Discovery of PELATON links to the INHBA gene in the TGF-β pathway in colorectal cancer using a combination of bioinformatics and experimental investigations

Colorectal cancer (CRC) is a major worldwide health issue, with high rates of both occurrence and mortality. Dysregulation of the transforming growth factor-beta (TGF-β) signaling pathway is recognized as a pivotal factor in CRC pathogenesis. Notably, the INHBA gene and long non-coding RNAs (lncRNAs) have emerged as key contributors to CRC progression. The aim of this research is to explore the immunological roles of INHBA and PELATON in CRC through a combination of computational predictions and experimental validations, with the goal of enhancing diagnostic and therapeutic strategies. In this study, we utilized bioinformatics analyses, which involved examining differential gene expression (DEG) in the TCGA-COAD dataset and exploring the INHBA gene in relation to the TGF-β pathway. Additionally, we analyzed mutations of INHBA, evaluated the microenvironment and tumor purity, investigated the INHBA's connection to immune checkpoint inhibitors, and measured its potential as an immunotherapy target using the TIDE score. Utilizing bioinformatics analyses of the TCGA-COAD dataset beside experimental methodologies such as RT-qPCR, our investigation revealed significant upregulation of INHBA in CRC. As results, our analysis of the protein-protein interaction network associated with INHBA showed 10 interacting proteins that play a role in CRC-associated processes. We observed a notable prevalence of mutations within INHBA and explored its correlation with the response to immune checkpoint inhibitors. Our study highlights INHBA as a promising target for immunotherapy in CRC. Moreover, our study identified PELATON as a closely correlated lncRNA with INHBA, with experimental validation confirming their concurrent upregulation in CRC tissues. Thus, these findings highlight the importance of INHBA and PELATON in driving CRC progression, suggesting their potential utility as diagnostic and prognostic biomarkers. By integrating computational predictions with experimental validations, this research enhances our understanding of CRC pathogenesis and uncovers prospects for personalized therapeutic interventions.