Identification of diagnostic signatures in ulcerative colitis patients via bioinformatic analysis integrated with machine learning.

Ulcerative colitis (UC) is an immune-related disorder with enhanced prevalence globally. Early diagnosis is critical for the effective treatment of UC. However, it still lacks specific diagnostic signatures. The aim of our study was to explore efficient signatures and construct the diagnostic model for UC. Microarray data of GSE87473 and GSE48634, which were obtained from tissue biopsy samples, were downloaded from the Gene Expression Omnibus (GEO), and differently expressed genes (DEGs), GO, and KEGG analyses were performed. We constructed the PPI network via STRING database. The immune infiltration of the samples was evaluated using CIBERSORT methods combined with the LM22 feature matrix. The logistic regression model was constructed, with the expression of selected genes as the predictor variable, and the UC occurrence as the responsive variable. As a result, a total of 126 DEGs between the UC patients and normal counterparts were identified. The GO and KEGG analysis revealed that multiple biological processes, such as antimicrobial humoral immune response mediated by antimicrobial peptide and IL-17 signaling pathway, were enriched. The infiltration of eight immune cell types (B cells naive, Dendritic.cells.activated, Macrophages.M0, Macrophages.M2, Mast.cells.resting, Neutrophils, Plasma.cells, and T.cells.follicular.helper) was significantly different between patients with UC and normal counterparts. The top 50 most significant DEGs were selected for the construction of the PPI network. The average AUC of the logistic regression model in the fivefold cross-validation was 0.8497 in the training set, GSE87473. The AUC of another independent verification set of GSE48634 from the GEO database was 0.7208. In conclusion, we identified potential hub genes, including REG3A, REG1A, DEFA6, REG1B, and DEFA5, which might be significantly associated with UC progression. The logistic regression model based on the five genes could reliably diagnose UC patients.

Lu J ，Wang Z ，Maimaiti M ，Hui W ，Abudourexiti A ，Gao F ... -  《Human Cell》

Identifying Hub Genes, Key Pathways and Immune Cell Infiltration Characteristics in Pediatric and Adult Ulcerative Colitis by Integrated Bioinformatic Analysis.

In the present study, we investigated the differentially expressed genes (DEGs), pathways and immune cell infiltration characteristics of pediatric and adult ulcerative colitis (UC). We conducted DEG analysis using the microarray dataset GSE87473 containing 19 pediatric and 87 adult UC samples downloaded from the Gene Expression Omnibus. Gene ontology and pathway enrichment analyses were conducted using Metascape. We constructed the protein-protein interaction (PPI) network and the drug-target interaction network of DEGs and identified hub modules and genes using Cytoscape and analyzed immune cell infiltration in pediatric and adult UC using CIBERSORT. In total, 1700 DEGs were screened from the dataset. These genes were enriched mainly in inter-cellular items relating to cell junctions, cell adhesion, actin cytoskeleton and transmembrane receptor signaling pathways and intra-cellular items relating to the splicing, metabolism and localization of RNA. CDC42, POLR2A, RAC1, PIK3R1, MAPK1 and SRC were identified as hub DEGs. Immune cell infiltration analysis revealed higher proportions of naive B cells, resting memory T helper cells, regulatory T cells, monocytes, M0 macrophages and activated mast cells in pediatric UC, along with lower proportions of memory B cells, follicular helper T cells, γδ T cells, M2 macrophages, and activated dendritic cells. Our study suggested that hub genes CDC42, POLR2A, RAC1, PIK3R1, MAPK1 and SRC and immune cells including B cells, T cells, monocytes, macrophages and mast cells play vital roles in the pathological differences between pediatric and adult UC and may serve as potential biomarkers in the diagnosis and treatment of UC.

Xiu MX ，Liu YM ，Chen GY ，Hu C ，Kuang BH ... -  《-》

Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning.

Wang Y ，Huang J ，Zhang J ，Wang F ，Tang X ... -  《-》

Identification of shared gene signatures and molecular mechanisms between chronic kidney disease and ulcerative colitis.

There is a complex interaction between chronic kidney disease (CKD) and ulcerative colitis (UC), but the pathophysiological mechanisms underlying the coexistence of CKD and UC are unclear. This study aimed to investigate the key molecules and pathways that may mediate the co-occurrence of CKD and UC through quantitative bioinformatics analysis based on a public RNA-sequencing database. The discovery datasets of CKD (GSE66494) and UC (GSE4183), as well as validation datasets of CKD (GSE115857) and UC (GSE10616), were downloaded from the Gene Expression Omnibus (GEO) database. After identifying differentially expressed genes (DEGs) with GEO2R online tool, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the DEGs were performed. Next, protein-protein interaction network was constructed with Search Tool for the Retrieval of Interacting Genes (STRING) and visualized by Cytoscape. Gene modules were identified by the plug-in MCODE and hub genes were screened using the plug-in CytoHubba. Then, correlation between immune cell infiltration and hub genes was analyzed, and the receiver operating characteristic curves were used to assess the predictive value of hub genes. Finally, immunostaining of human specimens was used to validate the relevant findings. A total of 462 common DEGs were identified and selected for further analyses. GO and KEGG enrichment analyses indicated that these DEGs were primarily enriched in immune- and inflammation-related pathways. Among them, the PI3K-Akt signaling pathway ranked top in both discovery and validation cohorts, and the key signal molecule phosphorylated Akt (p-Akt) was shown to be significantly overexpressed in human CKD kidneys and UC colons, and further elevated in CKD-UC comorbidity specimens. Moreover, nine candidate hub genes, including CXCL8, CCL2, CD44, ICAM1, IL1A, CXCR2, PTPRC, ITGAX, and CSF3, were identified, of which ICAM1 was validated as a common hub gene. Besides, immune infiltration analysis revealed that neutrophils, macrophages, and CD4+ T memory cells significantly accumulated in both diseases, and ICAM1 was remarkably associated with neutrophil infiltration. Furthermore, intercellular adhesion molecule1 (ICAM1)-mediated neutrophil infiltration was validated to be upregulated in kidney and colon biopsies of CKD and UC patients, and further increased in patients diagnosed with both CKD and UC. Finally, ICAM1 had shown critical value as a diagnostic marker for the co-occurrence of CKD and UC. Our study elucidated that immune response, PI3K-Akt signaling pathway, and ICAM1-mediated neutrophil infiltration might be the common pathogenesis of CKD and UC, and identified ICAM1 as a key potential biomarker and therapeutic target for the comorbidity of these two diseases.

Liang Z ，Hu X ，Lin R ，Tang Z ，Ye Z ，Mao R ，Chen W ，Zhou Y ... -  《Frontiers in Immunology》

Identification of Immune-Related Gene Signature and Prediction of CeRNA Network in Active Ulcerative Colitis.

Ulcerative colitis (UC) is an inflammatory disease of the intestinal mucosa, and its incidence is steadily increasing worldwide. Intestinal immune dysfunction has been identified as a central event in UC pathogenesis. However, the underlying mechanisms that regulate dysfunctional immune cells and inflammatory phenotype remain to be fully elucidated. Transcriptome profiling of intestinal mucosa biopsies were downloaded from the GEO database. Robust Rank Aggregation (RRA) analysis was performed to identify statistically changed genes and differentially expressed genes (DEGs). Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore potential biological mechanisms. CIBERSORT was used to evaluate the proportion of 22 immune cells in biopsies. Weighted co-expression network analysis (WGCNA) was used to determine key module-related clinical traits. Protein-Protein Interaction (PPI) network and Cytoscape were performed to explore protein interaction network and screen hub genes. We used a validation cohort and colitis mouse model to validate hub genes. Several online websites were used to predict competing endogenous RNA (ceRNA) network. RRA integrated analysis revealed 1838 statistically changed genes from four training cohorts (adj. p-value < 0.05). GSEA showed that statistically changed genes were enriched in the innate immune system. CIBERSORT analysis uncovered an increase in activated dendritic cells (DCs) and M1 macrophages. The red module of WGCNA was considered the most critical module related to active UC. Based on the results of the PPI network and Cytoscape analyses, we identified six critical genes and transcription factor NF-κB. RT-PCR revealed that andrographolide (AGP) significantly inhibited the expression of hub genes. Finally, we identified XIST and three miRNAs (miR-9-5p, miR-129-5p, and miR-340-5p) as therapeutic targets. Our integrated analysis identified four hub genes (CXCL1, IL1B, MMP1, and MMP10) regulated by NF-κB. We further revealed that AGP decreased the expression of hub genes by inhibiting NF-κB activation. Lastly, we predicted the involvement of ceRNA network in the regulation of NF-κB expression. Collectively, our results provide valuable information in understanding the molecular mechanisms of active UC. Furthermore, we predict the use of AGP and small RNA combination for the treatment of UC.

Xu M ，Kong Y ，Chen N ，Peng W ，Zi R ，Jiang M ，Zhu J ，Wang Y ，Yue J ，Lv J ，Zeng Y ，Chin YE ... -  《Frontiers in Immunology》