Computational Analysis to Identify Novel Drug Targets for Esophageal Cancer

Cancer is a multigene and widespread disease. Increasing drug resistance leads to the development of new therapeutic targets. Recent research indicates that various cellular components called stress granules (SGs) are engaged in the cancer-related signaling pathway. The phosphoinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling, considered a master regulator in cancer, has been shown through genomic profiling studies to play a key role in esophageal cancer (EC). In this study, we performed the in silico analysis of an RNA sequencing dataset to investigate the effects of omipalisib, a PI3K/mTOR inhibitor, on EC cell lines. Our objective was to identify novel molecular targets, particularly SG-related proteins, that contribute to drug resistance in EC. Using computational approaches including differential gene expression analysis, pathway analysis, and functional enrichment, we examined the transcriptomic changes in response to omipalisib treatment. Our analysis revealed downregulation of the PI3K/mTOR signaling pathway and upregulation of compensatory pathways such as FOXO and JAK-STAT signaling in response to omipalisib. Notably, we identified 16 SG-related proteins that were significantly upregulated, suggesting their potential role in drug resistance mechanisms. These findings provide new insights into the molecular mechanisms underlying drug resistance in EC and highlight potential novel targets for therapeutic intervention. Currently, EC is limited by the number of potential drugs for treatment and poor prognosis and is prone to chemotherapeutic resistance to existing clinically proven drugs. Our computational analysis offers valuable insights into targeting SGs for cancer drug discovery, potentially enhancing the development of new therapeutic strategies for EC. These results provide a strong foundation for future experimental validation and drug development efforts aimed at overcoming resistance to EC treatment. Received: 1 July 2024 | Revised: 20 September 2024 | Accepted: 20 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the NCBI Gene Expression Omnibus (GEO) database under accession number GSE143462 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143462. Author Contribution Statement Vinod Jangid: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Visualization. Chandrasekar Narayanan Rahul: Conceptualization, Methodology, Validation, Investigation, Writing – original draft. Aarthi Rashmi B: Validation, Writing – review & editing. Kanagaraj Sekar: Validation, Writing – review & editing, Supervision, Project administration.