Significant pathway and biomarker identification of pancreatic cancer associated lung cancer

In-silico discovery of common molecular signatures for which SARS-CoV-2 infections and lung diseases stimulate each other, and drug repurposing

COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases.

Investigation of berberine and its derivatives in Sars Cov-2 main protease structure by molecular docking, PROTOX-II and ADMET methods: in machine learning and in silico study

Bioactive compounds found in plants also have pharmacological antiviral effects. Berberine (BBR), an alkaloid found naturally in plants, is one of the phytochemicals with a wide range of biological activities, including antiviral, anticancer, anti-inflammatory and anti-inflammatory. In this study, we firstly aimed to predict pIC50 values for selcted compounds and then extract the binding patterns of berberine and its derivatives in the Sars Cov-2 Master Protease structure via employing molecular docking approache. Our results showed that berberine and its derivatives have good binding affinities towared Sars Cov2 main protease protein. Based on docking results the pharamaccokinetic studies for berberine, berberrubine, demethylen-berberine, jatrorrhizin, and thalifendine, were conducted and showed a good pharamacokinetic properties as an oral drugs. For deep inspection, we utiilized molecular dynmaics simulation to examine the Sars Cov2 main protease-ligand stabilities. The molecular dynamics simulation and PCA investigations revealed that thalifendine have a strong willing to act as good bindinder to SARS-CoV-2 protease. Further, the network based pharamacology showed that these drugs mediate different pathways such as human T-cell leukemia virus 1 infection, viral carcinogenesis, human immunodeficiency virus 1 infection, kaposi sarcoma-associated herpesvirus infection and epstein-Barr virus infection.The findings of this study have an important recomendation for thalifendine for more in vivo and in vitro studies to work.Communicated by Ramaswamy H. Sarma.

A Computational Approach to Justifying Stratifin as a Candidate Diagnostic and Prognostic Biomarker for Pancreatic Cancer

Pancreatic cancer (PC) is considered a silent killer because it does not show specific symptoms at an early stage. Thus, identifying suitable biomarkers is important to avoid the burden of PC. Stratifin (SFN) encodes the 14-3-3σ protein, which is expressed in a tissue-dependent manner and plays a vital role in cell cycle regulation. Thus, SFN could be a promising therapeutic target for several types of cancer. This study was aimed at investigating, using online bioinformatics tools, whether SFN could be used as a diagnostic and prognostic biomarker in PC. SFN expression was explored by utilizing the ONCOMINE, UALCAN, GEPIA2, and GENT2 tools, which revealed that SFN expression is higher in PC than in normal tissues. The clinicopathological analysis using the ULCAN tool showed that the intensity of SFN expression is commensurate with cancer progression. GEPIA2, R2, and OncoLnc revealed a negative correlation between SFN expression and survival probability in PC patients. The ONCOMINE, UCSC Xena, and GEPIA2 tools showed that cofilin 1 is strongly coexpressed with SFN. Moreover, enrichment and network analyses of SFN were performed using the Enrichr and NetworkAnalyst platforms, respectively. Receiver operating characteristic (ROC) curves revealed that tissue-dependent expression of the SFN gene could serve as a diagnostic and prognostic biomarker. However, further wet laboratory studies are necessary to determine the relevance of SFN expression as a biomarker.