRNAs and 1761 genes were recognized exhibiting the significant change in expression (p < 0.05). Further, we investigated the role of sub-network motif in patient survival, hsa-miR-5010 was found to be significantly linked with patient outcome in Lung Adenocarcinoma (LUAD) (p = 0.033) and Lung Squamous Cell Carcinoma (LUSC) (p = 0.013) while SMAD4 (p < 0.001) and NRG1 (p < 0.001) expression exhibited prognostic significance in LUAD cohort…
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