
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-02203
00031
10.1097/MD.0000000000039552
3
4500
Research Article
Diagnostic Accuracy Study
Identification and validation of coagulation and fibrinolytic-related diagnostic biomarkers for ulcerative colitis by bioinformatics analysis
Li Feng-Yun MMed 604291225@qq.com
a
https://orcid.org/0009-0007-4684-2472
Wu Xue MMed b*
Yao Mei-Fang MMed 1955706481@qq.com
c
Zhang Juan BS, ME 1473358746@qq.com
d
Mo Yuan-Jun MMed 630993339@qq.com
d
a The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China
b Department of Proctology, The First People’s Hospital of Bijie, Bijie City, Guizhou Province, China
c The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China
d Renhuai Hospital of Traditional Chinese Medicine, Renhuai City, Guizhou Province, China.
* Correspondence: Xue Wu, The First People’s Hospital of Bijie City, Qixingguan District, Bijie City, Guizhou Province, 551700, China (e-mail: 2415277954@qq.com).
06 9 2024
06 9 2024
103 36 e3955206 3 2024
11 5 2024
13 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Abnormalities in coagulation and fibrinolytic status have been demonstrated to be relevant to inflammatory bowel disease. Nevertheless, there is no study to methodically examine the role of the coagulation and fibrinolysis-related genes in the diagnosis of ulcerative colitis (UC). UC-related datasets (GSE169568 and GSE94648) were originated from the Gene Expression Omnibus database. The biomarkers related to coagulation and fibrinolysis were identified through combining differentially expressed analysis and machine learning algorithms. Moreover, Gene Set Enrichment Analysis and immune analysis were carried out. A total of 4 biomarkers (MAP2K1, CREBBP, TAF1, and HP) were identified, and biomarkers were markedly enriched in pathways related to immunity, such as T-cell receptor signaling pathway, primary immunodeficiency, chemokine signaling pathway, etc. In total, the infiltrating abundance of 4 immune cells between UC and control was markedly different, namely eosinophils, macrophage M0, resting mast cells, and regulatory T cells. And all biomarkers were significantly relevant to eosinophils. Our findings detected 4 coagulation and fibrinolysis-related biomarkers (MAP2K1, CREBBP, TAF1, and HP) for UC, which contributed to the advancement of UC for further clinical investigation.

bioinformatics analysis
biomarkers
coagulation and fibrinolysis
ulcerative colitis
OPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

Ulcerative colitis (UC) is a chronic inflammatory disease of the intestines that frequently occurs repeatedly and predominantly affects the colon and rectum. The main symptoms of this disease consist of abdominal pain, rectal bleeding, and diarrhea.[1] The incidence of UC is on the rise worldwide and is intractable. Approximately 15% of patients with UC undergo an aggressive course, and some even progress to developmental abnormalities and colorectal cancer (CRC).[2] The etiology of UC includes intestinal microbial factors and disorders of the immune response.[3] Current treatments for ulcerative colitis comprise surgical and nonsurgical therapies. Nonsurgical therapy consists of 5-aminosalicylic acid (5-ASA), glucocorticoids, immunosuppressants, biologics as well as probiotics, but they have a high recurrence rate and side effects.[4] Therefore, the identification of new biomarkers and exploration of their potential molecular mechanisms are essential for the early clinical diagnosis of ulcerative colitis and targeted therapy.

Inflammation and abnormal coagulation mechanisms develop an instrumental role in the pathology of UC. Activation of the coagulation cascade and abnormal fibrinolysis are relevant in the pathogenesis of UC, and many studies suggest a close association between inflammation and coagulation and fibrinolytic responses. Abnormalities in coagulation mechanisms are frequently observed qualitatively or quantitatively in patients with UC. In recent years, it has been shown that active intestinal inflammation can activate the coagulation and fibrinolytic cascades, and that the hypercoagulable and hyperfibrinolytic states in UC patients are secondary to intestinal inflammation.[5] In addition to platelet activation, the inflammatory process promotes the expression of tissue factors.[6] On the other hand, the inflammatory process increases the level of fibrinogen, which is considered an acute-phase reactant of inflammation associated with thrombosis.[7]

Therefore, this study explored the biomarkers associated with coagulation–fibrinolysis in ulcerative colitis based on the data set related to ulcerative colitis obtained from the Gene Expression Omnibus (GEO) database using a bioinformatics method. The enrichment analysis explored the possible pathways through which the biomarkers affect the disease and provided a theoretical basis and new directions for further exploration of the molecular mechanisms of coagulation–fibrinolysis-related genes in UC in the future.

2. Materials and methods

2.1. Data originations

UC-related datasets (GSE169568 and GSE94648) were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/). The GSE169568, including 58 UC and 30 control peripheral blood samples, was employed as the training set.[8] The GSE94648, containing 25 UC and 22 control peripheral whole blood samples, which was used as a validation set.[9] Altogether, 514 coagulation and fibrinolysis-related genes (CFRGs) were downloaded from the GeneCards database (http://www.genecards.org),[10] and 130 CFRGs with relevance score >5 were selected for subsequent analysis. Guidelines Flow Diagram illustrates the workflow chart of data preparation, processing, analysis, and validation.

2.2. Characterization and function enrichment analysis of differential expressed CFRGs (DE-CFRGs) in UC and control

Initially, the differentially expressed genes (DEGs) between UC and control in training set were detected by “limma” package[11] (v3.50.1) with adj P < .05 and |log2FC| > 0.5. The DEGs and CFRGs were taken the intersection to obtain DE-CFRGs. Afterwards, the “enrichR” R package was utilized to carry out the biological enrichment analysis of DE-CFRGs, based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) (P < .05).

2.3. Screening of the biomarkers

To identify the final biomarkers, we employed 3 machine learning algorithms on the DE-CFRGs. Initially, the least absolute shrinkage and selection operator (LASSO) was applied, retaining genes with nonzero coefficients as LASSO-selected biomarkers. Subsequently, the “RandomForest” R package (v4.7-1) ranked the DE-CFRGs by importance, selecting biomarkers from those with notably high importance scores. Lastly, support vector machine-recursive feature elimination via the “caret ” R package (v6.0-91) identified biomarkers by minimizing model errors. After that, the intersection of biomarkers from the 3 algorithms was taken to determine the final set of biomarkers. The therapeutic benefit of the biomarkers for the diagnosis of UC was appraised by the receiver operating characteristic curves in GSE169568 and GSE94648. Genes with an area under the curve (AUC) >0.7 were regarded as having good diagnostic value for UC. Furthermore, biomarker expression was measured by Wilcox test comparing the UC and control in GSE169568 and GSE94648 (P < .05). Eventually, the “rms” R package (v6.2-0) was adopted to establish the nomogram containing biomarkers. The reliability of the nomogram was assessed via corresponding calibration curves.

2.4. Gene Set Enrichment Analysis (GSEA) of biomarkers

For analyzing the biomarker enrichment pathways, we performed GSEA via “ClusterProfiler” R package (v 4.0.5) (adj P < .05).[12] Samples of the training set were categorized into high and low expressed according to the midpoint value of the biomarker expression, and differential analysis was carried out. All genes were ranked via logFC. The reference gene set was “c2.cp.kegg.v7.5.1.symbols.gmt.”

2.5. Immune analysis

The infiltrating abundance of 22 immune cells in UC and control was assessed via CIBERSORT algorithm. Variations in immune cell infiltration abundance between UC and control were compared by Wilcox test (P < .05). The correlation of immune cells with each other was analyzed via Spearman algorithm. Correlation among biomarkers and discrepant immune cells was estimated by Spearman algorithm.

2.6. Sample collection

In our study, 5 UC patients (UC group) and 5 healthy individuals (control group) at Renhuai Hospital of Traditional Chinese Medicine, Renhuai City, Guizhou Province from January 1, 2022 to January 1, 2023 were recruited. The following were the inclusion criteria for the samples: (1) Western diagnosis met the relevant criteria for ulcerative colitis in gastroenterology, and Chinese medicine diagnosis met the relevant criteria for damp-heat type in integrative surgery; (2) 18 years old ≤ age ≤ 65 years old; (3) no history of antimicrobial drugs or related systemic therapy in the last 1 month; (4) both primary or chronic relapsing types; (5) good compliance with the program. The investigation was endorsed by the Medical Ethical Review Report on “Identification and Mechanistic Analysis of Coagulation–Fibrinolysis Related Biomarkers in Patients with Ulcerative Colitis.” All patients submitted an informative declaration of consent.

2.7. Real time quantitative-polymerase chain reaction (RT-qPCR)

Initially, total RNA of peripheral blood samples was collected via TRIzol (Ambion, Austin) in accordance with instructions provided by the manufacturer. The SureScript-First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China) was utilized to perform reverse transcription of total RNA to cDNA the based on the manufacturer’s instructions. RT-qPCR was executed with 2xUniversal Blue SYBR Green qPCR Master Mix (Servicebio, Wuhan, China). The primer sequences for RT-qPCR were illustrated in Table 1. The internal reference gene was GAPDH. The 2−ΔΔCt approach was employed to count the expression of biomarkers.[13]

Table 1 The primer sequences of biomarkers.

Gene name	Primer sequence (5’-3’)	
MAP2K1-F	TAGTTGAGTGTGACGGGTGC	
MAP2K1-R	AGCTCTAGCTCCTCCAGCTT	
TAF1-F	TAGCTGCAGAGCAACGACTG	
TAF1-R	CATCCTACCTTCTGCTGTGTT	
HP-F	AACCAAGATGAGTGCCCTGG	
HP-R	CAGCCGTCATCTGCTTCACAT	
Reference gene-GAPDH-F	CGAAGGTGGAGTCAACGGATTT	
Reference gene-GAPDH-R	ATGGGTGGAATCATATTGGAAC	

2.8. Statistical analysis

Statistical analysis was undertaken by R studio. Discrepancies among groups were analyzed by Wilcox test. P < .05 represented a significant difference.

3. Results

3.1. Acquisition and enrichment analysis of DE-CFRGs

There existed 776 DEGs between UC and control, of which 338 up-regulated genes and 438 down-regulated genes (Fig. 1A and B). In total, 10 DE-CFRGs were gained through taking the intersection of DEGs with CFRGs, namely MAP2K1, CREBBP, TAF1, F13A1, ITGB3, HP, CXCL10, MMP9, ITGA2B, and F2RL1 (Fig. 1C). The 10 DE-CFRGs were transparently enriched in 412 Gene Ontology items (Table S1, Supplemental Digital Content, http://links.lww.com/MD/N499). For instance, the DE-CFRGs were involved in “cellular response to UV,” “platelet degranulation,” “platelet alpha granule,” “platelet alpha granule membrane,” “serine-type endopeptidase activity,” “histone acetyltransferase activity,” etc. (Fig. 1D). Of the KEGG pathways, the DE-CFRGs were markedly enriched in 74 KEGG pathways (Table S2, Supplemental Digital Content, http://links.lww.com/MD/N500), such as “microRNAs in cancer,” “TNF signaling pathway,” “thyroid hormone signaling pathway” (Fig. 1D).

Figure 1. Differential expression analysis and functional enrichment analysis. (A and B) The volcano map (A) and heat map (B) of DEGs between UC and control. (C) The Venn diagram of 10 DE-CFRGs. (D) The GO terms and KEGG pathways enriched in DE-CFRGs. DEGs, differentially expressed genes; UC, ulcerative colitis; DE-CFRGs, differentially expressed coagulation and fibrinolysis-related genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

3.2. Acquisition of the biomarkers

With respect to the LASSO algorithm, 7 signatures genes were derived, namely MAP2K1, CREBBP, TAF1, HP, CXCL10, MMP9, and ITGA2B (Fig. 2A). The 10 DE-CFRGs were ranked based on their importance via random forest algorithm. Since the importance of the seventh gene (F2RL1) decreased significantly compared to the sixth gene (HP), we obtained the 6 signature genes via random forest algorithm, namely MAP2K1, CREBBP, TAF1, MMP9, ITGA2B, and HP (Fig. 2B and C). In total, 7 signature genes were gained using support vector machine-recursive feature elimination algorithm, namely MAP2K1, CREBBP, TAF1, F2RL1, CXCL10, HP, and ITGB3 (Fig. 2D). Subsequently, 4 biomarkers were gained, namely MAP2K1, CREBBP, TAF1, and HP (Fig. 2E). In GSE169568, AUC values of all biomarkers were >0.7 (Fig. 3A–D). In GSE94648, the AUC values were 0.742 (MAP2K1), 0.651 (CREBBP), 0.727 (TAF), and 0.840 (HP) (Fig. 3E–H). These results demonstrated that 4 biomarkers have therapeutic benefit in the diagnosis of UC. In GSE169568, the expression of all biomarkers was dramatically dissimilar among the UC and normal (Fig. 4A). The expression of HP had evidently increased in UC, while the expression of the other 3 genes showed the opposite trend. In GSE94648, MAP2K1, TAF1, and HP showed the same expression trend as the training set, while the expression of CREBBP among UC and control was insignificant (Fig. 4B).

Figure 2. Identification of biomarkers. (A) The lamabda min/lamabda lse and coefficients of signature genes obtained by LASSO. Different colors indicate the change of regression coefficients for different genes with log lamabda change. (B) Error rate and the number of classification trees of random forest model. (C) The importance ranking of genes. (D) The results of support vector machine-recursive feature elimination (SVM-RFE). (E) The Venn diagram of 4 biomarkers obtained by above 3 machine learning algorithms. LASSO, least absolute shrinkage and selection operator; RF, random forest; SVM-RFE, support vector machine-recursive feature elimination.

Figure 3. Evaluation of diagnostic value of biomarkers for UC. (A–D) The receiver operating characteristic (ROC) curves of 4 biomarkers in the GSE169568 dataset. (E and F) The ROC curves of biomarkers in the GSE94648 dataset. ROC, receiver operating characteristic; AUC, area under the curve.

Figure 4. Expression of biomarkers in GSE169568 dataset (A) and GSE94648 dataset (B). ns, not significant; **P < .01; ***P < .001.

3.3. The creation of the nomogram

A nomogram incorporating 4 biomarkers was generated to predict UC progression (Fig. 5A). The corresponding calibration curves revealed that the predicted advancement was very proximate to the reality, demonstrating the superior forecasting precision of the nomogram for UC progression (Fig. 5B).

Figure 5. The construction and validation of nomogram based on 4 biomarkers. (A) The nomogram of 4 biomarkers. (B) The calibration curve of the nomogram.

3.4 . MAP2K1, CREBBP, and TAF1 were enriched in “T cell receptor signaling pathway”

In order to better comprehend the implications of biomarkers on the progression of UC, we undertook GSEA. The top5 KEGG pathways enriched were shown. MAP2K1, CREBBP, and TAF1 were involved in ‘oxidative phosphorylation’ in low expressed group (Fig. 6A–C). Additionally, MAP2K1, CREBBP, and TAF1 were enriched in “T cell receptor signaling pathway,” “JAK-STAT signaling pathway,” “B cell receptor signaling pathway,” ‘chemokine signaling pathway’, and so on in high expressed group (Fig. 6A–C). HP was participated in “antigen processing and presentation,” “primary immunodeficiency,” ‘cell adhesion molecules cams’, and so on in low expressed group (Fig. 6D).

Figure 6. Gene Set Enrichment Analysis (GSEA) of biomarkers. A: MAP2K1; (B) CREBBP; (C) TAF1; (D) HP.

3.5. MAP2K1 and HP were significantly relevant to resting mast cells

The infiltration enrichment of 22 immune cells in the UC and control was illustrated through column stacking chart (Fig. 7A). In total, the infiltrating abundance of 4 immune cells between UC and control was dramatically dissimilar, namely eosinophils, macrophage M0, resting mast cells, and regulatory T cells (Tregs) (Fig. 7B). Association analysis between immune cells revealed no significant association among most of the immune cells (Fig. 7C). Eosinophil was most markedly positively relevant to monocytes, and most markedly negatively relevant to Tregs (Fig. 7C). The CREBBP was significantly relevant to differential immune cells (Fig. 7D). And CREBBP was markedly positively related to Tregs and macrophage M0, and was markedly negatively related to eosinophils and resting mast cells (Fig. 7D). In addition, MAP2K1, and TAF1 were also significantly negatively correlated with eosinophil, while the HP was markedly positively related to eosinophils (Fig. 7E–G). MAP2K1 and HP were significantly relevant to resting mast cells (Fig. 7E and F). Therefore, HP was significantly positively correlated with macrophage M0 (Fig. 7F).

Figure 7. Immune infiltration analysis. (A) The abundance of 22 immune cells in UC and control. (B) The scores of differential immune cells in the UC and control groups. (C) The relevance of immune cells. (D–G) The relevance of biomarkers to differential immune cells. *P < .05; **P < .01.

3.6. MAP2K1, TAF1, and HP were significantly highly expressed in control

To further validate the biomarkers, the expression of MAP2K1, TAF1, and HP was analyzed via RT-qRCR. The results suggested these 3 biomarkers were significantly highly expressed in control (Fig. 8A–C). However, the expression trend of HP was opposite to the findings of the public database, which may be attributed to heterogeneity in the samples, as well as differences in the experimental design.

Figure 8. The expression of biomarkers by RT-qPCR. (A) HP; (B) MAP2K1; (C) TAF1.*P < .05.

4. Discussion

In this study, 4 biomarkers (MAP2K1, CREBBP, TAF1, and HP) were obtained by bioinformatics analysis. MAP2K1 is an interleukin-6 (IL-6) signaling pathway protein, and in a pig model consuming a high-calorie diet, intestinal inflammation was increased, and colonic mucosal IL-6 expression was elevated, and MAP2K1-related proteins were highly expressed.[14] Experiments in mice showed that high salt diet enhanced the expression of the pro-inflammatory gene Map2k1 and inhibited the expression of many cytokines and chemokine genes (e.g., Ccl3, Ccl4, Cxcl2, Cxcr4, Ccr7), thereby exacerbating colitis in mice and, to a lesser extent, affecting small intestinal mucosal immunity.[15] CREBBP is a human lysine acetyltransferase, a key node of the human protein interactome.[16] Ruifeng Song found that CREBBP can interact with STAT1 to involve in development and progression of UC.[17,18] Additionally, CREBBP is a novel target of miR-26b in colorectal tumor cells. Sixteen cellular studies have shown that inhibition of the CREBBP bromodomain in colon cancer cells leads to reduced c-Myc levels and decreased acetylation of H3K18 and H3K27.[19] TAF1 is the most important transcription factor involved in the development of various cancers, such as colorectal cancer and breast cancer.[20,21] Compared to healthy subjects, haptoglobin was moderately increased in the serum of colon cancer patients.[22] These studies provide evidence for our findings, and therefore, further exploration of the specific mechanisms linking these 4 key genes to UC is warranted.

The results of GSEA enrichment analysis suggest that biomarkers are enriched in antigen processing and presentation, primary immunodeficiency, and T-cell receptor signaling. Tomoya Iida et al showed that autophagy can play multiple roles in the pathogenesis of inflammatory bowel disease by altering intracellular dendritic cell antigen presentation and other processes.[23] Severe infantile ulcerative colitis develops into primary immunodeficiency, and RTEL1 mutations are associated with infantile ulcerative colitis and severe immunodeficiency.[24] Mucosal CD4 + T cells are amplified in UC.[25] In addition, key genes were enriched for cell adhesion molecule function. Cell adhesion molecule (CAM) is a collective term for numerous molecules that mediate mutual contact and binding between cells or between cells and the extracellular matrix (ECM). Adhesion molecules act as receptor–ligand binders and are engaged in cell recognition, cell activation and signal transduction and are the molecular foundation for several critical physiological and pathological processes, including immune response, inflammation, coagulation, tumor metastasis, and wound healing.[26] Some studies have shown that IgG can inhibit cell adhesion in patients with ulcerative colitis.[27]

Immune infiltration analysis showed that the infiltration abundance of 4 immune cells (eosinophil, macrophage M0, resting mast cell, and Tregs) differed between the UC and control. Niels Vande Casteele et al found that eosinophils are associated with the pathogenesis of UC.[28] Eosinophils, acting as effector cells within the lamina propria, exacerbate tissue damage through their hyperactive Th2 immune response. The activation of muscarinic receptors induces the secretion of corticotropin-releasing factor, which in turn stimulates nearby mast cells to undergo degranulation, consequently heightening barrier permeability.[29] Remarkably, eosinophils can release various cytotoxic proteins and pro-inflammatory cytokines, interacting with other immune cells, thereby further exacerbating the inflammatory environment.[30] Eosinophils can produce IL-6, and therefore, they may be related to MAP2K1, which is involved in the IL-6 signaling pathway. Another thing to mention is that, mast cell resting ratios also differed between UC and normal subjects.[31] They play a crucial role in preserving epithelial barrier integrity, facilitating neuro-immune communication, and modulating mucosal immune responses.[32] Mast cells are frequently present in mucosal tissues and are well-known for their function in allergic reactions. In a previous study on irritable bowel syndrome, it was found that the natural compound costunolide inhibits mast cell activity by upregulating p-CREB expression.[33] Our research revealed a slight positive correlation between mast cell activity and CREBBP or MAP2K1. However, their specific roles within mast cells still need comprehensive clarification. As well, Treg cells maintain intestinal mucosal health, and Treg cells can inhibit CX3CR1 + macrophage production of interleukin-23 (IL-23) and IL-1β by inhibiting interleukin-22 (IL-22) production by group 3 innate lymphoid cells (ILC3).[34] Tregs aid in the reduction of inflammation in UC by inhibiting the function of immune cells that promote inflammation. We discovered a positive correlation between CREBBP and Tregs. Additionally, expression of CREBBP was lower in patients with UC, which might contribute to insufficient differentiation and functions of regulatory T cells. Joseph Castillo et al also confirmed that in follicular lymphoma, CREBBP drove Tregs differentiation by altering pro-inflammatory cytokine secretion.[35] During the active phase of UC, the infiltration of macrophages induces the production of inflammatory cytokines such as IL-6, IL-18, TNF-α, and IL-1β, which play crucial roles in inflammation and immunity. In general, macrophages polarize into 2 phenotypes under different conditions.[36] While M1 macrophages predominantly fuel inflammation by breaking down the barrier and promoting apoptosis of epithelial cells, M2 macrophages act in an opposing manner by resolving inflammation and fostering tissue healing. Multiple studies have confirmed that modulating macrophage polarization to restore immune balance is a promising therapeutic strategy for UC.[37–40] Our research findings indicated a significant positive correlation between HP and M0 macrophages, which predominantly exhibit a pro-inflammatory cytokine profile.[41] Interestingly, the results also indicated a significant positive correlation between CREBBP and M0 macrophages, while showing a negative correlation with M1 macrophages. We speculated that CREBBP, as a protective factor, may promote the infiltration of M0 macrophages into the inflammatory environment, but primarily facilitate the polarization of macrophages towards the M2 phenotype rather than the M1 phenotype. Consequently, the alteration of gene expression may influence immune cell activity, thus contributing to the development of UC.[42] Therefore, amelioration of aberrant immune status by targeting 4 biomarkers may be a perspective therapy modality in targeted therapies for UC.

The discovery of MAP2K1, CREBBP, TAF1, and HP as possible biomarkers for UC opens up new avenues for improving the clinical identification and treatment of this condition. Current diagnostic approaches for UC, which rely heavily on endoscopic evaluation and histological assessment, can be invasive and may not always provide clear-cut results, particularly in early or mild cases.[43] The introduction of these biomarkers could offer a noninvasive or minimally invasive alternative that complements traditional methods, especially in the initial screening process. Biomarkers like MAP2K1, which is involved in the IL-6 signaling pathway, could be indicative of the inflammatory processes at play in UC. Elevated levels might suggest active inflammation, allowing for earlier detection of the disease. Monitoring the levels of these biomarkers over time could provide insights into disease progression and treatment response, which is particularly useful for managing flare-ups and adjusting therapeutic strategies.[44] The expression levels of these biomarkers could vary among individuals, reflecting the heterogeneity of UC. This information could be used to tailor treatment plans to individual patients, moving towards a more personalized approach to UC management. Some of these biomarkers, such as HP, which is involved in the acute-phase response, might have predictive value in determining the severity or the risk of complications in UC patients.[45] The use of these biomarkers could streamline the diagnostic process by providing quick and reliable results, reducing the need for repeat endoscopic procedures, and thereby lowering healthcare costs and patient discomfort. If validated through large-scale studies, these biomarkers could be integrated into clinical practice through the development of simple blood tests or other diagnostic assays. This would facilitate their use in routine checkups and in the emergency department for rapid assessment.[46] The identification of these biomarkers could also spur further research into the underlying pathophysiology of UC, potentially leading to the discovery of new therapeutic targets and the development of novel treatments. While these biomarkers hold promise, their implementation into clinical practice will require extensive validation studies to ensure their specificity and sensitivity for UC diagnosis. It will also be crucial to establish reference ranges and to understand how these biomarker levels are affected by confounding factors such as concomitant medications, other medical conditions, and the natural variability among individuals.

Among all the genes discussed, we identified 4 genes that may serve as potential biomarkers for UC, characterized by significant differential expression related to coagulation and fibrinolysis. Although our research has yielded meaningful results, it does have limitations. Despite ensuring the reliability of our findings through robust statistical methods and cross-validation of results, our small sample size may limit the universality of the outcomes. Therefore, caution should be exercised when interpreting the results. The study was also predominantly bioinformatics-based, necessitating broader experimental validation to understand the biological functions and mechanisms of the biomarkers. The heterogeneity of UC suggests that future research should account for differences among patient groups to enhance the applicability and clinical relevance of the findings. To address these limitations, future directions should include: (1) in vitro experiments: we plan to use cell culture models to test the impact of biomarkers on cellular functions. (2) Animal models: we are considering the use of relevant animal models to evaluate the biological roles of biomarkers in vivo. (3) Clinical sample analysis: we aim to analyze a large cohort of clinical samples to confirm the expression patterns of biomarkers and their correlation with disease states.

5. Conclusion

In summary, this investigation screened 4 biomarkers of UC species associated with coagulation–fibrinolysis to provide new ideas for the clinical management of UC. However, the study has some limitations, and we need to conduct more in-depth functional studies to investigate the mechanism of action.

Acknowledgments

We thank Renhuai Hospital of Traditional Chinese Medicine, Guizhou Province for their contribution in collecting clinical samples for this study. We are grateful to the GEO database for providing the data analyzed for this study.

Author contributions

Conceptualization: Xue Wu.

Data curation: Xue Wu.

Formal analysis: Feng-Yun Li.

Funding acquisition: Xue Wu.

Investigation: Xue Wu.

Methodology: Xue Wu.

Project administration: Xue Wu.

Resources: Xue Wu.

Software: Xue Wu.

Supervision: Xue Wu.

Validation: Xue Wu.

Visualization: Xue Wu.

Writing – original draft: Feng-Yun Li.

Writing – review & editing: Feng-Yun Li, Xue Wu, Mei-Fang Yao, Juan Zhang, Yuan-Jun Mo.

Supplementary Material

Abbreviations:

AUC area under the curve

CFRGs coagulation and fibrinolytic-related genes

DEGs differentially expressed genes

DE-CFRGs differential expressed CFRGs

GEO Gene Expression Omnibus

GSEA Gene Set Enrichment Analysis

KEGG Kyoto Encyclopedia of Genes and Genomes

LASSO least absolute shrinkage and selection operator

RT-qPCR real time quantitative-polymerase chain reaction

Tregs regulatory T cells

UC ulcerative colitis

This study was performed in line with the principles of the Declaration of Helsinki. All patients signed informed consent forms, and the study received approval from the Ethics Committee of Renhuai Hospital of Traditional Chinese Medicine.

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Li F-Y, Wu X, Yao M-F, Zhang J, Mo Y-J. Identification and validation of coagulation and fibrinolytic-related diagnostic biomarkers for ulcerative colitis by bioinformatics analysis. Medicine 2024;103:36(e39552).

F-YL and XW contributed equally to this work.

Guidelines Flow Diagram: Workflow of the research.

The UC-related datasets (GSE169568 and GSE94648) analyzed in this study were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/).
==== Refs
References

[1] Turner D Ricciuto A Lewis A . STRIDE-II: an update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): determining therapeutic goals for treat-to-target strategies in IBD. Gastroenterology. 2021;160 :1570–83.33359090
[2] Segal JP LeBlanc JF Hart AL . Ulcerative colitis: an update. Clin Med (Lond). 2021;21 :135–9.33762374
[3] Kaenkumchorn T Wahbeh G . Ulcerative colitis: making the diagnosis. Gastroenterol Clin North Am. 2020;49 :655–69.33121687
[4] Ama C Sandborn WJ D’Haens GR . Discordance between patient-reported outcomes and mucosal inflammation in patients with mild to moderate ulcerative colitis. Clin Gastroenterol Hepatol. 2020;18 :1760–8.31546056
[5] Alfarone L Dal Buono A Craviotto V . Cross-sectional imaging instead of colonoscopy in inflammatory bowel diseases: lights and shadows. J Clin Med. 2022;11 :353.35054047
[6] Grant RK Jones GR Plevris N . The ACE (Albumin, CRP and Endoscopy) index in acute colitis: a simple clinical index on admission that predicts outcome in patients with acute ulcerative colitis. Inflamm Bowel Dis. 2021;27 :451–7.32572468
[7] Luyendyk JP Schoenecker JG Flick MJ . The multifaceted role of fibrinogen in tissue injury and inflammation. Blood. 2019;133 :511–20.30523120
[8] Juzenas S Hübenthal M Lindqvist CM . Detailed transcriptional landscape of peripheral blood points to increased neutrophil activation in treatment-naïve inflammatory bowel disease. J Crohns Colitis. 2022;16 :1097–109.35022690
[9] Planell N Masamunt MC Leal RF . Usefulness of transcriptional blood biomarkers as a non-invasive surrogate marker of mucosal healing and endoscopic response in ulcerative colitis. J Crohns Colitis. 2017;11 :1335–46.28981629
[10] Ma Y Wang B He P . Coagulation- and fibrinolysis-related genes for predicting survival and immunotherapy efficacy in colorectal cancer. Front Immunol. 2022;13 :1023908.36532065
[11] Ritchie ME Phipson B Wu D . Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43 :e47.25605792
[12] Yu G Wang LG Han Y He Q-Y . Clusterprofiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16 :284–7.22455463
[13] Livak KJ Schmittgen TD . Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25 :402–8.11846609
[14] Sido A Radhakrishnan S Kim SW . A food-based approach that targets interleukin-6, a key regulator of chronic intestinal inflammation and colon carcinogenesis. J Nutr Biochem. 2017;43 :11–7.28193578
[15] Miranda PM De Palma G Serkis V . High salt diet exacerbates colitis in mice by decreasing Lactobacillus levels and butyrate production. Microbiome. 2018;6 :57.29566748
[16] Benderska N Dittrich AL Knaup S . miRNA-26b overexpression in ulcerative colitis-associated carcinogenesis. Inflamm Bowel Dis. 2015;21 :2039–51.26083618
[17] Shinzaki S Matsuoka K Tanaka H . Leucine-rich alpha-2 glycoprotein is a potential biomarker to monitor disease activity in inflammatory bowel disease receiving adalimumab: PLANET study. J Gastroenterol. 2021;56 :560–9.33942166
[18] Song R Li Y Hao W Wang B Yang L Xu F . Identification and analysis of key genes associated with ulcerative colitis based on DNA microarray data. Medicine (Baltimore). 2018;97 :e10658.29794741
[19] Brand M Clayton J Moroglu M . Controlling intramolecular interactions in the design of selective, high-affinity ligands for the CREBBP bromodomain. J Med Chem. 2021;64 :10102–23.34255515
[20] Rai S Singh MP Srivastava S . Integrated analysis identifies novel fusion transcripts in laterally spreading tumors suggestive of distinct etiology than colorectal cancers. J Gastrointest Cancer. 2023;54 :913–26.36480069
[21] Zhang S Liu X Chen W Zhang K Wu Q Wei Y . Targeting TAF1 with BAY-299 induces antitumor immunity in triple-negative breast cancer. Biochem Biophys Res Commun. 2023;665 :55–63.37148745
[22] Yasutomi E Inokuchi T Hiraoka S . Leucine-rich alpha-2 glycoprotein as a marker of mucosal healing in inflammatory bowel disease. Sci Rep. 2021;11 :11086.34045529
[23] Iida T Onodera K Nakase H . Role of autophagy in the pathogenesis of inflammatory bowel disease. World J Gastroenterol. 2017;23 :1944–53.28373760
[24] Ziv A Werner L Konnikova L . An RTEL1 mutation links to infantile-onset ulcerative colitis and severe immunodeficiency. J Clin Immunol. 2020;40 :1010–9.32710398
[25] Damico F Bonovas S Danese S . Faecal calprotectin and histologic remission in ulcerative colitis. Aliment Pharmacol Ther. 2020;51 :689–98.32048751
[26] Xiang BJ Jiang M Sun MJ Dai C . Optimal range of fecal calprotectin for predicting mucosal healing in patients with inflammatory bowel disease: a systematic review and meta-analysis. Visc Med. 2021;37 :338–48.34722717
[27] Kuwada T Shiokawa M Kodama Y . Identification of an anti-integrin αvβ6 autoantibody in patients with ulcerative colitis. Gastroenterology. 2021;160 :2383–94.e21.33582126
[28] Vande Casteele N Leighton JA Pasha SF . Utilizing deep learning to analyze whole slide images of colonic biopsies for associations between eosinophil density and clinicopathologic features in active ulcerative colitis. Inflamm Bowel Dis. 2022;28 :539–46.34106256
[29] Filippone RT Sahakian L Apostolopoulos V Nurgali K . Eosinophils in inflammatory bowel disease. Inflamm Bowel Dis. 2019;25 :1140–51.30856253
[30] Loktionov A . Eosinophils in the gastrointestinal tract and their role in the pathogenesis of major colorectal disorders. World J Gastroenterol. 2019;25 :3503–26.31367153
[31] Zhang J Shi G . Lymphocyte infiltration and key differentially expressed genes in the ulcerative colitis. Medicine (Baltimore). 2020;99 :e21997.32871953
[32] Albert-Bayo M Paracuellos I González-Castro AM . Intestinal mucosal mast cells: key modulators of barrier function and homeostasis. Cells. 2019;8 :135.30744042
[33] Li X Liu Q Yu J . Costunolide ameliorates intestinal dysfunction and depressive behaviour in mice with stress-induced irritable bowel syndrome via colonic mast cell activation and central 5-hydroxytryptamine metabolism. Food Funct. 2021;12 :4142–51.33977961
[34] Bauché D Joyce-Shaikh B Jain R . LAG3(+) regulatory T cells restrain interleukin-23-producing CX3CR1(+) gut-resident macrophages during group 3 innate lymphoid cell-driven colitis. Immunity. 2018;49 :342–52.e5.30097293
[35] Castillo J Wu E Lowe C . CBP/p300 drives the differentiation of regulatory T cells through transcriptional and non-transcriptional mechanisms. Cancer Res. 2019;79 :3916–27.31182547
[36] Zhang M Li X Zhang Q Yang J Liu G . Roles of macrophages on ulcerative colitis and colitis-associated colorectal cancer. Front Immunol. 2023;14 :1103617.37006260
[37] Wu MM Wang QM Huang BY . Dioscin ameliorates murine ulcerative colitis by regulating macrophage polarization. Pharmacol Res. 2021;172 :105796.34343656
[38] Liang L Liu L Zhou W . Gut microbiota-derived butyrate regulates gut mucus barrier repair by activating the macrophage/WNT/ERK signaling pathway. Clin Sci (Lond). 2022;136 :291–307.35194640
[39] Yang Z Lin S Feng W . A potential therapeutic target in traditional Chinese medicine for ulcerative colitis: macrophage polarization. Front Pharmacol. 2022;13 :999179.36147340
[40] Jia DJ Wang QW Hu YY . Lactobacillus johnsonii alleviates colitis by TLR1/2-STAT3 mediated CD206+ macrophagesIL-10 activation. Gut Microbes. 2022;14 :2145843.36398889
[41] Zhou D Yao M Zhang L . Adenosine alleviates necrotizing enterocolitis by enhancing the immunosuppressive function of myeloid-derived suppressor cells in newborns. J Immunol. 2022;209 :401–11.35777852
[42] Herppich S Hoenicke L Kern F . Zfp362 potentiates murine colonic inflammation by constraining Treg cell function rather than promoting Th17 cell differentiation. Eur J Immunol. 2023;53 :1425–32.
[43] Kiesslich R . Colour me blue: chromoendoscopy and advanced detection methods in ulcerative colitis. Curr Opin Gastroenterol. 2022;38 :67–71.34871195
[44] Na SY Lim YJ . Capsule endoscopy in inflammatory bowel disease: when? To whom? Diagnostics (Basel). 2021;11 :2240.34943477
[45] Hosoe N Hayashi Y Ogata H . Colon capsule endoscopy for inflammatory bowel disease. Clin Endosc. 2020;53 :550–4.31914721
[46] Matsubayashi M Kobayashi T Okabayashi S . Determining the usefulness of capsule scoring of ulcerative colitis in predicting relapse of inactive ulcerative colitis. J Gastroenterol Hepatol. 2021;36 :943–50.32805065
