Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein–protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body’s stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.

Colorectal cancer is currently the third most common cancer around the world. In this study, we chose a bioinformatics analysis method based on network analysis to dig out the pathological mechanism and key prognostic targets of rectal adenocarcinoma (READ). In this study, we downloaded the clinical information data and transcriptome data from the Cancer Genome Atlas database. Differentially expressed genes analysis was used to identify the differential expressed genes in READ. Community discovery algorithm analysis and Correlation analysis between gene modules and clinical data were performed to mine the key modules related to tumor proliferation, metastasis, and invasion. Genetic significance (GS) analysis and PageRank algorithm analysis were applied for find key genes in the key module. Finally, the importance of these genes was confirmed by survival analysis. Transcriptome datasets of 165 cancer tissue samples and 9 paracancerous tissue samples were selected. Gene coexpression networks were constructed, multilevel algorithm was used to divide the gene coexpression network into 11 modules. From GO enrichment analysis, module 11 significantly associated with clinical characteristic N, T, and event, mainly involved in 2 types of biological processes which were highly related to tumor metastasis, invasion, and tumor microenvironment regulation: cell development and differentiation; the development of vascular and nervous systems. Based on the results of survival analysis, 7 key genes were found negatively correlated to the survival rate of READ, such as MMP14, SDC2, LAMC1, ELN, ACTA2, ZNF532, and CYBRD1. Our study found that these key genes were predicted playing an important role in tumor invasion and metastasis, and being associated with the prognosis of READ. This may provide some new potential therapeutic targets and thoughts for the prognosis of READ.

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Increasing studies have demonstrated that ginsenoside Rg3 (Rg3) plays an important role in the prevention and treatment of various diseases, including allergic lower airway inflammation such as asthma. To investigate...

Colorectal cancer is one of the 3 most common cancers worldwide. In this study, a weighted network-based analysis method was proposed to explore the pathological mechanisms and prognostic targets of rectal adenocarcinoma (READ) at the deoxyribonucleic acid (DNA) methylation level. In this study, we downloaded clinical information and DNA methylation data from The Cancer Genome Atlas database. Differentially methylated gene analysis was used to identify the differential methylated genes in READ. Canonical correlation analysis was used to construct the weighted gene regulatory network for READ. Multilevel analysis and association analyses between gene modules and clinical information were used to mine key modules related to tumor metastasis evaluation. Genetic significance analysis was used to identify methylation sites in key modules. Finally, the importance of these methylation sites was confirmed using survival analysis. DNA methylation datasets from 90 cancer tissue samples and 6 paracancerous tissue samples were selected. A weighted gene regulatory network was constructed, and a multilevel algorithm was used to divide the gene co-expression network into 20 modules. From gene ontology enrichment analysis, characteristic M was related to biological processes such as the chemotaxis of fibroblast growth factors and the activation and regulation of immune cells etc and characteristic N was associated with the regulation of cytoskeleton formation, mainly microtubules and flagella, regulation of synapses, and regulation of cell mitosis. Based on the results of survival analysis, 7 key methylation sites were found closely correlated to the survival rate of READ, such as cg04441191 (microtubule-associated protein 4 [MAP4]), cg05658717 (KSR2), cg09622330 (GRIN2A), cg10698404 (YWHAG), cg17047993 (SPAG9), cg24504843 (CEP135), and cg24531267 (CEP250). Mutational and transcriptomic level studies revealed significant differences in DNA methylation, single nucleotide polymorphism, and transcript levels between YWHAG and MAP4 in normal tissues compared to tumor tissues, and differential expression of the 2 proteins in immunohistochemistry. Therefore, potential targeting drugs were screened for these 2 proteins for molecular docking, and artenimol was found to bind to MAP4 protein and 27-hydroxycholesterol to YWHAG. Our study found that key methylation sites played an important role in tumor metastasis and were associated with the prognosis of READ. Mutations and methylation may jointly regulate the transcription and translation of related genes, which in turn affect cancer progression. This may provide some new potential therapeutic targets and thoughts for the prognosis of READ.