Identification of metabolism-related subtypes and feature genes in Crohn’s disease
Abstract Crohn's disease is a chronic inflammatory disorder of the gastrointestinal tract, capable of causing disruptions in gastrointestinal function. However, the precise etiology of the disease remains unclear at present. This study aims to analyses metabolism-related signatures to identify feature genes. To investigate potential treatment targets to improve patient prognosis. We downloaded Crohn's disease (CD) datasets from the GEO database, we identified three subtypes (MCA, MCB, MCC) through consensus cluster, as well as the correlation with immune infiltration. We selected the MCA cluster for weighted gene correlation network analysis (WGCNA). After selecting the strongly correlated red module, we identified 20 hub genes. We conducted KEGG, GO functional enrichment analyses. Further analysis using LASSO, SVM and protein-protein interaction (PPI) methods narrowed down to 11 hub genes. Taking the intersection with differentially expressed genes, we finally obtained 4 feature genes: ACTN1, MMP2, THY1, and ZCCHC24. We constructed a nomogram and used the DGIdb database for candidate drug prediction. Molecular docking validation was performed using Schrödinger software to calculate and visualize the interactions between MMP2 and 19 drugs. Through the analysis of metabolism-related signatures, we have identified 4 feature genes and used them to construct a nomogram. This model is expected to offer novel insights for clinical treatment..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
ResearchSquare.com - (2024) vom: 05. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Xizhi [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.21203/rs.3.rs-3982266/v1 |
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| PPN (Katalog-ID): |
XRA042804418 |
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| 520 | |a Abstract Crohn's disease is a chronic inflammatory disorder of the gastrointestinal tract, capable of causing disruptions in gastrointestinal function. However, the precise etiology of the disease remains unclear at present. This study aims to analyses metabolism-related signatures to identify feature genes. To investigate potential treatment targets to improve patient prognosis. We downloaded Crohn's disease (CD) datasets from the GEO database, we identified three subtypes (MCA, MCB, MCC) through consensus cluster, as well as the correlation with immune infiltration. We selected the MCA cluster for weighted gene correlation network analysis (WGCNA). After selecting the strongly correlated red module, we identified 20 hub genes. We conducted KEGG, GO functional enrichment analyses. Further analysis using LASSO, SVM and protein-protein interaction (PPI) methods narrowed down to 11 hub genes. Taking the intersection with differentially expressed genes, we finally obtained 4 feature genes: ACTN1, MMP2, THY1, and ZCCHC24. We constructed a nomogram and used the DGIdb database for candidate drug prediction. Molecular docking validation was performed using Schrödinger software to calculate and visualize the interactions between MMP2 and 19 drugs. Through the analysis of metabolism-related signatures, we have identified 4 feature genes and used them to construct a nomogram. This model is expected to offer novel insights for clinical treatment. | ||
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