Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear. Methods: The microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues. Results: Compared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression. Conclusions: Our study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.

Aims/Introduction: The incidence of type 2 diabetes is increasing worldwide. Hepatic insulin resistance and liver lipid accumulation contributes to type 2 diabetes development. The aim of the present study was to investigate the key gene pathways and co‐expression networks in the livers of type 2 diabetes patients.Materials and Methods: Dataset GSE15653 containing nine healthy individuals and nine type 2 diabetes patients was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database. Differentially expressed genes were obtained from the livers of type 2 diabetes patients, annotated pathway enrichment and protein–protein interaction network analysis. Next, functional modules and transcription factor networks were constructed. Gene co‐expression networks were analyzed by weighted correlation network analysis to identify key modules related to clinical traits, and the candidate key genes were validated in hepatic insulin resistance models in vitro.Results: A total of 778 differentially expressed genes were filtered in the livers of type 2 diabetes patients, pathway enrichment analysis identified ke y pathways, such as the mitogen‐activated protein kinase signaling pathway, Hippo signaling pathway and hypoxia‐inducible factor‐1 signaling pathway, that were associated with type 2 diabetes. Several transcription factors of three functional modules identified from protein–protein interaction networks are likely to be implicated in type 2 diabetes. Furthermore, weighted correlation network analysis identified five modules that were shown to be highly correlated with type 2 diabetes and other clinical traits. Functional annotation showed that these modules were mainly enriched in pathways such as metabolic pathways, phosphoinositide 3‐kinase‐protein kinase B signaling pathway and natural killer cell‐mediated cytotoxicity. UBE2M and GPER were upregulated in L02 and HepG2 models, whereas P2RY11 only upregulated in L02 model, and UBE2N only downregulated in HepG2 model at a significant level.Conclusions: These results would offer new insights into hepatic insulin resistance, type 2 diabetes pathogenesis, development and drug discovery.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is now a global outbreak of disease. The antiviral treatment acts as one of the most important means of SARS-CoV-2 infection. Alteration of physiological characteristics in special populations may lead to the change in drug pharmacokinetics, which may result in treatment failure or increased adverse drug reactions. Some potential drugs have shown antiviral effects on SARS-CoV-2 infections, such as chloroquine, hydroxychloroquine, favipiravir, lopinavir/ritonavir, arbidol, interferon alpha, and remedsivir. Here, we reviewed the literature on clinical effects in COVID-19 patients of these antiviral agents and provided the potential antiviral agent options for pregnant women, elderly patients, liver or renal dysfunction patients, and severe or critically ill patients receiving renal replacement therapy or ECMO after SARS-CoV-2 infection.

Purpose: The number of people with type 2 diabetes (T2D) is growing rapidly worldwide. Islet β-cell dysfunction and failure are the main causes of T2D pathological processes. The aim of this study was to elucidate the underlying pathways and coexpression networks in T2D islets. Materials and methods: We analyzed the differentially expressed genes (DEGs) in the data set GSE41762, which contained 57 nondiabetic and 20 diabetic samples, and developed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed as well. Moreover, a weighted correlation network analysis (WGCNA) was applied to screen critical gene modules and coexpression networks and explore the biological significance. Results: We filtered 957 DEGs in T2D islets. Then GO and KEGG analyses identified that key pathways like inflammatory response, type B pancreatic cell differentiation, and calcium ion-dependent exocytosis were involved in human T2D. Three significant modules were filtered from the PPI network. Ribosome biogenesis, extrinsic apoptotic signaling pathway, and membrane depolarization during action potential were associated with the modules, respectively. Furthermore, coexpression network analysis by WGCNA identified 13 distinct gene modules of T2D islets and revealed four modules, which were strongly correlated with T2D and T2D biomarker hemoglobin A1c (HbA1c). Functional annotation showed that these modules mainly enriched KEGG pathways such as NF-kappa B signaling pathway, tumor necrosis factor signaling pathway, cyclic adenosine monophosphate signaling pathway, and peroxisome proliferators-activated receptor signaling pathway. Conclusion: The results provide potential gene pathways and underlying molecular mechanisms for the prevention, diagnosis, and treatment of T2D.