Analysis and validation of genes co-expressed in Crohn's disease and cervical cancer based on GEO database.

doi:10.21203/rs.3.rs-3133943/v1

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Analysis and validation of genes co-expressed in Crohn's disease and cervical cancer based on GEO database.

https://doi.org/10.21203/rs.3.rs-3133943/v1

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Background

In recent years, many studies have shown that Crohn's disease (CD) can increase the incidence of cervical cancer in women. However, the mechanism of the association is unclear, and this study aimed to investigate further the mechanisms underlying the joint development of both diseases.

Methods

Gene expression profiles of Crohn's disease (GSE95095) and cervical cancer (GSE63514) were downloaded from the GEO database. Differentially expressed genes (DEGs) in the experimental group and control group of Crohn's disease and cervical cancer were searched for, as well as co-expressed genes in the two conditions. These co-expressed genes were then subjected to functional annotation, protein-protein interaction (PPI) network and module construction, screening and co-expression analysis of key genes, and prediction and validation of transcription factors that exert regulatory effects on the corresponding key genes.

Results

A total of 63 co-expressed genes were screened from the differential genes of the two disease groups: 17 up-regulated and 46 down-regulated genes. Functional analysis revealed that hormone metabolic processes, steroid hormone synthesis processes and angiogenesis, play a crucial role in both diseases. In addition, 15 important core genes were screened. We used core genes that were upregulated in both diseases. The core genes upregulated in both conditions: AKR1B1, CHI3L1, CXCR4 and DUSP2, were used to construct risk models for predicting disease progression. Finally, it is expected that transcription factors such as MYC, VHL and SP1 may play a regulatory role on core genes, with the regulation of CXCR4 by the transcription factor MYC deserving an in-depth study. Combining the extent scores and high expression levels of both diseases, CXCR4 is considered the most risky gene and is expected to be a biomarker for both diseases. Finally, CXCR4 expression in cervical cancer was verified using immunohistochemistry and protein immunoblotting assays.

Conclusion

This study has provided initial insights into the common pathogenesis of Crohn's disease and cervical cancer and the potential co-expression of key genes. These common pathways and key gene mechanisms require further investigation and can provide a crucial reference for early clinical diagnosis and treatment.

Crohn's disease

cervical cancer

co-expression of core genes

CXCR4

MYC

Cervical cancer (CC), also known as cervical carcinoma, is the most common malignant tumor in gynecology and is associated with Human Papillomavirus (HPV) infection (primarily HPV subtypes 16 and 18), which occurs at the junction of squamous and columnar epithelium of the uterine cervix ^[1]. Clinical symptoms mainly include contact bleeding (after intercourse and during intercourse) in middle-aged and older women. The high incidence of carcinoma in situ is between 30 and 35 years old, and invasive carcinoma is mostly between 45 and 55 years old^{[2, 3]}. In recent years, there has been a trend of younger incidence. Currently, cervical smear cytology is mainly used for cervical cancer screening, and cervical and cervical canal biopsy are used for cervical cancer diagnosis ^[4]. As women have become more aware of the need for HPV vaccination, preventive HPV vaccination is very effective in preventing pre-invasive and invasive cervical, vulvovaginal, and anal diseases caused by HPV infection, etc.^{[5, 6]}. Moreover, with the popularization of cervical cytology screening, the increasing detectability of cervical cancer and precancerous lesions at an early stage, and the timely and aggressive treatment measures taken by patients, the incidence and mortality rate of cervical cancer have been significantly reduced ^{[7, 8]}.

Inflammatory bowel disease (IBD) is an idiopathic inflammatory disease of the intestine that primarily involves the ileum, rectum, and colon. The clinical manifestations are diarrhea, abdominal pain, and possibly bloody stools. Inflammatory bowel diseases are divided into two major groups, including Ulcerative colitis (UC) and Crohn's disease (CD)^[9]. Crohn's disease (CD), also known as restrictive enteritis, restrictive ileitis, segmental enteritis, and granulomatous enteritis, or Crohn's disease^[10]. Crohn's disease can occur in any part of the gastrointestinal tract, mainly in the terminal ileum and right hemicolectomy. The main symptoms are abdominal pain (mostly right lower abdominal pain as the typical symptom), diarrhea, and intestinal obstruction, often accompanied by fever, nutritional disorders, and other extraintestinal manifestations. Colonoscopy reveals a cobblestone or paving stone-like appearance with deep ulcers, and non-caseating granulomas are frequently observed during examination^[11]. The course of the disease is often recurrent, much delayed, complications are frequent, not easy to eradicate^[12]. Crohn's disease is an autoimmune disease. Anti-immune therapy is mainly used for moderate and severe patients, and glucocorticoids are mainly applied clinically, but no general eradication method has been found. When patients develop complications (primarily intestinal obstruction is common), surgical treatment is required^[13].

In recent years, an increasing number of studies have shown that patients with Crohn's disease have an elevated risk of developing certain extraintestinal malignancies, such as cervical cancer^[14] .Some women diagnosed with Crohn's disease are more likely to develop cervical tumor lesions than those without Crohn's disease^{[15, 16]}. This is thought to be related to the chronic use of immunosuppressive drugs in Crohn's patients. When glucocorticoids are used, the organism's ability to fight HPV infection decreases, allowing the HPV virus to take advantage and increase the risk of cervical cancer formation. Chronic inflammation appears to be involved in the pathophysiological processes of both Crohn's disease and cervical cancer. Therefore, this study aims to explore the co-expressed genes between Crohn's disease and cervical cancer using the public clinical database GEO and a bioinformatics approach to provide reliable biomarkers for elucidating the potential biomolecular mechanisms shared between Crohn's disease and cervical cancer.

2.1 Data Collection

The platform file and matrix file data corresponding to Crohn's disease (GSE95095)^[17] and cervical cancer (GSE63514)^[18] were downloaded from the public gene expression database GEO (Gene Expression Omnibus)

(https://www.ncbi.nlm.nih.gov/geo/) by searching for the relevant clinical data using the keywords "Crohn's disease" and "cervical cancer". GSE95095 included clinical information from 12 healthy patients without Crohn's disease and 48 patients with Crohn's disease. GSE63514 consisted of cervical tissue specimens from 128 women, including 24 normal specimens, 14 CIN1 lesions, 22 CIN2 lesions, 40 CIN3 lesions, and 28 cancer specimens.

2.2 Identification of DEGs

Genes expressed in the two diseases were annotated using the "limma" package in R (version R.4.2.1) ^[19]. The genes meeting the above conditions in the control group and the experimental group were considered as differential genes (DEGs) by screening at logFC = 0.585, a fold change of 1.5, and P value = 0.05. The DEGs in the two disease groups were visualized using a heat map and volcano map, and the packages "pheatmap" and "ggplot2" were used for visualization.

2.3 Visualization of up-regulated and down-regulated DEGs

To visualize the up-regulated and down-regulated DEGs in the two diseases, we first took the intersection of the differential genes for Crohn's disease and cervical cancer. Then, we classified the genes as up-regulated or down-regulated based on whether logFC was greater than or less than zero, respectively. The "VennDiagram" package in R was used to generate Venn diagrams of the up-regulated and down-regulated genes, with the filtered genes meeting the criteria of logFC > 0.585, difference multiplier of 1.5, and P value = 0.05.

2.4 Construction of PPI network and module analysis

The online website STRING (https://string-db.org/) can be used to find protein-protein relationships and construct complex PPI networks^[20]. Interactions with a composite score above 0.150 are considered statistically significant. The intersecting genes in Crohn's disease and cervical cancer were entered to construct the PPI network, which was visualized using Cytoscape software^[21].

2.5 Selection and analysis of core genes

Core genes were screened using the cytoHubba plug in Cytoscape software^[22]. The Degree algorithm was used to evaluate and select core genes. The Degree algorithm measures the number of edges (i.e., connections) of each node in the network, and the more the number of edges, the more likely the gene is to be the core gene of the network. The intersecting genes in the two diseases were entered, and the top 15 genes with the highest Degree scores were screened as the core genes of the network.

2.6 Construction of core gene interaction network

Subsequently, we searched for genes with protein interactions with the 15 core genes that were screened and for the functions in which these genes might be involved. We used a reliable tool for identifying internal associations in the genome, Gene MANIA (http://www.genemania.org/)^[23].

2.7 GO and KEGG enrichment analysis of core genes

Gene Ontology (GO) is a database created by the Gene Ontology Consortium that provides simple annotations of gene products in terms of their functions, biological processes, and cellular components^[24]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database that stores information on gene pathways of different species^[25]. The GO and KEGG enrichment analysis of core genes were analytically filtered using R language with P-value filter condition = 0.05 and corrected P-value = 1. The output bar, bubble, and circle plots visualized the cellular function and metabolic pathways.

2.8 Visualization of core gene expression levels and selection of risk genes

The expression levels of the screened core genes in the control and disease groups of the two conditions were visualized using the "limma" and "ggpubr" packages in R. The t-test was used to compare the two groups for each disease. P-values < 0.05 were considered significant, and the expression levels of the core genes were exported using "ggviolin" and plotted on violin plots. The significantly upregulated genes in the disease group were selected as risk genes.

2.9 Prediction of transcription factors (TF)

The website TRRUST (https://www.grnpedia.org/trrust/) is a database used for predicting transcriptional regulatory networks that contain target genes corresponding to TF and regulatory relationships between TFs^[26]. The site was used to predict the transcription factors (TFs) that might play a regulatory role on the core genes, which included the target genes corresponding to the TFs and the regulatory relationships with the TFs. The results were considered significant when the adjusted P value was < 0.05.

2.10 Verification of protein expression of core genes using immunohistochem-

ical experiments

An essential gene, CXCR4, which was co-expressed in both diseases, was obtained based on bioinformatic analysis, and its protein expression was verified in cervical cancer and paraneoplastic tissues using immunohistochemical experiments. The Department of Pathology provided pathological tissue section samples of cervical cancer and para-cancerous tissues, The First Affiliated Hospital of Dali University; CXCR4 rabbit-derived antibodies were purchased from Beijing Bioss Biotechnology Co., Ltd (Item No.: bs-1011R), and subsequent experiments were performed according to the dilution ratio of 1:400; DAB horseradish peroxidase color development kit was purchased from Biyuntian Biotechnology Co. Images were obtained using an AE2000 microscope (Shanghai Optical Instrument Factory, product no. 22060525).The experimental data were quantified by Image J. The data were analyzed and organized using GraphPad software, and the images were output. It was considered significant when the P value was < 0.05.

2.11 The expression level of core genes in cells was detected using the Western blot method.

Cervical cancer Hela cells were used as the cancer group, and human vaginal epithelial HESC cells were used as the healthy group. HESC and Hela cells were provided by Prof. Yuzhe Zhang of Dali University. The cells were cultured in DMEM basal medium supplemented with 10% FBS and 1% double antibody configuration. The cells were incubated at 37°C in a 5% CO2 incubator. When the cell growth density reached 90% or more, protein was extracted for subsequent Western Blot experiments. DMEM medium was purchased from Yunnan Roodbao Biotechnology Co., Ltd. with the product label number: C11995500BT; fetal bovine serum was purchased from SERANA with the product label number: S-FBS-SA-015; penicillin-streptomycin double antibody was purchased from Kunming Zanna Biotechnology Co. SDS-PAGE gel kit purchased from Kunming Zanner Biotechnology Co., Ltd (Biyuntian), product label number: P0012AC; Marker was purchased from Wuhan Servicebio Biotechnology Co., Ltd. with the item number: G2083; CXCR4 primary antibody was purchased from Beijing Bioss Biotechnology Co., Ltd (item number: bs-1011R) at a ratio of 1:2000 for the experiment; The internal reference GAPDH was purchased from Wuhan Servicebio Biotechnology Co., Ltd. with the item number: GB12002, and the investigation was conducted at a ratio of 1:2000; Secondary antibodies IgG were purchased from Beijing Bioss Biotechnology Co., Ltd (Item No.: bs-0295G (rabbit source); bs-0296G (mouse source)), and the experiments were performed at a ratio of 1:5000. The mini chemiluminescence imager was purchased from Beijing Saizhi Venture Technology Co. Protein grayscale values were measured using Image J, and relative protein expression was calculated, and the data were analyzed and collated using GraphPad software and the images were output. It was considered significant when the P value was < 0.05.

2.12 Statistical analysis

Image J software was used for statistical analysis of immunohistochemistry and WB results, and GraphPad software was used for data analysis. T-test was used for two-by-two comparisons, with P < 0.05 indicating a statistically significant difference.

3.1 Identification of common DEGs between Crohn's disease and cervical cancer

Platform files with matrix files for the Crohn's disease (CD) GSE95095 and cervical cancer (CC) GSE63514 datasets were downloaded from the NCBI-GEO website and screened for significant DEGs between the disease and regular groups for each dataset. These data were filtered at a different multiplier of 1.5, logFC = 0.585, and Pvalue = 0.05. From these data, we found 942 DEGs in the disease group versus the regular group in dataset GSE95095, with 303 upregulated genes and 576 downregulated genes; the dataset GSE63514 had 1852 DEGs in the disease and regular groups, with 975 upregulated genes and 814 downregulated genes. The expression of DEGs was visualized and output as volcano plots, with red representing differential genes upregulated in the dataset and green expressing genes downregulated in the dataset (Fig. 1A and B). These DEGs were used for further analysis.

3.2 Up-regulated and down-regulated DEGs between Crohn's and cervical cancer

Genes with logFC > 0 in each dataset represent up-regulated genes, and those with logFC < 0 represent down-regulated genes. These DEGs were further analyzed and screened for visualization using Venn diagrams, of which 17 DEGs showed up-regulation and 46 DEGs showed down-regulation in both diseases (Fig. 2A and B). A total of 63 intersecting genes were co-expressed in the two conditions.

3.3 PPI protein network construction and analysis

The STRING (https://cn.string-db.org/) website can be used to find protein-protein relationships, construct a PPI network of 63 intersecting genes between Crohn's disease and cervical cancer, and construct interactions of intersecting genes with a composite score greater than 0.150 (Fig. 3A). The PPI network was visualized using Cytoscape software, which includes 54 nodes and 78 interaction pairs, with 12 genes represented in red for up-regulation and 42 genes in green for down-regulation (Fig. 3B).

3.4 Selection of Core Genes and Interaction Network Analysis

The top 15 genes in terms of score ranking (CA12, CXCR4, SLPI, BCAS1, AKR1B1, PAPSS2, VSIG2, FUT3, CHI3L1, DUSP2, SPINK5, GJB2, PTK6, GCNT3, DHRS11) were calculated by seven algorithms (MCC, MNC, Degree, EPC, Closeness, Radiality, Stress) of the cytoHubba plugin in Cytoscape software, and the score ranking and gene names are shown in Table 1. In our follow-up study, we will use these genes as core genes between Crohn's disease and cervical cancer. The screening was performed again mainly based on Degree scores. The higher the score, the redder the color shown in the graph, and the more likely the gene is a core gene in the protein interaction network (Fig. 4A). We found that two genes, CA12 and CXCR4, were redder in color, indicating that they were more likely to act as crucial core genes. Using the GeneMANIA website (http://genemania.org/), we entered the 15 core genes and obtained the protein interaction network relationship map between the core genes (Fig. 4B). The inner circles represent the 15 core genes we screened. The outer circles represent the genes with reciprocal relationships with these core genes. If there is a link between two genes, it indicates a protein reciprocal relationship, and the different colors on the nodes represent that the genes have other functions.

Table 1

Names and scores of the top 15 genes
node name	MCC	MNC	Degree	EPC	Closeness	Radiality	Stress
CA12	8	1	8	24.296	23.9	7.43396	1920
CXCR4	8	3	7	25.134	22.65	7.20755	902
SLPI	7	3	6	23.999	21.21667	7.01887	454
BCAS1	6	2	6	22.126	21.21786	6.84906	860
AKR1B1	6	2	6	21.805	20.39286	6.79245	528
PAPSS2	6	3	5	21.937	19.52976	6.60377	340
VSIG2	5	2	5	19.386	19.74286	6.77358	508
FUT3	5	2	5	19.899	19.6595	6.73585	364
CHI3L1	5	2	5	24.419	21.26667	7.0566	486
DUSP2	4	2	4	21.682	19.07619	6.69811	344
SPINK5	5	3	4	20.041	17.85952	6.41509	230
GJB2	4	2	4	19.572	17.86667	6.54717	588
PTK6	4	1	4	20.15	19.69286	6.92453	454
GCNT3	4	2	4	19.87	19.73333	6.92453	426
DHRS11	4	2	4	20.925	17.89643	6.33962	144

3.5 Functional characterization of core genes

GO and KEGG pathway enrichment analyses were performed for 15 core genes to analyze the biological functions and pathways of the core genes. The results of GO analysis showed that these genes were mainly enriched in the metabolic hormone process, angiogenesis regulation, and vasculature development regulation were increased (Fig. 5A and B) Regarding the KEGG pathway, the three significantly enriched metabolic pathways were sulfur metabolism, selenium compound metabolism, and nitrogen metabolism (Fig. 5D and E). And in this way, string diagrams were output on GO and KEGG enrichment (Fig. 5C and E); the left semicircle represents the gene, and the right semicircle is the critical pathway enriched by the gene. The gene enrichment was found to be most significant in the steroid hormone biosynthesis process, hormone metabolism process pathway (Fig. 5C) and most associated with the sulfur metabolism pathway (Fig. 5F). These results strongly suggest that these pathways are jointly involved in the development and progression of both Crohn's disease and cervical cancer.

3.6 Visualization of core gene expression levels and selection of risk genes

The expression levels of the 15 core genes selected for Crohn's disease and cervical cancer were visualized using the "limma" and "ggpubr" packages in R language and exported as a violin plot using the "ggviolin" package. In the Crohn's disease samples, blue color indicates normal samples and red color represents Crohn's disease samples (Fig. 6A-O). In cervical cancer, the cancer samples were divided into three stages: blue for regular cervical epithelial samples, red for cervical intraepithelial neoplasia, and green for cervical squamous epithelial carcinoma (Fig. 6A'-O'). Among these, AKR1B1, CHI3L1, CXCR4, and DUSP2 were significantly upregulated in both Crohn's disease and cervical cancer compared to regular samples, whereas SLPI, FUT3, GJB2, GCNT3, SPINK5, BCAS1, VSIG2, DHRS11, PTK6, PAPSS2, and CA12 were significantly downregulated. These upregulated genes in the disease group are of particular interest, and it might be possible to construct a risk model for the co-occurrence of these two diseases consisting of these four core genes. Furthermore, in our previous analysis of PPI network interactions, we found that CA12 and CXCR4 are more likely to be the critical core genes of the protein interaction network based on the score and module color visualization. Moreover, CXCR4 showed higher expression levels in both diseases, suggesting that it is a high-risk gene in both disorders and should be given greater attention.

3.7 Prediction of transcription factors (TF)

Using transcription factor enrichment analysis at TRRUST (http://www.grnpedia.org/trrust/), we predicted the transcription factors that can regulate the core genes. The analysis identified MYC as a transcription factor that can regulate CXCR4 and FUT3 genes, VHL as a regulator of CA12 and CXCR4, and SP1 as a regulator of CHI3L1 and PTK6 (Fig. 7). Since CXCR4 was identified as a high-risk gene, the finding that MYC can regulate CXCR4 gene expression warrants further investigation. MYC is a transcription factor known for its broad regulatory effects, and its role in regulating CXCR4 and FUT3 genes could be crucial in the development and progression of Crohn's disease and cervical cancer.

3.8 Expression of CXCR4 in cervical cancer and adjacent para-cancerous tissues

The combined bioinformatics analysis showed that CXCR4 had an abnormally high expression in cervical cancer, which piqued our interest. To validate this finding, we conducted in vitro immunohistochemical experiments, and images were captured using an AE2000 microscope at 10×200 magnification (Fig. 8A). Quantification was performed using ImageJ software, and data analysis was done with GraphPad Prism software. Our findings revealed that CXCR4 expression was significantly higher in cervical cancer tissues than in adjacent para-cancerous tissues (P < 0.05) (Fig. 8B). These results were in line with the bioinformatics analysis and provided further robust evidence supporting CXCR4 as a potential tumor marker for cervical cancer associated with Crohn's disease.

3.9 Validation of CXCR4 expression in cervical cancer cells (Hela) versus human vaginal epithelial cells (HESC) using Western Blot assay

The expression of CXCR4 in the cells was verified by culturing Hela cells with HESC cells and extracting the corresponding proteins according to Western Blot assays. The grayscale values of the strips were analyzed using Image J software, and the experimental data were organized using GraphPad Prism software. The results showed that CXCR4 was highly expressed in Hela cells and expressed at a lower level in HESC cells (P < 0.01) (Fig. 9A and B). The experiments were further validated at the cellular level, and CXCR4 should be closely monitored as a risk protein for cervical cancer caused by Crohn's disease.

Many studies have shown that the immune system plays a vital role in the development of tumors, which cannot be ignored^[27]. The interaction between the tumor and the immune system leads to metabolic competition in the tumor ecosystem, limiting the availability of nutrients and altering the microenvironment, which hinders the function of immune cells^[28]. Interestingly, metabolic processes are also essential in maintaining the homeostasis of various types of immune cells in the organism^[29]. Some diseases, such as Crohn's disease, can damage the immune system, and long-term use of immunosuppressants, such as glucocorticoids, may exacerbate this effect, leading to extra-intestinal manifestations, including cervical cancer. However, few studies have investigated this relationship. This study aimed to investigate the co-expression of genes in both diseases using a bioinformatics approach based on Crohn's patients with impaired immune systems due to long-term medication, to provide insights for preventing disease progression in clinical practice.

It is important to note that while immunosuppressive drugs are a primary treatment for Crohn's disease, they can have significant side effects on the immune system, which may increase the risk of developing other diseases such as cervical cancer. Further research is needed to understand the mechanisms by which these drugs affect the immune system and contribute to the development of cervical cancer in patients with Crohn's disease. The identification of MYC as a regulator of the risk gene CXCR4 provides a potential target for future therapeutic interventions to prevent or treat cervical cancer in this population. However, additional studies are necessary to validate these findings and develop effective treatment strategies. Gene-C-X-C Motif Chemokine Receptor Type 4 (CXCR4) is a specific receptor for chemokine stromal cell-derived factor-1 (CXCL12), a 352 amino acid G protein-coupled receptor with a seven times per membrane structure. CXCR4 is expressed in most tissues and organs in the body, and CXCR4 regulates a variety of essential processes of normal physiology, including embryonic development, tissue repair, angiogenesis, and immune cell trafficking. Tumors can use these fundamental processes to stimulate cancer cell proliferation, invasion, and metastasis directly^[30]. CXCR4 signalling contributes to critical functions of stromal cells in cancer, including angiogenesis and multiple cell types in the tumor immune environment^{[30, 31]} ; this is consistent with the results of the GO pathway analysis for the core genes in this paper. Studies have shown that IgG-secreting plasma cells (PCs) express increased levels of CXCR4 in the presence of intestinal inflammation and that the frequency of CXCR4(+)/IgG (+) PCs correlates with the severity of intestinal inflammatory disease^[32]. CXCR4 has constitutive and inflammatory effects in the intestinal mucosa and can be considered for selective therapeutic manipulation in managing inflammatory bowel disease^[33]. CXCR4, an inflammatory chemokine capable of modulating the immune response, is highly expressed in inflammatory bowel disease, and its role cannot be ignored^{[34, 35]}. And in gastrointestinal tumors, CXCR4 has a close relationship with cancer development, for example, as a high-risk protein in colorectal cancer, pancreatic cancer, and gastric cancer, and is involved in tumor development and metastasis^[36–39]. The association between CXCL12/CXCR4 pathway and cervical cancer begins in the early stages of the disease, and CXCR4 has been reported to be an essential host factor for HPV carcinogenesis^[40]. It has long been considered a cofactor for HPV infection and a risk factor for cervical cancer. CXCR4 expression was a key determinant of cervical cancer metastasis, and the CXCL12/CXCR4 axis plays a central role in cervical cancer pathogenesis, progression, and treatment response^[41].

In the process of disease onset and progression, CXCR4 can play a role in disease signaling not only by affecting the immune response to the disease and the expression of associated inflammatory chemokines but also by affecting the metabolic processes of the cells involved. In the analysis presented here, sulfur metabolism, selenium compound metabolism, and nitrogen metabolism were found to play a role in both diseases, with sulfur metabolism being particularly well represented. Metabolic dysregulation is a prominent feature of inflammatory diseases and cancer. Cellular metabolism involves a series of redox reactions capable of generating the energy and biomass required for tumor growth. Cells need various molecular species with constituent sulfur atoms to facilitate these processes^[42]. It has been shown that dysregulation of sulfur metabolism in Crohn's disease can lead to long-term effects on the associated intestinal flora, potentially contributing to the development of inflammatory bowel diseases like Crohn's. In addition to considering the results of the analysis presented in this paper, it may also be worth exploring whether gut microbes can play a role in regulating chemokine-dependent immune cell accumulation and whether Crohn's disease can impact gut microbes, potentially leading to increased risk for cervical cancer formation if these microbes pass through the bloodstream (eg. via the portal venous circulation). This could contribute to the development and progression of cervical cancer. Indeed, studying the interaction between gut microbiota and host metabolism is a promising approach to better understand the pathogenesis of inflammatory diseases and cancer. By analyzing the microbiota-metabolite network, we may identify potential biomarkers that could help predict disease progression, response to treatment, or even risk of developing cervical cancer in women with Crohn's disease. Additionally, targeted modulation of the gut microbiota through interventions such as probiotics, prebiotics, or fecal microbiota transplantation may represent a novel therapeutic approach to prevent or delay the onset and progression of Crohn's disease and its associated risk of cervical cancer. However, more research is needed to fully understand the complex interplay between the gut microbiota, host metabolism, and disease pathogenesis to develop effective interventions. Based on the analysis in this paper, highly consistently expressed genes were found between Crohn's disease and cervical cancer, which hopefully can provide new thinking in the direction of immunotherapy and anti-tumor immune response for tumors and clinically encourage patients with Crohn's disease to undergo annual cervical cancer screening in the hope of early detection in the clinical prevention stage. And we hope that the analysis based on this paper can bring new insights and essential theoretical support to the clinical treatment of cervical cancer.

This study screened 15 genes co-expressed in Crohn's disease and cervical cancer, including SLPI, FUT3, GJB2, CXCR4, AKR1B1, and GCNT3, CHI3L1, SPINK5, BCAS1, VSIG2, DUSP2, DHRS11, PTK6, PAPSS2, and CA12. Based on the gene expression data, four genes that were significantly upregulated in both diseases (AKR1B1, CHI3L1, CXCR4, and DUSP2) were considered risk genes, and risk models were constructed to predict disease progression. Based on the analysis, the regulatory factors MYC, VHL, and SP1 were predicted to act on the core genes. Among them, MYC was identified as a regulator of the high-risk gene CXCR4 and should be given particular attention. The expression of the CXCR4 protein was verified at both the tissue and cellular levels through immunohistochemistry and Western Blot assays. The experimental results were consistent with the bioinformatics analysis, indicating that CXCR4 could potentially serve as a biomarker for cervical cancer caused by Crohn's disease as a high-risk factor for developing both diseases.

Crohn's disease

Differentially expressed genes

DEGs

Protein-protein interaction

PPI

Cervical cancer

Human Papillomavirus

HPV

Inflammatory bowel disease

IBD

Ulcerative colitis

Gene Expression Omnibus

GEO

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

KEGG

transcription factors

TFs

Gene-C-X-C Motif Chemokine Receptor Type 4

CXCR4

Chemokine stromal cell-derived factor-1

CXCL12

Plasma cells

PCs

Ethical Approval：

In this paper, immunohistochemical experiments using human cervical cancer tissue samples were reviewed by the Medical Ethics Committee of Dali University in Dali, Yunnan Province, China, and were in accordance with the principles of medical ethics and the relevant regulations of national medical experimental ethics, and could be conducted. Approval number of medical ethics review approval letter: MECDU-202301-10

Competing interests：

All authors disclosed no relevant relationships. No potential conflict of interest was reported by the authors.

Authors' contributions：

Author 1 (first author): Le Zhang: Presentation of ideas and thoughts on the study, methodology, experiments, software, investigations, formal analysis, writing-original manuscript.

Author 2: Jing Zou: Data Curation.

Author 3&4: Zhen Wang, Hongru Liu: Image organization and visualization.

Author 5: Bei Jiang: Software, Writing - Review & Editing

Corresponding authors: Yi Liang and Yuzhe Zhang: Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review & Editing.

Funding ：

This work was supported by Joint Special Project for Local Universities in Yunnan Province (202001BA070001-064, 202101BA070001-102); Yunnan Provincial Education Department Scientific Research Fund Project (2023Y0950, 2022Y80); Open Project of Yunnan Provincial Key Laboratory of Pathogen Resistant Plant Resources Screening Research in Western Yunnan Province (APKL2101); Dali University Doctoral Research Start-up Fund Project (KYBS2018012).

Availability of data and materials:

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Goodman, A. HPV testing as a screen for cervical cancer. BMJ (Clinical research ed). 2015;350:h2372.doi:10.1136/bmj.h2372.
Moore, DH. Cervical cancer. Obstetrics and gynecology. 2006;107(5):1152-61.doi:10.1097/01.Aog.0000215986.48590.79.
Cohen, PA, Jhingran, A, Oaknin, A, et al. Cervical cancer. Lancet (London, England). 2019;393(10167):169-82.doi:10.1016/s0140-6736(18)32470-x.
Zhang, S, Batur, P. Human papillomavirus in 2019: An update on cervical cancer prevention and screening guidelines. Cleveland Clinic journal of medicine. 2019;86(3):173-8.doi:10.3949/ccjm.86a.18018.
Zhu, Y, Wang, Y, Hirschhorn, J, et al. Human Papillomavirus and Its Testing Assays, Cervical Cancer Screening, and Vaccination. Advances in clinical chemistry. 2017;81:135-92.doi:10.1016/bs.acc.2017.01.004.
Athanasiou, A, Bowden, S, Paraskevaidi, M, et al. HPV vaccination and cancer prevention. Best practice & research Clinical obstetrics & gynaecology. 2020;65:109-24.doi:10.1016/j.bpobgyn.2020.02.009.
Wang, R, Pan, W, Jin, L, et al. Human papillomavirus vaccine against cervical cancer: Opportunity and challenge. Cancer letters. 2020;471:88-102.doi:10.1016/j.canlet.2019.11.039.
Kjaer, SK, Dehlendorff, C, Belmonte, F, et al. Real-World Effectiveness of Human Papillomavirus Vaccination Against Cervical Cancer. Journal of the National Cancer Institute. 2021;113(10):1329-35.doi:10.1093/jnci/djab080.
Flynn, S, Eisenstein, S. Inflammatory Bowel Disease Presentation and Diagnosis. The Surgical clinics of North America. 2019;99(6):1051-62.doi:10.1016/j.suc.2019.08.001.
Baumgart, DC, Sandborn, WJ. Crohn's disease. Lancet (London, England). 2012;380(9853):1590-605.doi:10.1016/s0140-6736(12)60026-9.
Loftus, EV, Jr. Crohn's Disease: Etiology, Complications, Assessment, Therapy, and Management. Gastroenterology clinics of North America. 2017;46(3):xiii-xv.doi:10.1016/j.gtc.2017.06.001.
Freeman, HJ. Natural history and long-term clinical course of Crohn's disease. World journal of gastroenterology. 2014;20(1):31-6.doi:10.3748/wjg.v20.i1.31.
Mazal, J. Crohn disease: pathophysiology, diagnosis, and treatment. Radiologic technology. 2014;85(3):297-316; quiz 7-20
Garg, SK, Velayos, FS, Kisiel, JB. Intestinal and Nonintestinal Cancer Risks for Patients with Crohn's Disease. Gastroenterology clinics of North America. 2017;46(3):515-29.doi:10.1016/j.gtc.2017.05.006.
Rungoe, C, Simonsen, J, Riis, L, et al. Inflammatory bowel disease and cervical neoplasia: a population-based nationwide cohort study. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2015;13(4):693-700.e1.doi:10.1016/j.cgh.2014.07.036.
Mann, S, Jess, T, Allin, K, et al. Risk of Cervical Cancer in Inflammatory Bowel Disease: A Meta-Analysis of Population-Based Studies. Clinical and translational gastroenterology. 2022;13(7):e00513.doi:10.14309/ctg.0000000000000513.
Yao, Z, Zhang, B, Niu, G, et al. Neutrophil Infiltration Characterized by Upregulation of S100A8, S100A9, S100A12 and CXCR2 Is Associated With the Co-Occurrence of Crohn's Disease and Peripheral Artery Disease. Frontiers in immunology. 2022;13:896645.doi:10.3389/fimmu.2022.896645.
Zhao, K, Ma, Z, Zhang, W. Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer. Frontiers in genetics. 2021;12:732822.doi:10.3389/fgene.2021.732822.
Ritchie, ME, Phipson, B, Wu, D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research. 2015;43(7):e47.doi:10.1093/nar/gkv007.
Szklarczyk, D, Gable, AL, Nastou, KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic acids research. 2021;49(D1):D605-d12.doi:10.1093/nar/gkaa1074.
Doncheva, NT, Morris, JH, Gorodkin, J, et al. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. Journal of proteome research. 2019;18(2):623-32.doi:10.1021/acs.jproteome.8b00702.
Doncheva, NT, Morris, JH, Holze, H, et al. Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks. Journal of proteome research. 2023;22(2):637-46.doi:10.1021/acs.jproteome.2c00651.
Franz, M, Rodriguez, H, Lopes, C, et al. GeneMANIA update 2018. Nucleic acids research. 2018;46(W1):W60-w4.doi:10.1093/nar/gky311.
Gene Ontology Consortium: going forward. Nucleic acids research. 2015;43(Database issue):D1049-56.doi:10.1093/nar/gku1179.
Kanehisa, M, Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research. 2000;28(1):27-30.doi:10.1093/nar/28.1.27.
Han, H, Cho, JW, Lee, S, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic acids research. 2018;46(D1):D380-d6.doi:10.1093/nar/gkx1013.
Abbott, M, Ustoyev, Y. Cancer and the Immune System: The History and Background of Immunotherapy. Seminars in oncology nursing. 2019;35(5):150923.doi:10.1016/j.soncn.2019.08.002.
Xia, L, Oyang, L, Lin, J, et al. The cancer metabolic reprogramming and immune response. Molecular cancer. 2021;20(1):28.doi:10.1186/s12943-021-01316-8.
Leone, RD, Powell, JD. Metabolism of immune cells in cancer. Nature reviews Cancer. 2020;20(9):516-31.doi:10.1038/s41568-020-0273-y.
Luker, GD, Yang, J, Richmond, A, et al. At the Bench: Pre-clinical evidence for multiple functions of CXCR4 in cancer. Journal of leukocyte biology. 2021;109(5):969-89.doi:10.1002/jlb.2bt1018-715rr.
Furusato, B, Mohamed, A, Uhlén, M, et al. CXCR4 and cancer. Pathology international. 2010;60(7):497-505.doi:10.1111/j.1440-1827.2010.02548.x.
Buckner, CM, Moir, S, Kardava, L, et al. CXCR4/IgG-expressing plasma cells are associated with human gastrointestinal tissue inflammation. The Journal of allergy and clinical immunology. 2014;133(6):1676-85.e5.doi:10.1016/j.jaci.2013.10.050.
Dotan, I, Werner, L, Vigodman, S, et al. CXCL12 is a constitutive and inflammatory chemokine in the intestinal immune system. Inflammatory bowel diseases. 2010;16(4):583-92.doi:10.1002/ibd.21106.
Werner, L, Guzner-Gur, H, Dotan, I. Involvement of CXCR4/CXCR7/CXCL12 Interactions in Inflammatory bowel disease. Theranostics. 2013;3(1):40-6.doi:10.7150/thno.5135.
Werner, L, Elad, H, Brazowski, E, et al. Reciprocal regulation of CXCR4 and CXCR7 in intestinal mucosal homeostasis and inflammatory bowel disease. Journal of leukocyte biology. 2011;90(3):583-90.doi:10.1189/jlb.0111101.
Yu, X, Wang, D, Wang, X, et al. CXCL12/CXCR4 promotes inflammation-driven colorectal cancer progression through activation of RhoA signaling by sponging miR-133a-3p. Journal of experimental & clinical cancer research : CR. 2019;38(1):32.doi:10.1186/s13046-018-1014-x.
Biasci, D, Smoragiewicz, M, Connell, CM, et al. CXCR4 inhibition in human pancreatic and colorectal cancers induces an integrated immune response. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(46):28960-70.doi:10.1073/pnas.2013644117.
Ding, X, Huang, R, Zhong, Y, et al. CTHRC1 promotes gastric cancer metastasis via HIF-1α/CXCR4 signaling pathway. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie. 2020;123:109742.doi:10.1016/j.biopha.2019.109742.
Xue, S, Ma, M, Bei, S, et al. Identification and Validation of the Immune Regulator CXCR4 as a Novel Promising Target for Gastric Cancer. Frontiers in immunology. 2021;12:702615.doi:10.3389/fimmu.2021.702615.
Meuris, F, Carthagena, L, Jaracz-Ros, A, et al. The CXCL12/CXCR4 Signaling Pathway: A New Susceptibility Factor in Human Papillomavirus Pathogenesis. PLoS pathogens. 2016;12(12):e1006039.doi:10.1371/journal.ppat.1006039.
Lecavalier-Barsoum, M, Chaudary, N, Han, K, et al. Targeting the CXCL12/CXCR4 pathway and myeloid cells to improve radiation treatment of locally advanced cervical cancer. International journal of cancer. 2018;143(5):1017-28.doi:10.1002/ijc.31297.
Ward, NP, DeNicola, GM. Sulfur metabolism and its contribution to malignancy. International review of cell and molecular biology. 2019;347:39-103.doi:10.1016/bs.ircmb.2019.05.001.

No competing interests reported.

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Analysis and validation of genes co-expressed in Crohn's disease and cervical cancer based on GEO database.

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Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and Methods

2.1 Data Collection

2.2 Identification of DEGs

2.3 Visualization of up-regulated and down-regulated DEGs

2.4 Construction of PPI network and module analysis

2.5 Selection and analysis of core genes

2.6 Construction of core gene interaction network

2.7 GO and KEGG enrichment analysis of core genes

2.8 Visualization of core gene expression levels and selection of risk genes

2.9 Prediction of transcription factors (TF)

2.10 Verification of protein expression of core genes using immunohistochem-

2.11 The expression level of core genes in cells was detected using the Western blot method.

2.12 Statistical analysis

3. Results

3.1 Identification of common DEGs between Crohn's disease and cervical cancer

3.2 Up-regulated and down-regulated DEGs between Crohn's and cervical cancer

3.3 PPI protein network construction and analysis

3.4 Selection of Core Genes and Interaction Network Analysis

3.5 Functional characterization of core genes

3.6 Visualization of core gene expression levels and selection of risk genes

3.7 Prediction of transcription factors (TF)

3.8 Expression of CXCR4 in cervical cancer and adjacent para-cancerous tissues

4. Discussion

5. Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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