Shared biomarkers LCN2 and CXCL11 for ulcerative colitis and colon cancer: Bioinformatics analysis and diagnostic model construction

Shared biomarkers LCN2 and CXCL11 for ulcerative colitis and colon cancer: Bioinformatics analysis and diagnostic model construction | 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 Shared biomarkers LCN2 and CXCL11 for ulcerative colitis and colon cancer: Bioinformatics analysis and diagnostic model construction Zhihao Wu, Xiaofen Sun, Binglong Li, Zhiyuan Xie, Jing Zheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7039177/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Digestive Diseases and Sciences → Version 1 posted 9 You are reading this latest preprint version Abstract Background Long-term chronic inflammation is an important risk factor for colon cancer. Ulcerative colitis is a complex chronic inflammatory disease. Related studies have shown that the risk of colorectal cancer in patients with ulcerative colitis is 2–3 times higher than that in the general population. Method Transcriptome data from GEO and TCGA databases were analyzed using RStudio. Differential expression was analyzed with “limma” and disease-related genes identified via WGCNA. Core genes were screened by GO, KEGG, and PPI analyses, and further refined using MCC, LASSO, and RF algorithms. Expression levels and diagnostic value were evaluated via ROC curves; a disease diagnosis model was constructed. Immune cell infiltration was assessed with CIBERSORT, and GSEA analysis was performed based on gene expression. Result 87 common genes were identified through differential analysis and WGCNA. Using these genes, a PPI network was built and top 15 genes were selected by MCC algorithm. LASSO and RF algorithms identified LCN2 and CXCL11 as characteristic genes, highly expressed in the disease group with AUC > 0.7. The diagnosis model performed well. GO, KEGG, and GSEA analyses showed immune and inflammatory responses were important in the disease, with characteristic genes enriched in immune response and cell proliferation pathways. Conclusion It was found that ulcerative colitis and colon cancer have common diagnostic markers and similar pathogenic pathways, and also show similarities in the immune cell infiltration microenvironment. The disease diagnosis model constructed by combining genes is superior to the diagnosis effect of single gene on disease. ulcerative colitis colon cancer bioinformatic methods characteristic genes immune cells infiltration diagnosis model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Ulcerative colitis(UC) and colon cancer(COAD) are two diseases with important clinical significance in the digestive system. Although they are different, they are closely related [ 1 ] . Ulcerative colitis is a chronic inflammatory bowel disease(IBD), which is characterized by persistent chronic inflammation of the colonic mucosa. The disease mostly occurs in the rectum and sigmoid colon, sometimes involving the right colon or even the whole colon. The clinical manifestations are abdominal pain, diarrhea, mucous pus and bloody stools and other symptoms [ 2 ] . It has the characteristics of repeated attacks and remission, which seriously affects the quality of life of patients. The pathogenesis of the disease is not yet fully understood. It is currently believed to be the result of a combination of genetic susceptibility, environmental factors, intestinal microflora disorders, and immune system abnormalities [ 3 ] . Colon cancer is a malignant tumor originating from the colon, which occurs at the junction of the rectum and the sigmoid colon. According to the cancer statistics of the World Health Organization(WHO) in 2022, the incidence of colon cancer ranks third among all cancers in the world, and the mortality rate ranks second [ 4 ] . The occurrence of colon cancer is associated with a variety of factors, including genetic factors, dietary habits(such as high-fat, low-fiber diet), chronic inflammation, and changes in intestinal microbiota [ 5 ] . The early symptoms of colon cancer are usually not obvious. As the disease progresses, symptoms such as hematochezia, changes in bowel habits, abdominal pain, and weight loss may occur [ 6 ] . In recent years, more and more studies have shown that there is a close relationship between ulcerative colitis and colon cancer [ 1 ] . Patients with long-term ulcerative colitis have a significantly increased risk of colon cancer. This link may be related to gene mutations, cell proliferation and decreased immune surveillance function caused by repeated stimulation of chronic inflammation [ 7 ] . In addition, gut microbiota also plays an important role in the development of ulcerative colitis and colon cancer [ 8 ] . Related studies have shown that the risk of colon cancer in patients with ulcerative colitis is 2 to 3 times higher than that in the general population [ 9 , 10 ] . At present, there is no radical cure for ulcerative colon, which can only be controlled by drugs. The course of disease is generally longer. In the context of this long-term intestinal inflammatory stimulation, we should be more vigilant against the occurrence of colon cancer. Therefore, in-depth exploration of the relationship between ulcerative colitis and colon cancer not only helps to better understand the pathogenesis of these two diseases, but also may provide new ideas for early diagnosis and treatment. Methods Batch transcriptome data preprocessing For ulcerative colitis(UC), we selected the GSE87466 series data from the GEO database ( Https://www.ncbi.nlm.nih.gov/geo ) as the training set, including 87 UC samples and 21 healthy control samples(HC), and combined the GSE59071 and GSE66407 series data as the external validation set, including 114 UC samples and 39 HC samples. For colon cancer(COAD), we selected the GSE44076 series data from the GEO database as the training set, including 98 tumor samples(CA) and 98 normal control samples(NC), and the TCGA-COAD data from the TCGA database ( Https://portal.gdc.cancer.gov ) as the external validation set, including 453 CA samples and 41 NC samples. The above samples were obtained by intestinal lesions or normal tissue biopsy. For the validation set TCGA-COAD data, we used the log2(X + 1) method to normalize the final expression matrix. For the GSE series data in the GEO database, the RStudio software was uniformly used for processing. In the preprocessing process, the “normalizeBetweenArrays” function in the “limma” package was used to normalize the transcriptome data. Screening of Core Genes Differential gene expression(DEG) analysis was performed on the GSE87466 and GSE44076 datasets using the “limma” package [ 11 ] , and the meaningful differential gene screening criteria was P.adj.Val 1. The DEGs results were visualized in the form of heat maps and volcano maps using “pheatmap”, “ggpubr”, and “ggthemes” software packages. The common differential genes UC-COAD DEGs of UC and COAD were obtained by intersection of the screened differential genes. The WGCNA software package was used to perform weighted gene co-expression network analysis on the GSE87466 and GSE44076 datasets [ 12 ] . The genes with the top 25% variance in each dataset were selected according to the previous relevant literatureto construct the input matrix [ 13 , 14 ] . Topology calculation was performed using soft thresholds from 1 to 20, and the best soft threshold was selected. When calculating the topological overlap matrix(TOM), TOM Type was selected as “unsigned”, that is, unsigned type, to avoid introducing unnecessary symbol differences in the calculation process. At the same time, in order to ensure that the identified modules have a certain scale and representativeness, the minModuleSize was set to 30. Then, the “Pearson” method was used to calculate the correlation between the merged module and the occurrence of the disease, and the module with higher correlation with the disease was selected as the core module. The screened disease-related genes were intersected to obtain the common related UC-COAD WGCNAs genes of UC and COAD. Finally, the common differential genes of UC and COAD were intersected with the common related genes to obtain the WGCNA-DEG genes. GO and KEGG enrichment analysis GO(Gene Ontology) and KEGG(Kyoto Encyclopedia of Genes and Genomes) enrichment analysis were performed on the screened WGCNA-DEG genes using the “clusterProfiler” package [ 15 ] . GO is used to annotate the biological processes, molecular functions and cellular components of genes. KEGG is used to annotate genes involved in pathways. We believe that P < 0.05 indicates that the enrichment analysis is statistically significant. The metascape database( Http://metascape.org ) was used to further analyze the role of core shared genes. PPI network construction The common core genes(WGCNA-DEG) obtained by difference analysis and weighted gene co-expression network analysis were input into the String platform( https://cn.string-db.org/ ), and the minimum required interaction score was selected as 0.4. After removing independent genes, the PPI network was modified by Cytoscape software to construct a visual network. The core TOP15 genes in the PPI network were calculated using the MCC algorithm(identifying nodes with centrality in the largest cluster in the network) of the cytoHubba plug-in [ 16 ] . Machine learning screens characteristic genes Two machine learning algorithms were used to screen characteristic genes. LASSO regression analysis based on the “glmnet” package and random forest analysis(RF) based on the “rfFuncs” function in the “caret” package. The final LASSO-RF characteristic genes were obtained by intersecting the result genes screened by UC and COAD through machine learning. In order to verify the reliability of the selected characteristic genes, we used the “pROC” package to draw the working characteristic curve(ROC) to evaluate the predictive performance of these genes for the disease, and calculated the area under the curve(AUC) [ 17 ] . AUC greater than 0.7 indicates good diagnostic performance. Expression of characteristic genes in disease and normal groups The expression of the selected characteristic genes in the samples was visualized using the “ggpubr” and “ggsci” packages to compare the differences between the disease group and the normal group. The significance of the difference was calculated using the wilcox.test method. And verify it in the external data set. Construction of disease prediction model based on feature genes Based on the selected characteristic genes, a logistic regression model was constructed using the “lrm” function in the “rms” package to predict the occurrence of related diseases, and the “nomogram” function was used to visualize the model [ 18 ] . The gene expression was converted to “Scores”, and the sum of the “Scores” of all genes was obtained to represent the “Total Score” of each sample, and then the “Total Score” was converted to the disease probability of each sample. Then, the AUC value of the ROC curve was used to evaluate the recognition effect of the model on the disease. At the same time, the calibration curve and the decision curve were used to evaluate the consistency between the prediction and the actual observation, and the corresponding verification set was used for external verification again. Immune infiltration analysis Immunoinfiltration analysis was performed using the “CIBERSORT.R” resource package and the “LM22” document(expression of 22 immune cells in genes) to evaluate immune cell infiltration in each sample. After removing immune cells with all abundances of zero, the results were visualized using the “rainbow” function. According to the sample type, the difference of immune cell infiltration between the disease group and the normal group was compared. The “wilcox.test” method was used to calculate the significance of the difference. The difference between the disease group and the normal group was P < 0.05. Visualization was performed using the “ggpubr” and “ggsci” packages. Using the “corrplot” and “ggcorrplot” packages, the “Spearman” method was used to calculate the correlation between the characteristic genes and the immune cells, and the related heat maps were drawn. GSEA enrichment analysis of characteristic genes The selected characteristic genes were divided into high and low groups according to the level of expression. The high group was greater than the median, and the low group was less than the median. The difference analysis was performed using the “limma” package based on the level of gene expression. The genes were arranged from large to small according to the difference analysis results LogFC, GSEA enrichmentanalysis was performed using the “clusterProfiler” package, and the enrichment results were visualized using the “enrichplot” package [ 19 ] . Results Screening of Differential Genes A total of 1003 differentially expressed genes were identified from UC’s GSE87466 dataset, including 638 up-regulated genes and 365 down-regulated genes. The heat map showed the top 50 most significantly up-regulated and down-regulated genes(Fig. 1 A), and the volcano map visually showed the identification of all differentially expressed genes(Fig. 1 C). A total of 1927 differentially expressed genes were identified from COAD’s GSE44076 dataset, including 874 up-regulated genes and 1053 down-regulated genes. The heat map showed the top 50 most significantly up-regulated and down-regulated genes(Fig. 1 B), and the volcano map showed all the identified differentially expressed genes(Fig. 1 D). Finally, the differential genes obtained from the UC and COAD datasets were intersected to obtain 329 overlapping common differential genes UC-COAD DEGs(Fig. 1 E). Weighted gene co-expression network analysis WGCNA analysis was performed on the GSE87466 dataset of UC. Firstly, the samples were classified into disease and normal groups, and the samples were clustered. After removing the discrete samples, the optimal soft threshold was selected as 12(Fig. 2 A). Finally, 15 modules(Fig. 2 C,E) were identified in the UC dataset. Among them, the salmon module(r = 0.74) and the turquoise module(r = 0.72) had a strong correlation with UC and the P value was significantly less than 0.05. A total of 1529 related genes were obtained in the two modules. Similarly, WGCNA analysis was performed on the GSE44076 dataset of COAD. After removing discrete samples, the optimal soft threshold was selected as 12(Fig. 2 B). Finally, nine modules(Fig. 2 D, F) were identified in the COAD dataset. Among them, the turquoise module(r = 0.92), grey module (r = 0.81) and yellow module(r = 0.61) were strongly correlated with COAD and the P value was significantly less than 0.05. A total of 2485 related genes were screened out in the three modules. Finally, the related genes of UC and COAD were intersected to obtain 355 common related UC-COAD WGCNAs genes(Fig. 2 G). Enrichment analysis of core genes In order to find more meaningful characteristic genes, we intersected 329 overlapping UC-COAD DEGs genes obtained by differential analysis with 355 common related UC-COAD WGCNAs genes obtained by WGCNA analysis, and obtained 87 core WGCNA-DEG genes that were highly correlated with COAD and UC and had significant differential expression in the disease and normal groups(Fig. 3 A). These genes may play a similar and important role in the development of COAD and UC. In order to understand the specific functions of these genes and the pathways involved, we used the “clusterProfiler” package in RStudio to perform GO(Fig. 3 B) and KEGG(Fig. 3 C) enrichment analysis on these genes. The results showed that these genes were mainly involved in the antimicrobial humoral immune, response mediated by antimicrobial peptide, humoral immune response, Cytokine − cytokine receptor interaction, IL − 17 signaling pathway and so on. At the same time, the above genes were input into the metascape database, and the results showed that inflammatory response was of great significance in the common pathogenesis of the two diseases(Fig. 3 D, E). Screening of characteristic genes by PPI network and machine learning The above 87 core WGCNA-DEG genes were input into the STRING database. After removing the independent genes, a protein interaction network consisting of 61 nodes and 292 edges was obtained, and the network was beautified by cytoscape software(Fig. 4 A). Using the MCC algorithm of the cytoHubba plug-in, the TOP15 nodes in the network are screened and visualized(Fig. 4 B). The above 15 genes were subjected to LASSO regression analysis and random forest analysis. In the LASSO regression analysis, the seeds were set to 2024. The UC dataset(GSE87466) screened 11 related genes(Fig. 4 C, E), and the COAD dataset(GSE44076) screened 14 related genes(Fig. 4 D, F). Similarly, in the random forest analysis, the seeds were set to 20252, 7 related genes were screened by UC(Fig. 4 G), and 8 related genes were screened by COAD(Fig. 4 H). Finally, the above four parts of genes were intersected to obtain two overlapping genes, LCN2 and CXCL11(Fig. 4 I). Expression of characteristic genes and identification of diagnostic efficacy In order to identify the expression of characteristic genes in the disease group and the normal group, and the ability to identify the disease, we visualize the expression of characteristic genes in the form of histograms and verify them in the validation set. In the training set of UC(GSE87466) and COAD(GSE44076), LCN2 and CXCL11 genes were significantly highly expressed in the disease group, and had significant statistical differences(Fig. 5 A, B). Similarly, in the validation set UC(GSE59071 and GSE66407) and COAD(TCGA-COAD), the two characteristic genes also showed significant high expression in the disease group, and had statistical differences(Fig. 5 C, D). Then, the ROC curve was used to evaluate the diagnostic predictive value of the characteristic genes. Among them, in the UC training set LCN2(AUC = 0.98), CXCL11(AUC = 0.961) (Fig. 5 A), in the COAD training set LCN2(AUC = 0.852), CXCL11(AUC = 0.804) (Fig. 5 B). In the UC validation set, LCN2(AUC = 0.957), CXCL11(AUC = 0.909) (Fig. 5 C), in the COAD validation set, LCN2(AUC = 0.796), CXCL11(AUC = 0.871) (Fig. 5 D). Based on the above results, the AUC values of the two characteristic genes in each data set were greater than 0.7, indicating that these two genes have good diagnostic performance and may become a common diagnostic marker for UC and COAD. Diagnostic model construction based on characteristic genes According to the ROC results, the above two characteristic genes have a good recognition effect on the disease. In order to further determine their diagnostic prediction ability, we use the two genes to construct a disease diagnosis model. The datasets UC(GSE87466) and COAD(GSE44076) were used as training sets, and UC(GSE59071 and GSE66407) and COAD(TCGA-COAD) were used as validation sets to construct diagnostic models. Use the lrm function to construct a logistic regression model and visualize it with a nomogram. Among them, (Fig. 6 A, B) shows the nomogram model of UC and COAD training sets. The ROC curve was used to evaluate the disease prediction accuracy of the diagnostic model in the four databases. The results showed that the AUC values of the model in the training set and the validation set were all more than 0.85, indicating that the diagnostic effect was good. In addition, the calibration curve and decision curve analysis show more detailed content of the model. From the above results, it can be seen that the diagnostic model of the two genes is superior to the single gene in the diagnosis of diseases in various databases(Fig. 6 C-F). Analysis of immune cell infiltration Because the GO and KEGG enrichment analysis results show that the immune response is crucial in the development of the two diseases, we used the CIBERSORT algorithm to observe the infiltration of various immune cells in the tissues of related diseases, and display the results in the form of rainbow diagrams(Fig. 7 A, B). Then the samples were divided into the disease group and the normal group, and the group comparison diagram was drawn to show the difference of immune cell infiltration between the samples(Fig. 7 C, D). The results showed that the infiltration of T cells CD4 memory activated, T cells follicular helper, Macrophages and Neutrophils in UC was significantly increased. The infiltration of T cells CD4 memory activated, Macrophages, Mast cells activated, Neutrophils and other cells was significantly increased in COAD. Finally, we calculated the correlation between the characteristic genes and immune cell infiltration, and drew the correlation heat map(Fig. 7 E, F). The results showed that the two characteristic genes were strongly correlated with T cells CD4 memory activated, Macrophages M0, Macrophages M1, Mast cells activated, Neutrophils. GSEA enrichment analysis Finally, we divided the disease samples in the UC(GSE87466) and COAD(GSE44076) datasets into high and low groups according to the expression levels of the two characteristic genes, and performed GSEA enrichment analysis to explore the potential pathways(Fig. 8 A-D). The results showed that HALLMARK_ ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_GAMMA_RESPONSE and other pathways were enriched in the LCN2 and CXCL11 high expression group in the UC data set. HALLMARK_FATTY_ACID_METABOLISM, HALLMARK_ OXIDATIVE_PHOSPHORYLATION and other pathways were enriched in LCN2 and CXCL11 low expression groups. In COAD data, HALLMARK_E2F_TARGETS, HALLMARK_G2M_CHECKPOINT, HALLMARK_MYC_TARGETS_V1 and other pathways were enriched in LCN2 and CXCL11 high expression groups. Pathways such as HALLMARK_MYOGENESIS and HALLMARK_ADIPOGENESIS were enriched in the LCN2 and CXCL11 low expression groups. Discussion Ulcerative colitis is a chronic non-specific inflammatory bowel disease with unclear etiology. Its pathology mainly involves the colorectal mucosa, forming a continuous and diffuse superficial inflammatory ulcer. [ 2 ] The common symptoms of patients include recurrent abdominal pain, diarrhea, mucopurulent bloody stools, while some patients may also have non-specific manifestations outside the intestine, such as arthritis, rash, etc. Nowadays, there is no cure for this disease, and only symptomatic control with drugs can be used. Therefore, the course of the patient 's disease usually lasts for decades, and it is this repeated inflammatory stimulation that provides a hotbed for the occurrence of colon cancer [ 20 , 21 ] . Colon cancer is a malignant tumor originating from colonic mucosal epithelial cells. Patients usually have no obvious symptoms in the early stage. As the disease progresses, symptoms such as hematochezia, anemia, and weight loss will occur [ 6 ] . Due to the absence of symptoms in the early stage and the lack of regular physical examination, it is often relatively late when symptoms occur, which is a blow to the patient’s body and mind and has always been a burden on medical care. Because the symptoms of ulceration are similar to those of colon cancer, the occurrence of colon cancer is often covered up. Therefore, it is necessary to be alert to the early occurrence of colon cancer for patients with ulceration [ 22 ] . Although one is a benign inflammatory disease and the other is a malignant tumor, the two diseases have an important relationship, and the specific mechanism remains to be studied. Therefore, the identification of shared biomarkers and pathogenesis may provide new ideas for the diagnosis and treatment of diseases. We selected transcriptome data of ulcerative colitis and colon cancer from the public databases GEO and TCGA. After differential analysis and WGCNA analysis, 87 overlapping genes related to both diseases were obtained. GO and KEGG enrichment analysis showed that immune response and inflammatory response were important in the occurrence and development of the two diseases, which provided ideas for subsequent analysis. Subsequently, we performed PPI network analysis on 87 cores and then screened the TOP15 genes by the algorithm. Finally, the 15 genes were identified by machine learning methods LASSO and RF, and two key characteristic genes LCN2 and CXCL11 were obtained. According to the grouping comparison, these two genes are highly expressed in ulcerative colitis and colon cancer, and have a good diagnostic effect on the disease. It can be seen that these two genes are of great significance in the pathogenesis of the disease. Subsequently, we focused on a series of analysis of these two genes. LCN2 is a secreted glycoprotein, also known as neutrophil gelatinase-associated lipocalin (NGAL) or oncogene 24p3. It is expressed in a variety of cells and is significantly up-regulated in inflammation, infection, and tissue damage. LCN2 has many functions, including : limiting the growth of bacteria and fungi by chelating iron, thereby exerting antibacterial and antifungal effects [ 23 ] , can be used as a biomarker for acute kidney injury (AKI) and other inflammatory diseases, may promote neurotoxicity in the nervous system may also promote neuroprotection [ 24 ] . At the same time, the role of LCN2 in tumors has also been extensively studied. The occurrence and development of lung cancer, breast cancer, and pancreatic cancer have their own shadows [ 25 – 27 ] . LCN2 is a ferroptosis-related gene that plays an important role in inflammatory response, cell death and autophagy, and tumor progression [ 28 – 30 ] . Previous studies have shown that LCN2 can inhibit the invasion and liver metastasis of colon cancer [ 31 ] , indicating that LCN2 may be a candidate metastasis suppressor in colon cancer cells. Recent studies have shown that LCN2 may promote the progression of colon cancer by inhibiting ferroptosis [ 32 ] . It can be seen that the role of this gene is still a research hotspot.Related studies have attempted to use LCN2 as a diagnostic marker for colon cancer [ 33 , 34 ] , and LCN2 has also shown good diagnostic value in ulcerative colitis [ 35 ] . Recent studies have shown that LCN2 has a pro-inflammatory effect in colonic epithelial cells and provides a potential target for inhibiting UC cell pyroptosis.Because LCN2 has significant protease resistance [ 36 ] , the expression of LCN2 can be directly detected from feces, which also provides a natural advantage as a diagnostic marker. Compared with colonoscopic biopsy, fecal examination is both affordable and reduces patient pain.Based on a large number of studies, it can be seen that the role of LCN2 gene in ulceration and colon cancer is very important, and previous studies have already made some exploration of the role of LCN2 in ulceration and colon cancer [ 37 ] . CXCL11 is a small molecule cytokine belonging to the CXC chemokine family, also known as interferon-inducible T cell alpha chemokine(I-TAC) or interferon-inducible protein 9(IP-9). It is mainly induced by IFN-α, IFN-β and IFN-γ. CXCL11 has a chemotactic effect on activated T cells, neutrophils and monocytes by binding to the chemokine receptor CXCR3. It can also bind to the chemokine receptor CCR3 and prevent the binding of CCR3 ligands [ 38 ] . In the inflammatory response, CXCL11 plays an important role in the occurrence and development of a variety of inflammatory diseases, such as allergic contact dermatitis, mycosis fungoides and immune multiple sclerosis. CXCL11 has a dual role in the tumor microenvironment. On the one hand, it can recruit immune cells into the tumor tissue and enhance the anti-tumor immune response; on the other hand, abnormal expression of CXCL11 may be associated with tumor progression and prognosis [ 39 ] . For example, studies have shown that elevated CXCL11 is an independent prognostic biomarker for COAD patients, which can promote anti-tumor immunity to prolong survival [ 40 ] . At the same time, studies have shown that CXCL10 and CXCL11 secreted by neuroendocrine-like cells can recruit tumor-associated macrophages to infiltrate tumor tissues, thereby enhancing the proliferation and invasion of colorectal cancer cells and leading to poor prognosis [ 41 ] . Studies in ulcerative colitis have shown that genes such as CXCL11 are significantly up-regulated in the first few years of the disease and can be used as potential disease diagnostic factors [ 42 ] . In addition, previous studies have shown that CXCL11 may be a candidate gene involved in the development of colitis-associated colorectal cancer [ 43 ] . This suggests that the role of CXCL11 in ulceration and colon cancer has been partially explored. From the results of GO and KEGG enrichment analysis, it can be seen that immune response and inflammatory response play an important role. Ulcerative colitis itself is a long-term chronic inflammatory disease, and chronic inflammation is also a significant risk factor in the occurrence and development of colon cancer. At the same time, the inflammatory response is often accompanied by the activation of these various immune mechanisms, so this result is consistent with previous studies. From the results of GSEA enrichment analysis, immune rejection, interferon γ response markers, cell cycle regulation and proliferation were co-enriched in CXCL11 and LCN2 up-regulated groups. It can be seen that the immune response is still more or less involved in the occurrence and development of the two diseases. Therefore, considering the important role of immune function in the common pathogenesis of COAD and UC, we conducted CIBERSORT immune cell infiltration analysis to understand the pattern of immune cell infiltration in these two diseases. The results showed that COAD and UC showed similar immune infiltration patterns. CD4 T cells, Macrophages, Neutrophils were significantly increased in ulceration and colon cancer, and these cells were also highly correlated with immune response and inflammatory response, which again confirmed the results of our previous enrichment analysis. CD4 T cells play a central coordinating role in the immune system by secreting a variety of cytokines, activating and regulating other immune cells, such as macrophages, B cells and CD8 T cells [ 44 ] . At the same time, CD4 T cells can directly or indirectly target tumor cells through a variety of mechanisms in the tumor microenvironment to promote anti-tumor effects. Macrophages can secrete a variety of cytokines and chemokines when activated, such as TNF-α, IL-1β, IL-6 and IL-12, which can recruit other immune cells and regulate the inflammatory response [ 45 ] . Macrophages are divided into M0, M1, and M2 types. Among them, M1 is a pro-inflammatory type, which is mainly involved in inflammatory response and pathogen clearance. From our cell infiltration analysis results, it can be seen that M1 type accounts for a large proportion, which also better supports the mechanism of disease. Neutrophils are one of the most important innate immune cells in the human immune system. They are mainly responsible for rapid response to pathogen invasion and tissue damage. They also secrete a variety of cytokines such as TNF-α, IL-1β, IL-6 and so on when activated [ 46 ] . According to the results of the correlation heat map, it can be seen that our characteristic genes CXCL11 and LCN2 have this high correlation with the above immune cells, which once again reflects the mechanism of these two genes in the occurrence and development of the disease, and provides ideas for subsequent research. Previous studies have discussed the role of LCN2 and CXCL11 genes in two diseases [ 47 – 49 ] , and tried to use these genes as diagnostic markers for diseases [ 33 ] . Our study also found these two genes at the same time, and observed that there is also an interaction between the two genes. Therefore, we intentionally included the two genes in the logistic regression model and constructed a nomogram. The results showed that with the increase of gene expression level, the patient’s disease score was also higher, and the score corresponded to the probability of disease. It can be seen that LCN2 and CXCL11 genes are risk factors for two diseases, and the higher the expression, the higher the probability of disease. At the same time, the ROC curve of the model shows that the combined diagnosis model of the two genes is better than the diagnosis effect of a single gene on the disease, and it is well verified in both the training set and the validation set. Calibration curve and decision curve analysis also show the good application of the model. Therefore, the above results show that our model may have great predictive potential, and combining the two genes as a common biomarker for colon cancer and ulceration can improve the accuracy of diagnosis. In previous studies, most of them considered the influence of genes on diseases alone. Our research adopts the model constructed by joint analysis to consider the influence of multiple factors on diseases, which not only improves the accuracy, but also provides ideas and solutions for subsequent analysis. Although our research ideas are novel, there are still some limitations. First of all, our research is based on the analysis of public data sets, and the effective sample size is limited. Secondly, because the clinical information of some samples is not perfect enough, we did not classify the samples. Finally, our samples are concentrated in some specific areas. When expanding the geographical scope and race, the universality of the results is still worth exploring. In conclusion, this study found the common potential biomarkers and targets of COAD and UC through bioinformatics analysis, which provided new ideas for the study of the pathogenesis and clinical treatment of the two diseases. Conclusion Through data mining of public databases and combined with machine learning models, we found that ulcerative colitis and colon cancer have common potential biomarkers LCN2 and CXCL11. Enrichment analysis results show that the two diseases may have similar pathogenic pathways, and the two diseases also show similarities in the immune cell infiltration microenvironment. The ROC curve shows that the LCN2 and CXCL11 genes have a good diagnostic effect on the disease. Finally, we found that the disease diagnosis model constructed by the combined gene is better than the single gene for the diagnosis of the disease. Our model may have new application potential in clinical diagnosis. Declarations Disclosure statement No potential conflict of interest was reported by the authors. Funding The current study received financial support from, the Key Research and Development Plan Project of Hainan Province (Grant Nos.ZDYF2023SHFZ142), the Hainan Provincial Health Science and Technology Innovation Project (Grant Nos.WSJK2024MS136), and the National Natural Science Foundation of China (Grant Nos.82260343). Author Contribution All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas. Jianqiang Chen, Xiaofen Sun and Ying Guan conceptualized the paper. Zhihao Wu and Binglong Li drafted the manuscript; Zhiyuan Xie, Jing Zheng and Nianqing Sun were substantially involved in revising the manuscript. All the authors have checked the final manuscript before submission. Data Availability For ulcerative colitis(UC), we selected the GSE87466 series data from the GEO database (Https://www.ncbi.nlm.nih.gov/geo) as the training set, including 87 UC samples and 21 healthy control samples(HC), and combined the GSE59071 and GSE66407 series data as the external validation set, including 114 UC samples and 39 HC samples. For colon cancer(COAD), we selected the GSE44076 series data from the GEO database as the training set, including 98 tumor samples(CA) and 98 normal control samples(NC), and the TCGA-COAD data from the TCGA database (Https://portal.gdc.cancer.gov) as the external validation set, including 453 CA samples and 41 NC samples. References Yao D, Dong M, Dai C, Wu S. Inflammation and Inflammatory Cytokine Contribute to the Initiation and Development of Ulcerative Colitis and Its Associated Cancer. Inflamm Bowel Dis . Sep 18 2019;25(10):1595–1602. doi: 10.1093/ibd/izz149 Ordás I, Eckmann L, Talamini M, Baumgart DC, Sandborn WJ. Ulcerative colitis. 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Aug 21 2017;37:103–130. doi: 10.1146/annurev-nutr-071816-064559 Huang Z, Rui X, Yi C, et al. Silencing LCN2 suppresses oral squamous cell carcinoma progression by reducing EGFR signal activation and recycling. J Exp Clin Cancer Res . Mar 11 2023;42(1):60. doi: 10.1186/s13046-023-02618-z Lee HJ, Lee EK, Lee KJ, Hong SW, Yoon Y, Kim JS. Ectopic expression of neutrophil gelatinase-associated lipocalin suppresses the invasion and liver metastasis of colon cancer cells. Int J Cancer . May 15 2006;118(10):2490–7. doi: 10.1002/ijc.21657 Chaudhary N, Choudhary BS, Shah SG, et al. Lipocalin 2 expression promotes tumor progression and therapy resistance by inhibiting ferroptosis in colorectal cancer. Int J Cancer . Oct 1 2021;149(7):1495–1511. doi: 10.1002/ijc.33711 Kou F, Cheng Y, Shi L, et al. LCN2 as a Potential Diagnostic Biomarker for Ulcerative Colitis-Associated Carcinogenesis Related to Disease Duration. Front Oncol . 2021;11:793760. doi: 10.3389/fonc.2021.793760 Roli L, Pecoraro V, Trenti T. Can NGAL be employed as prognostic and diagnostic biomarker in human cancers? A systematic review of current evidence. Int J Biol Markers . Mar 2 2017;32(1):e53-e61. doi: 10.5301/jbm.5000245 Wang J, Li L, Chen P, He C, Niu X. Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms. Sci Rep . Jul 1 2024;14(1):15009. doi: 10.1038/s41598-024-65481-8 Nielsen OH, Gionchetti P, Ainsworth M, et al. Rectal dialysate and fecal concentrations of neutrophil gelatinase-associated lipocalin, interleukin-8, and tumor necrosis factor-alpha in ulcerative colitis. Am J Gastroenterol . Oct 1999;94(10):2923–8. doi: 10.1111/j.1572-0241.1999.01439.x Derakhshani A, Javadrashid D, Hemmat N, et al. Identification of Common and Distinct Pathways in Inflammatory Bowel Disease and Colorectal Cancer: A Hypothesis Based on Weighted Gene Co-Expression Network Analysis. Front Genet . 2022;13:848646. doi: 10.3389/fgene.2022.848646 Gao Q, Zhang Y. CXCL11 Signaling in the Tumor Microenvironment. Adv Exp Med Biol . 2021;1302:41–50. doi: 10.1007/978-3-030-62658-7_4 Kureshi CT, Dougan SK. Cytokines in cancer. Cancer Cell . Jan 13 2025;43(1):15–35. doi: 10.1016/j.ccell.2024.11.011 Cao Y, Jiao N, Sun T, et al. CXCL11 Correlates With Antitumor Immunity and an Improved Prognosis in Colon Cancer. Front Cell Dev Biol . 2021;9:646252. doi: 10.3389/fcell.2021.646252 Zeng YJ, Lai W, Wu H, et al. Neuroendocrine-like cells -derived CXCL10 and CXCL11 induce the infiltration of tumor-associated macrophage leading to the poor prognosis of colorectal cancer. Oncotarget . May 10 2016;7(19):27394–407. doi: 10.18632/oncotarget.8423 Bergemalm D, Andersson E, Hultdin J, et al. Systemic Inflammation in Preclinical Ulcerative Colitis. Gastroenterology . Nov 2021;161(5):1526–1539.e9. doi: 10.1053/j.gastro.2021.07.026 Lu C, Zhang X, Luo Y, Huang J, Yu M. Identification of CXCL10 and CXCL11 as the candidate genes involving the development of colitis-associated colorectal cancer. Front Genet . 2022;13:945414. doi: 10.3389/fgene.2022.945414 Chen X, Ghanizada M, Mallajosyula V, et al. Differential roles of human CD4(+) and CD8(+) regulatory T cells in controlling self-reactive immune responses. Nat Immunol . Jan 13 2025; doi: 10.1038/s41590-024-02062-x Rodríguez-Morales P, Franklin RA. Macrophage phenotypes and functions: resolving inflammation and restoring homeostasis. Trends Immunol . Dec 2023;44(12):986–998. doi: 10.1016/j.it.2023.10.004 Herro R, Grimes HL. The diverse roles of neutrophils from protection to pathogenesis. Nat Immunol . Dec 2024;25(12):2209–2219. doi: 10.1038/s41590-024-02006-5 Luo Q, An M, Wu Y, et al. Bioinformatics analysis reveals potential crosstalk genes and molecular mechanisms between ulcerative colitis and psoriasis. Arch Dermatol Res . Dec 14 2024;317(1):118. doi: 10.1007/s00403-024-03617-6 Chen ZA, Sun YF, Wang QX, Ma HH, Ma ZZ, Yang CJ. Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis. Front Genet . 2021;12:697514. doi: 10.3389/fgene.2021.697514 Nielsen BS, Borregaard N, Bundgaard JR, Timshel S, Sehested M, Kjeldsen L. Induction of NGAL synthesis in epithelial cells of human colorectal neoplasia and inflammatory bowel diseases. Gut . Mar 1996;38(3):414–20. doi: 10.1136/gut.38.3.414 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Digestive Diseases and Sciences → Version 1 posted Editorial decision: Revision requested 24 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers invited by journal 07 Jul, 2025 Editor assigned by journal 04 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 03 Jul, 2025 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. We do this by developing innovative software and high quality services for the global research community. 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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-7039177","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481477018,"identity":"efdaa875-d57f-4f16-86d0-d15ddfca20f9","order_by":0,"name":"Zhihao Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDAC9v7HDyQqbHgY29sPPiBOC88ZNgOLM2kyzD1nkg2I0yKRwyBR2XLYhn1GgpkEUToMDuQeMLjZcJiHt+dAWsWbMgZ5frEDhLScS3g4c0c6j2R747Gbc84xGM6cnYBfi9nBBgNjyTPWPIZAW27ztjEkGNwmpOUwg4H03zZmHvsbCWbFxGk5xmMgIdnmzMMI9D4zUVrsz7ClGUicSeNhBAay5JxzEoT9Ijn/8WFQVNqDovLDmzIbeX5pAlpQAQ8bcVGDooVUHaNgFIyCUTASAAA8Kkj2rhex6AAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Wu","suffix":""},{"id":481477019,"identity":"b125cb4c-8b8c-446d-883b-af437392e527","order_by":1,"name":"Xiaofen Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofen","middleName":"","lastName":"Sun","suffix":""},{"id":481477020,"identity":"13c733fe-7489-4729-b8a5-99b071e677bb","order_by":2,"name":"Binglong Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Binglong","middleName":"","lastName":"Li","suffix":""},{"id":481477021,"identity":"95b087d0-b3c2-4fe5-bfa1-e647fad2ef40","order_by":3,"name":"Zhiyuan Xie","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Xie","suffix":""},{"id":481477022,"identity":"7b4aeb21-2253-4a63-aac7-16d76274a0be","order_by":4,"name":"Jing Zheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zheng","suffix":""},{"id":481477023,"identity":"493d59be-d103-47d0-b6ac-37b8aaa35358","order_by":5,"name":"Nianqing Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nianqing","middleName":"","lastName":"Sun","suffix":""},{"id":481477024,"identity":"5dfd46ff-0c54-4414-a22d-efb1307d07aa","order_by":6,"name":"Ying Guan","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Guan","suffix":""},{"id":481477025,"identity":"0d27898d-0f32-42bc-b763-24c7bb9e7d5b","order_by":7,"name":"Jianqiang Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianqiang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-07-03 14:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7039177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7039177/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10620-025-09443-8","type":"published","date":"2025-10-14T15:58:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86298481,"identity":"9c7fe9f7-59e0-40f8-87d2-002ea840eba7","added_by":"auto","created_at":"2025-07-09 05:54:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2679201,"visible":true,"origin":"","legend":"\u003cp\u003e(A) UC training set difference analysis heat map, showing the top 50 up-regulated and down-regulated most obvious genes. (B) COAD training set difference analysis heat map, showing the top 50 most significantly up-regulated and down-regulated genes. (C) UC training set difference analysis volcano map, red up, blue down. (D) COAD training set difference analysis volcano map, red up, blue down. (E) Wayne diagram: UC differential genes and COAD differential genes intersect to obtain common differential genes. H: normal control, P: disease group. CADEG: COAD differential genes. UCDEG: UC differential genes.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/9c4332e9c1b9e0f0914f5a01.jpeg"},{"id":86298476,"identity":"30546553-7e06-4dfc-ba7e-7543d7eec3dc","added_by":"auto","created_at":"2025-07-09 05:54:54","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1228885,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The optimal soft threshold of UC training set is 12. (B) The best soft threshold of COAD training set is 12. (C, E) Gene clustering of UC training set and the relationship between modules and diseases. (D, F) COAD training set gene clustering and the relationship between modules and diseases. (G) Wayne diagram: UC-related module genes and COAD-related module genes intersect to obtain common related genes. CAWGCNA: COAD-related genes. UCWGCNA: UC-related genes.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/9fd3cb3653cc6eefedcfa751.jpeg"},{"id":86299098,"identity":"802f484d-d466-4062-9cc2-6eb7670a0839","added_by":"auto","created_at":"2025-07-09 06:02:55","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2064337,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Wayne diagram: The common differential genes of UC and COAD were intersected with the common related genes to obtain the core genes. (B, C)GO and KEGG enrichment analysis results of core genes. (D) Metascape database enrichment analysis results. (E) The relationship between biological processes or signaling pathways.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/e1b98006f7cb08afa170cc96.jpeg"},{"id":86298478,"identity":"12e63257-3368-4b49-bf35-eea62d35eeb6","added_by":"auto","created_at":"2025-07-09 05:54:54","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1467592,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of core genes. (B) TOP15 genes screened by MCC and their interaction. (C, E) UC lasso regression analysis chart. (D, F) COAD lasso regression analysis chart. (G) UC random forest graph. (H) COAD random forest graph. (I) Wayne diagram: The genes screened by UC and COAD through machine learning methods were intersected to obtain characteristic genes.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/ce442e32cdc1a862e5764d55.jpeg"},{"id":86298487,"identity":"9420fb5e-b4f3-42af-9dcf-78262b6504f9","added_by":"auto","created_at":"2025-07-09 05:54:55","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":758067,"visible":true,"origin":"","legend":"\u003cp\u003e(A-D)The expression of characteristic genes in the disease and normal groups and the ROC curve: ( A ) UC training set (B) COAD training set (C) UC validation set (D) COAD validation set. *** indicates that the difference was statistically significant. H normal control, P disease group.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/aa7a30e6e22114f8e694c0c0.jpeg"},{"id":86298483,"identity":"e2b073f8-4a55-4b96-bc20-517bc539f69e","added_by":"auto","created_at":"2025-07-09 05:54:55","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1337424,"visible":true,"origin":"","legend":"\u003cp\u003e(A) UC training set nomogram diagnostic model. (B) COAD training set nomogram diagnostic model. ROC curve, calibration curve and decision analysis curve: (C)UC training set, (D)COAD training set, (E) UC validation set, (F) COAD validation set.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/bf5be1d4ebd63c5c272d5fab.jpeg"},{"id":86298480,"identity":"6aee6dc2-f60f-42b3-8c0c-e13337470545","added_by":"auto","created_at":"2025-07-09 05:54:54","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2072626,"visible":true,"origin":"","legend":"\u003cp\u003e(A, B) Rainbow diagram of immune cell infiltration in UC and COAD. (C, D) UC and COAD disease group compared with the normal group of immune cell infiltration, * represents a statistically significant difference, ns indicates no statistical difference. H normal control, P disease group. (E, F) Heat map of correlation between UC and COAD characteristic genes and immune cells.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/02deea4cfb1c252fc52b682e.jpeg"},{"id":86298489,"identity":"aa3f7080-a2e8-47e3-9aab-75e9c5ae3d46","added_by":"auto","created_at":"2025-07-09 05:54:55","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":989947,"visible":true,"origin":"","legend":"\u003cp\u003e(A,B)GSEA enrichment analysis of LCN2 and CXCL11 expression high and low groups in UC training set. (C, D) COAD training set LCN2, CXCL11 expression high and low group GSEA enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/b473662fc3dd89d829b7e727.jpeg"},{"id":93956062,"identity":"748c7317-521f-4c8c-b79c-8a139002b599","added_by":"auto","created_at":"2025-10-20 16:09:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13138820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7039177/v1/b3187642-7f55-456e-bc03-72e7b789ec7c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shared biomarkers LCN2 and CXCL11 for ulcerative colitis and colon cancer: Bioinformatics analysis and diagnostic model construction","fulltext":[{"header":"Background","content":"\u003cp\u003eUlcerative colitis(UC) and colon cancer(COAD) are two diseases with important clinical significance in the digestive system. Although they are different, they are closely related\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Ulcerative colitis is a chronic inflammatory bowel disease(IBD), which is characterized by persistent chronic inflammation of the colonic mucosa. The disease mostly occurs in the rectum and sigmoid colon, sometimes involving the right colon or even the whole colon. The clinical manifestations are abdominal pain, diarrhea, mucous pus and bloody stools and other symptoms\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It has the characteristics of repeated attacks and remission, which seriously affects the quality of life of patients. The pathogenesis of the disease is not yet fully understood. It is currently believed to be the result of a combination of genetic susceptibility, environmental factors, intestinal microflora disorders, and immune system abnormalities\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Colon cancer is a malignant tumor originating from the colon, which occurs at the junction of the rectum and the sigmoid colon. According to the cancer statistics of the World Health Organization(WHO) in 2022, the incidence of colon cancer ranks third among all cancers in the world, and the mortality rate ranks second\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The occurrence of colon cancer is associated with a variety of factors, including genetic factors, dietary habits(such as high-fat, low-fiber diet), chronic inflammation, and changes in intestinal microbiota\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The early symptoms of colon cancer are usually not obvious. As the disease progresses, symptoms such as hematochezia, changes in bowel habits, abdominal pain, and weight loss may occur\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In recent years, more and more studies have shown that there is a close relationship between ulcerative colitis and colon cancer\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Patients with long-term ulcerative colitis have a significantly increased risk of colon cancer. This link may be related to gene mutations, cell proliferation and decreased immune surveillance function caused by repeated stimulation of chronic inflammation\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In addition, gut microbiota also plays an important role in the development of ulcerative colitis and colon cancer\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Related studies have shown that the risk of colon cancer in patients with ulcerative colitis is 2 to 3 times higher than that in the general population\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. At present, there is no radical cure for ulcerative colon, which can only be controlled by drugs. The course of disease is generally longer. In the context of this long-term intestinal inflammatory stimulation, we should be more vigilant against the occurrence of colon cancer. Therefore, in-depth exploration of the relationship between ulcerative colitis and colon cancer not only helps to better understand the pathogenesis of these two diseases, but also may provide new ideas for early diagnosis and treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eBatch transcriptome data preprocessing\u003c/p\u003e\u003cp\u003eFor ulcerative colitis(UC), we selected the GSE87466 series data from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHttps://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://Https://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the training set, including 87 UC samples and 21 healthy control samples(HC), and combined the GSE59071 and GSE66407 series data as the external validation set, including 114 UC samples and 39 HC samples. For colon cancer(COAD), we selected the GSE44076 series data from the GEO database as the training set, including 98 tumor samples(CA) and 98 normal control samples(NC), and the TCGA-COAD data from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"http://Https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the external validation set, including 453 CA samples and 41 NC samples. The above samples were obtained by intestinal lesions or normal tissue biopsy. For the validation set TCGA-COAD data, we used the log2(X\u0026thinsp;+\u0026thinsp;1) method to normalize the final expression matrix. For the GSE series data in the GEO database, the RStudio software was uniformly used for processing. In the preprocessing process, the \u0026ldquo;normalizeBetweenArrays\u0026rdquo; function in the \u0026ldquo;limma\u0026rdquo; package was used to normalize the transcriptome data.\u003c/p\u003e\u003cp\u003eScreening of Core Genes\u003c/p\u003e\u003cp\u003eDifferential gene expression(DEG) analysis was performed on the GSE87466 and GSE44076 datasets using the \u0026ldquo;limma\u0026rdquo; package\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and the meaningful differential gene screening criteria was P.adj.Val\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |LogFC| \u0026gt;1. The DEGs results were visualized in the form of heat maps and volcano maps using \u0026ldquo;pheatmap\u0026rdquo;, \u0026ldquo;ggpubr\u0026rdquo;, and \u0026ldquo;ggthemes\u0026rdquo; software packages. The common differential genes UC-COAD DEGs of UC and COAD were obtained by intersection of the screened differential genes. The WGCNA software package was used to perform weighted gene co-expression network analysis on the GSE87466 and GSE44076 datasets\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The genes with the top 25% variance in each dataset were selected according to the previous relevant literatureto construct the input matrix\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Topology calculation was performed using soft thresholds from 1 to 20, and the best soft threshold was selected. When calculating the topological overlap matrix(TOM), TOM Type was selected as \u0026ldquo;unsigned\u0026rdquo;, that is, unsigned type, to avoid introducing unnecessary symbol differences in the calculation process. At the same time, in order to ensure that the identified modules have a certain scale and representativeness, the minModuleSize was set to 30. Then, the \u0026ldquo;Pearson\u0026rdquo; method was used to calculate the correlation between the merged module and the occurrence of the disease, and the module with higher correlation with the disease was selected as the core module. The screened disease-related genes were intersected to obtain the common related UC-COAD WGCNAs genes of UC and COAD. Finally, the common differential genes of UC and COAD were intersected with the common related genes to obtain the WGCNA-DEG genes.\u003c/p\u003e\u003cp\u003eGO and KEGG enrichment analysis\u003c/p\u003e\u003cp\u003eGO(Gene Ontology) and KEGG(Kyoto Encyclopedia of Genes and Genomes) enrichment analysis were performed on the screened WGCNA-DEG genes using the \u0026ldquo;clusterProfiler\u0026rdquo; package\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. GO is used to annotate the biological processes, molecular functions and cellular components of genes. KEGG is used to annotate genes involved in pathways. We believe that P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates that the enrichment analysis is statistically significant. The metascape database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHttp://metascape.org\u003c/span\u003e\u003cspan address=\"http://Http://metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to further analyze the role of core shared genes.\u003c/p\u003e\u003cp\u003ePPI network construction\u003c/p\u003e\u003cp\u003eThe common core genes(WGCNA-DEG) obtained by difference analysis and weighted gene co-expression network analysis were input into the String platform(\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), and the minimum required interaction score was selected as 0.4. After removing independent genes, the PPI network was modified by Cytoscape software to construct a visual network. The core TOP15 genes in the PPI network were calculated using the MCC algorithm(identifying nodes with centrality in the largest cluster in the network) of the cytoHubba plug-in\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMachine learning screens characteristic genes\u003c/p\u003e\u003cp\u003eTwo machine learning algorithms were used to screen characteristic genes. LASSO regression analysis based on the \u0026ldquo;glmnet\u0026rdquo; package and random forest analysis(RF) based on the \u0026ldquo;rfFuncs\u0026rdquo; function in the \u0026ldquo;caret\u0026rdquo; package. The final LASSO-RF characteristic genes were obtained by intersecting the result genes screened by UC and COAD through machine learning. In order to verify the reliability of the selected characteristic genes, we used the \u0026ldquo;pROC\u0026rdquo; package to draw the working characteristic curve(ROC) to evaluate the predictive performance of these genes for the disease, and calculated the area under the curve(AUC)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. AUC greater than 0.7 indicates good diagnostic performance.\u003c/p\u003e\u003cp\u003eExpression of characteristic genes in disease and normal groups\u003c/p\u003e\u003cp\u003eThe expression of the selected characteristic genes in the samples was visualized using the \u0026ldquo;ggpubr\u0026rdquo; and \u0026ldquo;ggsci\u0026rdquo; packages to compare the differences between the disease group and the normal group. The significance of the difference was calculated using the wilcox.test method. And verify it in the external data set.\u003c/p\u003e\u003cp\u003eConstruction of disease prediction model based on feature genes\u003c/p\u003e\u003cp\u003eBased on the selected characteristic genes, a logistic regression model was constructed using the \u0026ldquo;lrm\u0026rdquo; function in the \u0026ldquo;rms\u0026rdquo; package to predict the occurrence of related diseases, and the \u0026ldquo;nomogram\u0026rdquo; function was used to visualize the model\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The gene expression was converted to \u0026ldquo;Scores\u0026rdquo;, and the sum of the \u0026ldquo;Scores\u0026rdquo; of all genes was obtained to represent the \u0026ldquo;Total Score\u0026rdquo; of each sample, and then the \u0026ldquo;Total Score\u0026rdquo; was converted to the disease probability of each sample. Then, the AUC value of the ROC curve was used to evaluate the recognition effect of the model on the disease. At the same time, the calibration curve and the decision curve were used to evaluate the consistency between the prediction and the actual observation, and the corresponding verification set was used for external verification again.\u003c/p\u003e\u003cp\u003eImmune infiltration analysis\u003c/p\u003e\u003cp\u003eImmunoinfiltration analysis was performed using the \u0026ldquo;CIBERSORT.R\u0026rdquo; resource package and the \u0026ldquo;LM22\u0026rdquo; document(expression of 22 immune cells in genes) to evaluate immune cell infiltration in each sample. After removing immune cells with all abundances of zero, the results were visualized using the \u0026ldquo;rainbow\u0026rdquo; function. According to the sample type, the difference of immune cell infiltration between the disease group and the normal group was compared. The \u0026ldquo;wilcox.test\u0026rdquo; method was used to calculate the significance of the difference. The difference between the disease group and the normal group was P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Visualization was performed using the \u0026ldquo;ggpubr\u0026rdquo; and \u0026ldquo;ggsci\u0026rdquo; packages. Using the \u0026ldquo;corrplot\u0026rdquo; and \u0026ldquo;ggcorrplot\u0026rdquo; packages, the \u0026ldquo;Spearman\u0026rdquo; method was used to calculate the correlation between the characteristic genes and the immune cells, and the related heat maps were drawn.\u003c/p\u003e\u003cp\u003eGSEA enrichment analysis of characteristic genes\u003c/p\u003e\u003cp\u003eThe selected characteristic genes were divided into high and low groups according to the level of expression. The high group was greater than the median, and the low group was less than the median. The difference analysis was performed using the \u0026ldquo;limma\u0026rdquo; package based on the level of gene expression. The genes were arranged from large to small according to the difference analysis results LogFC, GSEA enrichmentanalysis was performed using the \u0026ldquo;clusterProfiler\u0026rdquo; package, and the enrichment results were visualized using the \u0026ldquo;enrichplot\u0026rdquo; package\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eScreening of Differential Genes\u003c/p\u003e\n\u003cp\u003eA total of 1003 differentially expressed genes were identified from UC\u0026rsquo;s GSE87466 dataset, including 638 up-regulated genes and 365 down-regulated genes. The heat map showed the top 50 most significantly up-regulated and down-regulated genes(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA), and the volcano map visually showed the identification of all differentially expressed genes(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). A total of 1927 differentially expressed genes were identified from COAD\u0026rsquo;s GSE44076 dataset, including 874 up-regulated genes and 1053 down-regulated genes. The heat map showed the top 50 most significantly up-regulated and down-regulated genes(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB), and the volcano map showed all the identified differentially expressed genes(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Finally, the differential genes obtained from the UC and COAD datasets were intersected to obtain 329 overlapping common differential genes UC-COAD DEGs(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eWeighted gene co-expression network analysis\u003c/p\u003e\n\u003cp\u003eWGCNA analysis was performed on the GSE87466 dataset of UC. Firstly, the samples were classified into disease and normal groups, and the samples were clustered. After removing the discrete samples, the optimal soft threshold was selected as 12(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Finally, 15 modules(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC,E) were identified in the UC dataset. Among them, the salmon module(r\u0026thinsp;=\u0026thinsp;0.74) and the turquoise module(r\u0026thinsp;=\u0026thinsp;0.72) had a strong correlation with UC and the P value was significantly less than 0.05. A total of 1529 related genes were obtained in the two modules. Similarly, WGCNA analysis was performed on the GSE44076 dataset of COAD. After removing discrete samples, the optimal soft threshold was selected as 12(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Finally, nine modules(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD, F) were identified in the COAD dataset. Among them, the turquoise module(r\u0026thinsp;=\u0026thinsp;0.92), grey module (r\u0026thinsp;=\u0026thinsp;0.81) and yellow module(r\u0026thinsp;=\u0026thinsp;0.61) were strongly correlated with COAD and the P value was significantly less than 0.05. A total of 2485 related genes were screened out in the three modules. Finally, the related genes of UC and COAD were intersected to obtain 355 common related UC-COAD WGCNAs genes(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e\n\u003cp\u003eEnrichment analysis of core genes\u003c/p\u003e\n\u003cp\u003eIn order to find more meaningful characteristic genes, we intersected 329 overlapping UC-COAD DEGs genes obtained by differential analysis with 355 common related UC-COAD WGCNAs genes obtained by WGCNA analysis, and obtained 87 core WGCNA-DEG genes that were highly correlated with COAD and UC and had significant differential expression in the disease and normal groups(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). These genes may play a similar and important role in the development of COAD and UC. In order to understand the specific functions of these genes and the pathways involved, we used the \u0026ldquo;clusterProfiler\u0026rdquo; package in RStudio to perform GO(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) and KEGG(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC) enrichment analysis on these genes. The results showed that these genes were mainly involved in the antimicrobial humoral immune, response mediated by antimicrobial peptide, humoral immune response, Cytokine\u0026thinsp;\u0026minus;\u0026thinsp;cytokine receptor interaction, IL\u0026thinsp;\u0026minus;\u0026thinsp;17 signaling pathway and so on. At the same time, the above genes were input into the metascape database, and the results showed that inflammatory response was of great significance in the common pathogenesis of the two diseases(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD, E).\u003c/p\u003e\n\u003cp\u003eScreening of characteristic genes by PPI network and machine learning\u003c/p\u003e\n\u003cp\u003eThe above 87 core WGCNA-DEG genes were input into the STRING database. After removing the independent genes, a protein interaction network consisting of 61 nodes and 292 edges was obtained, and the network was beautified by cytoscape software(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Using the MCC algorithm of the cytoHubba plug-in, the TOP15 nodes in the network are screened and visualized(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). The above 15 genes were subjected to LASSO regression analysis and random forest analysis. In the LASSO regression analysis, the seeds were set to 2024. The UC dataset(GSE87466) screened 11 related genes(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, E), and the COAD dataset(GSE44076) screened 14 related genes(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD, F). Similarly, in the random forest analysis, the seeds were set to 20252, 7 related genes were screened by UC(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG), and 8 related genes were screened by COAD(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eH). Finally, the above four parts of genes were intersected to obtain two overlapping genes, LCN2 and CXCL11(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eI).\u003c/p\u003e\n\u003cp\u003eExpression of characteristic genes and identification of diagnostic efficacy\u003c/p\u003e\n\u003cp\u003eIn order to identify the expression of characteristic genes in the disease group and the normal group, and the ability to identify the disease, we visualize the expression of characteristic genes in the form of histograms and verify them in the validation set. In the training set of UC(GSE87466) and COAD(GSE44076), LCN2 and CXCL11 genes were significantly highly expressed in the disease group, and had significant statistical differences(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). Similarly, in the validation set UC(GSE59071 and GSE66407) and COAD(TCGA-COAD), the two characteristic genes also showed significant high expression in the disease group, and had statistical differences(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). Then, the ROC curve was used to evaluate the diagnostic predictive value of the characteristic genes. Among them, in the UC training set LCN2(AUC\u0026thinsp;=\u0026thinsp;0.98), CXCL11(AUC\u0026thinsp;=\u0026thinsp;0.961) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), in the COAD training set LCN2(AUC\u0026thinsp;=\u0026thinsp;0.852), CXCL11(AUC\u0026thinsp;=\u0026thinsp;0.804) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). In the UC validation set, LCN2(AUC\u0026thinsp;=\u0026thinsp;0.957), CXCL11(AUC\u0026thinsp;=\u0026thinsp;0.909) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC), in the COAD validation set, LCN2(AUC\u0026thinsp;=\u0026thinsp;0.796), CXCL11(AUC\u0026thinsp;=\u0026thinsp;0.871) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). Based on the above results, the AUC values of the two characteristic genes in each data set were greater than 0.7, indicating that these two genes have good diagnostic performance and may become a common diagnostic marker for UC and COAD.\u003c/p\u003e\n\u003cp\u003eDiagnostic model construction based on characteristic genes\u003c/p\u003e\n\u003cp\u003eAccording to the ROC results, the above two characteristic genes have a good recognition effect on the disease. In order to further determine their diagnostic prediction ability, we use the two genes to construct a disease diagnosis model. The datasets UC(GSE87466) and COAD(GSE44076) were used as training sets, and UC(GSE59071 and GSE66407) and COAD(TCGA-COAD) were used as validation sets to construct diagnostic models. Use the lrm function to construct a logistic regression model and visualize it with a nomogram. Among them, (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B) shows the nomogram model of UC and COAD training sets. The ROC curve was used to evaluate the disease prediction accuracy of the diagnostic model in the four databases. The results showed that the AUC values of the model in the training set and the validation set were all more than 0.85, indicating that the diagnostic effect was good. In addition, the calibration curve and decision curve analysis show more detailed content of the model. From the above results, it can be seen that the diagnostic model of the two genes is superior to the single gene in the diagnosis of diseases in various databases(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC-F).\u003c/p\u003e\n\u003cp\u003eAnalysis of immune cell infiltration\u003c/p\u003e\n\u003cp\u003eBecause the GO and KEGG enrichment analysis results show that the immune response is crucial in the development of the two diseases, we used the CIBERSORT algorithm to observe the infiltration of various immune cells in the tissues of related diseases, and display the results in the form of rainbow diagrams(Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Then the samples were divided into the disease group and the normal group, and the group comparison diagram was drawn to show the difference of immune cell infiltration between the samples(Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC, D). The results showed that the infiltration of T cells CD4 memory activated, T cells follicular helper, Macrophages and Neutrophils in UC was significantly increased. The infiltration of T cells CD4 memory activated, Macrophages, Mast cells activated, Neutrophils and other cells was significantly increased in COAD. Finally, we calculated the correlation between the characteristic genes and immune cell infiltration, and drew the correlation heat map(Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE, F). The results showed that the two characteristic genes were strongly correlated with T cells CD4 memory activated, Macrophages M0, Macrophages M1, Mast cells activated, Neutrophils.\u003c/p\u003e\n\u003cp\u003eGSEA enrichment analysis\u003c/p\u003e\n\u003cp\u003eFinally, we divided the disease samples in the UC(GSE87466) and COAD(GSE44076) datasets into high and low groups according to the expression levels of the two characteristic genes, and performed GSEA enrichment analysis to explore the potential pathways(Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA-D). The results showed that HALLMARK_ ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_GAMMA_RESPONSE and other pathways were enriched in the LCN2 and CXCL11 high expression group in the UC data set. HALLMARK_FATTY_ACID_METABOLISM, HALLMARK_ OXIDATIVE_PHOSPHORYLATION and other pathways were enriched in LCN2 and CXCL11 low expression groups. In COAD data, HALLMARK_E2F_TARGETS, HALLMARK_G2M_CHECKPOINT, HALLMARK_MYC_TARGETS_V1 and other pathways were enriched in LCN2 and CXCL11 high expression groups. Pathways such as HALLMARK_MYOGENESIS and HALLMARK_ADIPOGENESIS were enriched in the LCN2 and CXCL11 low expression groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUlcerative colitis is a chronic non-specific inflammatory bowel disease with unclear etiology. Its pathology mainly involves the colorectal mucosa, forming a continuous and diffuse superficial inflammatory ulcer.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e The common symptoms of patients include recurrent abdominal pain, diarrhea, mucopurulent bloody stools, while some patients may also have non-specific manifestations outside the intestine, such as arthritis, rash, etc. Nowadays, there is no cure for this disease, and only symptomatic control with drugs can be used. Therefore, the course of the patient 's disease usually lasts for decades, and it is this repeated inflammatory stimulation that provides a hotbed for the occurrence of colon cancer\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Colon cancer is a malignant tumor originating from colonic mucosal epithelial cells. Patients usually have no obvious symptoms in the early stage. As the disease progresses, symptoms such as hematochezia, anemia, and weight loss will occur\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Due to the absence of symptoms in the early stage and the lack of regular physical examination, it is often relatively late when symptoms occur, which is a blow to the patient\u0026rsquo;s body and mind and has always been a burden on medical care. Because the symptoms of ulceration are similar to those of colon cancer, the occurrence of colon cancer is often covered up. Therefore, it is necessary to be alert to the early occurrence of colon cancer for patients with ulceration\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Although one is a benign inflammatory disease and the other is a malignant tumor, the two diseases have an important relationship, and the specific mechanism remains to be studied. Therefore, the identification of shared biomarkers and pathogenesis may provide new ideas for the diagnosis and treatment of diseases.\u003c/p\u003e\u003cp\u003eWe selected transcriptome data of ulcerative colitis and colon cancer from the public databases GEO and TCGA. After differential analysis and WGCNA analysis, 87 overlapping genes related to both diseases were obtained. GO and KEGG enrichment analysis showed that immune response and inflammatory response were important in the occurrence and development of the two diseases, which provided ideas for subsequent analysis. Subsequently, we performed PPI network analysis on 87 cores and then screened the TOP15 genes by the algorithm. Finally, the 15 genes were identified by machine learning methods LASSO and RF, and two key characteristic genes LCN2 and CXCL11 were obtained. According to the grouping comparison, these two genes are highly expressed in ulcerative colitis and colon cancer, and have a good diagnostic effect on the disease. It can be seen that these two genes are of great significance in the pathogenesis of the disease. Subsequently, we focused on a series of analysis of these two genes.\u003c/p\u003e\u003cp\u003eLCN2 is a secreted glycoprotein, also known as neutrophil gelatinase-associated lipocalin (NGAL) or oncogene 24p3. It is expressed in a variety of cells and is significantly up-regulated in inflammation, infection, and tissue damage. LCN2 has many functions, including : limiting the growth of bacteria and fungi by chelating iron, thereby exerting antibacterial and antifungal effects\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, can be used as a biomarker for acute kidney injury (AKI) and other inflammatory diseases, may promote neurotoxicity in the nervous system may also promote neuroprotection\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. At the same time, the role of LCN2 in tumors has also been extensively studied. The occurrence and development of lung cancer, breast cancer, and pancreatic cancer have their own shadows\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. LCN2 is a ferroptosis-related gene that plays an important role in inflammatory response, cell death and autophagy, and tumor progression\u003csup\u003e[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that LCN2 can inhibit the invasion and liver metastasis of colon cancer\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, indicating that LCN2 may be a candidate metastasis suppressor in colon cancer cells. Recent studies have shown that LCN2 may promote the progression of colon cancer by inhibiting ferroptosis\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. It can be seen that the role of this gene is still a research hotspot.Related studies have attempted to use LCN2 as a diagnostic marker for colon cancer\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, and LCN2 has also shown good diagnostic value in ulcerative colitis\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Recent studies have shown that LCN2 has a pro-inflammatory effect in colonic epithelial cells and provides a potential target for inhibiting UC cell pyroptosis.Because LCN2 has significant protease resistance\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, the expression of LCN2 can be directly detected from feces, which also provides a natural advantage as a diagnostic marker. Compared with colonoscopic biopsy, fecal examination is both affordable and reduces patient pain.Based on a large number of studies, it can be seen that the role of LCN2 gene in ulceration and colon cancer is very important, and previous studies have already made some exploration of the role of LCN2 in ulceration and colon cancer\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCXCL11 is a small molecule cytokine belonging to the CXC chemokine family, also known as interferon-inducible T cell alpha chemokine(I-TAC) or interferon-inducible protein 9(IP-9). It is mainly induced by IFN-α, IFN-β and IFN-γ. CXCL11 has a chemotactic effect on activated T cells, neutrophils and monocytes by binding to the chemokine receptor CXCR3. It can also bind to the chemokine receptor CCR3 and prevent the binding of CCR3 ligands\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. In the inflammatory response, CXCL11 plays an important role in the occurrence and development of a variety of inflammatory diseases, such as allergic contact dermatitis, mycosis fungoides and immune multiple sclerosis. CXCL11 has a dual role in the tumor microenvironment. On the one hand, it can recruit immune cells into the tumor tissue and enhance the anti-tumor immune response; on the other hand, abnormal expression of CXCL11 may be associated with tumor progression and prognosis\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. For example, studies have shown that elevated CXCL11 is an independent prognostic biomarker for COAD patients, which can promote anti-tumor immunity to prolong survival\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. At the same time, studies have shown that CXCL10 and CXCL11 secreted by neuroendocrine-like cells can recruit tumor-associated macrophages to infiltrate tumor tissues, thereby enhancing the proliferation and invasion of colorectal cancer cells and leading to poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Studies in ulcerative colitis have shown that genes such as CXCL11 are significantly up-regulated in the first few years of the disease and can be used as potential disease diagnostic factors\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. In addition, previous studies have shown that CXCL11 may be a candidate gene involved in the development of colitis-associated colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. This suggests that the role of CXCL11 in ulceration and colon cancer has been partially explored.\u003c/p\u003e\u003cp\u003eFrom the results of GO and KEGG enrichment analysis, it can be seen that immune response and inflammatory response play an important role. Ulcerative colitis itself is a long-term chronic inflammatory disease, and chronic inflammation is also a significant risk factor in the occurrence and development of colon cancer. At the same time, the inflammatory response is often accompanied by the activation of these various immune mechanisms, so this result is consistent with previous studies. From the results of GSEA enrichment analysis, immune rejection, interferon γ response markers, cell cycle regulation and proliferation were co-enriched in CXCL11 and LCN2 up-regulated groups. It can be seen that the immune response is still more or less involved in the occurrence and development of the two diseases.\u003c/p\u003e\u003cp\u003eTherefore, considering the important role of immune function in the common pathogenesis of COAD and UC, we conducted CIBERSORT immune cell infiltration analysis to understand the pattern of immune cell infiltration in these two diseases. The results showed that COAD and UC showed similar immune infiltration patterns. CD4 T cells, Macrophages, Neutrophils were significantly increased in ulceration and colon cancer, and these cells were also highly correlated with immune response and inflammatory response, which again confirmed the results of our previous enrichment analysis. CD4 T cells play a central coordinating role in the immune system by secreting a variety of cytokines, activating and regulating other immune cells, such as macrophages, B cells and CD8 T cells\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. At the same time, CD4 T cells can directly or indirectly target tumor cells through a variety of mechanisms in the tumor microenvironment to promote anti-tumor effects. Macrophages can secrete a variety of cytokines and chemokines when activated, such as TNF-α, IL-1β, IL-6 and IL-12, which can recruit other immune cells and regulate the inflammatory response\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Macrophages are divided into M0, M1, and M2 types. Among them, M1 is a pro-inflammatory type, which is mainly involved in inflammatory response and pathogen clearance. From our cell infiltration analysis results, it can be seen that M1 type accounts for a large proportion, which also better supports the mechanism of disease. Neutrophils are one of the most important innate immune cells in the human immune system. They are mainly responsible for rapid response to pathogen invasion and tissue damage. They also secrete a variety of cytokines such as TNF-α, IL-1β, IL-6 and so on when activated\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. According to the results of the correlation heat map, it can be seen that our characteristic genes CXCL11 and LCN2 have this high correlation with the above immune cells, which once again reflects the mechanism of these two genes in the occurrence and development of the disease, and provides ideas for subsequent research.\u003c/p\u003e\u003cp\u003ePrevious studies have discussed the role of LCN2 and CXCL11 genes in two diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e, and tried to use these genes as diagnostic markers for diseases\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Our study also found these two genes at the same time, and observed that there is also an interaction between the two genes. Therefore, we intentionally included the two genes in the logistic regression model and constructed a nomogram. The results showed that with the increase of gene expression level, the patient\u0026rsquo;s disease score was also higher, and the score corresponded to the probability of disease. It can be seen that LCN2 and CXCL11 genes are risk factors for two diseases, and the higher the expression, the higher the probability of disease. At the same time, the ROC curve of the model shows that the combined diagnosis model of the two genes is better than the diagnosis effect of a single gene on the disease, and it is well verified in both the training set and the validation set. Calibration curve and decision curve analysis also show the good application of the model. Therefore, the above results show that our model may have great predictive potential, and combining the two genes as a common biomarker for colon cancer and ulceration can improve the accuracy of diagnosis. In previous studies, most of them considered the influence of genes on diseases alone. Our research adopts the model constructed by joint analysis to consider the influence of multiple factors on diseases, which not only improves the accuracy, but also provides ideas and solutions for subsequent analysis.\u003c/p\u003e\u003cp\u003eAlthough our research ideas are novel, there are still some limitations. First of all, our research is based on the analysis of public data sets, and the effective sample size is limited. Secondly, because the clinical information of some samples is not perfect enough, we did not classify the samples. Finally, our samples are concentrated in some specific areas. When expanding the geographical scope and race, the universality of the results is still worth exploring. In conclusion, this study found the common potential biomarkers and targets of COAD and UC through bioinformatics analysis, which provided new ideas for the study of the pathogenesis and clinical treatment of the two diseases.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough data mining of public databases and combined with machine learning models, we found that ulcerative colitis and colon cancer have common potential biomarkers LCN2 and CXCL11. Enrichment analysis results show that the two diseases may have similar pathogenic pathways, and the two diseases also show similarities in the immune cell infiltration microenvironment. The ROC curve shows that the LCN2 and CXCL11 genes have a good diagnostic effect on the disease. Finally, we found that the disease diagnosis model constructed by the combined gene is better than the single gene for the diagnosis of the disease. Our model may have new application potential in clinical diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe current study received financial support from, the Key Research and Development Plan Project of Hainan Province (Grant Nos.ZDYF2023SHFZ142), the Hainan Provincial Health Science and Technology Innovation Project (Grant Nos.WSJK2024MS136), and the National Natural Science Foundation of China (Grant Nos.82260343).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas. Jianqiang Chen, Xiaofen Sun and Ying Guan conceptualized the paper. Zhihao Wu and Binglong Li drafted the manuscript; Zhiyuan Xie, Jing Zheng and Nianqing Sun were substantially involved in revising the manuscript. All the authors have checked the final manuscript before submission.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eFor ulcerative colitis(UC), we selected the GSE87466 series data from the GEO database (Https://www.ncbi.nlm.nih.gov/geo) as the training set, including 87 UC samples and 21 healthy control samples(HC), and combined the GSE59071 and GSE66407 series data as the external validation set, including 114 UC samples and 39 HC samples. For colon cancer(COAD), we selected the GSE44076 series data from the GEO database as the training set, including 98 tumor samples(CA) and 98 normal control samples(NC), and the TCGA-COAD data from the TCGA database (Https://portal.gdc.cancer.gov) as the external validation set, including 453 CA samples and 41 NC samples.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYao D, Dong M, Dai C, Wu S. Inflammation and Inflammatory Cytokine Contribute to the Initiation and Development of Ulcerative Colitis and Its Associated Cancer. \u003cem\u003eInflamm Bowel Dis\u003c/em\u003e. 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Mar 1996;38(3):414\u0026ndash;20. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gut.38.3.414\u003c/span\u003e\u003cspan address=\"10.1136/gut.38.3.414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"digestive-diseases-and-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ddsj","sideBox":"Learn more about [Digestive Diseases and Sciences](http://link.springer.com/journal/10620)","snPcode":"10620","submissionUrl":"https://submission.nature.com/new-submission/10620/3","title":"Digestive Diseases and Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ulcerative colitis, colon cancer, bioinformatic methods, characteristic genes, immune cells infiltration, diagnosis model","lastPublishedDoi":"10.21203/rs.3.rs-7039177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7039177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLong-term chronic inflammation is an important risk factor for colon cancer. Ulcerative colitis is a complex chronic inflammatory disease. Related studies have shown that the risk of colorectal cancer in patients with ulcerative colitis is 2\u0026ndash;3 times higher than that in the general population.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eTranscriptome data from GEO and TCGA databases were analyzed using RStudio. Differential expression was analyzed with \u0026ldquo;limma\u0026rdquo; and disease-related genes identified via WGCNA. Core genes were screened by GO, KEGG, and PPI analyses, and further refined using MCC, LASSO, and RF algorithms. Expression levels and diagnostic value were evaluated via ROC curves; a disease diagnosis model was constructed. Immune cell infiltration was assessed with CIBERSORT, and GSEA analysis was performed based on gene expression.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003e87 common genes were identified through differential analysis and WGCNA. Using these genes, a PPI network was built and top 15 genes were selected by MCC algorithm. LASSO and RF algorithms identified LCN2 and CXCL11 as characteristic genes, highly expressed in the disease group with AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7. The diagnosis model performed well. GO, KEGG, and GSEA analyses showed immune and inflammatory responses were important in the disease, with characteristic genes enriched in immune response and cell proliferation pathways.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIt was found that ulcerative colitis and colon cancer have common diagnostic markers and similar pathogenic pathways, and also show similarities in the immune cell infiltration microenvironment. The disease diagnosis model constructed by combining genes is superior to the diagnosis effect of single gene on disease.\u003c/p\u003e","manuscriptTitle":"Shared biomarkers LCN2 and CXCL11 for ulcerative colitis and colon cancer: Bioinformatics analysis and diagnostic model construction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 05:54:49","doi":"10.21203/rs.3.rs-7039177/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-24T07:21:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T02:44:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T14:30:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20423837217154161175421440675249359641","date":"2025-08-10T12:03:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246513764605333392453634562401878668364","date":"2025-08-10T11:33:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-07T04:45:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-04T23:51:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T15:17:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Digestive Diseases and Sciences","date":"2025-07-03T14:17:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"digestive-diseases-and-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ddsj","sideBox":"Learn more about [Digestive Diseases and Sciences](http://link.springer.com/journal/10620)","snPcode":"10620","submissionUrl":"https://submission.nature.com/new-submission/10620/3","title":"Digestive Diseases and Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"26b315d3-2c5a-4a03-89b2-6b88cbd9b552","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:04:10+00:00","versionOfRecord":{"articleIdentity":"rs-7039177","link":"https://doi.org/10.1007/s10620-025-09443-8","journal":{"identity":"digestive-diseases-and-sciences","isVorOnly":false,"title":"Digestive Diseases and Sciences"},"publishedOn":"2025-10-14 15:58:17","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-07-09 05:54:49","video":"","vorDoi":"10.1007/s10620-025-09443-8","vorDoiUrl":"https://doi.org/10.1007/s10620-025-09443-8","workflowStages":[]},"version":"v1","identity":"rs-7039177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7039177","identity":"rs-7039177","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}