Identification and Analysis of Biomarkers Associated With Lipid Metabolism and Ferroptosis in Ulcerative Colitis

Cellular senescence, a hallmark of aging, significantly contributes to the pathology of ulcerative colitis (UC). Despite this, the role of senescence-related genes in UC remains largely undefined. This study seeks to clarify the impact of cellular senescence on UC by identifying key senescence-related genes and developing diagnostic models with potential clinical utility. Clinical data and gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Senescence-related differentially expressed genes (sene-DEGs) between patients with UC and healthy controls were identified using various bioinformatics techniques. Functional enrichment and immune infiltration analyses were performed to understand subtype characteristics derived from sene-DEGs through consensus clustering. Machine learning algorithms were employed to select feature genes from sene-DEGs, and their expression was validated across multiple independent datasets and human specimens. A nomogram incorporating these feature genes was created and assessed, with its diagnostic performance evaluated using receiver operating characteristic (ROC) analysis on independent datasets. Fourteen senescence-related differential genes were identified between patients with UC and healthy controls. These genes enabled the classification of patients with UC into molecular subtypes via unsupervised clustering. ABCB1 and LCN2 emerged as central hub genes through machine learning and feature importance analysis. ROC analysis verified their diagnostic value across various datasets. Validation in independent datasets and human specimens supported the bioinformatics findings. Furthermore, the expression levels of ABCB1 and LCN2 showed significant associations with immune cell profiles. The logistic regression (LR) model based on these genes demonstrated accurate UC prediction, as confirmed by ROC curve analysis. The nomogram model, constructed with feature genes, exhibited outstanding prediction capabilities, supported by DCA, C index, and calibration curve assessments. This integrated bioinformatics approach identified ABCB1 and LCN2 as significant biomarkers associated with cellular senescence. These findings enhance the understanding of cellular senescence in UC pathogenesis and propose its potential as a valuable diagnostic biomarker.

BackgroundThe incidence of ulcerative colitis (UC) is rapidly increasing worldwide, but existing therapeutics are limited. Neutrophil extracellular traps (NETs), which have been associated with the development of various autoimmune diseases, may serve as a novel therapeutic target for UC treatment.MethodsBioinformatics analysis was performed to investigate UC-related datasets downloaded from the GEO database, including GSE87466, GSE75214, and GSE206285. Differentially expressed genes (DEGs) related to NETs in UC patients and healthy controls were identified using Limma R package and WGCNA, followed by functional enrichment analysis. To identify potential diagnostic biomarkers, we applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE) model, and Random Forest (RF) algorithm, and constructed Receiver Operating Characteristic (ROC) curves to evaluate accuracy. Additionally, immune infiltration analysis was conducted to identify immune cells potentially involved in the regulation of NETs. Finally, the expression of core genes in patients was validated using Quantitative real-time PCR (qRT-PCR), and potential therapeutic drugs for UC were explored through drug target databases.ResultDifferential analysis of transcriptomic sequencing data from UC samples identified 29 DEGs related to NETs. Enrichment analysis showed that these genes primarily mediate UC-related damage through biological functions such as leukocyte activation, migration, immune receptor activity, and the IL-17 signaling pathway. Three machine learning algorithms successfully identified core NETs-related genes in UC (IL1B, MMP9 and DYSF). According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Additionally, Immune infiltration analysis revealed that the expression of these core genes was closely associated with neutrophils infiltration and CD4+ memory T cell activation, and negatively associated with M2 macrophage infiltration. qRT-PCR showed that the core genes were significantly overexpressed in UC patients. Gevokizumab, canakinumab and carboxylated glucosamine were predicted as potential therapeutic drugs for UC.ConclusionBy combining three machine learning algorithms and bioinformatics, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of UC, which may provide valuable insights into the mechanism of NETs in UC and potential related therapies.

Neutrophil extracellular traps (NETs), web-like structures released by neutrophils during the process NETosis, aiding in gut mucosal defense against microbial infections but potentially exacerbating inflammation and tissue damage. In Ulcerative Colitis (UC), NET formation is increased and may contribute to gut inflammation. However, the role of NETs in diagnosis of UC remains unclear. This study aims to identify NETs-related genes with diagnostic potential in UC and to develop a diagnostic predictive model based on these genes. The transcriptome dataset of UC retrieved from the GEO database. Differential expression analysis and Gene Set Enrichment Analysis (GSEA) were performed on the training set using R software. The "CIBERSORT" algorithm was utilized to evaluate the immune cell infiltration in UC. Subsequently, differentially expressed NETs (DE-NETs) were identified by intersecting key module genes from Weighted Gene Co-Expression Network Analysis (WGCNA), NETs-related genes, and differentially expressed genes (DEGs). The diagnostic genes were identified through three machine learning algorithms (Least Absolute Shrink-age And Selection Operator (LASSO) algorithm, Random Forests (RF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE)). The results of the three algorithms were integrated to identify NETs-related diagnostic genes (NDGs). A clinical diagnostic predictive model was constructed based on NDGs, and its performance was assessed using calibration curves, Clinical Impact Curve (CIC), Decision Curve Analysis (DCA), and Receiver Operating Characteristic Curve (ROC) to evaluate its discrimination ability and clinical utility. Immunohistochemical (IHC) examination was performed on colon tissue from UC patients. Additionally, a DSS-induced UC mouse model was constructed, and NDG expression in mouse colon tissues was analyzed by qRT-PCR, Western-blot, and IHC. The UC group exhibits significant enrichment of immune-related pathways, including the IL-17 signaling pathway, cytokine-cytokine receptor interactions, and TNF signaling pathway, as well as enhanced infiltration of immune cells, as demonstrated by GSEA and immune infiltration analysis. Fourteen genes were identified through intersecting DEGs, key module genes in WGCNA, and NETs-related genes. The identification of IL-1β, MMP-9 and CXCR2 as NDGs was using three machine learning methods. The clinical diagnostic predictive model of UC based on NDGs was constructed, and the AUC value of the model was 0.9715 in the training set, and 0.9595 and 0.9597 in external validation sets, respectively. Significant positive correlations were found between NDGs and Mast cells, Neutrophils, resting NK cells, M1 Macrophages, Tregs and Eosinophils. IHC analysis of clinical samples shows high expression of NDGs in the colon of UC patients. A clinical-associated nomogram was constructed from the IHC scores of the clinical samples, exhibiting an AUC of 100%. In DSS-induced UC mice model, NDG expression was up-regulated compared to normal control mice. A mouse-associated nomogram was constructed based on IHC scores of mouse tissues, and the AUC was also 100%. We constructed a clinical diagnostic predictive model for UC based on IL-1β, MMP-9 and CXCR2 and validated the ability of NDGs to predict UC in the clinical patient and the animal model. The model has excellent diagnostic efficiency and can provide a new idea for clinical diagnosis of UC.

BackgroundChronic kidney disease (CKD) is one of the most significant cardiovascular risk factors, playing vital roles in various cardiovascular diseases such as calcific aortic valve disease (CAVD). We aim to explore the CKD-associated genes potentially involving CAVD pathogenesis, and to discover candidate biomarkers for the diagnosis of CKD with CAVD.MethodsThree CAVD, one CKD-PBMC and one CKD-Kidney datasets of expression profiles were obtained from the GEO database. Firstly, to detect CAVD key genes and CKD-associated secretory proteins, differentially expressed analysis and WGCNA were carried out. Protein-protein interaction (PPI), functional enrichment and cMAP analyses were employed to reveal CKD-related pathogenic genes and underlying mechanisms in CKD-related CAVD as well as the potential drugs for CAVD treatment. Then, machine learning algorithms including LASSO regression and random forest were adopted for screening candidate biomarkers and constructing diagnostic nomogram for predicting CKD-related CAVD. Moreover, ROC curve, calibration curve and decision curve analyses were applied to evaluate the diagnostic performance of nomogram. Finally, the CIBERSORT algorithm was used to explore immune cell infiltration in CAVD.ResultsThe integrated CAVD dataset identified 124 CAVD key genes by intersecting differential expression and WGCNA analyses. Totally 983 CKD-associated secretory proteins were screened by differential expression analysis of CKD-PBMC/Kidney datasets. PPI analysis identified two key modules containing 76 nodes, regarded as CKD-related pathogenic genes in CAVD, which were mostly enriched in inflammatory and immune regulation by enrichment analysis. The cMAP analysis exposed metyrapone as a more potential drug for CAVD treatment. 17 genes were overlapped between CAVD key genes and CKD-associated secretory proteins, and two hub genes were chosen as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning. Furthermore, SLPI/MMP9 expression patterns were confirmed in our external cohort and the nomogram could serve as novel diagnosis models for distinguishing CAVD. Finally, immune cell infiltration results uncovered immune dysregulation in CAVD, and SLPI/MMP9 were significantly associated with invasive immune cells.ConclusionsWe revealed the inflammatory-immune pathways underlying CKD-related CAVD, and developed SLPI/MMP9-based CAVD diagnostic nomogram, which offered novel insights into future serum-based diagnosis and therapeutic intervention of CKD with CAVD.

BackgroundsAsthma and ulcerative colitis (UC) are chronic inflammatory diseases linked through the “gut-lung axis,” but their shared mechanisms remain unclear. This study aims to identify common biomarkers and pathways between asthma and UC using bioinformatics.MethodsGene expression data for asthma and UC were retrieved from the GEO database, and differentially expressed genes (DEGs) were analyzed. Weighted Gene Coexpression Network Analysis (WGCNA) identified UC-associated gene modules. Shared genes between asthma and UC were derived by intersecting DEGs with UC-associated modules, followed by functional enrichment and protein-protein interaction (PPI) analysis. Machine learning identified hub genes, validated through external datasets using ROC curves, nomograms, and boxplots. Gene Set Enrichment Analysis (GSEA) explored pathway alterations, while immune infiltration patterns were analyzed using the CIBERSORT algorithm. Molecular docking (MD) was performed to predict therapeutic compounds, followed by molecular dynamics simulations on the top-ranked docked complex to assess its binding stability.ResultsA total of 41 shared genes were identified, linked to inflammatory and immune pathways, including TNF, IL-17, and chemokine signaling. Four key hub genes—NOS2, TCN1, CHI3L1, and TIMP1—were validated as diagnostic biomarkers. Immune infiltration analysis showed strong correlations with multiple immune cells. Molecular docking identified several potential therapeutic compounds, with PD 98059, beclomethasone, and isoproterenol validated as promising candidates. The stability of the TIMP1-Beclomethasone complex was determined through molecular dynamics simulations.ConclusionThis study highlights NOS2, TCN1, CHI3L1, and TIMP1 as potential biomarkers and therapeutic targets for asthma and UC, providing insights into shared mechanisms and new strategies for diagnosis and treatment.

Lung adenocarcinoma (LUAD), the predominant form of non-small-cell lung cancer, is frequently complicated by acute respiratory distress syndrome (ARDS), which increases mortality risks. Investigating the prognostic implications of ARDS-related genes in LUAD is crucial for improving clinical outcomes. Data from TCGA, GEO and GTEx were used to identify 276 ARDS-related genes in LUAD via differential expression analysis. Univariate Cox regression, consensus clustering and machine learning algorithms were used to develop a prognostic risk scoring model. Functional enrichment, immune infiltration analyses, copy number variations and mutational burdens were examined, and the results were validated at the single-cell level. ARDS-related genes significantly impact the prognosis of LUAD patients. A machine learning-based risk scoring model accurately predicted survival rates. Functional enrichment and immune infiltration analyses revealed that these genes are primarily involved in cell cycle regulation and immune cell infiltration. Single-cell expression data supported these findings, and the assessments of copy number variations and mutational burdens highlighted distinct genetic characteristics. This study establishes the prognostic relevance of ARDS-associated genes in LUAD and provides potential biomarkers for personalized therapy and prognosis. Future studies will validate these findings and explore their clinical applications.

BackgroundMoyamoya disease (MMD) is a progressive cerebrovascular disorder characterized by stenosis or occlusion of the terminal portions of the internal carotid arteries and their proximal branches, accompanied by the formation of abnormal collateral vessel networks. It represents a leading cause of ischemic and hemorrhagic stroke in both pediatric and adult populations. However, a comprehensive understanding of the molecular drivers underlying the hallmark vascular pathology of MMD remains elusive. Emerging evidence indicates that dysregulated lipid metabolism significantly contributes to MMD susceptibility and disease severity; nevertheless, its precise mechanistic roles in MMD pathogenesis have not been thoroughly investigated.MethodsWe integrated three publicly available gene expression datasets comprising MMD patients and non-MMD controls (GSE189993, GSE157628, and GSE141024). Following rigorous batch-effect correction, differential expression analysis was performed to identify differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and machine learning approaches were then integrated to prioritize hub genes. Immune cell infiltration analysis was conducted for the identified hub genes. Subsequently, functional enrichment analysis, immune infiltration profiling, and protein-protein interaction (PPI) network construction were further performed. Validation was carried out using an independent external dataset (GSE249254) as well as in vitro experiments-including hypoxia-treated human endothelial cells and patient-derived tissue samples-to assess mRNA and protein expression levels. Finally, a Transcription Factor (TF)-miRNA-mRNA regulatory network was constructed, and potential therapeutic compounds targeting MMD were predicted via computational screening.ResultsA total of 2,288 DEGs were identified. GSEA revealed significant enrichment of pathways related to lipid metabolism and immune responses. WGCNA identified MMD-associated co-expression modules, and integrative machine learning prioritized four hub genes: LPIN3, PPT2, ACSS1, and INPPL1. A diagnostic nomogram built upon these four genes demonstrated robust predictive performance, with an area under the curve (AUC) of 0.91. Immune infiltration analysis revealed that the abundance of B cells in the MMD patient group was significantly lower than that in the control group, with statistical significance. Notably, LPIN3 expression was significantly upregulated in MMD. It was the only hub gene whose upregulation at the mRNA level was consistently validated in both the external validation set (GSE249254) and in vitro models. Subsequent immunohistochemical (IHC) experiments further corroborated this finding at the protein level, highlighting its potential as an independent biomarker. Furthermore, leveraging the hub gene network, seven candidate compounds with potential therapeutic relevance to MMD were predicted.ConclusionThis study delineates the immune-lipid metabolic transcriptomic characteristics of MMD, identifies novel molecular determinants of disease pathogenesis, and validates LPIN3 as a promising diagnostic biomarker. Collectively, these findings provide critical mechanistic insights into MMD etiology and offer a foundation for developing improved diagnostic strategies and targeted therapeutic interventions.

BackgroundAir pollution may crosstalk with immune system to promote hepatocellular carcinoma (HCC) development, but its precise mechanisms and prognostic significance remain unclear.ObjectiveThis study aims to construct a prognostic signature for HCC based on air pollutant-related immune genes (APIGs).MethodsWe obtained mRNA-seq and scRNA of HCC from GEO, TCGA and ICGC. AP-related target genes were retrieved from several online databases. APIGs were obtained using WGCNA, differential gene expression analysis and immune infiltration analysis. Molecular subtypes were conducted based on APIG expression to characterize immune features. A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). Furthermore, we performed qRT-PCR, survival analyses, functional enrichment, immune infiltration and single-cell analyses. Subsequently, LASSO, RF, and RFE-SVM were employed to identify diagnostic genes, followed by pan-cancer analysis.ResultsWe identified 19 APIGs. HCC samples were divided into 3 subtypes, with C1 exhibiting a pro-tumor immune microenvironment and poorer prognosis. APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. The biological pathway differences between APIGPS-based high- and low-risk groups involved immune responses and cell proliferation and migration. APIGPS genes had stable binding to 7 APs and were mainly expressed in macrophages, with HRG exhibiting higher macrophage abundance. CDC25C was identified as the hub gene after intersecting diagnostic genes and APIGPS genes. CDC25C was associated with survival of 10 cancers, MSI in 10 cancers, TMB in 21 cancers, and immune cell abundance in 13 cancers.ConclusionsWe identified key APIGs and constructed a robust APIG-based prognostic signature for HCC. CDC25C was a key target through which APs impact HCC and multiple other cancers.

AimVasculogenic mimicry (VM), a process in which cancer cells form endothelial cell-independent vascular networks, is a hallmark of tumor aggressiveness in lung adenocarcinoma (LUAD) and supports tumor growth and metastasis. This study aims to identify and validate key genes associated with VM formation in LUAD, and to elucidate their functional roles and clinical significance.MethodsTranscriptomic data from LUAD samples were analyzed using differential expression analysis (DEA) and weighted gene co-expression network analysis (WGCNA) to identify VM-associated genes. Machine learning algorithms were applied to refine the selection and identify hub genes. Functional enrichment and immune infiltration analyses were performed. The role of SULF1 in VM was further validated through in vitro and in vivo experiments.ResultsWe identified 10,810 differentially expressed genes. WGCNA revealed 101 VM-associated genes, predominantly within the “yellow” and “brown” modules. Machine learning pinpointed three key regulators: downregulated decorin (DCN) and upregulated nucleoplasmin 3 (NPM3) and sulfatase 1 (SULF1). Functional enrichment analysis highlighted their involvement in extracellular matrix (ECM) organization and ribosomal pathways. Immune infiltration analysis indicated a positive correlation between DCN and immune cell presence, whereas NPM3 and SULF1 showed negative correlations. Critically, SULF1 overexpression promoted VM formation in vitro by enhancing cell migration and invasion, mediated through the vascular endothelial growth factor (VEGF)/transforming growth factor beta (TGF-β)/Vimentin signaling axis, and accelerated tumor growth in vivo.ConclusionWe identified DCN, NPM3, and SULF1 as key biomarkers of VM in LUAD. SULF1, in particular, plays a central role in driving VM formation and tumor progression. These findings offer novel mechanistic insights and highlight potential therapeutic targets for LUAD treatment.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00432-025-06361-0.

Non-alcoholic fatty liver disease (NAFLD) and ulcerative colitis (UC) are two common health issues that have gained significant global attention. Previous studies have suggested a possible connection between NAFLD and UC, but the underlying pathophysiology remains unclear. This study investigates common genes, underlying pathogenesis mechanisms, identification of diagnostic markers applicable to both conditions, and exploration of potential therapeutic targets shared by NAFLD and UC. We obtained datasets for NAFLD and UC from the GEO database. The DEGs in the GSE89632 dataset of the NAFLD and GSE87466 of the UC dataset were analyzed. WGCNA, a powerful tool for identifying modules of highly correlated genes, was employed for both datasets. The DEGs of NAFLD and UC and the modular genes were then intersected to obtain shared genes. Functional enrichment analysis was conducted on these shared genes. Next, we utilize the STRING database to establish a PPI network. To enhance visualization, we employ Cytoscape software. Subsequently, the Cytohubba algorithm within Cytoscape was used to identify central genes. Diagnostic biomarkers were initially screened using LASSO regression and SVM methods. The diagnostic value of ROC curve analysis was assessed to detect diagnostic genes in both training and validation sets for NAFLD and UC. A nomogram was also developed to evaluate diagnostic efficacy. Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. Finally, we investigated the correlation between hub gene expression, diagnostic gene expression, and immune infiltration levels. We identified 34 shared genes that were found to be associated with both NAFLD and UC. These genes were subjected to enrichment analysis, which revealed significant enrichment in several pathways, including the IL-17 signaling pathway, Rheumatoid arthritis, and Chagas disease. One optimal candidate gene was selected through LASSO regression and SVM: CCL2. The ROC curve confirmed the presence of CCL2 in both the NAFLD and UC training sets and other validation sets. This finding was further validated using a nomogram in the validation set. Additionally, the expression levels of CCL2 for NAFLD and UC showed a significant correlation with immune cell infiltration. This study identified a gene (CCL2) as a biomarker for NAFLD and UC, which may actively participate in the progression of NAFLD and UC. This discovery holds significant implications for understanding the progression of these diseases and potentially developing more effective diagnostic and treatment strategies.

Ulcerative colitis (UC) is an inflammatory bowel disease influenced by genetic, immune, and environmental factors. This study investigates the link between ferroptosis, a cell death process related to oxidative stress and iron metabolism, and immune infiltration in UC. We analyzed UC patient transcription data from the Gene Expression Omnibus (GEO) and identified ferroptosis-related genes using FerrDB. Using STRING and Cytoscape, we analyzed protein-protein interactions to identify hub UC Differentially Expressed Genes (UCDEGs) and performed functional enrichment with GO and KEGG pathways. Machine learning helped further identify key UC Differentially Expressed Ferroptosis-related genes (UCDE-FRGs), which were validated using additional GEO datasets and immunohistochemical staining. A total of 11hub UCDEGs (CCL2, ICAM1, TLR2, CXCL9, MMP9, CXCL10, IL1B, CXCL8, PTPRC, FCGR3A, and IL1A) and 3 key UCDE-FRGs (DUOX2, LCN2 and IDO1) were identified. GO and KEGG functional enrichment indicates that these genes play a role in immunity and ferroptosis. Analysis of immune cell infiltration showed that there were a large number of Plasma cells, Monocytes, M0/M1 Macrophages and Neutrophils in the UC. Correlation analysis revealed 3 key UCDE-FRGs associated with immune-infiltrated cells in UC. IHC results showed that the expression levels of 3 key UCDE-FRGs in UC were all higher than that in the healthy controls. In summary, this study identified three key genes related to UC ferroptosis and immunity, namely DUOX2, IDO1 and LCN2. These findings suggest that immune infiltration plays an important role in UC caused by ferroptosis, and that there is mutual regulation between UC and immune-infiltrated cells. Our research revealed the potential application of immune and ferroptosis in the diagnosis, treatment and prognosis of UC, providing new strategies for clinical management.

Identification of autophagy-related biomarker and analysis of immune infiltrates in diabetic nephropathy: PTGER1 protein macromolecular structure and function.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by intestinal inflammation and autoimmune responses. This study aimed to identify diagnostic biomarkers for UC through bioinformatics analysis and machine learning, and to validate these findings through immunofluorescence staining of clinical samples. Differential expression analysis was conducted on expression profile datasets from 4 UC samples. Key biomarkers were selected using LASSO logistic regression, SVM-RFE, and Random Forest algorithms. The diagnostic performance of these biomarkers was evaluated using receiver operating characteristic (ROC) curves. Functional enrichment analysis assessed the biological functions of these biomarkers. The CIBERSORT algorithm was used to analyze immune cell infiltration. Regulatory networks for diagnostic markers were constructed. Additionally, immunofluorescence staining was performed on clinical samples to validate the expression levels of key biomarkers. Differential analysis identified 199 significantly differentially expressed genes. SLC6A14 was selected as a key diagnostic biomarker, demonstrating excellent diagnostic performance in training and validation sets (AUC values: 0.973, 0.984, and 0.970). Immune cell infiltration analysis revealed significant increases in Neutrophils and activated Mast cells in UC samples, whereas resting Mast cells were relatively downregulated. Furthermore, SLC6A14 showed strong correlations with various immune cells. The ceRNA network identified 22 lncRNAs and 10 miRNAs associated with SLC6A14. Immunofluorescence staining of clinical samples confirmed that SLC6A14 expression is significantly higher in UC patients compared to normal intestinal mucosa, and its expression increases with UC activity. SLC6A14 has been confirmed as a key diagnostic marker for UC, validated both through bioinformatics analysis and immunofluorescence staining of clinical samples. It maintains regulatory relationships with various non-coding RNAs and plays a significant role in the pathogenesis of UC through its interactions with immune cells.

Cuproptosis is a novel form of cell death, mediated by protein lipid acylation and highly associated with mitochondrial metabolism, which is regulated in the cell. Ulcerative colitis (UC) is a chronic inflammatory bowel disease that recurs frequently, and its incidence is increasing worldwide every year. Currently, a growing number of studies have shown that cuproptosis-related genes (CRGs) play a crucial role in the development and progression of a variety of tumors. However, the regulatory role of CRGs in UC has not been fully elucidated. Firstly, we identified differentially expressed genes in UC, Likewise, CRGs expression profiles and immunological profiles were evaluated. Using 75 UC samples, we typed UC based on the expression profiles of CRGs, followed by correlative immune cell infiltration analysis. Using the weighted gene co-expression network analysis (WGCNA) methodology, the cluster’s differentially expressed genes (DEGs) were produced. Then, the performances of extreme gradient boosting models (XGB), support vector machine models (SVM), random forest models (RF), and generalized linear models (GLM) were constructed and predicted. Finally, the effectiveness of the best machine learning model was evaluated using five external datasets, receiver operating characteristic curve (ROC), the area under the curve of ROC (AUC), a calibration curve, a nomogram, and a decision curve analysis (DCA). A total of 13 CRGs were identified as significantly different in UC and control samples. Two subtypes were identified in UC based on CRGs expression profiles. Immune cell infiltration analysis of subtypes showed significant differences between immune cells of different subtypes. WGCNA results showed a total of 8 modules with significant differences between subtypes, with the turquoise module being the most specific. The machine learning results showed satisfactory performance of the XGB model (AUC = 0.981). Finally, the construction of the final 5-gene-based XGB model, validated by the calibration curve, nomogram, decision curve analysis, and five external datasets (GSE11223: AUC = 0.987; GSE38713: AUC = 0.815; GSE53306: AUC = 0.946; GSE94648: AUC = 0.809; GSE87466: AUC = 0.981), also proved to predict subtypes of UC with accuracy. Our research presents a trustworthy model that can predict the likelihood of developing UC and methodically outlines the complex relationship between CRGs and UC.

BackgroundAssociations between ulcerative colitis (UC) and ankylosing spondylitis (AS) have been reported in multiple studies, but the common etiologies of UC and AS remain unknown. Thus, in the current study, we aimed to investigate the shared genes and relevant mechanisms in UC and AS.MethodsUsing datasets for UC (GSE113079) and AS (GSE1797879), we initially identified differentially expressed genes (DEGs) through differential expression analysis. The DEGs from both datasets were intersected to identify common DEGs, relevant to both UC and AS, which were used in receiver operating characteristic (ROC) curve analysis to confirm key genes in the shared pathway. Gene set enrichment analysis (GSEA) was used to obtain information on key gene pathways and interactions with UC or AS-related diseases, followed by immune infiltration analysis. Finally, peripheral blood samples of AS and UC were used to verify the mRNA expression of the eight key genes using reverse transcription-polymerase chain reaction (RT-PCR).ResultsOur results revealed that GMFG, GNG11, CLEC4D, CMTM2, VAMP5, S100A8, S100A12 and DGKQ are potential diagnostic biomarkers of AS and UC. Rimegepant, eptinezumab, methotrexate, atogepant, and ubrogepant were identified as potential drugs for S100A12 and S100A8 in patients with UC and AS. GSEA showed that these key genes were associated with antigen processing and presentation, natural killer cell mediated cytotoxicity and the T cell receptor signaling pathway in AS and UC, and were significantly associated with immune cells in various immune-related pathways. Subsequent functional experiments revealed significant increases in the mRNA expressions of S100A12 and VAMP5 in patients with AS and UC. Additionally, CLEC4D mRNA expression was notably higher in patients with UC than in healthy controls.ConclusionKey genes and shared pathways were identified in UC and AS, which may improve understanding of their relationship and guide diagnosis and treatment strategies.