Bioinformatics Analysis of Key Candidate Genes and Pathways in Ulcerative Colitis

Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study

Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils' accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting.

In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations

Most recently, the adenosine is considered as one of the most promising targets for treating pain, with few side effects. It exists in the central nervous system, and plays a key role in nociceptive afferent pathway. It is reported that the A1 receptor (A1R) could inhibit Ca²⁺ channels to reduce the pain like analgesic mechanism of morphine. And, A2a receptor (A2aR) was reported to enhance the accumulation of AMP (cAMP) and released peptides from sensory neurons, resulting in constitutive activation of pain. Much evidence showed that A1R and A2aR could be served as the interesting targets for the treatment of pain. Herein, virtual screening was utilized to identify the small molecule compounds towards A1R and A2aR, and top six molecules were considered as candidates via amber scores. The molecular dynamic (MD) simulations and molecular mechanics/generalized born surface area (MM/GBSA) were employed to further analyze the affinity and binding stability of the six molecules towards A1R and A2aR. Moreover, energy decomposition analysis showed significant residues in A1R and A2aR, including His1383, Phe1276, and Glu1277. It provided basics for discovery of novel agonists and antagonists. Finally, the agonists of A1R (ZINC19943625, ZINC13555217, and ZINC04698406) and inhibitors of A2aR (ZINC19370372, ZINC20176051, and ZINC57263068) were successfully recognized. Taken together, our discovered small molecules may serve as the promising candidate agents for future pain research.

Identification of Potential Biomarkers and Immune Infiltration Characteristics in Ulcerative Colitis by Combining Results from Two Machine Learning Algorithms

Objective

This study was designed to identify potential biomarkers for ulcerative colitis (UC) and analyze the immune infiltration characteristics in UC.

Methods

Datasets containing human UC and normal control tissues (GSE87466, GSE107597, and GSE13367) were downloaded from the GEO database. Then, the GSE87466 and GSE107597 datasets were merged, and the differentially expressed genes (DEGs) between UC and normal control tissues were screened out by the “limma R” package. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) were performed to screen out the best biomarkers. The GSE13367 dataset was used as a validation cohort, and the receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance. Finally, the immune infiltration characteristics in UC were explored by CIBERSORT, and we further analyzed the correlation between potential biomarkers and different immune cells.

Results

A total of 76 DEGs were screened out, among which 56 genes were upregulated and 20 genes were downregulated. Functional enrichment analysis revealed that these DEGs were mainly involved in immune response, chemokine signaling, IL−17 signaling, cytokine receptor interactions, inflammatory bowel disease, etc. ABCG2, HSPB3, SLC6A14, and VNN1 were identified as potential biomarkers for UC and validated in the GSE13367 dataset (AUC = 0.889, 95% CI: 0.797~0.961). Immune infiltration analysis by CIBERSORT revealed that there were significant differences in immune infiltration characteristics between UC and normal control tissues. A high level of memory B cells, γδ T cells, activated mast cells, M1 macrophages, neutrophils, etc. were found in the UC group, while a high level of M2 type macrophages, resting mast cells, eosinophils, CD8+ T cells, etc. were found in the normal control group.

Conclusion

ABCG2, HSPB3, SLC6A14, and VNN 1 were identified as potential biomarkers for UC. There was an obvious difference in immune infiltration between UC and normal control tissues, which may provide help to guide individualized treatment and develop new research directions.

Increased SERPINA3 Level Is Associated with Ulcerative Colitis

Ulcerative colitis (UC) is a recurrent, chronic intestinal disease that is currently incurable. Its pathogenesis remains to be further understood. Therefore, seeking new biomarkers and potential drug targets is urgent for the effective treatment of UC. In this study, the gene expression profile GSE38713 was obtained from the GEO (Gene Expression Omnibus) database. Data normalisation and screening of the differentially expressed genes (DEGs) were conducted using R software, and gene ontology (GO) enrichment was performed using Metascape online tools. The PubMed database was used to screen new genes that have not been reported, and SERPINA3 was selected. The correlation between SERPINA3 and other inflammatory factors was analysed by Spearman correlation analysis. Finally, colitis model mice and an in-vitro model were established to validate the function of the SERPINA3 gene. SERPINA3 gene expression was markedly increased in UC patient samples, colitis models and in-vitro models and showed an association with other inflammatory factors. ROC analysis indicated that SERPINA3 could represent a potential biomarker of active UC. Additionally, silencing SERPINA3 in an in-vitro intestinal epithelial inflammatory model significantly decreased the mRNA level of inflammatory factors. This study provides supportive evidence that SERPINA3 may act as a key biomarker and potential drug target in UC treatment.