Cellular senescence defining the disease characteristics of Crohn’s disease
BackgroundCrohn’s disease (CD) is a complex and heterogeneous inflammatory disease whose most important feature is immune dysregulation. As a basic cell response, cellular senescence (CS) can regulate the immune response involved in a variety of inflammatory diseases. However, the role of CS in the...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1616531/full |
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| author | Wenyu Zhang Xianzong Ma Xianzong Ma Wenqing Tian Yongsheng Teng Meihua Ji |
| author_facet | Wenyu Zhang Xianzong Ma Xianzong Ma Wenqing Tian Yongsheng Teng Meihua Ji |
| author_sort | Wenyu Zhang |
| collection | DOAJ |
| description | BackgroundCrohn’s disease (CD) is a complex and heterogeneous inflammatory disease whose most important feature is immune dysregulation. As a basic cell response, cellular senescence (CS) can regulate the immune response involved in a variety of inflammatory diseases. However, the role of CS in the pathogenesis and diagnosis prediction of CD are still unknown.MethodsWe utilized CD-related datasets from the GEO database for differential gene expression analysis, and CS related differentially expressed genes (CSRDEGs) in CD by a comprehensive bioinformatics analysis encompassing GSEA, WGCNA, and various interaction networks. The support vector machine (SVM) algorithm, random forest algorithm and LASSO regression analysis was used to construct a diagnostic model. And based on CSRDEGs, we further constructed a Cellular senescence score (CSscore) model. Different disease subtypes (cluster1/cluster2) were identified by the consensus clustering method. The assessment of immune cell infiltration and its correlation with CSRDEGs was analyzed by ssGAEA and CIBERSORT.ResultsWe identified 10 hub CS related differentially expressed genes (CSRDEGs) in CD. Based on CSRDEGs, we further constructed a diagnostic model (AUC = 0.880) containing 5 CSRDEGs (CDKN1A, IL1A, PML, SIRT1, and STAT3) through machine learning algorithm and other methods and analyzed the correlation with immune cell infiltration. In addition, a CS Scores model (Low or High) based on the 7 CSRDEGs (CDKN2B, IGFBP7, IL1A, IL6, PML, SIRT1, and STAT3) shows different characteristics, reaffirming the inflammatory regulatory role of CS in CD. Finally, the subtype construction (cluster1 and cluster2) based on 10 CSRDEGs shows the heterogeneity of the disease and affirms that CS is a prominent feature of CD.ConclusionsThese results suggest that CS is an important feature of CD, and CSRDEGs can be used to construct disease diagnostic models and distinguish disease subtypes. Further investigation of the mechanism of immune dysregulation caused by CS can deepen our understanding of the pathogenesis of CD. |
| format | Article |
| id | doaj-art-5d1db242542b4118bea4d431e91b1ef2 |
| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-5d1db242542b4118bea4d431e91b1ef22025-08-20T02:37:46ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-06-011610.3389/fimmu.2025.16165311616531Cellular senescence defining the disease characteristics of Crohn’s diseaseWenyu Zhang0Xianzong Ma1Xianzong Ma2Wenqing Tian3Yongsheng Teng4Meihua Ji5School of Nursing, Capital Medical University, Beijing, ChinaSenior Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, ChinaDepartment of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, ChinaDepartment of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Gastroenterology, Chongqing General Hospital, Chongqing University, Chongqing, ChinaSchool of Nursing, Capital Medical University, Beijing, ChinaBackgroundCrohn’s disease (CD) is a complex and heterogeneous inflammatory disease whose most important feature is immune dysregulation. As a basic cell response, cellular senescence (CS) can regulate the immune response involved in a variety of inflammatory diseases. However, the role of CS in the pathogenesis and diagnosis prediction of CD are still unknown.MethodsWe utilized CD-related datasets from the GEO database for differential gene expression analysis, and CS related differentially expressed genes (CSRDEGs) in CD by a comprehensive bioinformatics analysis encompassing GSEA, WGCNA, and various interaction networks. The support vector machine (SVM) algorithm, random forest algorithm and LASSO regression analysis was used to construct a diagnostic model. And based on CSRDEGs, we further constructed a Cellular senescence score (CSscore) model. Different disease subtypes (cluster1/cluster2) were identified by the consensus clustering method. The assessment of immune cell infiltration and its correlation with CSRDEGs was analyzed by ssGAEA and CIBERSORT.ResultsWe identified 10 hub CS related differentially expressed genes (CSRDEGs) in CD. Based on CSRDEGs, we further constructed a diagnostic model (AUC = 0.880) containing 5 CSRDEGs (CDKN1A, IL1A, PML, SIRT1, and STAT3) through machine learning algorithm and other methods and analyzed the correlation with immune cell infiltration. In addition, a CS Scores model (Low or High) based on the 7 CSRDEGs (CDKN2B, IGFBP7, IL1A, IL6, PML, SIRT1, and STAT3) shows different characteristics, reaffirming the inflammatory regulatory role of CS in CD. Finally, the subtype construction (cluster1 and cluster2) based on 10 CSRDEGs shows the heterogeneity of the disease and affirms that CS is a prominent feature of CD.ConclusionsThese results suggest that CS is an important feature of CD, and CSRDEGs can be used to construct disease diagnostic models and distinguish disease subtypes. Further investigation of the mechanism of immune dysregulation caused by CS can deepen our understanding of the pathogenesis of CD.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1616531/fullCrohn’s diseasecellular senescencemachine learningimmune infiltration heterogeneitybiomarker |
| spellingShingle | Wenyu Zhang Xianzong Ma Xianzong Ma Wenqing Tian Yongsheng Teng Meihua Ji Cellular senescence defining the disease characteristics of Crohn’s disease Frontiers in Immunology Crohn’s disease cellular senescence machine learning immune infiltration heterogeneity biomarker |
| title | Cellular senescence defining the disease characteristics of Crohn’s disease |
| title_full | Cellular senescence defining the disease characteristics of Crohn’s disease |
| title_fullStr | Cellular senescence defining the disease characteristics of Crohn’s disease |
| title_full_unstemmed | Cellular senescence defining the disease characteristics of Crohn’s disease |
| title_short | Cellular senescence defining the disease characteristics of Crohn’s disease |
| title_sort | cellular senescence defining the disease characteristics of crohn s disease |
| topic | Crohn’s disease cellular senescence machine learning immune infiltration heterogeneity biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1616531/full |
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