Abstract Objective Ulcerative Colitis (UC) manifests as a chronic inflammatory condition of the intestines, marked by ongoing immune system dysregulation. Disulfidptosis, a newly identified cell death mechanism, is intimately linked to the onset and advancement of inflammation. However, the role of disulfidptosis in UC remains unclear. Methods We screened differentially expressed genes (DEGs) associated with disulfidptosis in multiple UC datasets, narrowed down the target gene number using lasso regression, and conducted immune infiltration analysis and constructed a clinical diagnostic model. Additionally, we explored the association between disulfidptosis-related key genes and disease remission in UC patients receiving biologic therapy. Finally, we confirmed the expression of key genes in FHC cells and UC tissue samples. Results In the differential analysis, we identified 20 DEGs associated with disulfidptosis. Immune infiltration results revealed that five genes (PDLIM1, SLC7A11, MYH10, NUBPL, OXSM) exhibited strong correlations with immune cells and pathways. Using GO, KEGG and WGCNA analyses, we discovered that gene modules highly correlated with disulfidptosis-related gene expression were significantly enriched in inflammation-related pathways. Additionally, we developed a nomogram based on these five immune-related disulfidptosis genes for UC diagnosis, showing robust diagnostic capability and clinical efficacy. Kaplan-Meier survival analysis revealed a significant link between changes in the expression levels of these cell genes and disease remission in UC patients receiving biologic therapy. In line with previous studies, similar expression changes of the target gene were seen in both UC cell models and tissue samples. Conclusions This study utilized bioinformatic analysis and machine learning to identify and analyze features associated with disulfidptosis in multiple UC datasets. This enhances our comprehension of the role disulfidptosis plays in intestinal immunity and inflammation in UC, providing new perspectives for developing innovative treatments for UC. Keywords: Ulcerative Colitis, Disulfidptosis, GEO dataset, Biologics, Immune, Inflammatory bowel disease 1. Introduction Ulcerative Colitis (UC), a predominant form of inflammatory bowel disease (IBD) affecting the colon and rectum, is characterized by recurrent and incurable features, clinically presenting as mucopurulent bloody stools and persistent intestinal inflammation [[27]1]. The etiology of UC remains unclear, likely involving factors such as genetics, environment, lifestyle, gut microbiota, and immune dysregulation [[28]2]. In recent years, the incidence of UC worldwide has steadily increased alongside improving living standards, placing a considerable strain on the global economy and healthcare systems [[29]3]. Treatment of UC often involves medications like 5-aminosalicylic acid, steroids, immunosuppressants, and biologics. However, extended use of these drugs can result in resistance and additional side effects [[30]4]. Moreover, UC often faces challenges in clinical diagnosis, potentially resulting in delayed systemic treatment and affecting the efficacy of later-stage interventions for patients [[31]1,[32]5]. Therefore, elucidating the pathogenesis of UC is crucial for discovering new therapeutic targets and diagnostic markers, significantly enhancing clinical symptom relief for UC patients and improving the efficiency of UC drug treatments. As an immune-imbalanced disease, cell death plays a crucial role in the pathogenesis and progression of UC, essential for maintaining stable intestinal environments and regulating gut ecology [[33]6]. Disulfidptosis, a recently discovered cell death mechanism induced by disulfide bond stress, represents a novel mechanism closely associated with inflammation initiation and progression. An abnormal accumulation of intracellular disulfides, such as cysteine, induces disulfide stress, which is highly toxic to cells and triggers cell death [[34]7,[35]8]. Nicotinamide adenine dinucleotide phosphate (NADPH), as a reducing agent, can reduce disulfides and prevent cell damage [[36]7]. Disulfidptosis is considered to play a crucial role in cancer metabolism therapy [[37]9]. The accumulation of dead cells is likely to activate immune cells, induce inflammatory responses, and potentially impact local metabolism [[38]10]. However, in the immune dysregulation of UC, there is recurrent intestinal inflammation leading to disrupted gut metabolism. Whether disulfidptosis participates in the inflammation within the UC intestine is not yet clear. Therefore, this study aims to explore the role of disulfidptosis in UC intestinal immunity and inflammation, providing further theoretical evidence for how cell death influences the disease progression of UC. In this study, we downloaded three datasets ([39]GSE107499, [40]GSE87466, [41]GSE59071) containing gene expression data from UC mucosal tissue in the GEO database. After merging and screening for differentially expressed genes (log|FC| > 0, p-value <0.05), we intersected these genes with disulfidptosis-related genes, resulting in 20 differentially expressed genes. Subsequently, we used lasso regression to further filter the genes obtained in the previous step, ultimately identifying seven differentially expressed disulfidptosis-related genes ([42]Fig. 1). Additionally, we observed the immune infiltration landscape within UC intestines, selecting five immune-related disulfidptosis genes based on their correlation with immune cells or pathways (up = 2, down = 3). To further explore their diagnostic capabilities in UC, we constructed a nomogram using these five key genes for diagnosing UC and evaluated the clinical efficacy of this diagnostic model. Finally, we downloaded dataset [43]GSE73661 to validate the performance of these five genes in UC biologic therapy (vedolizumab (VDZ) and infliximab (IFX)). Fig. 1. [44]Fig. 1 [45]Open in a new tab The flow chart of the study, including experimental grouping design and research process. (UC: Ulcerative colitis; DRGs: Disulfidptosis Related Genes). 2. Materials and methods 2.1. Data sources We searched the GEO database for UC-related datasets and selected [46]GSE107499, [47]GSE87466, [48]GSE59071, and [49]GSE73661 for analysis. A total of 312 colon mucosal tissue samples were included, comprising 236 samples from UC patients in the active disease phase and 76 samples from normal colon mucosa. We integrated the gene expression data from [50]GSE107499, [51]GSE87466, and [52]GSE59071, and eliminated batch effects from the resulting expression matrix for differential analysis. After identifying the target genes, we used the [53]GSE73661 dataset to evaluate the influence of VDZ and IFX on these genes' expression. This enabled us to determine the effectiveness of pivotal genes in predicting whether UC patients achieve disease remission following biologic therapy. 2.2. Identification of differentially expressed genes associated with disulfidptosis We collected 24 genes related to disulfidptosis through the literature review for subsequent analysis [[54]8,[55]11,[56]12]. In order to further narrow down the selection of target genes, we identified differentially expressed genes in the merged UC colon mucosal expression matrix using the criteria of fold change (log|FC|) > 0 and p < 0.05. Subsequently, we intersected the differentially expressed genes with those associated with disulfidptosis, resulting in a total of 20 genes. To illustrate these findings, we generated a Venn diagram. 2.3. Lasso regression We applied Lasso regression to sift through the 20 differentially expressed genes identified in the preceding step. Employing a 10-fold cross-validation approach for iterative analysis, we derived a model exhibiting outstanding performance while utilizing the fewest variables. The outcome revealed 7 pivotal genes, encompassing 3 upregulated genes and 4 downregulated genes. 2.4. Analyses of immune infiltration and immune correlation To increase the reliability of the immune infiltration results, we utilized both the “CIBERSORT” and “ssGSEA” algorithms for analyzing the UC dataset [[57]13,[58]14]. We conducted correlation analyses on the previously selected 7 differentially expressed genes associated with disulfidptosis, using a significance threshold of p < 0.05. Based on the criteria of |(Ps)| ≥ 0.50 in both CIBERSORT and ssGSEA results, we ultimately identified 5 genes associated with disulfidptosis that showed significant correlation with immune dysregulation in the UC intestinal context. Subsequent analysis involved WGCNA (Weighted Gene Co-expression Network Analysis). 2.5. WGCNA analysis To further investigate the intrinsic connections among disulfidptosis-related genes significantly correlated with immune dysregulation in the UC intestinal context, we employed Weighted Gene Co-expression Network Analysis (WGCNA). This method facilitated the construction of a gene co-expression network to identify gene modules exhibiting co-expression patterns with the 5 identified genes [[59]15,[60]16]. 2.6. Functional annotation and pathway enrichment analysis In order to further elucidate the role of differentially expressed genes in biological processes, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses [[61]17]. To ensure the reliability of the results, we utilized a significance threshold of p < 0.05 for the identification of significantly enriched GO terms and KEGG pathways. 2.7. Diagnostic model The selected 5 key genes were used to construct a diagnostic model for UC (Nomogram), and the expression matrix was randomly divided into training and validation sets in a 7:3 ratio. In the training set, we applied an algorithm to establish a clinical prediction nomogram. To further validate its accuracy, we subjected the constructed model to examination using a validation set. Finally, we plotted a calibration curve to assess the calibration performance of the nomogram prediction model. 2.8. Validation of the disulfidptosis related genes signature To validate the value of the target genes in the diagnosis of UC patients, we conducted ROC analysis to assess their predictive capability. We quantified the area under the ROC curve (AUC value) for each gene. Additionally, we employed unsupervised consensus clustering to group all UC patients and performed Kaplan-Meier (KM) survival analysis on different patient clusters [[62]18]. 2.9. Cell culture and treatment The human normal colonic epithelial cell line (FHC) used in this study was purchased from the American Type Culture Collection (ATCC, Rockville, Maryland), and cultured in DMEM high glucose medium supplemented with 10 % fetal bovine serum and 1 % penicillin/streptomycin. Cells were maintained in a humidified atmosphere with 5 % CO[2] at 37 °C and observed under a microscope every 24 h. To simulate the cellular environment of UC, FHC cells were seeded in 6-well plates and treated with 50 ng/ml LPS solution when the cell density reached an appropriate level. After 12 h, cells were harvested for RNA or protein extraction for subsequent experiments. 2.10. Collection of UC mucosal tissue specimens The four IBD clinical intestinal lesion tissue samples in this study were obtained from UC patients who underwent surgery at the Second Xiangya Hospital of Central South University from December 2022 to December 2023 (all patients were pathologically diagnosed with UC, including 2 males and 2 females). Relative normal intestinal control samples were taken from intestinal tissues approximately 5 cm away from the ulcer (confirmed by pathological examination to be normal intestinal mucosa). All tissue specimens were collected within 30 min of bowel specimen excision and stored directly in liquid nitrogen for subsequent research. Informed consent was obtained from all patients participating in this scientific study, and the study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University (Approval No: 2022-155). 2.11. qRT-PCR RNA was extracted from cells or intestinal mucosal tissues using TRIZOL reagent, reverse transcribed, and used to detect the mRNA expression levels of target genes. The primers used in this study are listed in [63]Supplementary Table 2. 2.12. Statistical analysis All data processing and statistical analyses were performed using R software (version 4.2.0), and the executable code can be found in the supplementary files. All figures were generated using Adobe Illustrator software (version 2022). A significance level of p < 0.05 was considered statistically significant, denoted as (*p < 0.05, **p < 0.01, ***p < 0.001). 3. Result 3.1. Identification of disulfidptosis related DEGs First, we merged the three UC datasets and removed batch effects ([64]Supplementary Figures 1A and B). The 3D PCA plot displayed distinct clustering of UC and control groups ([65]Fig. 2A). Applying the criteria of log|FC|>0 and p < 0.05, we identified 4558 upregulated genes and 6302 downregulated genes ([66]Fig. 2B). Next, the intersection of 24 disulfidptosis-related genes from literature review ([67]Supplementary Table 1) and differentially expressed genes yielded 20 genes, comprising 7 upregulated and 13 downregulated genes ([68]Fig. 2C). The volcano plot illustrated the expression profile of these genes in the dataset ([69]Fig. 3A). Further target gene selection was performed through Lasso regression on the expression data of these 20 genes, resulting in a model with excellent performance and minimal variables, identifying 7 key genes ([70]Fig. 3B and C). The heatmap displayed the expression differences of these 7 genes in UC ([71]Fig. 3E), correlation analysis indicated internal associations among these 7 differentially expressed genes ([72]Fig. 3D), and the boxplot depicted the expression level variations of target genes between UC and normal control groups ([73]Fig. 3F). Fig. 2. [74]Fig. 2 [75]Open in a new tab These 3 datasets [76]GSE107499, [77]GSE87466 and [78]GSE59071 were merged and normalized to a new dataset. A. The Principal Component Analysis of the new dataset. B. Volcano plot of DEGs, the red nodes represent the 4558 significantly upregulated DEGs and the blue nodes show the 6302 downregulated DEGs based on |log2 FC|> 0 and p < 0.05. C. Intersection of DEGs and disulfidptosis related genes. (UC: Ulcerative colitis; DEGs, differentially expressed genes; DRGs: Disulfidptosis Related Genes). (For interpretation of the references to color in this