The co-expression of Crohn’s disease and colon cancer network was analyzed by bioinformatics-CXCL1 Tumour microenvironment and prognosis-related gene CXCL1

The co-expression of Crohn’s disease and colon cancer network was analyzed by bioinformatics-CXCL1 Tumour microenvironment and prognosis-related gene CXCL1 | 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 The co-expression of Crohn’s disease and colon cancer network was analyzed by bioinformatics-CXCL1 Tumour microenvironment and prognosis-related gene CXCL1 Zijuan Mao, Yuyang Gu, Qiang Dai, Ganxue Tao, Zhenhua Fei, Yangjie Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4637273/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose This study aimed to investigate the molecular links and mechanisms between Crohn’s disease (CD) and colorectal cancer (CRC). Methods This study used the Gene Expression Omnibus (GEO) database to identify Differentially expressed genes (DEGs) in CD (GSE112366) and CRC (GSE110224), analyzed by 'edgeR' and 'limma'. The Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes explored DEG functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) informed the protein-protein interaction network construction visualized in Cytoscape (version 3.7.2). Cyto-Hubba identified key genes, whose biomarker potential for CD and CRC was evaluated. Results The study discovered 61 DEGs, with 44 up- and 17 down-regulated, linked to immune responses and signaling pathways. CXCL1, highly expressed in colon cancer, correlated with better prognosis and lower staging. It also showed associations with immune infiltration and checkpoint molecules, suggesting a role in cancer progression and retreat. Conclusion CXCL1 may play a role in the development of colorectal cancer from inflammatory bowel disease. Crohn’s disease colon cancer CXCL1 microenvironment inflammatory Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Inflammatory bowel disease (IBD) is a chronic disease that includes Crohn's disease (CD) and ulcerative colitis. Crohn's disease is an important category among them. Crohn's disease is characterized by transmural granulomatous inflammation that can affect the entire gastrointestinal tract from the mouth to the anus in a discontinuous manner 1 . Typical symptoms include abdominal pain, diarrhoea, and weight loss. While the exact cause of IBD is still unknown, there is a recognized correlation between IBD and factors such as immunity, microenvironment, genetics, and diet 2 . Crohn's disease is a chronic immune-mediated disease that is becoming more prevalent 3 . Furthermore, there is also a substantial link between CD and cancer. The risk of gastrointestinal and extraintestinal malignancies is much higher in those with Crohn's disease; colorectal cancer and lymphoma are the most frequent 4 . With Crohn's disease, the relative risk of colorectal cancer is 2.5 5 . Although significant research has been done in this area, it is still unclear what exactly caused this transition and how it happened. Along with the connection between chronic inflammation and cancer, D. Saul et al. have discovered many cell types that are responsible for CD-related gene expression patterns. Cells like ILC1 innate lymphoid cells may aid in the initiation and development of chronic intestinal inflammation that leads to CRC 6 . Early detection of colorectal cancer linked to IBD may aid in the treatment and prognosis of the disease by identifying high-risk patients and monitoring these patients appropriately. In this study, the Gene expression profiling of CD (GSE112366) and colorectal cancer (GSE110224) were obtained by utilizing the GEO database. Shared differentially expressed genes (DEGs) were identified for CD and colorectal cancer, and functional annotation, protein-protein interaction (PPI) network construction, and module assembly were performed to discover hub genes. In both the training and test datasets, receiver operating characteristic curves were utilized to evaluate the efficacy of hub genes as biomarkers for predicting CD and colorectal cancer. The external CD dataset was used to validate the expression level of pivotal genes and the external COAD dataset to validate the prognostic impact of pivotal genes in colon cancer. 2 Materials and methods 2.1 Data Sources and Identification of DEGs Using Crohn's disease and colorectal cancer as keywords, we looked for related gene expression datasets. As a result, we obtained two gene expression profile datasets, GSE112366 and GSE110224, from the GEO database available at http://www.ncbi.nlm.nih.gov/geo. The datasets' series matrix files were obtained from GEO using the R packages 'GEO query' and 'Limma'. The “Limma” package was utilized to extract the DEGs from the identified genes. Subsequently, the “heatmap” and “ggplot2” packages were employed to construct the heatmap and volcano plot for visualizing the DEGs. 2.2 GO and KEGG pathway enrichment analyses A common method for describing the biological process, cellular component, and molecular function of several genes is called Gene Ontology (GO). A database that systematically examines gene activities is called Kyoto Encyclopedia of Genes and Genomes (KEGG), and the KEEG pathway contains numerous biological pathways for various organisms 7 . We performed GO enrichment analysis and KEGG pathway enrichment analysis of DEGs to analyze the biological processes and key pathways, and P-value < 0.05 was regarded as statistically significant. 2.3 PPI Network and module analysis Protein-protein interaction (PPI) network exposes the specific and nonspecific interactions of proteins and discovers the core protein genes. The STRING database (https://cn.string-db.org/) is a widely used resource for searching known proteins and predicting relationships between them. The Cytoscape programme (version 3.7.2) was utilized to construct a PPI network of the DEGs with a combined score >0.4 in STRING. We used the MCODE plugin with the following parameters: K-core = 2, degree cutoff = 2, max depth = 100, and node score cutoff = 0.2 to find highly interconnected modules in the PPI network. 2.4 Selection and Analysis of Hub Genes The PPI network was analyzed using the CytoHubba plug-in of Cytoscape to identify hub genes. Seven standard algorithms (MCC, MNC, Degree, Closeness, Radiality, Stress, and EPC) were used to confirm the final hub genes. The functions of these hub genes were predicted using Metascape. The differences in expression of the screened pivotal genes in normal versus colorectal cancer tissues were validated against the overall prognosis of the patients by using the median relative expression of pivotal genes as a cut-off value through the GEPIA website. 2.5 Correlation analysis of CXCL1 and immune infiltration The TIMER database focuses on the correlation analysis of genes of interest with immune infiltration, and the TISIDB database focuses on the correlation analysis of genes with immune cells and immune molecules, which can be used for the corroboration of TIMER analysis results. Correlation analysis of CXCL1 expression in cancer with correlation analysis with TIL and immune checkpoint markers was analyzed by TISIDB and TIMER databases. 3 Results 3.1 Identification of DEGs Gene expression profiles and corresponding clinical information of the GSE110224 and GSE112366 datasets were obtained from the GEO database. The data were normalized, and the differentially expressed genes were identified by cluster analysis and DEGs (Figure 1). By taking the intersection of the Venn diagram (Fig. 2A, B), 61 DEGs with the same trend of expression were obtained, including 44 up-regulated genes and 17 down-regulated genes. 3.2 GO and KEGG pathway enrichment analyses The function of co-expressed genes was analyzed by GO and KEGG enrichment analysis. In the KEGG pathway, four important enrichment pathways are a humoral immune response, lipopolysaccharide response, response to bacterial-derived molecules, and cytokine-mediated signaling pathways (Figure 2C). The results of GO analysis showed that these genes were mainly enriched in humoral immune response, lipopolysaccharide response, and response to bacterial-derived molecules (Figure 2D). These findings strongly suggest that the humoral immune response is closely related to the occurrence and progression of these two diseases. 3.3 PPI Network and module analysis Using Cytoscape, a PPI network with a total score greater than 0.4 was created, which consists of 39 nodes and 150 interaction pairs (figure 3A), using the MCODE plug-in to get the closest gene module to constitute a sub-network. A subset of these (Score value = 10) included 12 common DEGs (figure 3C), the vast majority of which were chemokine family members. 3.4 Selection and Analysis of Hub Genes The first 15 hub genes were screened using the Cysto-Hubba plug-in (figure 4A). Including CXCL11, MMP3, CXCL3, MMP1, CXCL5, CXCL2, LCN2, CXCL1, CXCL6, Il1b, FPR1, IL1RN, CCL23, FCGR3B, and S100A9(figure 4A). The Gene-MANIA database was used to investigate the combination networks and related functions of these genes (figure 4B). These hub genes demonstrated a sophisticated PPI network with 38.13% co-expression, 29.49% physical interaction, and 18.71% common protein domain (figure 4B). These genes were shown to be mostly connected to chemokine-mediated signaling pathways, chemokine responses, cellular responses to chemokines, and so on. (figure 4D). Furthermore, KEGG pathway analysis confirmed that they were primarily enriched in lipids and arteriosclerosis, formation of neutrophil extracellular traps, leukocyte migration across endothelial cells, etc. (figure 4E, F). 3.5 Relationship between CXCL1 and immunity Analysis by the TISIDB database found a positive association of CXCL1 with immune cell infiltration in colon cancer, including neutrophil and CD4 effector T cells, CD8 effector T cells, etc. (figure 5B-H). At the same time, we analyzed the correlation with immune checkpoints and found that CXCL1 was positively correlated with immune checkpoint molecules that promote immune escape, such as PDCD1, CD274, etc. (figure 5J-M). What’s more, for verification of the expression levels for such hub genes, the TCGA-COAD dataset and an external CD dataset were employed. The findings revealed that all hub genes except CCL23 were significantly upregulated in colon cancer tissues in the external data set (TCGA-COAD) compared with the normal gut (figure 6). Similarly, in another data set (GSE102133), the expression of all genes except CCL23 was also higher than that of normal colon tissues (figure 7). The TCGA-COAD dataset was used to validate the prognostic impact of all 15 pivotal genes in colon cancer. The results showed that only CXCL1 was associated with prognosis in colon cancer, and its low expression was associated with poor prognosis (figure 8). Further, to clarify the role of CXCL1 in colon cancer, analysis of the TCGA-COAD data set found that low CXCL1 expression was associated with higher pathological stage, N stage, and M stage (figure 8G, H). 4 Discussion Recent research has established a strong correlation between inflammation and tumours, especially chronic inflammation, which is a major contributor to many tumours 8 . Research has indicated that patients with IBD-associated colorectal cancer have a lower overall survival rate compared to those with sporadic colorectal cancer. Additionally, some researchers have suggested that abnormal expression of certain proteins or activation of signalling pathways may be a significant factor in the transformation of Crohn's disease into colon cancer. In this study, we conducted bioinformatics analysis to identify key genes and mechanisms that may promote the transition from Crohn’s disease to colon cancer. GSE112366 and GSE110224 were used to perform differential analysis, and 61 overlapping DEGs were identified. The top 15 genes were obtained using the CystoHubba plug-in. These genes are mainly involved in chemokine-mediated signalling pathways and chemokine responses. Among them, CXCL1 is associated with colon cancer prognosis and immune cell infiltration. Chemokines play a pivotal role in regulating inflammation disease and in tumour growth, progression, metastasis, and prognosis 9,10 . A recent study has shown that chemokines produced within the gastrointestinal mucosa are essential in the transition from healthy physiological inflammation to pathophysiological inflammation, IBD, and colon cancer progression 11 . Our research has discovered a correlation between the chemokine CXCL1 and both inflammatory bowel disease and colorectal cancer. CXCL1 is primarily enriched in the formation of neutrophil extracellular traps (NETs) and leukocyte transendothelial migration (TEM). The increased presence of NET in patients with IBD suggests a possible association between CXCL1 and IBD 12 . Lena Seifert et al. have demonstrated that in pancreatic tumours, CXCL1 and Mincle promote tumourigenesis through induced immunosuppression, implying that CXCL1 plays a key role both in inflammatory diseases and various cancers 13 . However, our research has revealed that CXCL1 is correlated with colon cancer prognosis, and its low expression is associated with poor prognosis. Low expression of CXCL1 is linked to higher pathological stage, N stage and M stage. This is not consistent with most results. It has been observed that high levels of expression of certain cancer cell-derived chemokines lead to increased infiltration of CD8 T cells, CD4 T cells and natural killer T (NKT) cells into the cancer tissue, which may lead to the induction of anticancer immunity and suppress cancer progression 14 . CXCL1 may activate immune cells through the chemokine receptor signalling pathway, which in turn increases the infiltration of T cells and natural killer T cells, ultimately promoting tumour regression rather than promoting tumour growth. The tumour microenvironment (TME), is a complex network of tumour cells and non-tumour elements, including tumour-infiltrating immune cells, extracellular matrix (ECM), fibroblasts, and signalling molecules 15,16 . In the case of inflammatory diseases, damage to the colonic microenvironment results from chronic inflammation caused by an imbalance of homeostasis, immune dysfunction, and environmental and genetic factors 17 . The breakdown of homeostasis between Tregs and proinflammatory Th17 cells may be related to inflammation in IBD and even colon cancer 18 . CXCL1 is one of the significant factors in the production of inflammatory microenvironments. In cancer patients, CXCL1 helps to attract neutrophils to tumour sites, creating a microenvironment that supports tumour growth and metastasis 19,20 . Recent studies have shown that in gliomas, NETs (enriched with CXCL1) can influence the tumour microenvironment by regulating HMGB1/RAGE/IL-8 signalling and promoting glioma progression and migration 21 . Our findings suggest that CXCL1 was favourably linked with the infiltration of neutrophils, CD4 effector T cells, CD8 effector T cells, and other immune cells in colon cancer. Additionally, it has a favourable relationship with immunological checkpoint molecules like PDCD1 and CD274 that facilitate immune escape. Based on this, we hypothesize that CXCL1 plays a role in shaping the tumour microenvironment and facilitating the immune evasion of colon cancer cells through the abundance of neutrophils. Tumour Necrosis Factor-alpha (TNF-α) is an inflammatory cytokine that plays a crucial role in maintaining immune system homeostasis, inflammation, and host defence 22 . Chronic inflammation may cause excessive or inappropriate activation of TNF-α signalling and may affect the status of inflammatory bowel diseases such as CD 23 . Accumulating evidence has shown that TNF-αacts as a master switch in establishing the relationship between inflammation and cancer, contributing to cancer development by regulating proliferation 24 . Studies have shown that tumourigenesis and promotion are mediated by TNF-α activation of NF-κb, PKC α, and AP-1-dependent pathways TNF-α-induced tumour promotion is mainly dependent on NF-κb 25 . NF-κb activation is a common occurrence in both inflammation and carcinomas. Its deregulated activation is involved in the pathogenic processes of various inflammatory diseases such as IBD 26 . In addition, malignancy and tumour progression are associated with NF-κb activation, and NF-κb activation also provides a pathway for tumour cells to evade immune surveillance and resist treatment 27 . Several studies have shown that TAM/CXCL1 promotes breast cancer metastasis through NF-κb/SOX4 activation 28 . Our study, based on KEGG functional enrichment analysis, revealed a strong correlation between CXCL1 and colon cancer metastasis. The implicated pathways include TNF-α and NFΚB, which have long been linked to inflammation onset and resolution 29 . Therefore, we consider that CXCL1 may contribute to the development, progression and metastasis of colon cancer and the development of IBD such as CD by targeting TNF-α and NF-ΚB signalling pathways. TNF-αand NF-κB has pro-inflammatory effects and may indirectly promote the transformation of inflammation into cancer. This study identified genes that are co-expressed in both Crohn's disease and colorectal cancer. The study found that CXCL1 may play a role in promoting the transformation of Crohn's disease to colon cancer through specific signalling pathways. Additionally, CXCL1 was found to promote the metastasis of colorectal cancer through immune cell escape, but high expression of CXCL1 may lead to a good prognosis through immune cell infiltration. However, the study still has some limitations. Further experiments are required to better understand the mechanisms by which CXCL1 induces chronic inflammation and cancer. It still needs to be validated in future clinical trials. Abbreviations CD Crohn’s disease COAD colorectal cancer GEO Gene Expression Omnibus DEGs Differentially expressed genes PPI The protein-protein interaction IBD Inflammatory bowel disease GO Gene Ontology KEEG Kyoto Encyclopedia of Genes and Genomes STRING Search Tool for the Retrieval of Interacting Genes Declarations Data availability statement The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data that support the results of the current study is available on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) websites. Code availability The data analysis in this article was analyzed by the R. funding There is no funding received. Competing interests The authors declare no competing financial interests. Acknowledgements We are grateful to the researchers who built the public database and shared an enormous amount of research data, which made our study possible through their generous contributions. Author information Authors and Affiliations Department of Radiotherapy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China Zijuan Mao, Ganxue Tao, Zhenhua Fei Department of Oncology, The First Affiliated Hospital of Jiaxing University, No. 1882, Zhonghuan South Road, Jiaxing, 314000, Zhejiang People's Republic of China Yuyang Gu Department of Medical Oncology, Rui’an People’s Hospital, The Third Affiliated Hospital of Wenzhou Medical University, 108 Wansong Road, Ruian, 325200, China Qiang Dai Department of Oncology, Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, China Yangjie Xu Contributions Zijuan Mao and Yuyang Gu designed the study. Ganxue Tao performed data analysis. Qiang Dai drafted the manuscript. Zhenhua Fei and Yangjie Xu reviewed and revised the manuscript. Zijuan Mao and Yuyang Gu share the first authorship. All authors contributed to the article and approved the submitted version. Corresponding author Correspondence to Yangjie Xu and Zhenhua Fei. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics approval and consent to participate This project has been approved by the ethics committee of the First Affiliated Hospital of Wenzhou Medical University. Consent for publication Not applicable. References Atreya, Raja, and M. F. Neurath. 2010. ‘Chemokines in Inflammatory Bowel Diseases’. Digestive Diseases (Basel, Switzerland) 28 (3): 386–94. https://doi.org/10.1159/000320392. Balkwill, Frances. 2006. ‘TNF-Alpha in Promotion and Progression of Cancer’. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviews received at journal 26 Jul, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers invited by journal 13 Jul, 2024 Editor assigned by journal 12 Jul, 2024 Submission checks completed at journal 04 Jul, 2024 First submitted to journal 25 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-4637273","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331616542,"identity":"c6562bc7-b4f2-4baf-9f68-e80f539fa84a","order_by":0,"name":"Zijuan Mao","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zijuan","middleName":"","lastName":"Mao","suffix":""},{"id":331616543,"identity":"5b56ca4a-44c5-4b26-b157-42763bae3306","order_by":1,"name":"Yuyang Gu","email":"","orcid":"","institution":"Yuyang Gu, The First Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Yuyang","middleName":"","lastName":"Gu","suffix":""},{"id":331616544,"identity":"625ae142-feb1-44ce-89bc-22e065bae0e7","order_by":2,"name":"Qiang Dai","email":"","orcid":"","institution":"Rui’an People’s Hospital, The Third Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Dai","suffix":""},{"id":331616545,"identity":"bf5780e4-0427-4259-b3b4-3452222570c8","order_by":3,"name":"Ganxue Tao","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ganxue","middleName":"","lastName":"Tao","suffix":""},{"id":331616548,"identity":"04f37fc8-e398-4c13-9cda-34dd2a242dff","order_by":4,"name":"Zhenhua Fei","email":"data:image/png;base64,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","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Fei","suffix":""},{"id":331616550,"identity":"a15ad15b-fcd4-470f-889f-9ec13c8e4c84","order_by":5,"name":"Yangjie Xu","email":"","orcid":"","institution":"Yangjie Xu, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yangjie","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-06-25 14:44:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4637273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4637273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61342106,"identity":"f66cd2bd-1d8a-4e32-af58-29559ce8e75c","added_by":"auto","created_at":"2024-07-29 17:05:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":709156,"visible":true,"origin":"","legend":"\u003cp\u003e(A) clustering analysis heat map of the GSE110224 data set. (B) GSE110224 data set difference analysis volcano map. (C) clustering analysis heat map of GSE11236 data set. (D) GSE112366 data set differential analysis volcano map. Up-regulated genes are marked in red; down-regulated genes are marked in green.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/d65147b22b07763b560c7a96.png"},{"id":61342742,"identity":"49656eb0-b8b6-4fcc-921a-06a1dac9bf7a","added_by":"auto","created_at":"2024-07-29 17:13:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":337809,"visible":true,"origin":"","legend":"\u003cp\u003e(A)GSE110224 data set and GSE112366 data set both up-regulated the expression gene Venn diagram, (B) downregulated the expression gene Venn diagram, (C) enriched analysis of the co-differentially expressed gene KEGG, (D) co-differentially expressed gene Go, and (D) enriched analysis of the co-differentially expressed gene KEGG.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/eaa0a58f880f42e553c1b15d.png"},{"id":61343272,"identity":"abb5b03c-2d6e-459f-8090-ad2e9008268b","added_by":"auto","created_at":"2024-07-29 17:21:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":581135,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Overlapping differentially expressed gene protein interaction network (PPI) . (B) cytoscape-assisted visualization of protein interaction networks; red indicates gene upregulation, green indicates gene downregulation; (C-D)2 important subnetworks.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/cd79d9a4e8a945daa354d675.png"},{"id":61342102,"identity":"21eebb19-bf75-4f9a-827b-c262b2b43d22","added_by":"auto","created_at":"2024-07-29 17:05:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":653295,"visible":true,"origin":"","legend":"\u003cp\u003e(A)15 selected Hub genes. (b) Hub genes and their co-expressed genes were analyzed by GeneMANIA. (C-F) GO and KEGG enrichment analysis of hub genes. The outermost circle is the term on the right, and the inner circle on the left represents a significant p value for the corresponding pathway in the gene. Go, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and genomes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/08244b37fcb269cfc5eba85f.png"},{"id":61342100,"identity":"3f7e67b8-a7b5-47b9-95a5-c72097f95177","added_by":"auto","created_at":"2024-07-29 17:05:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":773954,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between CXCL1 expression and TIL in cancer was analyzed by TISIDB and TIMER database. (A) heatmap showing the association of CXCL1 and Tils in multiple cancers; (B-H) the linear graph showed that CXCL1 expression correlated with the infiltration levels of CD4 T cells, DC cells, CD8 T cells, neutrophil, Treg cells and Th17 cells in Coad. Correlation analysis between CXCL1 expression and immune checkpoint markers in COAD. (I) heat map of CXCL1 expression in relation to immune checkpoints in different cancer types. (J-M) CXCL1 expression was positively correlated with some immune checkpoint biomarkers, including CD274, CTLA4, CD244 and PDCD1 in Coad.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/938d21306e8930e7541a1db4.png"},{"id":61342743,"identity":"0cc81ce6-c392-4135-8897-3686315753c0","added_by":"auto","created_at":"2024-07-29 17:13:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":554268,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of key genes in normal and colon cancer tissues in the TCGA-COAD dataset. * P \u0026lt; 0.05, * * P \u0026lt; 0.01, * * * p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/b88768d906bd6d3d6c185362.png"},{"id":61342103,"identity":"b40a39d0-0020-4b63-9175-b9e3a669ab70","added_by":"auto","created_at":"2024-07-29 17:05:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":691556,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of key genes in normal and Crohn's disease tissues in the GSE102133 dataset. * P \u0026lt; 0.05, * * P \u0026lt;0.01, * * * p \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/60a45a6561cae172e945f718.png"},{"id":61342107,"identity":"c5816b4c-bfa1-4ae9-beaa-ae4ada091ef5","added_by":"auto","created_at":"2024-07-29 17:05:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":429564,"visible":true,"origin":"","legend":"\u003cp\u003e(A) one-way COX regression analysis forest plot of 19 key genes; (B) CXCL1 survival analysis plot. P \u0026lt;0.05 was considered statistically significant. Relationship between CXCL1 and pathological features of colon cancer. (C) sex, (D) age, (E) pathological stage, (F) t stage, (G) N stage, (H) m stage, Ig) CEA level, (J) history of colon cancer polyp, (K) present colon cancer polyp.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/312891adf0b72fa6b9017c7d.png"},{"id":61343273,"identity":"7dcd1893-8b8d-4777-bd43-790a922e8d7c","added_by":"auto","created_at":"2024-07-29 17:21:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4726427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4637273/v1/ddf3c97a-9ba2-4f79-b5e0-0f02491528e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The co-expression of Crohn’s disease and colon cancer network was analyzed by bioinformatics-CXCL1 Tumour microenvironment and prognosis-related gene CXCL1","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eInflammatory bowel disease (IBD) is a chronic disease that includes Crohn's disease (CD) and ulcerative colitis. Crohn's disease is an important category among them. Crohn's disease is characterized by transmural granulomatous inflammation that can affect the entire gastrointestinal tract from the mouth to the anus in a discontinuous manner\u003csup\u003e1\u003c/sup\u003e. Typical symptoms include abdominal pain, diarrhoea, and weight loss. While the exact cause of IBD is still unknown, there is a recognized correlation between IBD and factors such as immunity, microenvironment, genetics, and diet\u003csup\u003e2\u003c/sup\u003e. Crohn's disease is a chronic immune-mediated disease that is becoming more prevalent\u003csup\u003e3\u003c/sup\u003e. Furthermore, there is also a substantial link between CD and cancer.\u003c/p\u003e \u003cp\u003eThe risk of gastrointestinal and extraintestinal malignancies is much higher in those with Crohn's disease; colorectal cancer and lymphoma are the most frequent\u003csup\u003e4\u003c/sup\u003e. With Crohn's disease, the relative risk of colorectal cancer is 2.5\u003csup\u003e5\u003c/sup\u003e. Although significant research has been done in this area, it is still unclear what exactly caused this transition and how it happened. Along with the connection between chronic inflammation and cancer, D. Saul et al. have discovered many cell types that are responsible for CD-related gene expression patterns. Cells like ILC1 innate lymphoid cells may aid in the initiation and development of chronic intestinal inflammation that leads to CRC\u003csup\u003e6\u003c/sup\u003e. Early detection of colorectal cancer linked to IBD may aid in the treatment and prognosis of the disease by identifying high-risk patients and monitoring these patients appropriately.\u003c/p\u003e \u003cp\u003eIn this study, the Gene expression profiling of CD (GSE112366) and colorectal cancer (GSE110224) were obtained by utilizing the GEO database. Shared differentially expressed genes (DEGs) were identified for CD and colorectal cancer, and functional annotation, protein-protein interaction (PPI) network construction, and module assembly were performed to discover hub genes. In both the training and test datasets, receiver operating characteristic curves were utilized to evaluate the efficacy of hub genes as biomarkers for predicting CD and colorectal cancer. The external CD dataset was used to validate the expression level of pivotal genes and the external COAD dataset to validate the prognostic impact of pivotal genes in colon cancer.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Data Sources and Identification of DEGs\u003c/h2\u003e\n\u003cp\u003eUsing Crohn\u0026apos;s disease and colorectal cancer as keywords, we looked for related gene expression datasets. As a result, we obtained two gene expression profile datasets, GSE112366 and GSE110224, from the GEO database available at http://www.ncbi.nlm.nih.gov/geo. The datasets\u0026apos; series matrix files were obtained from GEO using the R packages \u0026apos;GEO query\u0026apos; and \u0026apos;Limma\u0026apos;. The \u0026ldquo;Limma\u0026rdquo; package was utilized to extract the DEGs from the identified genes. Subsequently, the \u0026ldquo;heatmap\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; packages were employed to construct the heatmap and volcano plot for visualizing the DEGs.\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;GO and KEGG pathway enrichment analyses\u003c/h2\u003e\n\u003cp\u003eA common method for describing the biological process, cellular component, and molecular function of several genes is called Gene Ontology (GO). A database that systematically examines gene activities is called Kyoto Encyclopedia of Genes and Genomes (KEGG), and the KEEG pathway contains numerous biological pathways for various organisms\u003csup\u003e7\u003c/sup\u003e. We performed GO enrichment analysis and KEGG pathway enrichment analysis of DEGs to analyze the biological processes and key pathways, and P-value \u0026lt; 0.05 was regarded as statistically significant.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp;PPI Network and module analysis\u003c/h2\u003e\n\u003cp\u003eProtein-protein interaction (PPI) network exposes the specific and nonspecific interactions of proteins and discovers the core protein genes. The STRING database (https://cn.string-db.org/) is a widely used resource for searching known proteins and predicting relationships between them. The Cytoscape programme (version 3.7.2) was utilized to construct a PPI network of the DEGs with a combined score \u0026gt;0.4 in STRING. We used the MCODE plugin with the following parameters: K-core = 2, degree cutoff = 2, max depth = 100, and node score cutoff = 0.2 to find highly interconnected modules in the PPI network.\u003c/p\u003e\n\u003ch2\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Selection and Analysis of Hub Genes\u003c/h2\u003e\n\u003cp\u003eThe PPI network was analyzed using the CytoHubba plug-in of Cytoscape to identify hub genes. Seven standard algorithms (MCC, MNC, Degree, Closeness, Radiality, Stress, and EPC) were used to confirm the final hub genes. The functions of these hub genes were predicted using Metascape. The differences in expression of the screened pivotal genes in normal versus colorectal cancer tissues were validated against the overall prognosis of the patients by using the median relative expression of pivotal genes as a cut-off value through the GEPIA website.\u003c/p\u003e\n\u003ch2\u003e2.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Correlation analysis of CXCL1 and immune infiltration\u003c/h2\u003e\n\u003cp\u003eThe TIMER database focuses on the correlation analysis of genes of interest with immune infiltration, and the TISIDB database focuses on the correlation analysis of genes with immune cells and immune molecules, which can be used for the corroboration of TIMER analysis results. Correlation analysis of CXCL1 expression in cancer with correlation analysis with TIL and immune checkpoint markers was analyzed by TISIDB and TIMER databases.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Identification of DEGs\u003c/h2\u003e\n\u003cp\u003eGene expression profiles and corresponding clinical information of the GSE110224 and GSE112366 datasets were obtained from the GEO database. The data were normalized, and the differentially expressed genes were identified by cluster analysis and DEGs (Figure 1). By taking the intersection of the Venn diagram (Fig. 2A, B), 61 DEGs with the same trend of expression were obtained, including 44 up-regulated genes and 17 down-regulated genes.\u003c/p\u003e\n\u003ch2\u003e3.2\u0026nbsp; \u0026nbsp; \u0026nbsp;GO and KEGG pathway enrichment analyses\u003c/h2\u003e\n\u003cp\u003eThe function of co-expressed genes was analyzed by GO and KEGG enrichment analysis. In the KEGG pathway, four important enrichment pathways are a humoral immune response, lipopolysaccharide response, response to bacterial-derived molecules, and cytokine-mediated signaling pathways (Figure 2C). The results of GO analysis showed that these genes were mainly enriched in humoral immune response, lipopolysaccharide response, and response to bacterial-derived molecules (Figure 2D). These findings strongly suggest that the humoral immune response is closely related to the occurrence and progression of these two diseases.\u003c/p\u003e\n\u003ch2\u003e3.3\u0026nbsp; \u0026nbsp; \u0026nbsp;PPI Network and module analysis\u003c/h2\u003e\n\u003cp\u003eUsing Cytoscape, a PPI network with a total score greater than 0.4 was created, which consists of 39 nodes and 150 interaction pairs (figure 3A), using the MCODE plug-in to get the closest gene module to constitute a sub-network. A subset of these (Score value = 10) included 12 common DEGs (figure 3C), the vast majority of which were chemokine family members.\u003c/p\u003e\n\u003ch2\u003e3.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Selection and Analysis of Hub Genes\u003c/h2\u003e\n\u003cp\u003eThe first 15 hub genes were screened using the Cysto-Hubba plug-in (figure 4A). Including CXCL11, MMP3, CXCL3, MMP1, CXCL5, CXCL2, LCN2, CXCL1, CXCL6, Il1b, FPR1, IL1RN, CCL23, FCGR3B, and S100A9(figure 4A). The Gene-MANIA database was used to investigate the combination networks and related functions of these genes (figure 4B). These hub genes demonstrated a sophisticated PPI network with 38.13% co-expression, 29.49% physical interaction, and 18.71% common protein domain (figure 4B). These genes were shown to be mostly connected to chemokine-mediated signaling pathways, chemokine responses, cellular responses to chemokines, and so on. (figure 4D). Furthermore, KEGG pathway analysis confirmed that they were primarily enriched in lipids and arteriosclerosis, formation of neutrophil extracellular traps, leukocyte migration across endothelial cells, etc. (figure 4E, F).\u003c/p\u003e\n\u003ch2\u003e3.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Relationship between CXCL1 and immunity\u003c/h2\u003e\n\u003cp\u003eAnalysis by the TISIDB database found a positive association of CXCL1 with immune cell infiltration in colon cancer, including neutrophil and CD4 effector T cells, CD8 effector T cells, etc. (figure 5B-H). At the same time, we analyzed the correlation with immune checkpoints and found that CXCL1 was positively correlated with immune checkpoint molecules that promote immune escape, such as PDCD1, CD274, etc. (figure 5J-M). What\u0026rsquo;s more, for verification of the expression levels for such hub genes, the TCGA-COAD dataset and an external CD dataset were employed. The findings revealed that all hub genes except CCL23 were significantly upregulated in colon cancer tissues in the external data set (TCGA-COAD) compared with the normal gut (figure 6). Similarly, in another data set (GSE102133), the expression of all genes except CCL23 was also higher than that of normal colon tissues (figure 7). The TCGA-COAD dataset was used to validate the prognostic impact of all 15 pivotal genes in colon cancer. The results showed that only CXCL1 was associated with prognosis in colon cancer, and its low expression was associated with poor prognosis (figure 8). Further, to clarify the role of CXCL1 in colon cancer, analysis of the TCGA-COAD data set found that low CXCL1 expression was associated with higher pathological stage, N stage, and M stage (figure 8G, H).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eRecent research has established a strong correlation between inflammation and tumours, especially chronic inflammation, which is a major contributor to many tumours\u003csup\u003e8\u003c/sup\u003e. Research has indicated that patients with IBD-associated colorectal cancer have a lower overall survival rate compared to those with sporadic colorectal cancer. Additionally, some researchers have suggested that abnormal expression of certain proteins or activation of signalling pathways may be a significant factor in the transformation of Crohn's disease into colon cancer. In this study, we conducted bioinformatics analysis to identify key genes and mechanisms that may promote the transition from Crohn\u0026rsquo;s disease to colon cancer. GSE112366 and GSE110224 were used to perform differential analysis, and 61 overlapping DEGs were identified. The top 15 genes were obtained using the CystoHubba plug-in. These genes are mainly involved in chemokine-mediated signalling pathways and chemokine responses. Among them, CXCL1 is associated with colon cancer prognosis and immune cell infiltration.\u003c/p\u003e \u003cp\u003eChemokines play a pivotal role in regulating inflammation disease and in tumour growth, progression, metastasis, and prognosis\u003csup\u003e9,10\u003c/sup\u003e. A recent study has shown that chemokines produced within the gastrointestinal mucosa are essential in the transition from healthy physiological inflammation to pathophysiological inflammation, IBD, and colon cancer progression\u003csup\u003e11\u003c/sup\u003e. Our research has discovered a correlation between the chemokine CXCL1 and both inflammatory bowel disease and colorectal cancer. CXCL1 is primarily enriched in the formation of neutrophil extracellular traps (NETs) and leukocyte transendothelial migration (TEM). The increased presence of NET in patients with IBD suggests a possible association between CXCL1 and IBD\u003csup\u003e12\u003c/sup\u003e. Lena Seifert et al. have demonstrated that in pancreatic tumours, CXCL1 and Mincle promote tumourigenesis through induced immunosuppression, implying that CXCL1 plays a key role both in inflammatory diseases and various cancers\u003csup\u003e13\u003c/sup\u003e. However, our research has revealed that CXCL1 is correlated with colon cancer prognosis, and its low expression is associated with poor prognosis. Low expression of CXCL1 is linked to higher pathological stage, N stage and M stage. This is not consistent with most results. It has been observed that high levels of expression of certain cancer cell-derived chemokines lead to increased infiltration of CD8 T cells, CD4 T cells and natural killer T (NKT) cells into the cancer tissue, which may lead to the induction of anticancer immunity and suppress cancer progression\u003csup\u003e14\u003c/sup\u003e. CXCL1 may activate immune cells through the chemokine receptor signalling pathway, which in turn increases the infiltration of T cells and natural killer T cells, ultimately promoting tumour regression rather than promoting tumour growth.\u003c/p\u003e \u003cp\u003eThe tumour microenvironment (TME), is a complex network of tumour cells and non-tumour elements, including tumour-infiltrating immune cells, extracellular matrix (ECM), fibroblasts, and signalling molecules\u003csup\u003e15,16\u003c/sup\u003e. In the case of inflammatory diseases, damage to the colonic microenvironment results from chronic inflammation caused by an imbalance of homeostasis, immune dysfunction, and environmental and genetic factors\u003csup\u003e17\u003c/sup\u003e. The breakdown of homeostasis between Tregs and proinflammatory Th17 cells may be related to inflammation in IBD and even colon cancer\u003csup\u003e18\u003c/sup\u003e. CXCL1 is one of the significant factors in the production of inflammatory microenvironments. In cancer patients, CXCL1 helps to attract neutrophils to tumour sites, creating a microenvironment that supports tumour growth and metastasis\u003csup\u003e19,20\u003c/sup\u003e. Recent studies have shown that in gliomas, NETs (enriched with CXCL1) can influence the tumour microenvironment by regulating HMGB1/RAGE/IL-8 signalling and promoting glioma progression and migration\u003csup\u003e21\u003c/sup\u003e. Our findings suggest that CXCL1 was favourably linked with the infiltration of neutrophils, CD4 effector T cells, CD8 effector T cells, and other immune cells in colon cancer. Additionally, it has a favourable relationship with immunological checkpoint molecules like PDCD1 and CD274 that facilitate immune escape. Based on this, we hypothesize that CXCL1 plays a role in shaping the tumour microenvironment and facilitating the immune evasion of colon cancer cells through the abundance of neutrophils.\u003c/p\u003e \u003cp\u003eTumour Necrosis Factor-alpha (TNF-α) is an inflammatory cytokine that plays a crucial role in maintaining immune system homeostasis, inflammation, and host defence\u003csup\u003e22\u003c/sup\u003e. Chronic inflammation may cause excessive or inappropriate activation of TNF-α signalling and may affect the status of inflammatory bowel diseases such as CD\u003csup\u003e23\u003c/sup\u003e. Accumulating evidence has shown that TNF-αacts as a master switch in establishing the relationship between inflammation and cancer, contributing to cancer development by regulating proliferation\u003csup\u003e24\u003c/sup\u003e. Studies have shown that tumourigenesis and promotion are mediated by TNF-α activation of NF-κb, PKC α, and AP-1-dependent pathways TNF-α-induced tumour promotion is mainly dependent on NF-κb\u003csup\u003e25\u003c/sup\u003e. NF-κb activation is a common occurrence in both inflammation and carcinomas. Its deregulated activation is involved in the pathogenic processes of various inflammatory diseases such as IBD\u003csup\u003e26\u003c/sup\u003e. In addition, malignancy and tumour progression are associated with NF-κb activation, and NF-κb activation also provides a pathway for tumour cells to evade immune surveillance and resist treatment\u003csup\u003e27\u003c/sup\u003e. Several studies have shown that TAM/CXCL1 promotes breast cancer metastasis through NF-κb/SOX4 activation\u003csup\u003e28\u003c/sup\u003e. Our study, based on KEGG functional enrichment analysis, revealed a strong correlation between CXCL1 and colon cancer metastasis. The implicated pathways include TNF-α and NFΚB, which have long been linked to inflammation onset and resolution\u003csup\u003e29\u003c/sup\u003e. Therefore, we consider that CXCL1 may contribute to the development, progression and metastasis of colon cancer and the development of IBD such as CD by targeting TNF-α and NF-ΚB signalling pathways. TNF-αand NF-κB has pro-inflammatory effects and may indirectly promote the transformation of inflammation into cancer.\u003c/p\u003e \u003cp\u003eThis study identified genes that are co-expressed in both Crohn's disease and colorectal cancer. The study found that CXCL1 may play a role in promoting the transformation of Crohn's disease to colon cancer through specific signalling pathways. Additionally, CXCL1 was found to promote the metastasis of colorectal cancer through immune cell escape, but high expression of CXCL1 may lead to a good prognosis through immune cell infiltration. However, the study still has some limitations. Further experiments are required to better understand the mechanisms by which CXCL1 induces chronic inflammation and cancer. It still needs to be validated in future clinical trials.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrohn\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecolorectal cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe protein-protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInflammatory bowel disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEEG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTRING\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSearch Tool for the Retrieval of Interacting Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data that support the results of the current study is available on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) websites.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eThe data analysis in this article was analyzed by the R.\u003c/p\u003e\n\u003cp\u003efunding\u003c/p\u003e\n\u003cp\u003eThere is no funding received.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe are grateful to the researchers who built the public database and shared an enormous amount of research data, which made our study possible through their generous contributions.\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eDepartment of Radiotherapy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China\u003c/p\u003e\n\u003cp\u003eZijuan Mao, Ganxue Tao, Zhenhua Fei\u003c/p\u003e\n\u003cp\u003eDepartment of Oncology, The First Affiliated Hospital of Jiaxing University, No. 1882, Zhonghuan South Road, Jiaxing, 314000, Zhejiang People\u0026apos;s Republic of China\u003c/p\u003e\n\u003cp\u003eYuyang Gu\u003c/p\u003e\n\u003cp\u003eDepartment of Medical Oncology, Rui\u0026rsquo;an People\u0026rsquo;s Hospital, The Third Affiliated Hospital of Wenzhou Medical University, 108 Wansong Road, Ruian, 325200, China\u003c/p\u003e\n\u003cp\u003eQiang Dai\u003c/p\u003e\n\u003cp\u003eDepartment of Oncology, Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, China\u003c/p\u003e\n\u003cp\u003eYangjie Xu\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eZijuan Mao and Yuyang Gu designed the study. Ganxue Tao performed data analysis. Qiang Dai drafted the manuscript. Zhenhua Fei and Yangjie Xu reviewed and revised the manuscript. Zijuan Mao and Yuyang Gu share the first authorship. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Yangjie Xu and Zhenhua Fei.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis project has been approved by the ethics committee of the First Affiliated Hospital of Wenzhou Medical University.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAtreya, Raja, and M. F. 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Moum. 2012. \u0026lsquo;Epidemiology and Clinical Course of Crohn\u0026rsquo;s Disease: Results from Observational Studies\u0026rsquo;. \u003cem\u003eWorld Journal of Gastroenterology\u003c/em\u003e 18 (15): 1723\u0026ndash;31. https://doi.org/10.3748/wjg.v18.i15.1723.\u003c/li\u003e\n\u003cli\u003eJang, Dan-In, A.-Hyeon Lee, Hye-Yoon Shin, Hyo-Ryeong Song, Jong-Hwi Park, Tae-Bong Kang, Sang-Ryong Lee, and Seung-Hoon Yang. 2021. \u0026lsquo;The Role of Tumour Necrosis Factor Alpha (TNF-\u0026alpha;) in Autoimmune Disease and Current TNF-\u0026alpha; Inhibitors in Therapeutics\u0026rsquo;. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e 22 (5): 2719. https://doi.org/10.3390/ijms22052719.\u003c/li\u003e\n\u003cli\u003e\u0026lsquo;KEGG: Kyoto Encyclopedia of Genes and Genomes - PubMed\u0026rsquo;. n.d. Accessed 8 June 2023. https://pubmed.ncbi.nlm.nih.gov/10592173/.\u003c/li\u003e\n\u003cli\u003eKoizumi, Keiichi, Shozo Hojo, Takuya Akashi, Kazuo Yasumoto, and Ikuo Saiki. 2007. \u0026lsquo;Chemokine Receptors in Cancer Metastasis and Cancer Cell-Derived Chemokines in Host Immune Response\u0026rsquo;. \u003cem\u003eCancer Science\u003c/em\u003e 98 (11): 1652\u0026ndash;58. https://doi.org/10.1111/j.1349-7006.2007.00606.x.\u003c/li\u003e\n\u003cli\u003eLi, Jinyang, Katelyn T. Byrne, Fangxue Yan, Taiji Yamazoe, Zeyu Chen, Timour Baslan, Lee P. Richman, et al. 2018. \u0026lsquo;Tumour Cell-Intrinsic Factors Underlie Heterogeneity of Immune Cell Infiltration and Response to Immunotherapy\u0026rsquo;. \u003cem\u003eImmunity\u003c/em\u003e 49 (1): 178-193.e7. https://doi.org/10.1016/j.immuni.2018.06.006.\u003c/li\u003e\n\u003cli\u003eLi, Lequn, and Vassiliki A. Boussiotis. 2013. \u0026lsquo;The Role of IL-17-Producing Foxp3+ CD4+ T Cells in Inflammatory Bowel Disease and Colon Cancer\u0026rsquo;. \u003cem\u003eClinical Immunology (Orlando, Fla.)\u003c/em\u003e 148 (2): 246\u0026ndash;53. https://doi.org/10.1016/j.clim.2013.05.003.\u003c/li\u003e\n\u003cli\u003eMutua, Victoria, and Laurel J. Gershwin. 2021. \u0026lsquo;A Review of Neutrophil Extracellular Traps (NETs) in Disease: Potential Anti-NETs Therapeutics\u0026rsquo;. \u003cem\u003eClinical Reviews in Allergy \u0026amp; Immunology\u003c/em\u003e 61 (2): 194\u0026ndash;211. https://doi.org/10.1007/s12016-020-08804-7.\u003c/li\u003e\n\u003cli\u003eNadeem, Muhammad Shahid, Vikas Kumar, Fahad A. Al-Abbasi, Mohammad Amjad Kamal, and Firoz Anwar. 2020. \u0026lsquo;Risk of Colorectal Cancer in Inflammatory Bowel Diseases\u0026rsquo;. \u003cem\u003eSeminars in Cancer Biology\u003c/em\u003e 64 (August): 51\u0026ndash;60. https://doi.org/10.1016/j.semcancer.2019.05.001.\u003c/li\u003e\n\u003cli\u003e\u0026lsquo;Neutrophil Extracellular Traps Mediate the Crosstalk between Glioma Progression and the Tumour Microenvironment via the HMGB1/RAGE/IL-8 Axis - PubMed\u0026rsquo;. n.d. Accessed 10 June 2023. https://pubmed.ncbi.nlm.nih.gov/32296583/.\u003c/li\u003e\n\u003cli\u003eOliver, Amanda J., Peter K. H. Lau, Ashleigh S. Unsworth, Sherene Loi, Phillip K. Darcy, Michael H. Kershaw, and Clare Y. Slaney. 2018. \u0026lsquo;Tissue-Dependent Tumour Microenvironments and Their Impact on Immunotherapy Responses\u0026rsquo;. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e 9: 70. https://doi.org/10.3389/fimmu.2018.00070.\u003c/li\u003e\n\u003cli\u003eRavi, Rajani, and Atul Bedi. 2004. \u0026lsquo;NF-kappaB in Cancer--a Friend Turned Foe\u0026rsquo;. \u003cem\u003eDrug Resistance Updates: Reviews and Commentaries in Antimicrobial and Anticancer Chemotherapy\u003c/em\u003e 7 (1): 53\u0026ndash;67. https://doi.org/10.1016/j.drup.2004.01.003.\u003c/li\u003e\n\u003cli\u003eSethi, Gautam, Bokyung Sung, and Bharat B. Aggarwal. 2008. \u0026lsquo;TNF: A Master Switch for Inflammation to Cancer\u0026rsquo;. \u003cem\u003eFrontiers in Bioscience: A Journal and Virtual Library\u003c/em\u003e 13 (May): 5094\u0026ndash;5107. https://doi.org/10.2741/3066.\u003c/li\u003e\n\u003cli\u003e\u0026lsquo;The Necrosome Promotes Pancreatic Oncogenesis via CXCL1 and Mincle-Induced Immune Suppression - PubMed\u0026rsquo;. n.d. Accessed 8 June 2023. https://pubmed.ncbi.nlm.nih.gov/27049944/.\u003c/li\u003e\n\u003cli\u003e\u0026lsquo;Tumour Microenvironment - PubMed\u0026rsquo;. n.d. Accessed 8 June 2023. https://pubmed.ncbi.nlm.nih.gov/31906017/.\u003c/li\u003e\n\u003cli\u003eVelloso, Fernando J., Marina Trombetta-Lima, Valesca Anschau, Mari C. Sogayar, and Ricardo G. Correa. 2019. \u0026lsquo;NOD-like Receptors: Major Players (and Targets) in the Interface between Innate Immunity and Cancer\u0026rsquo;. \u003cem\u003eBioscience Reports\u003c/em\u003e 39 (4): BSR20181709. https://doi.org/10.1042/BSR20181709.\u003c/li\u003e\n\u003cli\u003eWu, Y., and B. P. Zhou. 2010. \u0026lsquo;TNF-Alpha/NF-kappaB/Snail Pathway in Cancer Cell Migration and Invasion\u0026rsquo;. \u003cem\u003eBritish Journal of Cancer\u003c/em\u003e 102 (4): 639\u0026ndash;44. https://doi.org/10.1038/sj.bjc.6605530.\u003c/li\u003e\n\u003cli\u003eZimmerman, Noah P., Rebecca A. Vongsa, Michael K. Wendt, and Michael B. Dwinell. 2008. \u0026lsquo;Chemokines and Chemokine Receptors in Mucosal Homeostasis at the Intestinal Epithelial Barrier in Inflammatory Bowel Disease\u0026rsquo;. \u003cem\u003eInflammatory Bowel Diseases\u003c/em\u003e 14 (7): 1000\u0026ndash;1011. https://doi.org/10.1002/ibd.20480.\u003c/li\u003e\n\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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crohn’s disease, colon cancer, CXCL1, microenvironment, inflammatory","lastPublishedDoi":"10.21203/rs.3.rs-4637273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4637273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the molecular links and mechanisms between Crohn\u0026rsquo;s disease (CD) and colorectal cancer (CRC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study used the Gene Expression Omnibus (GEO) database to identify Differentially expressed genes (DEGs) in CD (GSE112366) and CRC (GSE110224), analyzed by 'edgeR' and 'limma'. The Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes explored DEG functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) informed the protein-protein interaction network construction visualized in Cytoscape (version 3.7.2). Cyto-Hubba identified key genes, whose biomarker potential for CD and CRC was evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study discovered 61 DEGs, with 44 up- and 17 down-regulated, linked to immune responses and signaling pathways. CXCL1, highly expressed in colon cancer, correlated with better prognosis and lower staging. It also showed associations with immune infiltration and checkpoint molecules, suggesting a role in cancer progression and retreat.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCXCL1 may play a role in the development of colorectal cancer from inflammatory bowel disease.\u003c/p\u003e","manuscriptTitle":"The co-expression of Crohn’s disease and colon cancer network was analyzed by bioinformatics-CXCL1 Tumour microenvironment and prognosis-related gene CXCL1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 17:05:32","doi":"10.21203/rs.3.rs-4637273/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-30T10:29:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-30T08:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67590820433360115797711496791328175320","date":"2024-08-26T09:29:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-26T08:18:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304307624875032386952285768940555248092","date":"2024-07-22T13:49:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-13T04:53:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-12T07:51:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-04T12:41:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-06-25T14:43:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47578863-54c0-4938-84e3-44981bf85926","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-03T07:38:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 17:05:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4637273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4637273","identity":"rs-4637273","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}