Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease
Crohn’s disease (CD), a subtype of inflammatory bowel disease (IBD), causes chronic gastrointestinal tract inflammation. Thirty percent of patients do not respond to anti-tumor necrosis factor (TNF) therapy. Sialylation is involved in the pathogenesis of IBD. We aimed to identify potential biomarker...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.1065297/full |
| _version_ | 1811224937735651328 |
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| author | Chenglin Ye Sizhe Zhu Yuan Gao Yabing Huang |
| author_facet | Chenglin Ye Sizhe Zhu Yuan Gao Yabing Huang |
| author_sort | Chenglin Ye |
| collection | DOAJ |
| description | Crohn’s disease (CD), a subtype of inflammatory bowel disease (IBD), causes chronic gastrointestinal tract inflammation. Thirty percent of patients do not respond to anti-tumor necrosis factor (TNF) therapy. Sialylation is involved in the pathogenesis of IBD. We aimed to identify potential biomarkers for diagnosing CD and predicting anti-TNF medication outcomes in CD. Three potential biomarkers (SERPINB2, TFPI2, and SLC9B2) were screened using bioinformatics analysis and machine learning based on sialylation-related genes. Moreover, the combined model of SERPINB2, TFPI2, and SLC9B2 showed excellent diagnostic value in both the training and validation cohorts. Importantly, a Sial-score was constructed based on the expression of SERPINB2, TFPI2, and SLC9B2. The Sial-low group showed a lower level of immune infiltration than the Sial-high group. Anti-TNF therapy was effective for 94.4% of patients in the Sial-low group but only 15.8% in the Sial-high group. The Sial-score had an outstanding ability to predict and distinguish between responders and non-responders. Our comprehensive analysis indicates that SERPINB2, TFPI2, and SLC9B2 play essential roles in pathogenesis and anti-TNF therapy resistance in CD. Furthermore, it may provide novel concepts for customizing treatment for individual patients with CD. |
| first_indexed | 2024-04-12T08:57:10Z |
| format | Article |
| id | doaj.art-935980f4eee0424097e6ea08e1150740 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-12T08:57:10Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-935980f4eee0424097e6ea08e11507402022-12-22T03:39:20ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-11-011310.3389/fgene.2022.10652971065297Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s diseaseChenglin Ye0Sizhe Zhu1Yuan Gao2Yabing Huang3Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, Wuhan, ChinaCrohn’s disease (CD), a subtype of inflammatory bowel disease (IBD), causes chronic gastrointestinal tract inflammation. Thirty percent of patients do not respond to anti-tumor necrosis factor (TNF) therapy. Sialylation is involved in the pathogenesis of IBD. We aimed to identify potential biomarkers for diagnosing CD and predicting anti-TNF medication outcomes in CD. Three potential biomarkers (SERPINB2, TFPI2, and SLC9B2) were screened using bioinformatics analysis and machine learning based on sialylation-related genes. Moreover, the combined model of SERPINB2, TFPI2, and SLC9B2 showed excellent diagnostic value in both the training and validation cohorts. Importantly, a Sial-score was constructed based on the expression of SERPINB2, TFPI2, and SLC9B2. The Sial-low group showed a lower level of immune infiltration than the Sial-high group. Anti-TNF therapy was effective for 94.4% of patients in the Sial-low group but only 15.8% in the Sial-high group. The Sial-score had an outstanding ability to predict and distinguish between responders and non-responders. Our comprehensive analysis indicates that SERPINB2, TFPI2, and SLC9B2 play essential roles in pathogenesis and anti-TNF therapy resistance in CD. Furthermore, it may provide novel concepts for customizing treatment for individual patients with CD.https://www.frontiersin.org/articles/10.3389/fgene.2022.1065297/fullcrohn’s diseaseanti-TNF therapysialylationimmune infiltrationbioinformatics analysis |
| spellingShingle | Chenglin Ye Sizhe Zhu Yuan Gao Yabing Huang Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease Frontiers in Genetics crohn’s disease anti-TNF therapy sialylation immune infiltration bioinformatics analysis |
| title | Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease |
| title_full | Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease |
| title_fullStr | Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease |
| title_full_unstemmed | Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease |
| title_short | Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease |
| title_sort | landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti tnf therapy in crohn s disease |
| topic | crohn’s disease anti-TNF therapy sialylation immune infiltration bioinformatics analysis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.1065297/full |
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