Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

Abstract

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

Figure 1

An illustration in schematic form of the study's entire procedure.

Figure 2

Differentially expressed genes (DEGs) of (A) COVID-19 and (B) IC are shown on volcano plots. With |log2(FC)| > 1 and a P-value < 0.05, red dots denoted up-regulated genes, blue dots denoted down-regulated genes, and grey dots denoted non-DEGs. The results of clustering analysis based on DEGs for (C) COVID-19 and (D) IC are displayed in heatmaps. A venn diagram then displayed the areas of GSE147507 and GSE11783 that overlapped.

Figure 3

Functional analysis of IC and COVID-19. (A) The histogram of the GO enrichment analysis; the letters BP, CC, and MF stand for biological process, cellular component, and molecular function, respectively. (B) KEGG pathway analysis bar plot.

Figure 4

Common genes in COVID-19 and IC are analysed using the PPI network and clustering methods. Based on the STRING web database, (A) a network visualisation of 198 common genes using Cytoscape. (B) The Cytoscape MCC algorithm located the crucial cluster.

Figure 5

The Venn diagram displayed 7 hub genes that were tested by 4 different methods.

Figure 6

The network of DEG-TF and DEG-miRNA regulatory interactions. (A) TFs are represented here as diamond nodes, while gene symbols operate as circle nodes to interact with TFs. (B) The square node in this case denotes the circle-shaped interaction between gene symbols and miRNAs.

Figure 7

The gene-disease association network is a representation of the diseases associated with common DEGs. The circle node and its subsequent gene symbols are linked to the square node, which specifies the top 10 related diseases.

Figure 8

Immune infiltrations were related to the hub genes. (A, B) Immune cell infiltration analysis of datasets GSE147507 and GSE11783. (C, D) Correlation analysis of immune-related hub genes and immune cell infiltration. *p < 0.05, **p < 0.01, and ***p < 0.001.

Figure 9

The validation of the diagnostic efficacy of seven immune-related hub genes, as well as their expression correlation. (A, B) ROC curves of seven immune-related hub genes in the datasets GSE147507 and GSE11783. (C, D) Correlations between the seven hub genes that are reciprocal.

Figure 10

Construction and validation of COVID-19 and IC diagnostic column line graph models. (A) Column line graphs are used to predict the occurrence of COVID-19. (B) Column line graphs are used to predict the occurrence of IC. (C) Calibration and ROC curves to assess the diagnostic value of the COVID-19-related column line graph model in the GSE147507 dataset. (D) Calibration and ROC curves to assess the diagnostic value of the IC-related column line graph model in the GSE11783 dataset. (E) GSE171110 dataset to verify the calibration and ROC curves for COVID-19. (E) GSE57560 dataset to verify the calibration and ROC curves for IC.