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.