Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis.

Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration. We obtained UC RNA sequencing data from the GEO database, which included the training set GSE92415 and the validation set GSE87473 and GSE72514. UC patients were classified based on GLS and its associated genes using consensus clustering analysis. We identified differentially expressed genes (DEGs) in different UC subtypes through a differential expression analysis of the training cohort. Machine learning algorithms, including Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were utilized to identify marker genes for UC. The CIBERSORT algorithm was used to determine the abundance of various immune cells in UC and their correlation with UC signature genes. Finally, we validated the expression of GLS through in vivo and ex vivo experiments. The expression of GLS was found to be elevated in patients with UC compared to normal patients. GLS and its related genes were able to classify UC patients into two subtypes, C1 and C2. The C1 subtype, as compared to the C2 subtype, showed a higher Mayo score and poorer treatment response. A total of 18 DEGs were identified in both subtypes, including 7 up-regulated and 11 down-regulated genes. Four UC signature genes (CWH43, HEPACAM2, IL24, and PCK1) were identified and their diagnostic value was validated in a separate cohort (AUC > 0.85). Furthermore, we found that UC signature biomarkers were linked to the immune cell infiltration. CWH43, HEPACAM2, IL24, and PCK1 may serve as potential biomarkers for diagnosing different subtypes of UC, which could contribute to the development of targeted molecular therapy and immunotherapy for UC.

Xie Y ，Li J ，Tao Q ，Wu Y ，Liu Z ，Chen Y ，Zeng C ... -  《Scientific Reports》

Age-related genes affecting the immune cell infiltration in ulcerative colitis revealed by weighted correlation network analysis and machine learning.

Chen HL ，Liu YH ，Tan CH 《-》

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

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

Bu M ，Cao X ，Zhou B 《-》

Screening and experimental validation of diagnostic gene in ulcerative colitis with anti-TNF-α therapy.

In clinical practice, the diagnosis of ulcerative colitis (UC) mainly relies on a comprehensive analysis of a series of signs and symptoms of patients. The current biomarkers for diagnosis of UC and prognostic prediction of anti-TNF-α therapy are inaccurate. The present study aimed to perform an integrative analysis of gene expression profiles in patients with UC. A total of seven datasets from the GEO database that met our strict inclusion criteria were included. After identifying differentially expressed genes (DEGs) between UC patients and healthy individuals, the diagnostic and prognostic utility of the DEGs were then analyzed via least absolute shrinkage and selection operator and support-vector machine recursive feature elimination. Subgroup analyses of the treated and untreated groups, as well as the treatment-response group and non-response group, were also performed. Furthermore, the relationship between the expressions of UC-related genes and infiltration of immune cells in the course of treatment was also investigated. Immunohistochemical (IHC) assay was used to verify the gene expression in inflamed UC tissues. When considering all the applied methods, DUOX2, PI3, S100P, MMP7, and S100A8 had priority to be defined as the characteristic genes among DEGs. The area under curve (AUC) of the five genes, which were all consistently over-expressed, based on an external validation dataset, were all above 0.94 for UC diagnosis. Four of the five genes (DUOX2, PI3, MMP7, and S100A8) were down-regulated between treatment-responsive and nonresponsive patients. A significant difference was also observed concerning the infiltration of immune cells, including macrophage and neutrophil, between the two groups (treatment responsive and nonresponsive). The changes in the expression of DUOX2 and MMP7 based on the IHC assay were highly consistent with the results obtained in the current study. This confirmed the mild to moderate diagnostic and predictive value of DUOX2 and MMP7 in patients with UC. The conducted analyses showed that the expression profile of the five identified biomarkers accurately detects UC, whereas four of the five genes evidently predicted the response to anti-TNF-α therapy.

Chen Y ，Li X ，Sun R ，Yang F ，Tian W ，Huang Q ... -  《-》