Cardiovascular complications combined with COVID-19 (SARS-CoV-2) lead to a poor prognosis in patients. The common pathogenesis of ischemic cardiomyopathy (ICM) and COVID-19 is still unclear. Here, we explored potential molecular mechanisms and biomarkers for ICM and COVID-19. Common differentially expressed genes (DEGs) of ICM (GSE5406) and COVID-19 (GSE164805) were identified using GEO2R. We performed enrichment and protein–protein interaction analyses and screened key genes. To confirm the diagnostic performance for these hub genes, we used external datasets (GSE116250 and GSE211979) and plotted ROC curves. Transcription factor and microRNA regulatory networks were constructed for the validated hub genes. Finally, drug prediction and molecular docking validation were performed using cMAP. We identified 81 common DEGs, many of which were enriched in terms of their relation to angiogenesis. Three DEGs were identified as key hub genes (HSP90AA1, HSPA9, and SRSF1) in the protein–protein interaction analysis. These hub genes had high diagnostic performance in the four datasets (AUC > 0.7). Mir-16-5p and KLF9 transcription factor co-regulated these hub genes. The drugs vindesine and ON-01910 showed good binding performance to the hub genes. We identified HSP90AA1, HSPA9, and SRSF1 as markers for the co-pathogenesis of ICM and COVID-19, and showed that co-pathogenesis of ICM and COVID-19 may be related to angiogenesis. Vindesine and ON-01910 were predicted as potential therapeutic agents. Our findings will contribute to a deeper understanding of the comorbidity of ICM with COVID-19.

Type 2 diabetes mellitus (T2DM) has been confirmed as an independent risk factor for colorectal cancer (CRC) in many studies. However, the mechanisms behind T2DM’s role in the progression of CRC remain unclear. This study aims to explore the potential biomarkers and molecular mechanisms involved in T2DM-promoted CRC progression. The limma package was used to identify differentially expressed genes in tumor tissue from CRC patients with or without T2DM. The key biological processes were screened by gene ontology and gene set enrichment analysis. A diagnostic model for co-morbidities was constructed by logistic regression model with least absolute shrinkage and selection operator (Lasso) regularization method. The diagnostic performance was assessed by supplementing external datasets to draw ROC curves on the diagnostic model. The diagnostic model was further screened for key genes by prognostic analysis. The relationship of key genes with immune cells and other cells was evaluated by immune infiltration algorithm and single-cell transcription analysis. Drug prediction was performed by cMAP and the obtained drugs were molecularly docked with the key genes. The differentially expressed genes of T2DM-promoted CRC progression were mainly enriched to O-linked glycosylation-related processes. The diagnostic model constructed based on Lasso logistic regression had good diagnostic performance (AUC > 0.8). COX11 was the key gene for co-morbidities: in tumor tissues, COX11 expression was significantly higher than that in normal colon tissues. However, COX11 gene expression was significantly lower in patients with comorbidities than in patients without T2DM in tumor tissue. External datasets confirmed from both mRNA and protein expression levels that low COX11 expression was significantly associated with poor CRC prognosis. Immune infiltration analysis suggested that its expression related to the proportion of M2 macrophages. Single-cell transcriptome analysis revealed a close association of COX11 expression with endothelial cells and macrophages. The top4 drugs predicted bound well to COX11. Our study revealed that the pathogenesis of T2DM-promoted CRC progression related to O-linked glycosylation. We constructed a diagnostic model for T2DM-CRC co-morbidity. Meanwhile, we identified COX11 as a potential immune-related molecular marker closely associated with T2DM-promoted CRC progression. These mechanisms and molecular markers may provide new ideas for further studies of T2DM-promoted CRC progression and contribute to drug discovery for the treatment of co-morbidities.

Objective: To investigate the molecular mechanism and simulated validation of Zhi Bai Di Huang Pill (ZBDHP) for the treatment of systemic lupus erythematosus (SLE) using network pharmacology and molecular docking techniques. Methods: The active ingredients of ZBDHP were obtained through the TCMSP database and the Canonical SMILES of the active ingredients were queried through Pubchem. The targets of the active ingredients were predicted in the SwissTarget database based on the SMILES. The SLE-related disease targets were obtained through the GeneCards, OMIM and DisGenets databases, and the intersection targets of ZBDHP and SLE were obtained using the Venny 2.1.0 online platform. Intersection targets build a visual protein interaction network (PPI) through the STRING database, and the core targets were identified by network topology analysis. GO analysis and KEGG pathway enrichment analysis of the intersecting targets were performed using the DAVID database. Finally, the molecular docking of the first four active ingredients and the first four core target genes were verified by Pubchem, the PDB database and CB-Dock online molecular docking technology. Results: ZBDHP screened 91 potential active ingredients and 816 potential targets. Among them, 141 genes were intersected by ZBDHP and SLE. The network topology analysis showed that the main active ingredients were Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, etc., and the key target genes were TNF, AKT1, EGFR, STAT3, etc. GO and KEGG enrichment analysis showed that common targets interfere with biological processes or molecular functions such as signal transduction protein phosphorylation, inflammatory response, transmembrane receptor protein tyrosine kinase activity, etc., through multiple signaling pathways, such as pathways in cancer, Kaposi sarcoma-associated herpesvirus infection, the PI3K-Akt signaling pathway, lipid and atherosclerosis, hepatitis B, etc. Molecular docking results showed that the active components of ZBDHP have good binding activity to the core targets of SLE. Conclusions: This study reveals that the ZBDHP treatment of SLE is a complex mechanistic process with multi-components, multi-targets and multi-pathways, and it may play a therapeutic role in SLE by inhibiting the production, proliferation and apoptosis of inflammatory factors. In conclusion, the present study provides a theoretical basis for further research on ZBDHP.

Background: Qianghuo Shengshi decoction (QHSSD), a traditional Chinese medicine formula, is used to treat ankylosing spondylitis (AS) in China. The pharmacological mechanism of QHSSD for AS remains to be clarified. In this study, we investigated the molecular mechanisms of QHSSD in the treatment of AS using network pharmacology and molecular docking. Methods: To obtain the chemical components and potential targets of QHSSD, we used the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP) and SwissTargetPrediction. AS potential targets were found in the GeneCards, OMIM, and DisGenets databases. A Venn diagram was used to screen QHSSD and AS common potential targets. The STRING website and Cytoscape software were used to create and analyze protein–protein interactions and component–target networks. The DAVID database was used for the gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was used to visualize drug–target interactions. Results: The component–target network consisted of 119 chemical components and 193 potential targets. QHSSD was implicated in various biological processes, such as inflammation and angiogenesis, and mediated multiple signaling pathways, such as the MAPK signaling pathway. Molecular docking revealed good binding ability between medicarpin, notoptol, vitetrifolin E, and cnidilin and EGFR, TNF-α, ALB, and VEGFA. Conclusions: The chemical compositions, potential targets, and pathways involved in the QHSSD treatment of AS were successfully predicted in this study. This study provides a solid foundation for the selection of drugs to treat AS.