Introduction: The survival rate for oral tongue squamous cell carcinoma (OTSCC) is the lowest compared to other types of carcinomas in the oral cavity. This study aims to employ novel and robust methodologies based on bioinformatics techniques to identify differentially expressed genes in OTSCC tissues compared to normal tongue samples to shed light on the underlying causes of OTSCC.Materials and Methods: The dataset GSE13601 was obtained from the GEO, and the OPLS-DA method was applied to identify genes differentially expressed in OTSCC tissues compared to the normal healthy oral mucosa. A protein interaction map (PIM) was constructed, and hubs were identified. Survival analysis was performed to identify prognostic hub genes. The expression levels of the identified prognostic markers were evaluated at both mRNA and protein levels. Furthermore, bioinformatics web tools were used to perform pathway and GO annotation analyses.Results: The study revealed 154 genes differentially expressed in OTSCC, with a statistically significant p<0.001 and a |Log2 fold change |>0.585. EIF2S1, EGF, NME1, NEDD8, EIF4A1, TOP1, and PSMA4 were found to have significant prognostic roles in OTSCC. The expression levels of all the identified prognostic markers were confirmed in HNSCC at the mRNA level and further validated the up-regulation of NME1, TOP1, and PSMA4 in HNSCC using immunohistochemical analysis. The FOXM1 was identified as the most important regulator of the hubs, while CDK2 was found to be the protein kinase acting on the transcription factors. The proteasome pathway and the regulation of ubiquitin-protein ligase activity were significantly enriched in OTSCC.Conclusions: This study provides valuable insights into the pathogenesis of OTSCC and may assist in improving the prognosis of OTSCC patients.

Background & Aims: Polycystic ovary syndrome (PCOS) is the most common female endocrine disease and often causes infertility in the women of reproductive age. This syndrome includes different genes and proteins, multiple pathways, and complex processes of hormone secretion. Therefore, a single factor cannot explain the pathogenesis of PCOS. Using Computational Biology and omicses including genomics, transcriptomics, proteomics, and metabolomics, can provide faster and more effective methods for studying the pathogenesis of complex diseases such as PCOS. Materials & Methods: In this study, to find the related studies, PubMed, Google Scholar, and Science Direct databases were searched without time constraints for 3 years using the keywords of "Polycystic Ovary Syndrome, Computational Biology, protein-protein interaction, Network Biology, and Pathways analysis". Results: Various databases have been designed and made available to the public to repository data, Protein-Protein interactions, networks, and biological pathways related to humans. Three databases PCOSBase, PCOSKB, and PCOSDB have been explicitly developed for polycystic ovary syndrome. Conclusion: Since the molecular mechanisms of polycystic ovary syndrome are still not completely understood, to realize this syndrome, besides experimental results using omics platforms, Computational Biology, and bioinformatics tools, it is necessary to identify the interaction between proteins and the pathways involved in it.

Acute gastroenteritis caused by Rotavirus remains the leading cause of child mortality worldwide. Rotavirus genotype G9 circulates in humans throughout the world. Antibodies against the outer glycoproteins VP7 and VP4 Rotavirus capsid are the main neutralization antibodies in the vaccine assessment. This study aimed to select an epitope to evoke T and B cells' response, as a favorable candidate for vaccine development using in Silico evaluation. In the present study, Rotavirus genotypes were determined in 100 stools specimens collected from children with acute diarrhea. The results showed predominant G genotype, G9 (38. 5%) followed by G2 (22. 9%), G1 (16. 5%), G12 (11. 4%), G4 (6. 4%), and G3 (4. 3%). The G9 was dominant in this study and other regions of Iran; thus, this study was conducted to select an epitope from Rotavirus genotype G9 as a promising epitope candidate for future vaccine development. For this reason, several works including a complete sequence of VP7 G9, phylogenetic analysis, Prediction of Protein Structure, Physicochemical Properties of Protein and Epitope prediction were carried out. The outcomes of this study revealed that the complete sequence of VP7 (G9) was comprised of 1062 nt with 326 amino acids (accession number MH824633). The selected epitope contained amino acid sequence of STLCLYYPTEASTQIGDTEWKN with the best score for T and B cells response. Based on data of Computational Biology, the selected epitope can optimistically have considered as an epitope candidate for Rotavirus vaccine development.