Identification of oxidative stress-related diagnostic marker genes and immune landscape in interstitial cystitis by bioinformatics and machine learning

Identification of oxidative stress-related diagnostic marker genes and immune landscape in interstitial cystitis by bioinformatics and machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of oxidative stress-related diagnostic marker genes and immune landscape in interstitial cystitis by bioinformatics and machine learning Chaowei Fu, Yuwei Zhang, Yu Zhao, Shiyu Wang, Yuhua Zhou, Jing Lv, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4642942/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Interstitial cystitis (IC) is a chronic inflammatory disease with autoimmune associations that is challenging to diagnose and treat. Recent findings indicate that oxidative stress (OS) is a crucial pathophysiological mechanism in IC. Moreover, the interactions between OS, inflammation, and immune cell infiltration are highly complex. Therefore, this study aims to identify biomarkers linked to OS in the development of IC and to elucidate their relationship with immune cell infiltration. These findings could provide new research directions for the diagnosis and treatment of IC. Methods The GSE711783 dataset from the GEO database was utilized to identify differentially expressed genes in IC, while OS-related genes were obtained from the GeneCards database. Hub genes associated with OS were identified through integrated analysis using WGCNA and protein-protein interaction networks. Gene regulatory networks involving transcription factors, TF-miRNA interactions and gene-disease associations were analyzed using relevant databases. Diagnostic marker genes associated with OS were refined using machine learning algorithms. Subsequently, a nomogram diagnostic prediction model was developed and validated through in vitro experiments. Potential drug candidates were identified using the DSigDB database, and the immune landscape in IC was explored using the CIBERSORT algorithm. Results We identified a total of 68 differentially expressed genes related to OS, alongside 15 hub genes. Among these, four genes—BMP2, MMP9, CCK and NOS3—were further selected as diagnostic markers. Using the ANN model, ROC curve analysis, and nomogram diagnostic prediction model, all four genes demonstrated excellent diagnostic efficacy. Additionally, these genes exhibited strong associations with T cells CD4 memory resting, T cells CD4 memory activated, and Eosinophils. Finally, decitabine emerged as the most promising drug molecule for IC treatment. Conclusion We identified four diagnostic marker genes related to OS that are pivotal in the pathogenesis of IC, influencing both OS and immune responses. These findings highlight new avenues for research in the diagnosis and treatment of IC. Interstitial cystitis Oxidative stress Diagnostic marker Bioinformatics Machine learning Immune landscape Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Interstitial cystitis (IC) is recognized as a chronic inflammatory disease of the bladder, typically resulting in inflammation and damage to the bladder. This condition causes persistent bladder pain, frequent urination, urgency, and dysuria[ 1 ]. The symptoms of IC significantly impair patients' quality of life, making daily activities challenging. It has been reported that due to long-term treatment failures, patients are prone to mental disorders such as depression and anxiety, with a high suicide risk of 38.10%[ 2 ]. However, the pathogenesis of IC remains unclear. Previous studies suggest that uroepithelial dysfunction, neurogenic inflammation, neural hyperactivity, or mast cell hyperactivation may play crucial roles in the pathophysiology of IC[ 3 – 5 ]. Inflammation and immune disorders have been identified as key components in the pathogenesis of IC in human studies. Excessive inflammatory mediators, such as IL-6 and TNF-α, are present in the urine of IC patients[ 6 , 7 ]. On one hand, the high expression of inflammatory mediators stimulates bladder afferent nerves to respond to pain substances. On the other hand, it disrupts the integrity of the urothelial structure and promotes epithelial cell apoptosis, thereby exacerbating the inflammatory environment and creating a vicious cycle[ 8 , 9 ]. In IC patients, there is an infiltration of immune cells, such as lymphocytes, macrophages, and mast cells, in the bladder tissue. The activation and increase of these immune cells may lead to inflammation and damage of the bladder mucosa, causing symptoms such as pain, urinary frequency, and urgency[ 10 ]. Studies have found that IC patients exhibit elevated counts of mast cells in the submucosal layer of the bladder wall and the layer of the detrusor muscle. Mast cells release active mediators such as histamine, 5-hydroxytryptamine, and prostaglandins, which can trigger inflammatory responses and pain sensations[ 11 ]. These abnormal changes indicate that IC patients suffer from inflammatory and immune disorders. Therefore, a comprehensive investigation of the mechanisms regulating inflammation and immune disorders in IC is of great significance for clinical intervention and treatment. Oxidative stress (OS) is a phenomenon characterized by the disruption of the intracellular redox balance, leading to the overproduction of free radicals and oxidized substances that surpass the scavenging capacity of the intracellular antioxidant system, this imbalance triggers cellular damage and abnormal function[ 12 ]. Recent research suggests that all currently recognized pathogeneses of IC are influenced by OS. This process may further exacerbate bladder inflammation and compromise urethral epithelial integrity, contributing to the development of IC[ 13 ]. OS impairs bladder contraction by affecting output pathways and interfering with cholinergic receptor signaling systems. Additionally, reactive oxygen species (ROS) produced by OS lead to an increase in peroxynitrite concentration in the uroepithelium and bladder smooth muscle. This elevation may trigger various pathological processes, including lipid peroxidation, protein oxidation, DNA damage, reduced glutathione depletion, oxidative damage, apoptosis, and cell necrosis. Previous animal studies have found that timely intervention with antioxidants can inhibit OS in IC/BPS rats and alleviate their pain and inflammatory responses, demonstrating a therapeutic effect[ 14 , 15 ]. Earlier research has shown that OS stimulates the production of inflammatory factors in mast cells through an APE/Ref-1-dependent pathway[ 16 ]. Furthermore, ROS play a role in FcεRI-dependent mast cell activation and degranulation. The use of peroxidase-containing substances can inhibit ROS accumulation and consequently suppress FcεRI-dependent mast cell activation[ 17 , 18 ]. Therefore, antioxidant measures are an important direction for the prevention and treatment of IC/BPS. In summary, OS plays a crucial role in the pathogenesis of IC and may contribute to the occurrence and progression of the disease through multiple pathways. Therefore, therapeutic strategies targeting OS could become a significant direction for IC treatment. In this study, we conducted a systematic bioinformatics analysis of OS-related genes to identify diagnostic maker genes involved in the occurrence of IC and to analyze their relationship with immune infiltration. The validation of these characterized genes was performed through in vitro experiments, enhancing the credibility of our findings. Materials and methods Acquisition of Dataset We obtained RNA sequencing datasets related to IC from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). Specifically, we utilized the GSE11783 dataset, comprising transcriptomic data from 11 human samples, including 5 IC cases (comprising 5 ulcerated and 5 non-ulcerated regions) and 6 control samples. During data preprocessing, we initially converted probe IDs to specific gene symbols using the platform annotation file, facilitating the mapping of raw data to the gene level. In this process, when multiple probes were associated with the same gene symbol, we opted to use the average value as the representative gene expression value to mitigate the impact of multiple probe mappings on the results. Furthermore, we identified 926 genes associated with OS from the GeneCards database ( https://www.genecards.org/ ) for subsequent analysis. In selecting these genes, we employed a correlation score greater than 7 as the screening criterion (see Additional file 1: Table S1 for details). Detailed information regarding the data is provided in Table 1 . Table 1 Summary of the data sets utilized in this research and their features. Dataset Database Platfrom Sample GSE11783 GEO GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array Bladder biopsy tissue from 5 IC patients and 6 controls Oxidative stress-related genes GeneCards Genecards Obtaining oxidative stress-related genes from GeneCards Identification of DEGs To identify the differentially expressed genes (DEGs) in the GSE11783 dataset, we first normalized the raw expression matrix using R software (version 4.3.3). Subsequently, we employed the “limma (version 3.56.2)” package to conduct a comprehensive analysis of gene expression between IC and controls. In this process, we utilized the Benjamini-Hochberg False Discovery Rate (FDR) method to address the issue of multiple testing while maintaining statistical significance. We established two primary screening criteria: adj.P 1. Here, a logFC > 1 denoted up-regulated genes, while a logFC < -1 indicated down-regulated genes. We utilized the “ggplot2 (version 3.5.0)” and “pheatmap (version 1.0.12)” packages to visually represent the expression patterns of DEGs through volcano plots and heatmaps, respectively. Construction of the co-expression network and identifies key gene modules by WGCNA Weighted gene co-expression network analysis (WGCNA) is a method commonly used to identify co-expressed gene modules and to explore the association relationship between gene networks and specific phenotypes. In our study, we constructed such a network using the “WGCNA (version 1.0.12)” package. First, we used hierarchical clustering with the "hclust" function to detect significant outliers. We used "pickSoftThreshold" to select the soft threshold power β, then converted the gene expression similarity matrix to a neighbor-joining matrix with "adjacency" and subsequently to a topological overlap matrix (TOM). Modules were detected using dynamic tree cutting approach. Pearson correlation analysis identified the modules most correlated with our phenotypes, designating them as key gene modules. Acquisition of intersecting genes Intersections between gene sets are obtained using the R package “ggVennDiagram (version 1.5.2)”. Common intersections between DEGs and WGCNA modules are referred to as key DEGs, and intersections between key DEGs and OS-related genes are identified as OS-related differentially expressed genes (OSDEGs). Functional enrichment analysis of key DEGs To gain insight into the pathogenesis of IC and the biological pathways that may be involved, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses of key DEGs using the “clusterProfiler (version 4.8.3)” package in R. GO analyses included the study of biological processes (BP), cellular components (CC), and molecular functions (MF). In this step, we set adj.P < 0.05 as the threshold for significant differences. Protein interaction network analysis To analyze protein interactions and their roles in biosignaling and metabolic responses, we generated a protein-protein interaction (PPI) network using oxidative stress-related differentially expressed genes (OSDEGs) in the STRING database[ 19 ] (version 12.0, https://cn.string-db.org/ ) with a confidence threshold of 0.4. Genes without interactions were removed to enhance network reliability. Hub genes were identified using “CytoHubba[ 20 ]” (a Cytoscape plugin) with four methods: Maximal Clique Centrality (MCC), Degree, Maximum Neighborhood Component (MNC), and Closeness, each providing the top 20 genes. We visualized these hub genes in Cytoscape (version 3.10.1), with circle size and color indicating the interaction degree. Overlapping hub genes from all methods were used to construct co-expression networks in GeneMANIA[ 21 ] ( http://www.genemania.org/ ). The correlation matrix was visualized using the “corrplot” package (version 0.92) to understand gene interactions and regulation. Construction of TF-gene and miRNA-gene regulatory networks We conducted comprehensive analyses to uncover transcriptional regulatory networks and key transcription factors (TFs) controlling hub genes. Gene expression profiling was performed using NetworkAnalyst[ 22 ] ( https://www.networkanalyst.ca/ ), integrating TF-gene and miRNA-gene interaction data from the JASPAR [ 23 ] and MiRTarBase databases[ 24 ], respectively. This provided insights into TF and miRNA interactions with hub genes. The results were visualized using Cytoscape (version 3.10.1) to clearly depict the regulatory network and key TFs. Additionally, GraphPad software (version 9.0.0) was used to compare TFs expression levels between the IC and control groups. Gene-disease association analysis Utilize the NetworkAnalyst platform to access the DisGeNET database[ 25 ] and establish relationships between genes and diseases. DisGeNET is a discovery platform containing one of the largest publicly available collections of genes and variants associated with human diseases. A comprehensive understanding of the molecular details of associated diseases can help identify comorbidities and advance our understanding of these diseases. Screening of candidate diagnostic markers by machine learning We employed Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) analysis, and Random Forest (RF) analysis for further screening of key genes, which are widely used in the literature. SVM-RFE ranks and selects features based on their importance, facilitating efficient feature selection, reducing model complexity and redundant features, and improving model generalization. LASSO, a machine learning algorithm for regression analysis and feature selection, controls model complexity, mitigates overfitting, and enhances model explanatory properties by adjusting the parameter λ. We determined the optimal value of λ through 10-fold cross-validation and selected the value with the minimum criterion. RF, an integrated learning method based on decision tree algorithms, enhances model accuracy and robustness by aggregating prediction results from multiple decision trees. We evaluated the error rate for the number of trees ranging from 1 to 500 and selected the tree with the lowest error rate. Finally, we determined feature importance scores for each candidate center gene and selected genes with importance values greater than zero. LASSO logistic regression analysis, RF analysis, and SVM-RFE analysis were conducted using the “glmnet (version 4.1.8)”, “randomForest (version 4.7.1.1)”, “e1071 (version 1.7.14)”, and “caret (version 6.0.94)” packages. The intersection genes of the three analysis methods were considered as the diagnostic marker genes. Subsequently, artificial neural network (ANN) models were constructed using the “neuralnet (version 1.44.2)” and “NeuralNetTools (version 1.5.3)” packages. The neural network model simulates the structure and function of the brain's neural network to derive a set of classification rules from complex and irregular data, thus building a highly accurate diagnostic model[ 26 ]. Additionally, we evaluated the predictive performance of individual genes using receiver operating characteristic (ROC) curve analysis, calculating the area under the curves (AUC), and visually representing these curves with the “pROC (version 1.18.5)” package. We categorized genes as having average diagnostic predictive value when AUC > 0.5, high diagnostic predictive value when AUC > 0.7, and excellent diagnostic predictive value when AUC > 0.9. Furthermore, we constructed a nomogram model based on genes using the “rms (version 6.8.0)” package, which is essential for clinical disease diagnosis[ 27 ]. Finally, we assessed the predictive ability and clinical utility of the nomogram model through calibration curve and decision curve analysis (DCA)[ 28 ]. Single-gene gene set enrichment analysis Single-Gene Set Enrichment Analysis (GSEA)[ 29 ] is a bioinformatics approach that leverages expression profiling to investigate the functions and signaling pathways associated with diagnostic markers in specific biological processes and diseases. In our study, we initially categorized the samples in the dataset into high and low expression groups based on the median expression levels of individual genes. Subsequently, we utilized the “clusterProfiler” package to conduct GSEA, aiming to elucidate the pathways implicated by the diagnostic marker genes. The gene set employed for this analysis was the KEGG (“c2.cp.kegg.v7.4.symbols.gmt”) gene set. We set the criteria for enrichment analysis as follows: P < 0.05, q 1. Based on the enrichment score ranking, we visually represented the top 5 pathways for visualization purposes. Immune landscape and correlation analysis with genes The immune landscape plays a crucial role in understanding the composition and activity of immune cells, offering valuable insights into disease progression prediction and therapy effectiveness. CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts) is an algorithm[ 30 ] that employs machine learning techniques to analyze gene expression data, enabling the inference of the relative content of each cell type from mixed gene expression data. In our study, we initially applied the CIBERSORT algorithm to evaluate the proportions of 22 immune cell populations in both the IC and control groups. Subsequently, we employed the Spearman correlation analysis to examine the association between marker genes and immune cell. Evaluation of applicant drugs Leveraging the Drug Signatures Database (DSigDB)[ 31 ] to pinpoint small molecule compounds that target hub genes represents a promising avenue of research. DSigDB encompasses a vast collection of 22,527 genes, capturing the effects of diverse drugs on gene expression. Accessible through the Enrichr platform[ 32 ], DSigDB offers a convenient resource for identifying drug entities based on acquired diagnostic marker genes. Employing a systematic approach, we can uncover potential pharmacological molecules capable of modulating gene expression. This approach enables targeted therapeutic interventions, presenting novel opportunities for disease treatment. Cell culture and IC in vitro model construction Two cell lines were used in this experiment. Human urothelial cells T24 (HTB-4, ATCC, Manassas, VA, USA) were cultured in McCoy's 5A medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Prior to all experiments, cells were grown to 80–90% confluence and washed three times with PBS. In all experiments, cells were cultured in 6-well plates at a density of 1×10^5 cells/well and incubated in standard medium for 24 hours before processing. Human normal urothelial cells SV-HUC-1 (CRL-9520, ATCC, Manassas, VA, USA) were cultured in F12K medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Prior to all experiments, cells were grown to 80–90% confluence and washed three times with PBS. In all experiments, cells were cultured in 6-well plates at a density of 2×10^5 cells/well and incubated in standard medium for 24 hours before treatment. TNF-α is a pro-inflammatory cytokine that plays a crucial role in the pathogenesis of IC. TNF-α released by mast cells acts on the urinary tract epithelium, causing inflammation. Furthermore, TNF-α expression is significantly increased in the urine of patients with IC compared to healthy controls. Therefore, in vitro models of bladder uroepithelial cells often use TNF-α to mimic the inflammatory and OS environment characteristic of IC. To mimic a classical inflammatory environment, cells were treated with 10 ng/ml TNF-α (Sigma-Aldrich, Arklow, Ireland) for 24 hours, according to previous studies[ 33 – 36 ]. RNA isolation and Reverse transcription-quantitative PCR (RT-qPCR) Total RNA was extracted using the FastRure® Cell/Tissue Total RNA Isolation Kit (Vazyme, Nanjing, China) and reverse transcribed using the HiScript IV All-in-One Ultra RT SuperMix (Vazyme, Nanjing, China). RT-qPCR analysis was performed on the QuantStudio™ Design & Analysis SE Software using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) in three independent replicates. The relative expression of target genes was calculated using the 2^ −ΔΔCt method, with GAPDH as the housekeeping gene. The sequences of the primers were as follows: CCK (forward, 5′-GTGCCTGTGCGTGCTGATG-3′ and reverse, 5′-GCCATCCGTTCTCTGCGATAC-3′), MMP9 (forward, 5′-GGCACCACCACCACAACATCACC-3′ and reverse, 5′-GGGCAAAGGCGTCGTCAATC-3′), BMP2 (forward, 5′-TCCCGACAGAACTCAGTGCTATC-3′ and reverse, 5′-ACCCACAACCCTCCACAACC-3′), NOS3 (forward, 5′-TCTCACCTTCTTCCTGGACATCAC-3′ and reverse, 5′-AACCACTTCCACTCCTCGTAGC-3′) and GAPDH (forward, 5′-GAAGGTGAAGGTCGGGAGTC-3′ and reverse, 5′-GAAGATGGTGATGGGGATTTC-3′). Statistical analysis GraphPad (version 9.0.0) and R (version 4.3.3) were employed for statistical analysis. Unpaired t-tests were used to compare the two data sets. Spearman correlation analysis was utilized to investigate relationships between infiltrating immune cells and marker genes, as well as among hub genes. A significance level of P < 0.05 was considered statistically significant. Results Identification of the DEGs of IC A flowchart outlining the study process is depicted in Fig. 1. Following the normalization of gene expression levels from the GSE11783 dataset, the results pre- and post-normalization are illustrated in Fig. 2. By applying statistical criteria (adj.P 1), a total of 1,893 DEGs were identified, comprising 823 up-regulated genes and 1,070 down-regulated genes (Additional file 1: Table S2). Subsequently, we visually presented the DEGs between the IC patient group and control group through volcano plot (Fig. 3A) and heat map (Fig. 3B). These findings underscore significant differences in gene expression between IC patients and controls, offering novel insights and targets for diagnosis and treatment. Construction of WGCNA in IC and identification of key modules To delve deeper into the key genes associated with IC, we employed WGCNA to pinpoint the most pertinent gene modules in the IC group. We visualized a clustering dendrogram of the modules, revealing no outliers in the included samples (Fig. 3C). After conducting the scale independence and mean connectivity evaluation, we selected a soft threshold power of 6 (Fig. 3D). Utilizing this power, we generated a total of 12 modules and elucidated the gene clustering outcomes (Fig. 3E). Furthermore, this analysis probed the correlation between IC and gene modules (Fig. 3F). Notably, the turquoise module exhibited a positive correlation with IC, encompassing 1701 genes (r = 0.87, P = 1e-05), while the blue module displayed a negative correlation with IC, encompassing 669 genes (r = -0.88, P = 8e-06). Consequently, we designated the turquoise and blue modules as the focal points for subsequent analysis. Within these modules, we identified 2370 key genes significantly associated with IC (Additional file 1: Table S3). We proceeded to intersect the DEGs with the key module genes identified through WGCNA to pinpoint the key DEGs, yielding a total of 1,324 key DEGs (Fig. 3G). These key DEGs were subsequently subjected to further enrichment analysis. Additionally, we intersected the key DEGs with OS-related genes, resulting in a total of 68 OSDEGs (Fig. 3H). These OSDEGs were subjected to further screening to identify hub genes. KEGG and GO analysis GO analysis and KEGG analysis provided insights into the biological traits and enrichment pathways of the key DEGs. Bubble plots illustrate the top 10 elements of GO terms for each category. In the BP subgroup, these key DEGs were significantly enriched in signaling pathways such as cell differentiation, cellular developmental, and regulation of cellular processes. In the CC subgroup, these key DEGs were involved in the extracellular region and vesicle. Furthermore, in the MF subgroup, these key DEGs were associated with identical protein binding and signaling receptor binding (Fig. 4A). KEGG enrichment analysis revealed that key DEGs expressed genes associated with cytokine-cytokine receptor interaction, chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor, NF-κB signaling pathway, Th17 cell differentiation, B cell receptor signaling pathway, IL-17 signaling pathway, and immunological and metabolic pathways (Fig. 4B). The detailed information regarding the enrichment results can be found in the Additional file 1: Table S4. These results suggest that patients with IC exhibit enrichment of inflammatory and immune-related pathways, some of which were also associated with OS. For instance, ROS can indirectly inhibit nitric oxide (NO) production by uncoupling endothelial nitric oxide synthase (eNOS), which serves as a negative regulator of Nuclear factor kappaB (NF-κB). Consequently, this process activates the NF-κB signaling pathway, leading to inflammation[ 37 , 38 ]. TH17 cells, a distinct subset of CD4 + T cells, have been implicated as causal agents in various autoimmune diseases like psoriasis, multiple sclerosis, rheumatoid arthritis, and inflammatory bowel disease. IL-17, primarily secreted by TH17 cells, plays a pivotal role in inducing a wide array of cytokines, chemokines, inflammatory factors, and antimicrobial proteins. These molecules target genes involved in autoimmunity and chronic inflammation[ 39 ]. Notably, recent research by Wang et al[ 40 ] demonstrated a correlation between the level of OS and Th17 activation in trichloroethylene-induced autoimmune diseases. Given these findings, therapeutic strategies targeting OS hold particular importance and promise in managing such conditions. Construction of PPI and acquisition of hub genes We utilized the online analysis tool STRING to construct a PPI network based on OSDEGs, revealing their interconnections. The resulting network consisted of 64 nodes and 249 edges (Fig. 5A; Additional file 1: Table S5). Subsequently, hub genes were identified using the “CytoHubba” plugin in Cytoscape. The MCC method, known for its high accuracy in detecting key proteins, identified the top 20 most influential genes. Additionally, the Degree, MCN, and Closeness algorithms were employed to identify the top 20 hub genes, respectively (Additional file 1: Table S6). Fifteen common hub genes were identified across these four gene sets, including TNF, MMP9, CXCL8, IFNG, LEP, PPARG, SELE, HMOX1, LCN2, NOS3, DPP4, CCK, MMP1, CCR7, and BMP2, which are considered core targets of IC (Fig. 5B), their interactions with other genes are shown in Fig. 5C. Furthermore, these hub genes were annotated using the GeneMANIA database, revealing associations with leukocyte migration, positive regulation of cell-cell adhesion, neutrophil migration, regulation of neutrophil chemotaxis, regulation of leukocyte migration, granulocyte chemotaxis, and neutrophil chemotaxis (Fig. 5D). Figure 5E illustrates the correlation between hub genes. Construction of the regulatory network Figure 6A shows regulatory interactions between miRNAs and genes, with the inner circle representing miRNAs and the outer circle indicating hub genes. Significant miRNAs include hsa-mir-27a-5p, hsa-mir-129-2-3p, hsa-mir-27a-3p, hsa-mir-210-3p, hsa-mir-7-5p, hsa-mir-155-5p, hsa-mir-146a-5p, and hsa-mir-34a-5p. Detailed miRNA regulatory networks are provided in Additional file 1: Table S7. Figure 6B depicts the interactions between TFs and hub genes, with the inner circle representing TFs and the outer circle representing hub genes. Significant TFs include FOXC1, GATA2, E2F1, YY1, FOXL1, NFKB1, FOS, JUN, TP53, and GATA. Detailed data are provided in Additional file 1: Table S8. These networks play a crucial role in understanding the intricate layers of gene regulation within the cell, elucidating the mechanisms by which genes are activated or suppressed, how miRNAs finely tune this regulation, and the delicate balance that maintains normal cellular function or leads to disease when dysregulated. Additionally, we validated the expression of TFs in the GSE11783 dataset and found that four TFs were significantly differentially expressed. Specifically, E2F1 was upregulated in the IC group compared to controls, while GATA2, GATA3, and TP53 were downregulated in the IC group (Fig. 6C). Identification of disease associations Connections are established through hub genes, and various diseases may exhibit interrelationships. Gene-disease association studies conducted on the NetworkAnalyst platform reveal intriguing connections. We observed that major depressive disorder, mental depression, proteinuria, schizophrenia, depressive disorder, nerve degeneration, and inflammation are strongly linked to hub genes (Fig. 6D). Detailed data can be found in Additional file 1: Table S9. Remarkably, most of these diseases are closely associated with inflammatory or immune responses in the organism. Interestingly, we found that these hub genes are closely associated with psychiatric symptoms, which is consistent with the previously reported presence of anxiety and depression in IC patients[ 41 – 43 ]. Hence, these hub genes might also contribute to the development of anxiety and depressive symptoms in patients with IC. Screening of candidate diagnostic markers by machine learning To further identify diagnostic marker genes for IC, we employed three machine learning algorithms: LASSO, SVM-RFE, and RF. Using LASSO logistic regression analysis, we extracted 6 genes from the hub genes (Fig. 7A). RF analysis identified 7 genes (Fig. 6B), while SVM-RFE analysis identified 7 genes (Fig. 7C). By overlapping the results from all three algorithms using a Venn diagram, we finally obtained 4 genes: BMP2, MMP9, CCK, and NOS3 (Fig. 7D; Additional file 1: Table S10). Among them, MMP9, CCK, and NOS3 exhibited higher expression in the IC group, while BMP2 had lower expression compared to the control group (Fig. 8A). Next, an ANN model was constructed using these four genes, which demonstrated high proficiency in distinguishing between IC and control samples (Fig. 8B; Additional file 1: Table S11). The diagnostic performance of these four genes was evaluated using ROC curves based on the expression data, with the AUC values for all four genes exceeding 0.9 (Fig. 8C). Corresponding nomogram models were constructed to demonstrate the diagnostic value of these genes, with the nomogram score used to predict the likelihood of having IC (Fig. 8D). Furthermore, DCA results indicated that the model had high clinical application value (Fig. 8E), and calibration curves showed that the nomogram model predicted IC very well (Fig. 8F). Overall, these findings suggest that these four genes have a strong ability to recognize IC, providing a feasible approach to diagnose and intervene in patients with IC. Single-GSEA analysis The KEGG pathway involving the four genes in IC was assessed using GSEA. Gene expression levels were categorized as high and low based on the median expression levels to explore the potential pathways these genes are involved in during IC occurrence (Fig. 9A, B, C, D; Additional file 1: Table S12). The top 10 significant terms for each gene are shown. The BMP2 high-expression group was predominantly enriched in the PPAR signaling pathway and basal cell carcinoma pathways, while the low-expression group was predominantly enriched with immune-related pathways. Conversely, the MMP9, CCK, and NOS3 low-expression groups were all predominantly enriched in metabolic pathways. These findings suggest that the high-expression groups were mainly associated with immune-related pathways, while the low-expression groups were more linked to metabolic pathways. Notably, all genes were associated with primary immunodeficiency and the PPAR signaling pathway. Therefore, we suggest that these four genes are strongly associated with inflammation as well as immune responses during IC. Immune landscape and correlation with diagnostic marker genes Based on the results of the previously described functional enrichment analysis, the immune pathway emerged as a key factor in the occurrence of IC. The immune landscape in IC disease was revealed using the CIBERSORT algorithm, which identified 22 immune cell subclasses. Figure 10A shows the proportions of 22 immune cell types. We observed significant differences between the IC group and control group in several immune cell types, including B cells memory, plasma cells, T cells CD4 naive, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, macrophages M0, macrophages M2, mast cells resting, eosinophils, and neutrophils (Fig. 10B; Additional file 1: Table S13). Furthermore, we analyzed the relationship between the level of infiltration of the 22 immune cells and the four genes of interest (Additional file 1: Table S14). Correlation analysis revealed that all four genes were associated with T cells CD4 memory resting, T cells CD4 memory activated, and Eosinophils. MMP9, CCK, and NOS3 were positively correlated with T cells CD4 memory activated and Eosinophils, whereas they were positively and negatively correlated with T cells CD4 memory resting, respectively. The correlation results for BMP2 were opposite to the other three genes (Fig. 10C, D). These findings suggest a change in the immune environment in IC patients and imply that these four genes may simultaneously influence an immune component of IC under the influence of OS. Exploration of potential drugs Using the DSigDB module in the EnrichR database, we identified potential drug candidates targeting the 4 genes of interest. By evaluating adj.P and combined score, we identified the most relevant drugs with promise in targeting potential therapeutic pathways for IC, deserving further exploration. The top 10 relevant drugs are listed in Table 2 : Decitabine, Roxarsone, DL-Mevalonic acid, Octreotide, ACMC-20mvek, 2,4-Diisocyanato-1-methylbenzene, TIRON, Phenylarsine oxide, and Diallyl disulfide. Particularly noteworthy is Decitabine, which was associated with all four genes. Table 2 Top 10 gene-targeted drugs for IC. Term Adjusted P-value Combined score Genes Decitabin 0.0018 701417.9666 BMP2; NOS3; CCK; MMP9 6401-97-4 3.34E-04 29570.47781 NOS3; MMP9 roxarsone 3.55E-04 23557.08871 NOS3; MMP9 DL-Mevalonic acid 7.94E-05 15288.15094 BMP2; NOS3; MMP9 Octreotide 7.67E-04 11214.62696 BMP2; CCK ACMC-20mvek 1.47E-04 8949.938225 BMP2; NOS3; MMP9 2,4-Diisocyanato-1-methylbenzene 8.85E-04 7698.982687 NOS3; MMP9 TIRON 8.85E-04 7698.982687 NOS3; MMP9 phenylarsine oxide 0.0010 6387.031681 NOS3; MMP9 DIALLYL DISULFIDE 0.0013 5271.69962 CCK; MMP9 External validation of maker genes The experimental results demonstrated that following TNF-α induction, mRNA expression of MMP9 and NOS3 significantly increased, whereas BMP2 mRNA expression notably decreased in SV-HUC-1 cells, consistent with our bioinformatics predictions (Fig. 11A). Similar trends were observed for MMP9, NOS3, and BMP2 mRNA expression in T24 cells (Fig. 11B). Importantly, CCK mRNA expression was found to be very low in both T24 and SV-HUC-1 cells. These experimental findings validate and reinforce the predictive value of bioinformatics analysis, thereby further supporting the involvement of MMP9, NOS3, and BMP2 in the pathogenesis of IC. We hypothesize that these uroepithelial cell lines, T24 and SV-HUC-1, do not express CCK, unlike neuroendocrine cells which are capable of CCK expression. Neuroendocrine cells present in bladder tissues within the uroepithelium can respond to certain chemicals, such as those produced by infecting bacteria, thereby influencing bladder activity[ 44 , 45 ]. This likely explains why CCK can be detected in bladder biopsies but not in T24 and SV-HUC-1 cells. Discussion IC is a troubling disease characterized by a complex pathogenesis involving numerous associated conditions. A majority of patients experience anxiety, depression, and other obstacles, which contribute to the complexity and chronicity of treatment. Moreover, misdiagnosis of IC as urinary tract infection, urethral syndrome, pelvic inflammatory disease, or overactive bladder[ 46 ] can lead to inappropriate treatment, worsening the patient's state. Enhancing the diagnostic precision of IC is crucial, necessitating the development of more sensitive and specific markers. Increased ROS production in IC/BPS patients is well documented. Jiang et al[ 47 , 48 ] showed elevated levels of urinary OS biomarkers (8-OHdG, 8-isoprostane), while Ener et al[ 49 ] found that the total antioxidant capacity of serum samples from IC/BPS patients was significantly lower than that of controls. These findings, along with the complex interactions between OS, inflammation, and immunity, suggest a new research direction. Therefore, the quest for novel diagnostic markers linked to OS is paramount. These markers not only offer fresh insights for early IC detection but also lay the groundwork for tailored treatment strategies. TFs serve as major orchestrators of biological processes, exerting control over the expression of multiple gene targets and forming intricate feedback loops. During the early stages of diseases like IC, numerous genes, including many TFs, undergo significant alterations. Previous research has highlighted the involvement of several TFs, such as E2F1, JUN, and TP53[ 50 , 51 ], in the occurrence process of IC, aligning with our findings. In our study, the TFs associated with hub genes, as predicted, hold promise as novel candidate genes for investigating the regulation of IC pathophysiological processes in future studies. Meanwhile, miRNA research represents a burgeoning area of interest across various scientific disciplines. A wealth of evidence now underscores the critical role of miRNAs in immune system development, as well as their contributions to innate and adaptive responses. miRNAs such as mir-155, mir-181a, mir-146a, mir-150, mir-223, and mir-17-92 have been demonstrated to play regulatory roles in immune cell development, differentiation, and function[ 52 , 53 ]. Moreover, in a mouse model of cyclophosphamide-induced cystitis, miRNAs like mir-34c-5p, mir-34b-3p, mir-212-3p, mir-449a-5p, and mir-21a-3p, as well as mir-376b-3p, mir-376b-5p, and mir-409-5p, have shown involvement in inflammation and smooth muscle function over the medium term[ 54 ]. However, the specific mechanisms by which the miRNAs identified in our study exert their effects on IC remain incompletely elucidated, warranting further investigation into their roles in IC pathology. In this study, we identified four diagnostic marker genes (BMP2, MMP9, CCK, and NOS3) significantly associated with IC using a combination of DEGs analysis, WGCNA, PPI networks, and machine learning. The ANN model, ROC analysis, and nomogram diagnostic model demonstrated that these markers have excellent discriminatory ability in distinguishing the IC group from controls. These results suggest that BMP2, MMP9, CCK, and NOS3 are significantly involved in the pathological processes of IC and have potential as diagnostic markers and therapeutic targets for this disease. BMP2(bone morphogenetic protein 2), a member of the TGF-β superfamily, is crucial for bone and cartilage development and repair[ 55 ]. Recent research indicates that BMP2 overexpression in osteoblasts (specifically MC3T3-E1) inhibits apoptosis, diminishes ROS production, and lowers the secretion of TNF-α, IL-6, and macrophage colony-stimulating factor (M-CSF)[ 56 ]. Consequently, BMP2 is deemed beneficial, aligning with our observation of reduced BMP2 expression in the IC patient group. While BMP2 has been extensively studied in orthopedic diseases, its role in IC remains uncertain. Subsequent investigations ought to delve into the precise mechanisms and therapeutic potential of BMP2 in IC, offering novel insights and treatment modalities. MMP9 (Matrix Metalloproteinase-9) is a member of the Zn 2+ -dependent enzyme family known as matrix metalloproteinases (MMPs). It is involved in various physiological processes, including embryonic development, tissue remodeling, and wound healing[ 57 ]. MMP9 not only contributes to tissue remodeling and inflammation but is also implicated in various autoimmune disorders such as systemic lupus erythematosus, Sjogren's syndrome, systemic sclerosis, rheumatoid arthritis, multiple sclerosis, polymyositis, and atherosclerosis[ 58 ]. Recent literature reports a significant elevation in MMP9 expression in a cyclophosphamide-induced rat model of IC[ 59 , 60 ], correlating with processes such as inflammatory response, cell migration, and tissue damage. This aligns with our discovery of elevated MMP9 expression in the IC patient group. Furthermore, MMPs are integral to the extracellular matrix proteasome and influence the remodeling and degradation of tight junctions (TJs). For instance, certain MMPs family genes, including MMP9, MMP7, and MMP2, have been shown to decrease the expression of TJ proteins[ 61 , 62 ], implicating MMP-9 in diverse pathogenic mechanisms of IC and suggesting its potential as a therapeutic target. CCK (Cholecystokinin) functions as both a neuropeptide and gut hormone, regulating pancreatic enzyme secretion, gastrointestinal motility, and satiety signaling. It is released by endocrine cells of the small intestine and various neurons in the gastrointestinal tract and central nervous system following ingestion [ 63 ]. A significant portion of current research has focused on its regulatory functions within the digestive, nervous, and endocrine systems. While bladder diseases remain poorly understood, our analysis suggests that CCK may contribute to IC occurrence through its impact on OS and immune responses. Consequently, conducting a comprehensive investigation into the mechanisms underlying CCK's involvement in bladder diseases, particularly its modulation of OS in bladder tissues, holds promise for identifying novel therapeutic targets. NOS3(Nitric Oxide Synthase 3), a subtype of nitric oxide synthase (NOS) referred to as eNOS, exhibits significantly higher levels in Hunner type IC bladder samples compared to non-Hunner IC bladder samples, indicating potential involvement of eNOS in the divergent pathogenesis of these IC subtypes[ 64 ]. eNOS, an enzyme crucial for NO production. NO serves as a signaling molecule with diverse physiological functions and extensive implications in both physiological and pathophysiological contexts. Its roles encompass the regulation of vascular tone, promotion of angiogenesis in wound healing, modulation of inflammatory responses, and involvement in pathologies such as ischemic cardiovascular disease and malignancies[ 65 ]. Under normal conditions, eNOS synthesizes substantial quantities of NO, contributing to the maintenance of homeostasis between the endothelium and adjacent tissues[ 66 ]. Similar to endothelial tissue, the urothelial epithelium of the lower urinary tract possesses NOS and produces NO. Inflammation resulting from chronic irritation or infection leads to increased NO production, and the upregulation of NOS in response to chronic inflammation may serve as an adaptive mechanism to enhance spinal nociceptive or reflexive responses triggered by nociceptive inputs from the bladder[ 67 ]. This phenomenon has been verified in a study by M V Souza-Fiho et al[ 68 ]. Previous studies have established that IC development involves the infiltration of various immune cells, including T cells, B cells, plasma cells, macrophages, neutrophils, and mast cell[ 10 , 69 ]. Our study conducted a comprehensive analysis of the infiltration extent of 22 immune cell types using the CIBERSORT algorithm. We observed differences in immune cell infiltration levels between the IC and control groups, consistent with previous findings. Furthermore, we discovered associations between the four genes and T cell CD4 memory resting, T cell CD4 memory activated, and eosinophils. CD4 + T cells play multifaceted roles in regulating immune responses, naive CD4 + T cells can differentiate into various subpopulations, including Th1, Th2, Th17, Th22, Tfh and CTLs, each with distinct phenotypes and protective functions. These subpopulations are crucial in pathogen response, immune regulation, and maintaining immune homeostasis[ 70 ]. CD4 + T cells play a critical immunoregulatory role in the occurrence and progression of IC[ 10 ], they recruit and activate other immune cells through the secretion of various cytokines and chemokines, thereby contributing significantly to the immune response in IC and research has indicated a significant increase in dysfunctional CD4 + T cells in IC patients[ 71 ]. Eosinophilic primarily maintain the tissue microenvironment during normal development and regulate host innate and adaptive immune responses[ 72 ]. Eosinophils express and release epithelial-mesenchymal transition mediators, including TGF-β, basic fibroblast growth factor, platelet-derived growth factor, matrix metalloproteinases, and vascular endothelial growth factor. They also release other repair/remodeling factors, such as nerve growth factor, neuropeptides, and cytokines like IL-1β and IL-6[ 73 ]. Despite extensive study, the specific role of eosinophils in IC remains unclear. The existing research suggests that eosinophils may play a role in the pathological process of IC, particularly through allergy-related mechanisms. Some scholars[ 74 , 75 ] speculate that allergic reactions could potentially trigger IC, with eosinophils playing a significant role in this response. They found significantly elevated levels of eosinophilic protein X in the morning urine of interstitial cystitis patients compared to controls, along with occasional eosinophil infiltration in the submucosal layer upon bladder tissue biopsy. Additionally, researchers[ 64 ] have observed more severe or moderate eosinophilic infiltration in Hunner IC bladder specimens compared to non-Hunner IC specimens. Therefore, increased eosinophils may contribute to the progression of IC. Further studies are necessary to elucidate the specific functions of eosinophils in IC disease progression and symptom presentation. To investigate the potential biological functions of the four genes in IC, we found, using GSEA, that they are associated with primary immunodeficiency and the peroxisome proliferator-activated receptor (PPAR) signaling pathway. PPARs are ligand-dependent transcription factors belonging to the nuclear hormone receptor superfamily. PPARα is one of its subtypes. PPARα is expressed in macrophages, dendritic cells (DCs), B cells, and T cells, actively participating in various aspects of immune regulation by modulating cytokine production in DCs and T cells, as well as lymphocyte proliferation[ 76 ]. Furthermore, research has confirmed that PPARα exhibits anti-inflammatory effects by inhibiting the NF-κB signaling pathway and reducing the expression of inducible NOS, cyclooxygenase-2, and TNF-α. This finding further demonstrates the association between OS, inflammation, and immunity. Additionally, the PPAR signaling pathway is considered a viable therapeutic target for treating chronic pain and its stress-related psychiatric complications[ 77 ]. Therefore, further investigation into the role of these four genes in the PPAR signaling pathway in IC is crucial. Through screening, we identified Decitabine as the most promising drug candidate for the treatment of IC. Decitabine (5-aza-2'-deoxycytidine, DAC) is a DNA methyltransferase inhibitor with a wide range of antimetabolic and anticancer activities. Recent studies have found that Decitabine alleviates symptoms of acute respiratory distress syndrome through its anti-inflammatory and antioxidant properties and by inhibiting the mitogen-activated protein kinase (MAPK) pathway[ 78 ]. Additionally, Chang Su et al[ 79 ] found that Decitabine increased the release of the inhibitory factor IL-10, decreased the pro-inflammatory factor IL-17, activated CD4 + Foxp3 + T cells, and elevated levels of zonula occludens 1 and occludin, thereby reducing symptoms of ulcerative colitis. Therefore, Decitabine has potential therapeutic value for the treatment of IC. There are several limitations to our study. First, it involved secondary data mining and analysis of a previously published dataset, where differences in dataset selection and analysis methods could influence outcomes. Second, due to the absence of clinical samples, validation was limited to the cellular level. Third, the impact of these oxidative stress-related markers on inflammatory and immune responses, contributing to IC occurrence, remains unclear. Therefore, future studies should include diverse populations and larger sample sizes, employing both in vitro and in vivo experiments for validation. Conclusions Through comprehensive bioinformatics analysis, we demonstrated differences in oxidative stress-related genes and immune cell infiltration between IC patients and controls, revealing for the first time the correlation between OS and immune infiltration in IC. We used machine learning algorithms to identify four mitochondria-related candidate core genes (BMP2, MMP9, CCK, and NOS3) and developed a nomogram for early diagnosis and monitoring of IC patients. This study also identified TFs, miRNAs, and drug molecules that may regulate the hub genes. These findings may offer new perspectives on potential therapeutic targets for IC, enhance understanding of IC mechanisms, and suggest new approaches for IC diagnosis. Abbreviations IC Interstitial Cystitis OS Oxidative Stress TF Transcription Factors NO Nitric Oxide ROS Reactive Oxygen Species DEGs Differentially Expressed Genes WGCNA Weighted Gene Co-expression Network Analysis Declarations Acknowledgements Not applicable Authors’ contributions FNH and LFP designed this study and provided clinical guidance as well as data interpretation. FCW, ZYW, ZY and WSY drafted the article. FCW was a major contributor in writing the manuscript. ZYH, LJ and JSK prepared the figures and tables for this study. FNH and LFP made significant contributions to the manuscript revision. All authors reviewed the manuscript, provided comments and approved the final version. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 82370777). 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Table S6: The top 20 hub genes for each method. Table S7: miRNA-gene regulatory networks. Table S8: TF-gene regulatory networks. Table S9: Gene-disease association networks. Table S10: Genes screened by three methods. Table S11: ANN model. Table S12: GSEA of four genes. Table S13: Immune cell infiltration analysis. Table S14: Links between immune cells and four genes. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 27 Jun, 2024 Submission checks completed at journal 27 Jun, 2024 First submitted to journal 26 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4642942","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320054228,"identity":"f08d9d67-21a5-420e-858d-631a73fe6cb8","order_by":0,"name":"Chaowei Fu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Chaowei","middleName":"","lastName":"Fu","suffix":""},{"id":320054229,"identity":"49044c04-b7c6-41ad-9088-f2d9b115ed8a","order_by":1,"name":"Yuwei Zhang","email":"","orcid":"","institution":"Nantong University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Zhang","suffix":""},{"id":320054230,"identity":"6f9fdc9a-af85-4b8a-afd0-adf274130f99","order_by":2,"name":"Yu Zhao","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhao","suffix":""},{"id":320054231,"identity":"01fbc8d3-9655-4174-9c10-b358581c7864","order_by":3,"name":"Shiyu Wang","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Shiyu","middleName":"","lastName":"Wang","suffix":""},{"id":320054232,"identity":"9e2f0183-6b61-4e6f-9586-fd255da8fa5c","order_by":4,"name":"Yuhua Zhou","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Yuhua","middleName":"","lastName":"Zhou","suffix":""},{"id":320054233,"identity":"45b9e638-3a80-4025-b850-109813283e74","order_by":5,"name":"Jing Lv","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Lv","suffix":""},{"id":320054234,"identity":"cb5de2e6-c4cd-4d6d-9ea2-b2722cd845bf","order_by":6,"name":"Shengkai Jin","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Shengkai","middleName":"","lastName":"Jin","suffix":""},{"id":320054235,"identity":"59bf4d77-d157-470d-b129-6212c12a5208","order_by":7,"name":"Fengping Liu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Fengping","middleName":"","lastName":"Liu","suffix":""},{"id":320054236,"identity":"5bb01b7e-1c7c-4665-a7f7-3d09aecc6c8b","order_by":8,"name":"Ninghan Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYFACxgdAwoaZDSGSQEgLswGQSCNdy2FkEQJazBmYGR8X/DrPzsd+9vDrgpo7DPzsOQYMP3fg1mLZwMxsPLPvNjMbT16a9Yxjzxgke94YMPaewa3F4AD/MWneHqAWhhwzYx62wwwGN3IMmBnb8GlhZv/N23OOmY3/DVDLv8MM9kRoYWPm+QEkJXKMH/O2AW2RIKTlMDOzNG9DMlDLGzNm3r7DPBJnnhUc7MWn5Xgz42eeP3bJ8v05xp95vh2W429P3vjgJx4tDMxADHRGMpBikwASPCDBA3g0QMEfBjuQ7g+EVY6CUTAKRsFIBADTH0enQKS/AAAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangnan University","correspondingAuthor":true,"prefix":"","firstName":"Ninghan","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2024-06-26 13:06:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4642942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4642942/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60667343,"identity":"d7c7a859-7b9a-4b45-9c40-c1f67ae9f966","added_by":"auto","created_at":"2024-07-19 09:38:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":267332,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of this research.\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/71b5e08954e21b6d376c8831.png"},{"id":60666160,"identity":"a199e133-d32d-4077-8368-4168327c0655","added_by":"auto","created_at":"2024-07-19 09:22:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":542943,"visible":true,"origin":"","legend":"\u003cp\u003eThe gene expression schematic of the chosen GEO datasets before and after standardization. (A) The GSE11783 dataset before standardization; (B) The GSE11783 dataset after standardization.\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/f70cdf96e4e674b2ec2f319c.png"},{"id":60666164,"identity":"d973364b-b8d2-472b-9f28-fd02976537a6","added_by":"auto","created_at":"2024-07-19 09:22:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1314062,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs analysis and WGCNA analysis. (A) The volcano plot of genes; (B) The gene heatmap of DEGs; (C) Cluster tree of 16 samples; (D) Choosing the best soft-threshold power, the red line indicates the minimum soft threshold of 6 for constructing the scale-free network; (E) Gene dendrogram for all DEGs; (F) Heatmap illustrating the relationships between modules and traits; (G) The Venn diagram of DEGs and WGCNA genes; (H) The Venn diagram of OS-related genes and key DEGs. DEGs, differentially expressed genes. WGCNA, Weighted gene co-expression network analysis. OS, oxidative stress.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/03a1376d44a1f5154fc5a221.png"},{"id":60666163,"identity":"f8d92f52-edf4-4566-b821-3d10f6eed04c","added_by":"auto","created_at":"2024-07-19 09:22:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":475547,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of key DEGs. (A) The GO enrichment analysis plot; (B) The KEGG enrichment analysis plot. BP, biological process; CC, cellular component; MF, molecular function.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/080f45e3e98185e6cbf26f6e.png"},{"id":60666162,"identity":"625b97d9-4efb-420d-bd10-eab21880add7","added_by":"auto","created_at":"2024-07-19 09:22:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2144939,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network analysis and identification of hub genes by cytoHubba. (A) Full overview of PPI networks of 64 genes. (B) The Venn diagram of the top 20 genes for the four methods. (C) PPI networks of hub genes. (D) Co-expression network of hub genes analyzed via GeneMANIA. (E) Correlations between the hub genes that are reciprocal. PPI, protein-protein interaction. ****, ***, **, * represent P\u0026lt;0.0001, P\u0026lt;0.001, P\u0026lt;0.01, P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/fbfcb2bab3324b60edf8b392.png"},{"id":60666167,"identity":"457861e9-7187-4259-bb54-fa05bec6a118","added_by":"auto","created_at":"2024-07-19 09:22:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":869532,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of regulatory interactions and associations. (A), (B) The network of gene-TF and gene-miRNA regulatory interactions. (C) Expression of TFs in GSE11783 datasets. (D) The gene-disease association network. TFs, transcription factors; miRNAs, microRNAs. ****, ***, **, * represent P\u0026lt;0.0001, P\u0026lt;0.001, P\u0026lt;0.01, P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/fd8b1bddb4f3028050c37104.png"},{"id":60666168,"identity":"35a50c92-b398-4cd0-8bf2-30393f43c4a5","added_by":"auto","created_at":"2024-07-19 09:22:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":350171,"visible":true,"origin":"","legend":"\u003cp\u003eScreening diagnostic marker genes using machine learning. (A) LASSO logistic regression analysis. (B) RF analysis. (C) SVM-RFE analysis. (D) Venn diagrams for three analysis method results. LASSO, Least Absolute Shrinkage and Selection Operator; SVM-RFE, Support Vector Machine-Recursive Feature Elimination; RF, Random Forest.\u003c/p\u003e","description":"","filename":"OnlineFig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/3acda480f121439d1d88b31e.png"},{"id":60666709,"identity":"2a7d2c45-2d5c-48e6-ac3d-852dfa3bddba","added_by":"auto","created_at":"2024-07-19 09:30:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":544486,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram model construction for IC diagnosis. (A) Expression of diagnostic marker genes in the GSE11783 dataset. (B) ANN model plot. Positive weights are connected by red lines, negative weights are connected by gray lines, and the thickness of the lines reflects the size of the weights. (C) ROC curves of the four genes and Nomogram model. (D) Nomogram to predict IC risk. (E) DCA curve to assess the practical efficacy of the nomogram. (F) Calibration curve evaluation for the diagnostic potential of the nomogram model. DCA, decision curve analysis; ROC, receiver operating characteristic; AUC, area under the ROC curve; ANN, artificial neural network. ****, ***, **, * represent P\u0026lt;0.0001, P\u0026lt;0.001, P\u0026lt;0.01, P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"OnlineFig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/153a6421b5354016635b6fa0.png"},{"id":60666169,"identity":"2d7fa554-b34b-424d-a128-1a278bb14186","added_by":"auto","created_at":"2024-07-19 09:22:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":293961,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA of 4 genes. (A) BMP2. (B) MMP9. (C) NOS3. (D) CCK.\u003c/p\u003e","description":"","filename":"OnlineFig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/1722d018caa3f77ee000728c.png"},{"id":60666711,"identity":"47fc7b03-c48d-4cc2-8a2a-ab168bf28f17","added_by":"auto","created_at":"2024-07-19 09:30:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":441514,"visible":true,"origin":"","legend":"\u003cp\u003eInfiltration pattern of immune cell subtypes. (A) A stacked plot of the expression of 22 types of immune cells in each sample; (B) The expression of 22 types of immune cells. (C), (D) The correlation analysis plot between 4 genes and 22 types of immune cells; ****, ***, **, * represent P\u0026lt;0.0001, P\u0026lt;0.001, P\u0026lt;0.01, P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"OnlineFig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/0e1dea29e8a04eeb5a4a8328.png"},{"id":60667348,"identity":"4c6959e6-a725-4536-9fac-6d56aa82b142","added_by":"auto","created_at":"2024-07-19 09:38:31","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":413082,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of BMP2, MMP9 and NOS3. (A) The expression levels of three genes in the SV-HUC-1 cell line. (B) The expression levels of three genes in the T24 cell line. ****, ***, **, * represent P\u0026lt;0.0001, P\u0026lt;0.001, P\u0026lt;0.01, P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"OnlineFig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/c608d541c46edfe926bac3af.png"},{"id":60666159,"identity":"b2836fa3-92b0-4c2b-b6e3-8a5652f34d7a","added_by":"auto","created_at":"2024-07-19 09:22:30","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":400232,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1:\u003c/strong\u003e Oxidative stress-related genes from GeneCards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2:\u003c/strong\u003e DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3:\u003c/strong\u003e Key gene modules of WGCNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4: \u003c/strong\u003eFunctional enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5:\u003c/strong\u003e Protein interaction network analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6: \u003c/strong\u003eThe top 20 hub genes for each method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7:\u003c/strong\u003e miRNA-gene regulatory networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S8: \u003c/strong\u003eTF-gene regulatory networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S9: \u003c/strong\u003eGene-disease association networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S10:\u003c/strong\u003e Genes screened by three methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S11: \u003c/strong\u003eANN model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S12: \u003c/strong\u003eGSEA of four genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S13:\u003c/strong\u003e Immune cell infiltration analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S14:\u003c/strong\u003e Links between immune cells and four genes.\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4642942/v1/9b6209587dd0d2ee143c6573.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of oxidative stress-related diagnostic marker genes and immune landscape in interstitial cystitis by bioinformatics and machine learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInterstitial cystitis (IC) is recognized as a chronic inflammatory disease of the bladder, typically resulting in inflammation and damage to the bladder. This condition causes persistent bladder pain, frequent urination, urgency, and dysuria[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The symptoms of IC significantly impair patients' quality of life, making daily activities challenging. It has been reported that due to long-term treatment failures, patients are prone to mental disorders such as depression and anxiety, with a high suicide risk of 38.10%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the pathogenesis of IC remains unclear. Previous studies suggest that uroepithelial dysfunction, neurogenic inflammation, neural hyperactivity, or mast cell hyperactivation may play crucial roles in the pathophysiology of IC[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Inflammation and immune disorders have been identified as key components in the pathogenesis of IC in human studies. Excessive inflammatory mediators, such as IL-6 and TNF-α, are present in the urine of IC patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. On one hand, the high expression of inflammatory mediators stimulates bladder afferent nerves to respond to pain substances. On the other hand, it disrupts the integrity of the urothelial structure and promotes epithelial cell apoptosis, thereby exacerbating the inflammatory environment and creating a vicious cycle[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In IC patients, there is an infiltration of immune cells, such as lymphocytes, macrophages, and mast cells, in the bladder tissue. The activation and increase of these immune cells may lead to inflammation and damage of the bladder mucosa, causing symptoms such as pain, urinary frequency, and urgency[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies have found that IC patients exhibit elevated counts of mast cells in the submucosal layer of the bladder wall and the layer of the detrusor muscle. Mast cells release active mediators such as histamine, 5-hydroxytryptamine, and prostaglandins, which can trigger inflammatory responses and pain sensations[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These abnormal changes indicate that IC patients suffer from inflammatory and immune disorders. Therefore, a comprehensive investigation of the mechanisms regulating inflammation and immune disorders in IC is of great significance for clinical intervention and treatment.\u003c/p\u003e \u003cp\u003eOxidative stress (OS) is a phenomenon characterized by the disruption of the intracellular redox balance, leading to the overproduction of free radicals and oxidized substances that surpass the scavenging capacity of the intracellular antioxidant system, this imbalance triggers cellular damage and abnormal function[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent research suggests that all currently recognized pathogeneses of IC are influenced by OS. This process may further exacerbate bladder inflammation and compromise urethral epithelial integrity, contributing to the development of IC[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. OS impairs bladder contraction by affecting output pathways and interfering with cholinergic receptor signaling systems. Additionally, reactive oxygen species (ROS) produced by OS lead to an increase in peroxynitrite concentration in the uroepithelium and bladder smooth muscle. This elevation may trigger various pathological processes, including lipid peroxidation, protein oxidation, DNA damage, reduced glutathione depletion, oxidative damage, apoptosis, and cell necrosis. Previous animal studies have found that timely intervention with antioxidants can inhibit OS in IC/BPS rats and alleviate their pain and inflammatory responses, demonstrating a therapeutic effect[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Earlier research has shown that OS stimulates the production of inflammatory factors in mast cells through an APE/Ref-1-dependent pathway[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, ROS play a role in FcεRI-dependent mast cell activation and degranulation. The use of peroxidase-containing substances can inhibit ROS accumulation and consequently suppress FcεRI-dependent mast cell activation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, antioxidant measures are an important direction for the prevention and treatment of IC/BPS.\u003c/p\u003e \u003cp\u003eIn summary, OS plays a crucial role in the pathogenesis of IC and may contribute to the occurrence and progression of the disease through multiple pathways. Therefore, therapeutic strategies targeting OS could become a significant direction for IC treatment. In this study, we conducted a systematic bioinformatics analysis of OS-related genes to identify diagnostic maker genes involved in the occurrence of IC and to analyze their relationship with immune infiltration. The validation of these characterized genes was performed through in vitro experiments, enhancing the credibility of our findings.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of Dataset\u003c/h2\u003e \u003cp\u003eWe obtained RNA sequencing datasets related to IC from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, we utilized the GSE11783 dataset, comprising transcriptomic data from 11 human samples, including 5 IC cases (comprising 5 ulcerated and 5 non-ulcerated regions) and 6 control samples. During data preprocessing, we initially converted probe IDs to specific gene symbols using the platform annotation file, facilitating the mapping of raw data to the gene level. In this process, when multiple probes were associated with the same gene symbol, we opted to use the average value as the representative gene expression value to mitigate the impact of multiple probe mappings on the results. Furthermore, we identified 926 genes associated with OS from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for subsequent analysis. In selecting these genes, we employed a correlation score greater than 7 as the screening criterion (see Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for details). Detailed information regarding the data is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the data sets utilized in this research and their features.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatfrom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE11783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBladder biopsy tissue from 5 IC patients and 6 controls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxidative stress-related genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneCards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenecards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObtaining oxidative stress-related genes from GeneCards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eTo identify the differentially expressed genes (DEGs) in the GSE11783 dataset, we first normalized the raw expression matrix using R software (version 4.3.3). Subsequently, we employed the \u0026ldquo;limma (version 3.56.2)\u0026rdquo; package to conduct a comprehensive analysis of gene expression between IC and controls. In this process, we utilized the Benjamini-Hochberg False Discovery Rate (FDR) method to address the issue of multiple testing while maintaining statistical significance. We established two primary screening criteria: adj.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFoldChange (FC)| \u0026gt; 1. Here, a logFC\u0026thinsp;\u0026gt;\u0026thinsp;1 denoted up-regulated genes, while a logFC \u0026lt; -1 indicated down-regulated genes. We utilized the \u0026ldquo;ggplot2 (version 3.5.0)\u0026rdquo; and \u0026ldquo;pheatmap (version 1.0.12)\u0026rdquo; packages to visually represent the expression patterns of DEGs through volcano plots and heatmaps, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the co-expression network and identifies key gene modules by WGCNA\u003c/h2\u003e \u003cp\u003eWeighted gene co-expression network analysis (WGCNA) is a method commonly used to identify co-expressed gene modules and to explore the association relationship between gene networks and specific phenotypes. In our study, we constructed such a network using the \u0026ldquo;WGCNA (version 1.0.12)\u0026rdquo; package. First, we used hierarchical clustering with the \"hclust\" function to detect significant outliers. We used \"pickSoftThreshold\" to select the soft threshold power β, then converted the gene expression similarity matrix to a neighbor-joining matrix with \"adjacency\" and subsequently to a topological overlap matrix (TOM). Modules were detected using dynamic tree cutting approach. Pearson correlation analysis identified the modules most correlated with our phenotypes, designating them as key gene modules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of intersecting genes\u003c/h2\u003e \u003cp\u003eIntersections between gene sets are obtained using the R package \u0026ldquo;ggVennDiagram (version 1.5.2)\u0026rdquo;. Common intersections between DEGs and WGCNA modules are referred to as key DEGs, and intersections between key DEGs and OS-related genes are identified as OS-related differentially expressed genes (OSDEGs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of key DEGs\u003c/h2\u003e \u003cp\u003eTo gain insight into the pathogenesis of IC and the biological pathways that may be involved, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses of key DEGs using the \u0026ldquo;clusterProfiler (version 4.8.3)\u0026rdquo; package in R. GO analyses included the study of biological processes (BP), cellular components (CC), and molecular functions (MF). In this step, we set adj.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the threshold for significant differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtein interaction network analysis\u003c/h2\u003e \u003cp\u003eTo analyze protein interactions and their roles in biosignaling and metabolic responses, we generated a protein-protein interaction (PPI) network using oxidative stress-related differentially expressed genes (OSDEGs) in the STRING database[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (version 12.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a confidence threshold of 0.4. Genes without interactions were removed to enhance network reliability. Hub genes were identified using \u0026ldquo;CytoHubba[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u0026rdquo; (a Cytoscape plugin) with four methods: Maximal Clique Centrality (MCC), Degree, Maximum Neighborhood Component (MNC), and Closeness, each providing the top 20 genes. We visualized these hub genes in Cytoscape (version 3.10.1), with circle size and color indicating the interaction degree. Overlapping hub genes from all methods were used to construct co-expression networks in GeneMANIA[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genemania.org/\u003c/span\u003e\u003cspan address=\"http://www.genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The correlation matrix was visualized using the \u0026ldquo;corrplot\u0026rdquo; package (version 0.92) to understand gene interactions and regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of TF-gene and miRNA-gene regulatory networks\u003c/h2\u003e \u003cp\u003eWe conducted comprehensive analyses to uncover transcriptional regulatory networks and key transcription factors (TFs) controlling hub genes. Gene expression profiling was performed using NetworkAnalyst[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), integrating TF-gene and miRNA-gene interaction data from the JASPAR [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and MiRTarBase databases[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], respectively. This provided insights into TF and miRNA interactions with hub genes. The results were visualized using Cytoscape (version 3.10.1) to clearly depict the regulatory network and key TFs. Additionally, GraphPad software (version 9.0.0) was used to compare TFs expression levels between the IC and control groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGene-disease association analysis\u003c/h2\u003e \u003cp\u003eUtilize the NetworkAnalyst platform to access the DisGeNET database[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and establish relationships between genes and diseases. DisGeNET is a discovery platform containing one of the largest publicly available collections of genes and variants associated with human diseases. A comprehensive understanding of the molecular details of associated diseases can help identify comorbidities and advance our understanding of these diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eScreening of candidate diagnostic markers by machine learning\u003c/h2\u003e \u003cp\u003eWe employed Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) analysis, and Random Forest (RF) analysis for further screening of key genes, which are widely used in the literature. SVM-RFE ranks and selects features based on their importance, facilitating efficient feature selection, reducing model complexity and redundant features, and improving model generalization. LASSO, a machine learning algorithm for regression analysis and feature selection, controls model complexity, mitigates overfitting, and enhances model explanatory properties by adjusting the parameter λ. We determined the optimal value of λ through 10-fold cross-validation and selected the value with the minimum criterion. RF, an integrated learning method based on decision tree algorithms, enhances model accuracy and robustness by aggregating prediction results from multiple decision trees. We evaluated the error rate for the number of trees ranging from 1 to 500 and selected the tree with the lowest error rate. Finally, we determined feature importance scores for each candidate center gene and selected genes with importance values greater than zero. LASSO logistic regression analysis, RF analysis, and SVM-RFE analysis were conducted using the \u0026ldquo;glmnet (version 4.1.8)\u0026rdquo;, \u0026ldquo;randomForest (version 4.7.1.1)\u0026rdquo;, \u0026ldquo;e1071 (version 1.7.14)\u0026rdquo;, and \u0026ldquo;caret (version 6.0.94)\u0026rdquo; packages. The intersection genes of the three analysis methods were considered as the diagnostic marker genes. Subsequently, artificial neural network (ANN) models were constructed using the \u0026ldquo;neuralnet (version 1.44.2)\u0026rdquo; and \u0026ldquo;NeuralNetTools (version 1.5.3)\u0026rdquo; packages. The neural network model simulates the structure and function of the brain's neural network to derive a set of classification rules from complex and irregular data, thus building a highly accurate diagnostic model[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, we evaluated the predictive performance of individual genes using receiver operating characteristic (ROC) curve analysis, calculating the area under the curves (AUC), and visually representing these curves with the \u0026ldquo;pROC (version 1.18.5)\u0026rdquo; package. We categorized genes as having average diagnostic predictive value when AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.5, high diagnostic predictive value when AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7, and excellent diagnostic predictive value when AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9. Furthermore, we constructed a nomogram model based on genes using the \u0026ldquo;rms (version 6.8.0)\u0026rdquo; package, which is essential for clinical disease diagnosis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Finally, we assessed the predictive ability and clinical utility of the nomogram model through calibration curve and decision curve analysis (DCA)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSingle-gene gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eSingle-Gene Set Enrichment Analysis (GSEA)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] is a bioinformatics approach that leverages expression profiling to investigate the functions and signaling pathways associated with diagnostic markers in specific biological processes and diseases. In our study, we initially categorized the samples in the dataset into high and low expression groups based on the median expression levels of individual genes. Subsequently, we utilized the \u0026ldquo;clusterProfiler\u0026rdquo; package to conduct GSEA, aiming to elucidate the pathways implicated by the diagnostic marker genes. The gene set employed for this analysis was the KEGG (\u0026ldquo;c2.cp.kegg.v7.4.symbols.gmt\u0026rdquo;) gene set. We set the criteria for enrichment analysis as follows: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, q\u0026thinsp;\u0026lt;\u0026thinsp;0.25, and an absolute value of Normalized Enrichment Score (NES)\u0026thinsp;\u0026gt;\u0026thinsp;1. Based on the enrichment score ranking, we visually represented the top 5 pathways for visualization purposes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImmune landscape and correlation analysis with genes\u003c/h2\u003e \u003cp\u003eThe immune landscape plays a crucial role in understanding the composition and activity of immune cells, offering valuable insights into disease progression prediction and therapy effectiveness. CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts) is an algorithm[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] that employs machine learning techniques to analyze gene expression data, enabling the inference of the relative content of each cell type from mixed gene expression data. In our study, we initially applied the CIBERSORT algorithm to evaluate the proportions of 22 immune cell populations in both the IC and control groups. Subsequently, we employed the Spearman correlation analysis to examine the association between marker genes and immune cell.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of applicant drugs\u003c/h2\u003e \u003cp\u003eLeveraging the Drug Signatures Database (DSigDB)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] to pinpoint small molecule compounds that target hub genes represents a promising avenue of research. DSigDB encompasses a vast collection of 22,527 genes, capturing the effects of diverse drugs on gene expression. Accessible through the Enrichr platform[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], DSigDB offers a convenient resource for identifying drug entities based on acquired diagnostic marker genes. Employing a systematic approach, we can uncover potential pharmacological molecules capable of modulating gene expression. This approach enables targeted therapeutic interventions, presenting novel opportunities for disease treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and IC in vitro model construction\u003c/h2\u003e \u003cp\u003eTwo cell lines were used in this experiment. Human urothelial cells T24 (HTB-4, ATCC, Manassas, VA, USA) were cultured in McCoy's 5A medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Prior to all experiments, cells were grown to 80\u0026ndash;90% confluence and washed three times with PBS. In all experiments, cells were cultured in 6-well plates at a density of 1\u0026times;10^5 cells/well and incubated in standard medium for 24 hours before processing. Human normal urothelial cells SV-HUC-1 (CRL-9520, ATCC, Manassas, VA, USA) were cultured in F12K medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Prior to all experiments, cells were grown to 80\u0026ndash;90% confluence and washed three times with PBS. In all experiments, cells were cultured in 6-well plates at a density of 2\u0026times;10^5 cells/well and incubated in standard medium for 24 hours before treatment. TNF-α is a pro-inflammatory cytokine that plays a crucial role in the pathogenesis of IC. TNF-α released by mast cells acts on the urinary tract epithelium, causing inflammation. Furthermore, TNF-α expression is significantly increased in the urine of patients with IC compared to healthy controls. Therefore, in vitro models of bladder uroepithelial cells often use TNF-α to mimic the inflammatory and OS environment characteristic of IC. To mimic a classical inflammatory environment, cells were treated with 10 ng/ml TNF-α (Sigma-Aldrich, Arklow, Ireland) for 24 hours, according to previous studies[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and Reverse transcription-quantitative PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using the FastRure\u0026reg; Cell/Tissue Total RNA Isolation Kit (Vazyme, Nanjing, China) and reverse transcribed using the HiScript IV All-in-One Ultra RT SuperMix (Vazyme, Nanjing, China). RT-qPCR analysis was performed on the QuantStudio\u0026trade; Design \u0026amp; Analysis SE Software using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) in three independent replicates. The relative expression of target genes was calculated using the 2^\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, with GAPDH as the housekeeping gene. The sequences of the primers were as follows: CCK (forward, 5\u0026prime;-GTGCCTGTGCGTGCTGATG-3\u0026prime; and reverse, 5\u0026prime;-GCCATCCGTTCTCTGCGATAC-3\u0026prime;), MMP9 (forward, 5\u0026prime;-GGCACCACCACCACAACATCACC-3\u0026prime; and reverse, 5\u0026prime;-GGGCAAAGGCGTCGTCAATC-3\u0026prime;), BMP2 (forward, 5\u0026prime;-TCCCGACAGAACTCAGTGCTATC-3\u0026prime; and reverse, 5\u0026prime;-ACCCACAACCCTCCACAACC-3\u0026prime;), NOS3 (forward, 5\u0026prime;-TCTCACCTTCTTCCTGGACATCAC-3\u0026prime; and reverse, 5\u0026prime;-AACCACTTCCACTCCTCGTAGC-3\u0026prime;) and GAPDH (forward, 5\u0026prime;-GAAGGTGAAGGTCGGGAGTC-3\u0026prime; and reverse, 5\u0026prime;-GAAGATGGTGATGGGGATTTC-3\u0026prime;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eGraphPad (version 9.0.0) and R (version 4.3.3) were employed for statistical analysis. Unpaired t-tests were used to compare the two data sets. Spearman correlation analysis was utilized to investigate relationships between infiltrating immune cells and marker genes, as well as among hub genes. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the DEGs of IC\u003c/h2\u003e \u003cp\u003eA flowchart outlining the study process is depicted in Fig.\u0026nbsp;1. Following the normalization of gene expression levels from the GSE11783 dataset, the results pre- and post-normalization are illustrated in Fig.\u0026nbsp;2. By applying statistical criteria (adj.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt; 1), a total of 1,893 DEGs were identified, comprising 823 up-regulated genes and 1,070 down-regulated genes (Additional file 1: Table S2). Subsequently, we visually presented the DEGs between the IC patient group and control group through volcano plot (Fig.\u0026nbsp;3A) and heat map (Fig.\u0026nbsp;3B). These findings underscore significant differences in gene expression between IC patients and controls, offering novel insights and targets for diagnosis and treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of WGCNA in IC and identification of key modules\u003c/h2\u003e \u003cp\u003eTo delve deeper into the key genes associated with IC, we employed WGCNA to pinpoint the most pertinent gene modules in the IC group. We visualized a clustering dendrogram of the modules, revealing no outliers in the included samples (Fig.\u0026nbsp;3C). After conducting the scale independence and mean connectivity evaluation, we selected a soft threshold power of 6 (Fig.\u0026nbsp;3D). Utilizing this power, we generated a total of 12 modules and elucidated the gene clustering outcomes (Fig.\u0026nbsp;3E). Furthermore, this analysis probed the correlation between IC and gene modules (Fig.\u0026nbsp;3F). Notably, the turquoise module exhibited a positive correlation with IC, encompassing 1701 genes (r\u0026thinsp;=\u0026thinsp;0.87, P\u0026thinsp;=\u0026thinsp;1e-05), while the blue module displayed a negative correlation with IC, encompassing 669 genes (r = -0.88, P\u0026thinsp;=\u0026thinsp;8e-06). Consequently, we designated the turquoise and blue modules as the focal points for subsequent analysis. Within these modules, we identified 2370 key genes significantly associated with IC (Additional file 1: Table S3).\u003c/p\u003e \u003cp\u003eWe proceeded to intersect the DEGs with the key module genes identified through WGCNA to pinpoint the key DEGs, yielding a total of 1,324 key DEGs (Fig.\u0026nbsp;3G). These key DEGs were subsequently subjected to further enrichment analysis. Additionally, we intersected the key DEGs with OS-related genes, resulting in a total of 68 OSDEGs (Fig.\u0026nbsp;3H). These OSDEGs were subjected to further screening to identify hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eKEGG and GO analysis\u003c/h2\u003e \u003cp\u003eGO analysis and KEGG analysis provided insights into the biological traits and enrichment pathways of the key DEGs. Bubble plots illustrate the top 10 elements of GO terms for each category. In the BP subgroup, these key DEGs were significantly enriched in signaling pathways such as cell differentiation, cellular developmental, and regulation of cellular processes. In the CC subgroup, these key DEGs were involved in the extracellular region and vesicle. Furthermore, in the MF subgroup, these key DEGs were associated with identical protein binding and signaling receptor binding (Fig.\u0026nbsp;4A). KEGG enrichment analysis revealed that key DEGs expressed genes associated with cytokine-cytokine receptor interaction, chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor, NF-κB signaling pathway, Th17 cell differentiation, B cell receptor signaling pathway, IL-17 signaling pathway, and immunological and metabolic pathways (Fig.\u0026nbsp;4B). The detailed information regarding the enrichment results can be found in the Additional file 1: Table S4. These results suggest that patients with IC exhibit enrichment of inflammatory and immune-related pathways, some of which were also associated with OS. For instance, ROS can indirectly inhibit nitric oxide (NO) production by uncoupling endothelial nitric oxide synthase (eNOS), which serves as a negative regulator of Nuclear factor kappaB (NF-κB). Consequently, this process activates the NF-κB signaling pathway, leading to inflammation[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. TH17 cells, a distinct subset of CD4\u003csup\u003e+\u003c/sup\u003e T cells, have been implicated as causal agents in various autoimmune diseases like psoriasis, multiple sclerosis, rheumatoid arthritis, and inflammatory bowel disease. IL-17, primarily secreted by TH17 cells, plays a pivotal role in inducing a wide array of cytokines, chemokines, inflammatory factors, and antimicrobial proteins. These molecules target genes involved in autoimmunity and chronic inflammation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Notably, recent research by Wang et al[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] demonstrated a correlation between the level of OS and Th17 activation in trichloroethylene-induced autoimmune diseases. Given these findings, therapeutic strategies targeting OS hold particular importance and promise in managing such conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of PPI and acquisition of hub genes\u003c/h2\u003e \u003cp\u003eWe utilized the online analysis tool STRING to construct a PPI network based on OSDEGs, revealing their interconnections. The resulting network consisted of 64 nodes and 249 edges (Fig.\u0026nbsp;5A; Additional file 1: Table S5). Subsequently, hub genes were identified using the \u0026ldquo;CytoHubba\u0026rdquo; plugin in Cytoscape. The MCC method, known for its high accuracy in detecting key proteins, identified the top 20 most influential genes. Additionally, the Degree, MCN, and Closeness algorithms were employed to identify the top 20 hub genes, respectively (Additional file 1: Table S6). Fifteen common hub genes were identified across these four gene sets, including TNF, MMP9, CXCL8, IFNG, LEP, PPARG, SELE, HMOX1, LCN2, NOS3, DPP4, CCK, MMP1, CCR7, and BMP2, which are considered core targets of IC (Fig.\u0026nbsp;5B), their interactions with other genes are shown in Fig.\u0026nbsp;5C. Furthermore, these hub genes were annotated using the GeneMANIA database, revealing associations with leukocyte migration, positive regulation of cell-cell adhesion, neutrophil migration, regulation of neutrophil chemotaxis, regulation of leukocyte migration, granulocyte chemotaxis, and neutrophil chemotaxis (Fig.\u0026nbsp;5D). Figure\u0026nbsp;5E illustrates the correlation between hub genes.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eConstruction of the regulatory network\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;6A shows regulatory interactions between miRNAs and genes, with the inner circle representing miRNAs and the outer circle indicating hub genes. Significant miRNAs include hsa-mir-27a-5p, hsa-mir-129-2-3p, hsa-mir-27a-3p, hsa-mir-210-3p, hsa-mir-7-5p, hsa-mir-155-5p, hsa-mir-146a-5p, and hsa-mir-34a-5p. Detailed miRNA regulatory networks are provided in Additional file 1: Table S7. Figure\u0026nbsp;6B depicts the interactions between TFs and hub genes, with the inner circle representing TFs and the outer circle representing hub genes. Significant TFs include FOXC1, GATA2, E2F1, YY1, FOXL1, NFKB1, FOS, JUN, TP53, and GATA. Detailed data are provided in Additional file 1: Table S8. These networks play a crucial role in understanding the intricate layers of gene regulation within the cell, elucidating the mechanisms by which genes are activated or suppressed, how miRNAs finely tune this regulation, and the delicate balance that maintains normal cellular function or leads to disease when dysregulated. Additionally, we validated the expression of TFs in the GSE11783 dataset and found that four TFs were significantly differentially expressed. Specifically, E2F1 was upregulated in the IC group compared to controls, while GATA2, GATA3, and TP53 were downregulated in the IC group (Fig.\u0026nbsp;6C).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of disease associations\u003c/h2\u003e \u003cp\u003eConnections are established through hub genes, and various diseases may exhibit interrelationships. Gene-disease association studies conducted on the NetworkAnalyst platform reveal intriguing connections. We observed that major depressive disorder, mental depression, proteinuria, schizophrenia, depressive disorder, nerve degeneration, and inflammation are strongly linked to hub genes (Fig.\u0026nbsp;6D). Detailed data can be found in Additional file 1: Table S9. Remarkably, most of these diseases are closely associated with inflammatory or immune responses in the organism. Interestingly, we found that these hub genes are closely associated with psychiatric symptoms, which is consistent with the previously reported presence of anxiety and depression in IC patients[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Hence, these hub genes might also contribute to the development of anxiety and depressive symptoms in patients with IC.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eScreening of candidate diagnostic markers by machine learning\u003c/h2\u003e \u003cp\u003eTo further identify diagnostic marker genes for IC, we employed three machine learning algorithms: LASSO, SVM-RFE, and RF. Using LASSO logistic regression analysis, we extracted 6 genes from the hub genes (Fig.\u0026nbsp;7A). RF analysis identified 7 genes (Fig.\u0026nbsp;6B), while SVM-RFE analysis identified 7 genes (Fig.\u0026nbsp;7C). By overlapping the results from all three algorithms using a Venn diagram, we finally obtained 4 genes: BMP2, MMP9, CCK, and NOS3 (Fig.\u0026nbsp;7D; Additional file 1: Table S10). Among them, MMP9, CCK, and NOS3 exhibited higher expression in the IC group, while BMP2 had lower expression compared to the control group (Fig.\u0026nbsp;8A). Next, an ANN model was constructed using these four genes, which demonstrated high proficiency in distinguishing between IC and control samples (Fig.\u0026nbsp;8B; Additional file 1: Table S11). The diagnostic performance of these four genes was evaluated using ROC curves based on the expression data, with the AUC values for all four genes exceeding 0.9 (Fig.\u0026nbsp;8C). Corresponding nomogram models were constructed to demonstrate the diagnostic value of these genes, with the nomogram score used to predict the likelihood of having IC (Fig.\u0026nbsp;8D). Furthermore, DCA results indicated that the model had high clinical application value (Fig.\u0026nbsp;8E), and calibration curves showed that the nomogram model predicted IC very well (Fig.\u0026nbsp;8F). Overall, these findings suggest that these four genes have a strong ability to recognize IC, providing a feasible approach to diagnose and intervene in patients with IC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eSingle-GSEA analysis\u003c/h2\u003e \u003cp\u003eThe KEGG pathway involving the four genes in IC was assessed using GSEA. Gene expression levels were categorized as high and low based on the median expression levels to explore the potential pathways these genes are involved in during IC occurrence (Fig.\u0026nbsp;9A, B, C, D; Additional file 1: Table S12). The top 10 significant terms for each gene are shown. The BMP2 high-expression group was predominantly enriched in the PPAR signaling pathway and basal cell carcinoma pathways, while the low-expression group was predominantly enriched with immune-related pathways. Conversely, the MMP9, CCK, and NOS3 low-expression groups were all predominantly enriched in metabolic pathways. These findings suggest that the high-expression groups were mainly associated with immune-related pathways, while the low-expression groups were more linked to metabolic pathways. Notably, all genes were associated with primary immunodeficiency and the PPAR signaling pathway. Therefore, we suggest that these four genes are strongly associated with inflammation as well as immune responses during IC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eImmune landscape and correlation with diagnostic marker genes\u003c/h2\u003e \u003cp\u003eBased on the results of the previously described functional enrichment analysis, the immune pathway emerged as a key factor in the occurrence of IC. The immune landscape in IC disease was revealed using the CIBERSORT algorithm, which identified 22 immune cell subclasses. Figure\u0026nbsp;10A shows the proportions of 22 immune cell types. We observed significant differences between the IC group and control group in several immune cell types, including B cells memory, plasma cells, T cells CD4 naive, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, macrophages M0, macrophages M2, mast cells resting, eosinophils, and neutrophils (Fig.\u0026nbsp;10B; Additional file 1: Table S13). Furthermore, we analyzed the relationship between the level of infiltration of the 22 immune cells and the four genes of interest (Additional file 1: Table S14). Correlation analysis revealed that all four genes were associated with T cells CD4 memory resting, T cells CD4 memory activated, and Eosinophils. MMP9, CCK, and NOS3 were positively correlated with T cells CD4 memory activated and Eosinophils, whereas they were positively and negatively correlated with T cells CD4 memory resting, respectively. The correlation results for BMP2 were opposite to the other three genes (Fig.\u0026nbsp;10C, D). These findings suggest a change in the immune environment in IC patients and imply that these four genes may simultaneously influence an immune component of IC under the influence of OS.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eExploration of potential drugs\u003c/h2\u003e \u003cp\u003eUsing the DSigDB module in the EnrichR database, we identified potential drug candidates targeting the 4 genes of interest. By evaluating adj.P and combined score, we identified the most relevant drugs with promise in targeting potential therapeutic pathways for IC, deserving further exploration. The top 10 relevant drugs are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Decitabine, Roxarsone, DL-Mevalonic acid, Octreotide, ACMC-20mvek, 2,4-Diisocyanato-1-methylbenzene, TIRON, Phenylarsine oxide, and Diallyl disulfide. Particularly noteworthy is Decitabine, which was associated with all four genes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 gene-targeted drugs for IC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecitabin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e701417.9666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP2; NOS3; CCK; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6401-97-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29570.47781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOS3; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eroxarsone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.55E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23557.08871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOS3; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL-Mevalonic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.94E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15288.15094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP2; NOS3; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctreotide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.67E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11214.62696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP2; CCK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACMC-20mvek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8949.938225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP2; NOS3; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2,4-Diisocyanato-1-methylbenzene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.85E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7698.982687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOS3; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIRON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.85E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7698.982687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOS3; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ephenylarsine oxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6387.031681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOS3; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIALLYL DISULFIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5271.69962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCK; MMP9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eExternal validation of maker genes\u003c/h2\u003e \u003cp\u003eThe experimental results demonstrated that following TNF-α induction, mRNA expression of MMP9 and NOS3 significantly increased, whereas BMP2 mRNA expression notably decreased in SV-HUC-1 cells, consistent with our bioinformatics predictions (Fig.\u0026nbsp;11A). Similar trends were observed for MMP9, NOS3, and BMP2 mRNA expression in T24 cells (Fig.\u0026nbsp;11B). Importantly, CCK mRNA expression was found to be very low in both T24 and SV-HUC-1 cells. These experimental findings validate and reinforce the predictive value of bioinformatics analysis, thereby further supporting the involvement of MMP9, NOS3, and BMP2 in the pathogenesis of IC. We hypothesize that these uroepithelial cell lines, T24 and SV-HUC-1, do not express CCK, unlike neuroendocrine cells which are capable of CCK expression. Neuroendocrine cells present in bladder tissues within the uroepithelium can respond to certain chemicals, such as those produced by infecting bacteria, thereby influencing bladder activity[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This likely explains why CCK can be detected in bladder biopsies but not in T24 and SV-HUC-1 cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIC is a troubling disease characterized by a complex pathogenesis involving numerous associated conditions. A majority of patients experience anxiety, depression, and other obstacles, which contribute to the complexity and chronicity of treatment. Moreover, misdiagnosis of IC as urinary tract infection, urethral syndrome, pelvic inflammatory disease, or overactive bladder[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] can lead to inappropriate treatment, worsening the patient's state. Enhancing the diagnostic precision of IC is crucial, necessitating the development of more sensitive and specific markers. Increased ROS production in IC/BPS patients is well documented. Jiang et al[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] showed elevated levels of urinary OS biomarkers (8-OHdG, 8-isoprostane), while Ener et al[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] found that the total antioxidant capacity of serum samples from IC/BPS patients was significantly lower than that of controls. These findings, along with the complex interactions between OS, inflammation, and immunity, suggest a new research direction. Therefore, the quest for novel diagnostic markers linked to OS is paramount. These markers not only offer fresh insights for early IC detection but also lay the groundwork for tailored treatment strategies.\u003c/p\u003e \u003cp\u003eTFs serve as major orchestrators of biological processes, exerting control over the expression of multiple gene targets and forming intricate feedback loops. During the early stages of diseases like IC, numerous genes, including many TFs, undergo significant alterations. Previous research has highlighted the involvement of several TFs, such as E2F1, JUN, and TP53[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], in the occurrence process of IC, aligning with our findings. In our study, the TFs associated with hub genes, as predicted, hold promise as novel candidate genes for investigating the regulation of IC pathophysiological processes in future studies. Meanwhile, miRNA research represents a burgeoning area of interest across various scientific disciplines. A wealth of evidence now underscores the critical role of miRNAs in immune system development, as well as their contributions to innate and adaptive responses. miRNAs such as mir-155, mir-181a, mir-146a, mir-150, mir-223, and mir-17-92 have been demonstrated to play regulatory roles in immune cell development, differentiation, and function[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Moreover, in a mouse model of cyclophosphamide-induced cystitis, miRNAs like mir-34c-5p, mir-34b-3p, mir-212-3p, mir-449a-5p, and mir-21a-3p, as well as mir-376b-3p, mir-376b-5p, and mir-409-5p, have shown involvement in inflammation and smooth muscle function over the medium term[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. However, the specific mechanisms by which the miRNAs identified in our study exert their effects on IC remain incompletely elucidated, warranting further investigation into their roles in IC pathology.\u003c/p\u003e \u003cp\u003eIn this study, we identified four diagnostic marker genes (BMP2, MMP9, CCK, and NOS3) significantly associated with IC using a combination of DEGs analysis, WGCNA, PPI networks, and machine learning. The ANN model, ROC analysis, and nomogram diagnostic model demonstrated that these markers have excellent discriminatory ability in distinguishing the IC group from controls. These results suggest that BMP2, MMP9, CCK, and NOS3 are significantly involved in the pathological processes of IC and have potential as diagnostic markers and therapeutic targets for this disease. BMP2(bone morphogenetic protein 2), a member of the TGF-β superfamily, is crucial for bone and cartilage development and repair[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Recent research indicates that BMP2 overexpression in osteoblasts (specifically MC3T3-E1) inhibits apoptosis, diminishes ROS production, and lowers the secretion of TNF-α, IL-6, and macrophage colony-stimulating factor (M-CSF)[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Consequently, BMP2 is deemed beneficial, aligning with our observation of reduced BMP2 expression in the IC patient group. While BMP2 has been extensively studied in orthopedic diseases, its role in IC remains uncertain. Subsequent investigations ought to delve into the precise mechanisms and therapeutic potential of BMP2 in IC, offering novel insights and treatment modalities. MMP9 (Matrix Metalloproteinase-9) is a member of the Zn\u003csup\u003e2+\u003c/sup\u003e-dependent enzyme family known as matrix metalloproteinases (MMPs). It is involved in various physiological processes, including embryonic development, tissue remodeling, and wound healing[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. MMP9 not only contributes to tissue remodeling and inflammation but is also implicated in various autoimmune disorders such as systemic lupus erythematosus, Sjogren's syndrome, systemic sclerosis, rheumatoid arthritis, multiple sclerosis, polymyositis, and atherosclerosis[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Recent literature reports a significant elevation in MMP9 expression in a cyclophosphamide-induced rat model of IC[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], correlating with processes such as inflammatory response, cell migration, and tissue damage. This aligns with our discovery of elevated MMP9 expression in the IC patient group. Furthermore, MMPs are integral to the extracellular matrix proteasome and influence the remodeling and degradation of tight junctions (TJs). For instance, certain MMPs family genes, including MMP9, MMP7, and MMP2, have been shown to decrease the expression of TJ proteins[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], implicating MMP-9 in diverse pathogenic mechanisms of IC and suggesting its potential as a therapeutic target. CCK (Cholecystokinin) functions as both a neuropeptide and gut hormone, regulating pancreatic enzyme secretion, gastrointestinal motility, and satiety signaling. It is released by endocrine cells of the small intestine and various neurons in the gastrointestinal tract and central nervous system following ingestion [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. A significant portion of current research has focused on its regulatory functions within the digestive, nervous, and endocrine systems. While bladder diseases remain poorly understood, our analysis suggests that CCK may contribute to IC occurrence through its impact on OS and immune responses. Consequently, conducting a comprehensive investigation into the mechanisms underlying CCK's involvement in bladder diseases, particularly its modulation of OS in bladder tissues, holds promise for identifying novel therapeutic targets. NOS3(Nitric Oxide Synthase 3), a subtype of nitric oxide synthase (NOS) referred to as eNOS, exhibits significantly higher levels in Hunner type IC bladder samples compared to non-Hunner IC bladder samples, indicating potential involvement of eNOS in the divergent pathogenesis of these IC subtypes[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. eNOS, an enzyme crucial for NO production. NO serves as a signaling molecule with diverse physiological functions and extensive implications in both physiological and pathophysiological contexts. Its roles encompass the regulation of vascular tone, promotion of angiogenesis in wound healing, modulation of inflammatory responses, and involvement in pathologies such as ischemic cardiovascular disease and malignancies[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Under normal conditions, eNOS synthesizes substantial quantities of NO, contributing to the maintenance of homeostasis between the endothelium and adjacent tissues[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Similar to endothelial tissue, the urothelial epithelium of the lower urinary tract possesses NOS and produces NO. Inflammation resulting from chronic irritation or infection leads to increased NO production, and the upregulation of NOS in response to chronic inflammation may serve as an adaptive mechanism to enhance spinal nociceptive or reflexive responses triggered by nociceptive inputs from the bladder[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This phenomenon has been verified in a study by M V Souza-Fiho et al[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have established that IC development involves the infiltration of various immune cells, including T cells, B cells, plasma cells, macrophages, neutrophils, and mast cell[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Our study conducted a comprehensive analysis of the infiltration extent of 22 immune cell types using the CIBERSORT algorithm. We observed differences in immune cell infiltration levels between the IC and control groups, consistent with previous findings. Furthermore, we discovered associations between the four genes and T cell CD4 memory resting, T cell CD4 memory activated, and eosinophils. CD4\u003csup\u003e+\u003c/sup\u003e T cells play multifaceted roles in regulating immune responses, naive CD4\u003csup\u003e+\u003c/sup\u003e T cells can differentiate into various subpopulations, including Th1, Th2, Th17, Th22, Tfh and CTLs, each with distinct phenotypes and protective functions. These subpopulations are crucial in pathogen response, immune regulation, and maintaining immune homeostasis[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. CD4\u003csup\u003e+\u003c/sup\u003e T cells play a critical immunoregulatory role in the occurrence and progression of IC[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], they recruit and activate other immune cells through the secretion of various cytokines and chemokines, thereby contributing significantly to the immune response in IC and research has indicated a significant increase in dysfunctional CD4\u003csup\u003e+\u003c/sup\u003e T cells in IC patients[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Eosinophilic primarily maintain the tissue microenvironment during normal development and regulate host innate and adaptive immune responses[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Eosinophils express and release epithelial-mesenchymal transition mediators, including TGF-β, basic fibroblast growth factor, platelet-derived growth factor, matrix metalloproteinases, and vascular endothelial growth factor. They also release other repair/remodeling factors, such as nerve growth factor, neuropeptides, and cytokines like IL-1β and IL-6[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Despite extensive study, the specific role of eosinophils in IC remains unclear. The existing research suggests that eosinophils may play a role in the pathological process of IC, particularly through allergy-related mechanisms. Some scholars[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] speculate that allergic reactions could potentially trigger IC, with eosinophils playing a significant role in this response. They found significantly elevated levels of eosinophilic protein X in the morning urine of interstitial cystitis patients compared to controls, along with occasional eosinophil infiltration in the submucosal layer upon bladder tissue biopsy. Additionally, researchers[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] have observed more severe or moderate eosinophilic infiltration in Hunner IC bladder specimens compared to non-Hunner IC specimens. Therefore, increased eosinophils may contribute to the progression of IC. Further studies are necessary to elucidate the specific functions of eosinophils in IC disease progression and symptom presentation. To investigate the potential biological functions of the four genes in IC, we found, using GSEA, that they are associated with primary immunodeficiency and the peroxisome proliferator-activated receptor (PPAR) signaling pathway. PPARs are ligand-dependent transcription factors belonging to the nuclear hormone receptor superfamily. PPARα is one of its subtypes. PPARα is expressed in macrophages, dendritic cells (DCs), B cells, and T cells, actively participating in various aspects of immune regulation by modulating cytokine production in DCs and T cells, as well as lymphocyte proliferation[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Furthermore, research has confirmed that PPARα exhibits anti-inflammatory effects by inhibiting the NF-κB signaling pathway and reducing the expression of inducible NOS, cyclooxygenase-2, and TNF-α. This finding further demonstrates the association between OS, inflammation, and immunity. Additionally, the PPAR signaling pathway is considered a viable therapeutic target for treating chronic pain and its stress-related psychiatric complications[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Therefore, further investigation into the role of these four genes in the PPAR signaling pathway in IC is crucial.\u003c/p\u003e \u003cp\u003eThrough screening, we identified Decitabine as the most promising drug candidate for the treatment of IC. Decitabine (5-aza-2'-deoxycytidine, DAC) is a DNA methyltransferase inhibitor with a wide range of antimetabolic and anticancer activities. Recent studies have found that Decitabine alleviates symptoms of acute respiratory distress syndrome through its anti-inflammatory and antioxidant properties and by inhibiting the mitogen-activated protein kinase (MAPK) pathway[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Additionally, Chang Su et al[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] found that Decitabine increased the release of the inhibitory factor IL-10, decreased the pro-inflammatory factor IL-17, activated CD4\u003csup\u003e+\u003c/sup\u003e Foxp3\u003csup\u003e+\u003c/sup\u003e T cells, and elevated levels of zonula occludens 1 and occludin, thereby reducing symptoms of ulcerative colitis. Therefore, Decitabine has potential therapeutic value for the treatment of IC.\u003c/p\u003e \u003cp\u003eThere are several limitations to our study. First, it involved secondary data mining and analysis of a previously published dataset, where differences in dataset selection and analysis methods could influence outcomes. Second, due to the absence of clinical samples, validation was limited to the cellular level. Third, the impact of these oxidative stress-related markers on inflammatory and immune responses, contributing to IC occurrence, remains unclear. Therefore, future studies should include diverse populations and larger sample sizes, employing both in vitro and in vivo experiments for validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThrough comprehensive bioinformatics analysis, we demonstrated differences in oxidative stress-related genes and immune cell infiltration between IC patients and controls, revealing for the first time the correlation between OS and immune infiltration in IC. We used machine learning algorithms to identify four mitochondria-related candidate core genes (BMP2, MMP9, CCK, and NOS3) and developed a nomogram for early diagnosis and monitoring of IC patients. This study also identified TFs, miRNAs, and drug molecules that may regulate the hub genes. These findings may offer new perspectives on potential therapeutic targets for IC, enhance understanding of IC mechanisms, and suggest new approaches for IC diagnosis.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interstitial Cystitis\u003c/p\u003e\n\u003cp\u003eOS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Oxidative Stress\u003c/p\u003e\n\u003cp\u003eTF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Transcription Factors\u003c/p\u003e\n\u003cp\u003eNO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Nitric Oxide\u003c/p\u003e\n\u003cp\u003eROS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Reactive Oxygen Species\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003eWGCNA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Weighted Gene Co-expression Network Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFNH and LFP designed this study and provided clinical guidance as well as data interpretation. FCW, ZYW, ZY and WSY drafted the article. FCW was a major contributor in writing the manuscript. ZYH, LJ and JSK prepared the figures and tables for this study. FNH and LFP made significant contributions to the manuscript revision. All authors reviewed the manuscript, provided comments and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82370777).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no Competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan de Merwe JP, Nordling J, Bouchelouche P, Bouchelouche K, Cervigni M, Daha LK, et al. 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Int J Mol Med. 2020;46:583\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Interstitial cystitis, Oxidative stress, Diagnostic marker, Bioinformatics, Machine learning, Immune landscape","lastPublishedDoi":"10.21203/rs.3.rs-4642942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4642942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInterstitial cystitis (IC) is a chronic inflammatory disease with autoimmune associations that is challenging to diagnose and treat. Recent findings indicate that oxidative stress (OS) is a crucial pathophysiological mechanism in IC. Moreover, the interactions between OS, inflammation, and immune cell infiltration are highly complex. Therefore, this study aims to identify biomarkers linked to OS in the development of IC and to elucidate their relationship with immune cell infiltration. These findings could provide new research directions for the diagnosis and treatment of IC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe GSE711783 dataset from the GEO database was utilized to identify differentially expressed genes in IC, while OS-related genes were obtained from the GeneCards database. Hub genes associated with OS were identified through integrated analysis using WGCNA and protein-protein interaction networks. Gene regulatory networks involving transcription factors, TF-miRNA interactions and gene-disease associations were analyzed using relevant databases. Diagnostic marker genes associated with OS were refined using machine learning algorithms. Subsequently, a nomogram diagnostic prediction model was developed and validated through in vitro experiments. Potential drug candidates were identified using the DSigDB database, and the immune landscape in IC was explored using the CIBERSORT algorithm.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified a total of 68 differentially expressed genes related to OS, alongside 15 hub genes. Among these, four genes\u0026mdash;BMP2, MMP9, CCK and NOS3\u0026mdash;were further selected as diagnostic markers. Using the ANN model, ROC curve analysis, and nomogram diagnostic prediction model, all four genes demonstrated excellent diagnostic efficacy. Additionally, these genes exhibited strong associations with T cells CD4 memory resting, T cells CD4 memory activated, and Eosinophils. Finally, decitabine emerged as the most promising drug molecule for IC treatment.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe identified four diagnostic marker genes related to OS that are pivotal in the pathogenesis of IC, influencing both OS and immune responses. These findings highlight new avenues for research in the diagnosis and treatment of IC.\u003c/p\u003e","manuscriptTitle":"Identification of oxidative stress-related diagnostic marker genes and immune landscape in interstitial cystitis by bioinformatics and machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 09:22:26","doi":"10.21203/rs.3.rs-4642942/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-06-28T00:36:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-28T00:35:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-06-26T13:04:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6de7203f-e6ad-40fd-bd6e-4895b450d479","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-19T09:22:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 09:22:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4642942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4642942","identity":"rs-4642942","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}