Biomarkers prediction and immune landscape in ulcerative colitis : Findings based on bioinformatics and machine learning
Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved..
BACKGROUND: Ulcerative colitis (UC) presents diagnostic and therapeutic difficulties. The primary objective of this study is to identify efficacious biomarkers for diagnosis and treatment, as well as acquire a deeper understanding of the immuneological characteristics associated with the disease.
METHODS: Datasets relating to UC were obtained from GEO database. Among these, three datasets were merged to create a metadata for bioinformatics analysis and machine learning. Additionally, one dataset specifically utilized for external validation. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) were employed to screen signature genes. The artificial neural network (ANN) model and receiver operating characteristic (ROC) curve were used to assess the diagnostic performance of signature genes. The single sample gene set enrichment analysis (ssGSEA) was applied to reveal the immune landscape. Finally, the relationship between the signature genes, immune infiltration, and clinical characteristics was investigated through correlation analysis.
RESULT: By intersecting the result of LASSO, RF and WGCNA, 8 signature genes were identified, including S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14. The biological progress of this gene mostly encompasses acute inflammatory response, aggregation and chemotaxis of leukocyte, and response to lipopolysaccharide by mediating IL-17 signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway. Immune infiltration analysis shows 25 immune cells are significantly elevated in UC samples. Moreover, these signature genes exhibit a strong correlation with various immune cells and a mild to moderate correlation with the Mayo score.
CONCLUSION: S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14 have been identified as credible potential biomarkers for the diagnosis and therapy of UC. The immune response mediated by these signature biomarkers plays a crucial role in the occurrence and advancement of UC by means of the reciprocal interaction between the signature biomarkers and immune-infiltrated cells.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:168 |
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| Enthalten in: |
Computers in biology and medicine - 168(2024) vom: 30. Jan., Seite 107778 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yang, Yuanming [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 10.01.2024 Date Revised 06.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2023.107778 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365610194 |
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| 100 | 1 | |a Yang, Yuanming |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Biomarkers prediction and immune landscape in ulcerative colitis |b Findings based on bioinformatics and machine learning |
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| 500 | |a Date Revised 06.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a BACKGROUND: Ulcerative colitis (UC) presents diagnostic and therapeutic difficulties. The primary objective of this study is to identify efficacious biomarkers for diagnosis and treatment, as well as acquire a deeper understanding of the immuneological characteristics associated with the disease | ||
| 520 | |a METHODS: Datasets relating to UC were obtained from GEO database. Among these, three datasets were merged to create a metadata for bioinformatics analysis and machine learning. Additionally, one dataset specifically utilized for external validation. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) were employed to screen signature genes. The artificial neural network (ANN) model and receiver operating characteristic (ROC) curve were used to assess the diagnostic performance of signature genes. The single sample gene set enrichment analysis (ssGSEA) was applied to reveal the immune landscape. Finally, the relationship between the signature genes, immune infiltration, and clinical characteristics was investigated through correlation analysis | ||
| 520 | |a RESULT: By intersecting the result of LASSO, RF and WGCNA, 8 signature genes were identified, including S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14. The biological progress of this gene mostly encompasses acute inflammatory response, aggregation and chemotaxis of leukocyte, and response to lipopolysaccharide by mediating IL-17 signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway. Immune infiltration analysis shows 25 immune cells are significantly elevated in UC samples. Moreover, these signature genes exhibit a strong correlation with various immune cells and a mild to moderate correlation with the Mayo score | ||
| 520 | |a CONCLUSION: S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14 have been identified as credible potential biomarkers for the diagnosis and therapy of UC. The immune response mediated by these signature biomarkers plays a crucial role in the occurrence and advancement of UC by means of the reciprocal interaction between the signature biomarkers and immune-infiltrated cells | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Biomarkers | |
| 650 | 4 | |a Deep learning model | |
| 650 | 4 | |a Immune infiltration | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Ulcerative colitis | |
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| 650 | 7 | |a Biomarkers |2 NLM | |
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| 700 | 1 | |a Zheng, Huan |e verfasserin |4 aut | |
| 700 | 1 | |a Jia, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Zhining |e verfasserin |4 aut | |
| 700 | 1 | |a Su, Guifang |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Yueming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Kai |e verfasserin |4 aut | |
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| 700 | 1 | |a Wu, Haomeng |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Shumin |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Shaogang |e verfasserin |4 aut | |
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