Screening of miRNAs as prognostic biomarkers and their associated hub targets across Hepatocellular carcinoma using survival-based bioinformatics approach.

BACKGROUND: The hepatocellular carcinoma (HCC) incident rate is gradually increasing yearly despite all the research and efforts taken by scientific communities and governing bodies. Approximately 90% of all liver cancer cases belong to HCC. Usually, HCC patients approach the treatment in the late stages of this malignancy which becomes the primary cause of high mortality rate. The knowledge about molecular pathogenesis of HCC is limited and needs more attention from researchers to identify the driver genes and miRNAs, which causes to translate this information into clinical practice. Therefore, the key regulators identification of miRNA-mRNA regulatory network is essential to identify HCC-associated genes. METHODOLOGY: We extracted microRNA (miRNA) and messenger RNA (mRNA) expression datasets of normal and tumor HCC patient samples from UCSC Xena followed by identifying differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Univariate and multivariate cox-proportional hazard models were utilized to identify DEMs having significant association with overall survival (OS). Kaplan-Meier (KM) plotter was used to validate the presence of prognostic DEMs. A risk-score model was used to evaluate the effectiveness of KM-plotter validated DEMs combination on risk of samples. Target DEGs of prognostic miRNAs were identified via sources such as miRTargetLink and miRWalk followed by their validation in an external microarray cohort and enrichment analysis. RESULTS: 562 DEGs and 388 DEMs were identified followed by seven prognostic miRNAs (i.e., miR-19a, miR-19b, miR-30d-5p, miR-424-5p, miR-3677-5p, miR-3913-5p, miR-7705) post univariate, multivariate, risk-score model evaluation and KM-plotter analyses. ANLN, MRO, CPEB3 were their targets and were also validated in GSE84005 dataset. CONCLUSIONS: The findings of this study decipher that most significant miRNAs and their identified target genes have association with apoptosis, inflammation, cell cycle regulation and cancer-related pathways, which appear to contribute to HCC pathogenesis and therefore, the discovery of new targets.

Identification of novel biomarkers and potential molecular targets for uterine cancer using network-based approach.

A better understanding of incidences at the cellular level in uterine cancer is necessary for its effective treatment and favourable prognosis. Till date, it lacks appropriate molecular target-based treatment because of unknown molecular mechanisms that proceed to cancer and no drug has shown the required results of treatment with less severe side effects. Uterine Cancer is one of the top five cancer diagnoses and among the ten most common death-causing cancer in the United States of America. There is no FDA-approved drug for it yet. Therefore, it became necessary to identify the molecular targets for molecular targeted therapy of this widely prevalent cancer type. For this study, we used a network-based approach to the list of the deregulated (both up and down-regulated) genes taking adjacent p-Value ≤ 0.05 as significance cut off for the mRNA data of uterine cancer. We constructed the protein-protein interaction (PPI) network and analyzed the degree, closeness, and betweenness centrality-like topological properties of the PPI network. Then we traced the top 30 genes listed from each topological property to find the key regulators involved in the endometrial cancer (ECa) network. We then detected the communities and sub-communities from the PPI network using the Cytoscape network analyzer and Louvain modularity optimization method. A set of 26 (TOP2A, CENPE, RAD51, BUB1, BUB1B, KIF2C, KIF23, KIF11, KIF20A, ASPM, AURKA, AURKB, PLK1, CDC20, CDKN2A, EZH2, CCNA2, CCNB1, CDK1, FGF2, PRKCA, PGR, CAMK2A, HPGDS, and CDCA8) genes were found to be key genes of ECa regulatory network altered in disease state and might be playing the regulatory role in complex ECa network. Our study suggests that among these genes, KIF11 and H PGDS appeared to be novel key genes identified in our research. We also identified these key genes interactions with miRNAs.

Uncovering the role of aquaporin and chromobox family members as potential biomarkers in head and neck squamous cell carcinoma via integrative multiomics and in silico approach.

Head and neck squamous cell carcinoma (HNSC) is a diverse group of tumors arising from oral cavity, oropharynx, larynx, and hypopharynx squamous epithelium, posing significant morbidity. Aquaporins (AQPs) are membrane proteins forming water channels, some associated with carcinomas. Chromobox (CBX) family is known to modulate physiological and oncological processes. In our study, we analyzed AQPs and CBXs having significant expression followed by their prognostic and mutational assessment. Next, we performed enrichment and tumor infiltration analysis followed by HPA validation. Lastly, we established a 3-node miRNA-TF-mRNA regulatory network and performed protein-protein docking of the highest-degree subnetwork motif between TF and mRNA. Significant upregulation of CBX3/2 and downregulation of AQP3/5/7 correlated with poor overall survival (OS) in HNSC patients. The most significant pathway, GO-BP, GO-MF, and GO-CC terms associated with AQP3 and CBX3 were passive transport by aquaporins, response to vitamin, glycerol channel activity, and condensed chromosome, centromeric region. AQP3 negatively correlated with [Formula: see text] T cells, positively with [Formula: see text] T cells and B cells, and negatively with tumor purity, whereas CBX3 positively correlated with [Formula: see text] T cells, negatively with [Formula: see text] T cells and B cells, and positively with tumor purity. Three-node miRNA-TF-mRNA regulatory network revealed a highest-degree subnetwork motif comprising one TF (SMAD3), one miRNA (miR-423-5p), and one mRNA (AQP3). Protein-protein interaction studies suggested a direct interaction between AQP3 and Smad3 proteins. We concluded that AQP3 and CBX3 hold potential as treatment strategies and individual prognostic biomarkers, while further protein-protein interaction studies of AQP3 could offer insights into its interactions with Smad3 proteins.

Nitric oxide as a double-edged sword in pulmonary viral infections: Mechanistic insights and potential therapeutic implications.

In the face of the global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), researchers are tirelessly exploring novel therapeutic approaches to combat coronavirus disease 2019 (COVID-19) and its associated complications. Nitric oxide (NO) has appeared as a multifaceted signaling mediator with diverse and often contrasting biological activities. Its intricate biochemistry renders it a crucial regulator of cardiovascular and pulmonary functions, immunity, and neurotransmission. Perturbations in NO production, whether excessive or insufficient, contribute to the pathogenesis of various diseases, encompassing cardiovascular disease, pulmonary hypertension, asthma, diabetes, and cancer. Recent investigations have unveiled the potential of NO donors to impede SARS-CoV- 2 replication, while inhaled NO demonstrates promise as a therapeutic avenue for improving oxygenation in COVID-19-related hypoxic pulmonary conditions. Interestingly, NO's association with the inflammatory response in asthma suggests a potential protective role against SARS-CoV-2 infection. Furthermore, compelling evidence indicates the benefits of inhaled NO in optimizing ventilation-perfusion ratios and mitigating the need for mechanical ventilation in COVID-19 patients. In this review, we delve into the molecular targets of NO, its utility as a diagnostic marker, the mechanisms underlying its action in COVID-19, and the potential of inhaled NO as a therapeutic intervention against viral infections. The topmost significant pathway, gene ontology (GO)-biological process (BP), GO-molecular function (MF) and GO-cellular compartment (CC) terms associated with Nitric Oxide Synthase (NOS)1, NOS2, NOS3 were arginine biosynthesis (p-value = 1.15 x 10-9) regulation of guanylate cyclase activity (p-value = 7.5 x 10-12), arginine binding (p-value = 2.62 x 10-11), vesicle membrane (p-value = 3.93 x 10-8). Transcriptomics analysis further validates the significant presence of NOS1, NOS2, NOS3 in independent COVID-19 and pulmonary hypertension cohorts with respect to controls. This review investigates NO's molecular targets, diagnostic potentials, and therapeutic role in COVID-19, employing bioinformatics to identify key pathways and NOS isoforms' significance.

Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins.

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host-virus protein-protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection. Objective: This study aims to identify therapeutic target proteins in humans that could act as virus-host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors. Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 ("hCoV-2"), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient's mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein. Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein. Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.