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. 2024 Nov 29;15(12):1548.
doi: 10.3390/genes15121548.

Exploring Immune Cell Infiltration and Small Molecule Compounds for Ulcerative Colitis Treatment

Yi Lu  1   2 , Dongqing Lu  3 , Chujie Li  4   5 , Luping Chen  6
Affiliations

Exploring Immune Cell Infiltration and Small Molecule Compounds for Ulcerative Colitis Treatment

Yi Lu et al. Genes (Basel). .

Abstract

Background/objectives: Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) with a relapsing nature and complex etiology. Bioinformatics analysis has been widely applied to investigate various diseases. This study aimed to identify crucial differentially expressed genes (DEGs) and explore potential therapeutic agents for UC.

Methods: The GSE47908 and GSE55306 colon tissue transcriptome gene datasets were downloaded from the Gene Expression Omnibus-NCBI (GEO) database. GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA). GO-BP analysis and KEGG enrichment analysis were performed on the intersecting differential genes via the Metascape website, while hub genes were analyzed by STRING11.0 and Cytoscape3.7.1. The expression of hub genes was verified in the dataset GSE38713 colon tissue specimens. Finally, the gene expression profiles of the validation set were analyzed by immuno-infiltration through the ImmuCellAI online tool, and the CMap database was used to screen for negatively correlated small molecule compounds.

Results: A total of 595 and 926 genes were screened by analysis of GSE47908 and GSE55306 datasets, respectively. Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC. GSEA analysis yielded intersecting pathways for both datasets (colorectal cancer pathway, base excision repair, cell cycle, apoptosis). GO-BP and KEGG enrichment analyses were performed to obtain key biological processes (inflammatory response, response to bacteria, leukocyte activation involved in the immune response, leukocyte-cell adhesion, apoptosis, positive regulation of immune effector processes) and key signaling pathways (cytokine-cytokine receptor interactions, IBD, NOD-like receptor signaling pathways). The immune cell infiltration analysis suggested that the incidence of UC was mainly related to the increase in CD4+T cells, depletion of T cells, T follicular helper cells, natural killer cells, γδ T cells and the decrease in CD8 naive T cells, helper T cells 17 and effector T cells. The CMap database results showed that small molecule compounds such as vorinostat, roxarsone, and wortmannin may be therapeutic candidates for UC.

Conclusions: This study not only aids in early prediction and prevention but also provides novel insights into the pathogenesis and treatment of UC.

Keywords: bioinformatics; differentially expressed genes; gene chip; potential therapeutic drugs; ulcerative colitis.

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Conflict of interest statement

Author Yi Lu was employed by the company Shanghai Tufeng Pharmaceutical Technology Co. Graphical Abstract and Figure 9 were created by Figdraw. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
DEGs in datasets of colon tissue samples. (A) Volcano of GSE47908. (B) Volcano of GSE53306.
Figure 2
Figure 2
Analysis of WGCNA co-expression module based on GSE47908 gene chip. (A) Sample cluster analysis. (B) Analysis of the scale-free fit index and the mean connectivity for various soft-thresholding powers (β = 18). (C) Dendrogram of genes clustered based on the dissimilarity measure. (D) Heatmap of the correlation between different modules with UC.
Figure 3
Figure 3
Analysis of WGCNA co-expression module based on GSE53306 gene chip. (A) Sample cluster analysis. (B) Analysis of the scale-free fit index and the mean connectivity for various soft-thresholding powers (β = 24). (C) Dendrogram of genes clustered based on the dissimilarity measure. (D) Heatmap of the correlation between different modules with UC.
Figure 4
Figure 4
Screening of hub genes. (A) Venn diagram of intersection DEGs. (B) PPI network construction. (C) Hub genes screening based on degree.
Figure 5
Figure 5
Enrichment analysis of core targets through GO and KEGG. (A) GO-BP pathway enrichment analysis. (B) KEGG pathway enrichment analysis.
Figure 6
Figure 6
Top 12 hub gene expression in the GSE38713 ulcerative colitis dataset.Significance: **, p < 0.01.
Figure 7
Figure 7
Signaling pathways in the GSE47908 and GSE55306 datasets via GSEA analysis.
Figure 8
Figure 8
Immune cell infiltration in ulcerative colitis and control groups. Significance: NS, p > 0.05; *, p < 0.05; **, p < 0.01.
Figure 9
Figure 9
The impact of hub genes expression on the immune cell infiltration in the colonic mucosa with the therapeutic activity of the best drug candidates (vorinostat and wortmannin).
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