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

Abstract

The crosstalk between age and immunity in the context of ulcerative colitis (UC) remains incompletely understood. Our objective is to elucidate the specific age-associated genetic factors that modulate immune cell infiltration in UC, with the aim of identifying innovative therapeutic targets for the treatment of this disease. Potential batch effects between samples were removed by R package "inSilicoMerging". Unsupervised clustering analysis via the "ConsensusClusterPlus" R package was utilized to perform consensus molecular subtyping of immune subtypes in UC. The construction of a heat map was accomplished through the utilization of the R package "pheatmap", while functional enrichment analysis was executed by means of the Metascape database. The identification of the age-related gene module was achieved by performing weighted gene co-expression network analysis (WGCNA) analysis using the R package "WGCNA". The support vector machine (SVM), least absolute shrinkage and selector operation (LASSO), and random forest algorithms were performed via the "e1071", "glmnet" and "randomForest" packages in R, respectively. The diagnostic performance of the parameter was assessed using the receiver operating characteristic (ROC) curve. Correlation analysis was performed by Spearman correlation. The "XSum" package in R was employed to identify potential small-molecule drugs for UC utilizing the Connectivity Map (CMap) database. Molecular docking was performed with Autodock Vina molecular docking software. A significantly greater frequency of UC patients aged below 40 years was observed in the group with extensive disease extent as compared to those with non-extensive disease extent (70% vs. 47%; Chi-square test, p = 0.02). The application of unsupervised clustering analysis allowed for the stratification of UC patients into two distinct immune subtypes, namely cluster C1 and cluster C2. The distribution of immune subtypes was significantly different between different age categories (Chi-square test, p = 0.00219). The UC samples that were grouped under cluster C1 were distinguished by a higher abundance of macrophages and an elevated number of neutrophils relative to those in cluster C2. Based on both WGCNA and Limma analysis, 146 age-related genes were identified, which exhibited a predominant enrichment in the biological process of cellular senescence. Two age-related genes (MIDN, and PLD6) affecting the immune cell infiltration in UC were identified based on machine learning algorithms (SVM, LASSO, and random forest). The diagnostic performance of MIDN (AUC = 0.93) and PLD6 (AUC = 0.90) in discerning UC patients belonging to cluster C1 was found to be satisfactory, as demonstrated by ROC curve analysis. MIDN demonstrated a positive correlation (r = 0.50, p < 0.0001) with Neutrophil, while PLD6 exhibited a negative correlation (r = -0.52, p < 0.0001) with Neutrophil levels. The "XSum" algorithm revealed that Entinostat has therapeutic potential for UC. The docking glide score between Entinostat and MIDN, and PLD6 protein was -8.9 kcal/mol and -6.8 kcal/mol, respectively. We have identified two age-related genes, MIDN and PLD6, that are involved in immune cell infiltration in patients with ulcerative colitis. Furthermore, a small molecule drug (Entinostat) with potential therapeutic effects for UC was screened out. This study presented new perspectives on personalized clinical management and therapy research for UC.