Identification of candidate target genes of oral squamous cell carcinoma using high-throughput RNA-Seq data and in silico studies of their interaction with naturally occurring bioactive compounds

AbstractOral Squamous Cell Carcinoma (OSCC) accounts for more than 90% of all kinds of oral neoplasms that develop in the oral cavity. It is a type of malignancy that shows high morbidity and recurrence rate, but data on the disease's target genes and biomarkers is still insufficient. In this study, in silico studies have been performed to find out the novel target genes and their potential therapeutic inhibitors for the effective and efficient treatment of OSCC. The DESeq2 package of RStudio was used in the current investigation to screen and identify differentially expressed genes for OSCC. As a result of gene expression analysis, the top 10 novel genes were identified using the Cytohubba plugin of Cytoscape, and among them, the ubiquitin-conjugating enzyme (UBE2D1) was found to be upregulated and playing a significant role in the progression of human oral cancers. Following this, naturally occurring compounds were virtually evaluated and simulated against the discovered novel target as prospective drugs utilizing the Maestro, Schrodinger, and Gromacs software. In a simulated screening of naturally occurring potential inhibitors against the novel target UBE2D1, Epigallocatechin 3-gallate, Quercetin, Luteoline, Curcumin, and Baicalein were identified as potent inhibitors. Novel identified gene UBE2D1 has a significant role in the proliferation of human cancers through suppression of 'guardian of genome' p53 via ubiquitination dependent pathway. Therefore, the treatment of OSCC may benefit significantly from targeting this gene and its discovered naturally occurring inhibitors.Communicated by Ramaswamy H. SarmaKeywords: Oral squamous cell carcinomaRNA-Seq datanetwork/hub gene analysisUbiquitin-Conjugating enzyme (UBE2D1)natural inhibitorsmolecular dockingmolecular dynamic simulation AcknowledgmentThe authors are thankful to the Department of Applied Sciences, Indian Institute of Information Technology for providing the in silico facilities and encouragements.Author contributionsUnnati Soni: Conceptualization, Methodology, Investigation, Software, Validation, Data Curation, Writing the original draft, Review and Editing; Anirudh Singh: Software, Data curation, Review and Editing; Ramendra Soni: Review and Editing; Sintu Samanta: Review and Editing; Pritish Kumar Varadwaj: Conceptualization, Supervision, Resources, Review and Editing; Krishna Misra: Conceptualization, Supervision, Resources, Review and Editing.Disclosure statementThere is no conflict of interest among authors. Besides the facilities provided by the institute the present work has not been funded by any agency.Data availability statementThe data generated for the present study are included in the article and supplementary material file.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.