Association of polymorphisms in C1orf106, IL1RN, and IL10 with post-induction infliximab trough level in Crohn’s disease patients

Tang, Jian; Zhang, Cai-Bin; Lyu, Kun-Sheng; Jin, Zhongming; Guan, Su; You, Na; Huang, Min; Wang, Xueding; Gao, Xiang

doi:10.1093/gastro/goz056

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…Moreover, a study found that IL1RN can be used as a biomarker of CD (33). IL1RN genetic variants were linked to anti-TNF medication responsiveness in CD patients (34).…”

Section: Discussionmentioning

confidence: 99%

Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

Cao

Xiao

et al. 2023

Preprint

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Background: Atherosclerosis (AS) is a major contributor to cardiovascular mortality and morbidity globally. There is currently a dearth of information about Crohn's disease (CD) and its relation to the risk of atherosclerotic cardiovascular disease. However, recent studies were unable to identify the biological mechanism at the genetic level. Methods: Four microarray datasets (GSE43292, GSE28829, GSE186582, and GSE102133) were downloaded from the Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS and CD. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms were applied to identify hub genes. Then the hub genes were calculated based on the receiver operating characteristic (ROC) curve. Subsequently, we conducted single-gene GSEA analysis and immune infiltration analysis to further investigated the possible mechanism of the hub genes. Results: A total of 24 common upregulated DEGs and 10 common downregulated DEGs were observed in AS and CD. According to enrichment analyses, these genes were connected with immune-related and inflammation-related signaling pathways. Three hub genes (IL1RN, TNFSF13B, PRDM1) were identified. The AUC of hub genes was higher than 0.7 and the AUC of the hub genes-based logistic regression model was 0.873. Single-gene GSEA analysis and immune infiltration analysis found the hub genes were associated with AS in CD patients, and there were some associations between hub genes and 22 immune cells. Conclusion: We identified and validated 3 hub genes (IL1RN, TNFSF13B, PRDM1) as biomarkers in AS and CD. This study may provide a new perspective on the pathogenesis of AS and CD comorbidity.

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“…Moreover, a study found that IL1RN can be used as a biomarker of CD (33). IL1RN genetic variants were linked to anti-TNF medication responsiveness in CD patients (34).…”

Section: Discussionmentioning

confidence: 99%

Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

Cao

Xiao

et al. 2023

Preprint

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“…Overexpression of C1orf106 was associated with invasive breast cancer and its poor prognosis and could be used as a novel marker to predict the aggressiveness and prognosis of breast cancer ( 38 ). In addition, 2 SNPs (rs442905 and rs59457695) in the C1orf106 gene and protein expression levels could be used to predict the therapeutic effect of infliximab in patients with Crohn’s disease ( 39 ). Based on the above, it appears particularly interesting to further explore the relationships between the C1orf106 gene and DM, C1orf106 , and CD4 + T cells.…”

Section: Discussionmentioning

confidence: 99%

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Huang

Zhang

et al. 2022

Front. Immunol.

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BackgroundDermatomyositis is an autoimmune disease characterized by damage to the skin and muscles. CD4+ T cells are of crucial importance in the occurrence and development of dermatomyositis (DM). However, there are few bioinformatics studies on potential pathogenic genes and immune cell infiltration of DM. Therefore, this study intended to explore CD4+ T-cell infiltration–associated key genes in DM and construct a new model to predict the level of CD4+ T-cell infiltration in DM.MethodsGSE46239, GSE142807, GSE1551, and GSE193276 datasets were downloaded. The WGCNA and CIBERSORT algorithms were performed to identify the most correlated gene module with CD4+ T cells. Matascape was used for GO enrichment and KEGG pathway analysis of the key gene module. LASSO regression analysis was used to identify the key genes and construct the prediction model. The correlation between the key genes and CD4+ T-cell infiltration was investigated. GSEA was performed to research the underlying signaling pathways of the key genes. The key gene-correlated transcription factors were identified through the RcisTarget and Gene-motif rankings databases. The miRcode and DIANA-LncBase databases were used to build the lncRNA-miRNA-mRNA network.ResultsIn the brown module, 5 key genes (chromosome 1 open reading frame 106 (C1orf106), component of oligomeric Golgi complex 8 (COG8), envoplakin (EVPL), GTPases of immunity-associated protein family member 6 (GIMAP6), and interferon-alpha inducible protein 6 (IFI6)) highly associated with CD4+ T-cell infiltration were identified. The prediction model was constructed and showed better predictive performance in the training set, and this satisfactory model performance was validated in another skin biopsy dataset and a muscle biopsy dataset. The expression levels of the key genes promoted the CD4+ T-cell infiltration. GSEA results revealed that the key genes were remarkably enriched in many immunity-associated pathways, such as JAK/STAT signaling pathway. The cisbp_M2205, transcription factor-binding site, was enriched in C1orf106, EVPL, and IF16. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network.ConclusionThe C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4+ T-cell infiltration. The prediction model constructed based on the 5 key genes may better predict the level of CD4+ T-cell infiltration in damaged muscle and lesional skin of DM. These key genes could be recognized as potential biomarkers and immunotherapeutic targets of DM.

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“…Potentially relevant parameters have been explored extensively in previous studies and several clinical risk factors were found to be associated with the therapeutic efficacy of IFX, including sex [ 6 ], duration of disease [ 7 ], albumin [ 8 ], and CRP levels [ 9 , 23 ]. However, the results are inconsistent between studies.…”

Section: Discussionmentioning

confidence: 99%

Multi-alleles predict primary non-response to infliximab therapy in Crohn’s disease

Zhang

Tang

Wang

et al. 2020

Gastroenterology Report

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Background Infliximab (IFX) is the first-line treatment for patients with Crohn’s disease (CD) and is noted for its relatively high cost. The therapeutic efficacy of IFX has noticeable individual differences. Known single-gene polymorphisms (SNPs) are inadequate for predicting non-response to IFX. In this study, we aimed to identify new genetic factors associated with IFX-therapy failure and to predict non-response to IFX by developing a multivariate predictive model. Methods In this retrospective study, we collected and analysed the data of Chinese patients with CD who received IFX therapy at one hospital between June 2013 and June 2019. Primary non-response (PNR) and non-durable response (NDR) were evaluated using a simple endoscopic score for CD (SES-CD). A total of 125 SNPs within 44 genes were genotyped. A multivariate logistic-regression model was established to predict non-response to IFX. An area-under-the-receiver-operating-characteristics curve (AUROC) was applied to evaluate the predictive model performance. Results Forty-two of 206 (20.4%) patients experienced PNR and 15 of 159 (9.4%) patients experienced NDR. Nine SNPs were associated with PNR (P < 0.05). A PNR predictive model was established, incorporating 2-week high-sensitivity C-reactive protein (hs-CRP), rs61886887, rs61740234, rs357291, rs2269330, and rs111504845, and the AUROC on training and testing data sets were 0.818 (P < 0.001) and 0.888 (P < 0.001), respectively. At week 14, hs-CRP levels ≥ 2.25 mg/L were significantly associated with NDR (AUROC = 0.815, P < 0.001). PNR-associated SNPs were not mutually associated with NDR, suggesting distinct mechanisms between PNR and NDR. Conclusion Genetic polymorphisms are significantly associated with response to IFX among Chinese CD patients.

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Association of polymorphisms in C1orf106, IL1RN, and IL10 with post-induction infliximab trough level in Crohn’s disease patients

Cited by 9 publications

References 33 publications

Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Multi-alleles predict primary non-response to infliximab therapy in Crohn’s disease

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