Hepatocellular carcinoma (HCC) is a type of malignant tumor. miRNAs are noncoding RNAs and their differential expression patterns are observed in HCC-induced by alcoholism, HBV and HCV infections. By acting as a competing endogenous RNA (ceRNA), circRNA regulates the miRNA function, indirectly controlling the gene expression and leading to HCC progression. In the present study, data mining was performed to screen out all miRNAs and circRNA involved in alcohol, HBV or HCV-induced HCC with statistically significant (≤0.05%) expression levels reported in various studies. Further, the interaction of miRNAs and circRNA was also investigated to explore their role in HCC due to various causative agents. Together, these study data provide a deeper understanding of the circRNA–miRNA regulatory mechanisms in HCC. These screened circRNA, miRNA and their interactions can be used as prognostic biomarkers or therapeutic targets for the treatment of HCC.

Hepatitis E Virus (HEV) is a major cause of acute and chronic hepatitis. The severity of HEV infection increases manyfold in pregnant women and immunocompromised patients. Despite the extensive research on HEV in the last few decades, there is no widely available vaccine yet. In the current study, immunoinformatic analyses were applied to predict a multi-epitope vaccine candidate against HEV. From the ORF2 region, 41 conserved and immunogenic epitopes were prioritized. These epitopes were further analyzed for their probable antigenic and non-allergenic combinations with several linkers. The stability of the vaccine construct was confirmed by molecular dynamic simulations. The vaccine construct is potentially antigenic and docking analysis revealed stable interactions with TLR3. These results suggest that the proposed vaccine can efficiently stimulate both cellular and humoral immune responses. However, further studies are needed to determine the immunogenicity of the vaccine construct.

Diabetes is a chronic metabolic disorder that occurs due to impaired secretion of insulin, insulin resistance, or both. Recent studies show that the antidiabetic drugs used to control hyperglycemic levels are associated with undesirable adverse effects. Therefore, developing a safe and effective medicine with antidiabetic potential is needed. In this context, in silico studies are considered a rapid, effectual, and cost‐effective method in drug discovery procedures. It is evident from the literature that plant‐based natural components have shown promising outcomes in drug development to alleviate various diseases and hence have diversified the screening of potential antidiabetic agents. Purposely, in the present study, an in silico approach was performed on three Punica granatum peel metabolites (punicalin, punicalagin, and ellagic acid). All these three compounds were docked against nine protein targets involved in glucose metabolism (GFAT, PTP1β, PPAR‐ᵞ, TKIR, RBP4, α‐amylase, α‐glucosidase, GCK, and AQP‐2). These three pomegranate‐specific compounds demonstrated significant interactions with GFAT, PTP1β, PPAR‐ᵞ, TKIR, RBP4, α‐amylase, α‐glucosidase, GCK, and AQP‐2 protein targets. Specifically, punicalin, punicalagin, and ellagic acid revealed significant binding scores (−9.2, −9.3, −8.1, −9.1, −8.5, −11.3, −9.2, −9.5, −10.1 kcal/mol; −10, −9.9, −8.5, −8.9, −10.4, −9.0, −10.2, −9.4, −9.0 kcal/mol; and −8.1, −8.0, −8.0, −6.8, −8.7, −7.8, −8.3, −8.1, −8.1 kcal/mol, respectively), with nine protein targets mentioned above. Hence, punicalin, punicalagin, and ellagic acid can be promising candidates in drug discovery to manage diabetes. Furthermore, in vivo and clinical trials must be conducted to validate the outcomes of the current study.

Many PTMs dysregulation is known to be the major cause of many cancers including HCV induced HCC. PTMs of hepatitis C virus (HCV) regions NS3/4A, NS5A and NS5B are crucial for proper protein functions and replication that directly affect the generation of infectious virus particles and completion of its life cycle. In this study, we have performed comprehensive analysis of PTMs within HCV non-structural proteins (NS3/4A, NS5A and NS5B) through bioinformatics analysis to examine post-translational crosstalk between phosphorylation, palmitoylation, methylation, acetylation and ubiquitination sites in selected viral proteins. Our analysis has revealed many highly putative PTMs sites that are also conserved among major genotypes conferring the importance of these sites. We have also analysed viral 3D structures in their modified and unmodified forms to address extent and signatures of structural changes upon PTM. This study provides evidence that PTMs induce significant conformational changes and make viral proteins more stable. To find the potential role of PTMs in HCV induced HCC, docking analysis between selected viral proteins and p38-MAPK has been performed which also confirms their strong association with HCV induced HCC. The major findings proposed that PTMs at specific sites of HCV viral proteins could dysregulate specific pathways that cause the development of HCC.

Aims and Objectives: Hepatitis C virus (HCV) infection is a significant risk factor for the development of hepatocellular carcinoma (HCC). Serum‐derived circular RNAs (circRNAs) play several crucial roles in HCV and HCC. They represent a promising area of research for improving the diagnosis and understanding the mechanisms of HCV‐HCC. This study aims to identify a serum‐derived circular RNA (circRNA) diagnostic panel for HCV‐HCC and to elucidate the regulatory mechanisms underlying their role in cancer progression.Methods: In this study, data mining and in silico analysis were conducted to identify the role of circular RNAs (hsa_circ_0003288, circ‐RNF13, hsa_circ_0004277, circANRIL, circUHRF1, hsa_circ_103047) and their associated biomarkers (IL‐6 and NF‐κB) in HCV‐HCC pathogenesis. Additionally, RT‐PCR was performed to assess their expression levels across different study groups (G0 = control, G1 = HCV, G2 = HCC, and G3 = HCV‐induced HCC).Results: The expression levels of circular RNAs, including hsa_circ_0003288, circ‐RNF13, hsa_circ_0004277, circANRIL, circUHRF1, and hsa_circ_103047, as well as the biomarkers IL‐6 and NF‐κB, were significantly elevated in the G3 group compared to the G0 group. ROC analysis also revealed significantly different expression rates for G3 group and G0 group.Conclusion: The data revealed that cricRNAs panel (hsa_circ_0003288, circ‐RNF13, circANRIL, circUHRF1, and hsa_circ_103047) could serve as a diagnostic biomarker and therapeutic target for HCV‐induced HCC.

An in-depth analysis of first-wave SARS-CoV-2 genome is required to identify various mutations that significantly affect viral fitness. In the present study, we performed a comprehensive in silico mutational analysis of 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), and spike (S) proteins with the aim of gaining important insights into first-wave virus mutations and their functional and structural impact on SARS-CoV-2 proteins. Our integrated analysis gathered 6000 SARS-CoV-2 sequences and identified 92 mutations in S, 37 in RdRp, and 11 in 3CLpro regions. The impact of these mutations was also investigated using various in silico approaches. Among these, 32 mutations in S, 15 in RdRp, and 3 in 3CLpro proteins were found to be deleterious in nature and could alter the structural and functional behavior of the encoded proteins. The D614G mutation in spike and the P323Lmutation in RdRp are the globally dominant variants with a high frequency. Most of the identified mutations were also found in the binding moiety of the viral proteins which determine their critical involvement in host–pathogen interactions and may represent drug targets. Furthermore, potential CD4⁺ and CD8⁺ T cell epitopes were predicted, and their overlap with genetic variations was explored. This study also highlights several hot spots in which HLA and drug selective pressure overlap. The findings of the current study may allow a better understanding of COVID-19 diagnostics, vaccines, and therapeutics.