Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning
Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers...
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| Veröffentlicht in: | Biochemical genetics 2024-02, Vol.62 (1), p.371-384 |
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| creator | Tang, Qiong Shi, Xiang Xu, Ying Zhou, Rongrong Zhang, Songnan Wang, Xiujuan Zhu, Junfeng |
| description | Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the “limma” package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine–cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD. |
| doi_str_mv | 10.1007/s10528-023-10422-9 |
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This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the “limma” package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine–cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. 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A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine–cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.</description><subject>Algorithms</subject><subject>Biochemistry</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>CD4 antigen</subject><subject>CD4-positive T-lymphocytes</subject><subject>Chemokines</subject><subject>Computational Biology</subject><subject>Cytokines</subject><subject>Dendritic cells</subject><subject>Diagnostic systems</subject><subject>digestive tract</subject><subject>disease diagnosis</subject><subject>Gastrointestinal system</subject><subject>Gastrointestinal tract</subject><subject>Gene expression</subject><subject>gene expression regulation</subject><subject>gene ontology</subject><subject>Genes</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Immune system</subject><subject>Immunological memory</subject><subject>Inflammatory bowel disease</subject><subject>Inflammatory bowel diseases</subject><subject>Inflammatory Bowel Diseases - diagnosis</subject><subject>Inflammatory Bowel Diseases - genetics</subject><subject>Inflammatory diseases</subject><subject>Intestine</subject><subject>Lymphocytes</subject><subject>Lymphocytes T</subject><subject>Machine Learning</subject><subject>Macrophages</subject><subject>Mast cells</subject><subject>Medical Microbiology</subject><subject>Memory cells</subject><subject>Original Article</subject><subject>Polymerase chain reaction</subject><subject>quantitative polymerase chain reaction</subject><subject>Signal transduction</subject><subject>Support vector machines</subject><subject>Zoology</subject><issn>0006-2928</issn><issn>1573-4927</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1v1DAQhi0EosvCH-CALHHhEhh_5MPHUgqstFUvFddokoy3Lold7KzQ_gF-d92mgNRDexrNzOP3tf0y9lbARwFQf0oCStkUIFUhQEtZmGdsJcpaFdrI-jlbAUBVSCObI_YqpavcGtD6JTtStSpFLcyK_dkM5GdnXY-zC56jH_gPHN2wtMHy-ZL4F4c7H9Lsen6G8SfFxG2IfOPtiNOEc4gH_jn8pjGTiTAR7_LABeczlveuT_zY43hILt1ZnGF_6TzxLWH0zu9esxcWx0Rv7uuaXXw9vTj5XmzPv21OjrdFr6GZCzmAoa7DEggQBl1Z1dSlhWEwqlIdyKbTFrQtq9JaMp0QGjuhCQ01QqBasw-L7HUMv_aU5nZyqadxRE9hn1olSlWBrrV5EpWNNDrz-ffX7P0D9CrsY35upoySNVSZzJRcqD6GlCLZ9jq6CeOhFdDe5tkuebZZsb3Ls729xbt76X030fDvyN8AM6AWIOWV31H87_2I7A3xR6vg</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Tang, Qiong</creator><creator>Shi, Xiang</creator><creator>Xu, Ying</creator><creator>Zhou, Rongrong</creator><creator>Zhang, Songnan</creator><creator>Wang, Xiujuan</creator><creator>Zhu, Junfeng</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SS</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20240201</creationdate><title>Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning</title><author>Tang, Qiong ; 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This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the “limma” package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine–cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>37351719</pmid><doi>10.1007/s10528-023-10422-9</doi><tpages>14</tpages></addata></record> |
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| subjects | Algorithms Biochemistry Bioinformatics Biomarkers Biomedical and Life Sciences Biomedicine CD4 antigen CD4-positive T-lymphocytes Chemokines Computational Biology Cytokines Dendritic cells Diagnostic systems digestive tract disease diagnosis Gastrointestinal system Gastrointestinal tract Gene expression gene expression regulation gene ontology Genes Human Genetics Humans Immune system Immunological memory Inflammatory bowel disease Inflammatory bowel diseases Inflammatory Bowel Diseases - diagnosis Inflammatory Bowel Diseases - genetics Inflammatory diseases Intestine Lymphocytes Lymphocytes T Machine Learning Macrophages Mast cells Medical Microbiology Memory cells Original Article Polymerase chain reaction quantitative polymerase chain reaction Signal transduction Support vector machines Zoology |
| title | Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning |
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