Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning
Ulcerative colitis (UC) is an inflammatory bowel disease of unknown cause that typically affects the colon and rectum. Innate intestinal immunity, including macrophages, plays a significant role in the pathological development of UC. Using the CIBERSORT algorithm, we observed elevated levels of 22 t...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
|
| Series: | Mediators of Inflammation |
| Online Access: | http://dx.doi.org/10.1155/2023/4373840 |
| _version_ | 1826992961483702272 |
|---|---|
| author | Shaocheng Hong Hongqian Wang Shixin Chan Jiayi Zhang Bangjie Chen Xiaohan Ma Xi Chen |
| author_facet | Shaocheng Hong Hongqian Wang Shixin Chan Jiayi Zhang Bangjie Chen Xiaohan Ma Xi Chen |
| author_sort | Shaocheng Hong |
| collection | DOAJ |
| description | Ulcerative colitis (UC) is an inflammatory bowel disease of unknown cause that typically affects the colon and rectum. Innate intestinal immunity, including macrophages, plays a significant role in the pathological development of UC. Using the CIBERSORT algorithm, we observed elevated levels of 22 types of immune cell infiltrates, as well as increased M1 and decreased M2 macrophages in UC compared to normal colonic mucosa. Weighted gene coexpression network analysis (WGCNA) was used to identify modules associated with macrophages and UC, resulting in the identification of 52 macrophage-related genes (MRGs) that were enriched in macrophages at single-cell resolution. Consensus clustering based on these 52 MRGs divided the integrated UC cohorts into three subtypes. Machine learning algorithms were used to identify ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), sodium- and chloride-dependent neutral and basic amino acid transporter B(0+) (SLC6A14), and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) in the training set, and their diagnostic value was validated in independent validation sets. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) revealed the main biological effects, and that interleukin-17 was one of several signaling pathways enriched by the three genes. We also constructed a competitive endogenous RNA (CeRNA) network reflecting a potential posttranscriptional regulatory mechanism. Expression of diagnostic markers was validated in vivo and in biospecimens, and our immunohistochemistry (IHC) results confirmed that HMGCS2 gradually decreased during the transformation of UC to colorectal cancer. In conclusion, ENPP1, SLC6A14, and HMGCS2 are associated with macrophages and the progression of UC pathogenesis and have good diagnostic value for patients with UC. |
| first_indexed | 2024-03-11T14:11:19Z |
| format | Article |
| id | doaj.art-83d02800b61c455d990673ea17043fca |
| institution | Directory Open Access Journal |
| issn | 1466-1861 |
| language | English |
| last_indexed | 2025-02-18T08:57:55Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Mediators of Inflammation |
| spelling | doaj.art-83d02800b61c455d990673ea17043fca2024-11-02T23:54:13ZengHindawi LimitedMediators of Inflammation1466-18612023-01-01202310.1155/2023/4373840Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine LearningShaocheng Hong0Hongqian Wang1Shixin Chan2Jiayi Zhang3Bangjie Chen4Xiaohan Ma5Xi Chen6Department of GastroenterologyDepartment of GastroenterologyDepartment of General SurgeryDepartment of GastroenterologyDepartment of OncologyDepartment of GastroenterologyDepartment of GastroenterologyUlcerative colitis (UC) is an inflammatory bowel disease of unknown cause that typically affects the colon and rectum. Innate intestinal immunity, including macrophages, plays a significant role in the pathological development of UC. Using the CIBERSORT algorithm, we observed elevated levels of 22 types of immune cell infiltrates, as well as increased M1 and decreased M2 macrophages in UC compared to normal colonic mucosa. Weighted gene coexpression network analysis (WGCNA) was used to identify modules associated with macrophages and UC, resulting in the identification of 52 macrophage-related genes (MRGs) that were enriched in macrophages at single-cell resolution. Consensus clustering based on these 52 MRGs divided the integrated UC cohorts into three subtypes. Machine learning algorithms were used to identify ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), sodium- and chloride-dependent neutral and basic amino acid transporter B(0+) (SLC6A14), and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) in the training set, and their diagnostic value was validated in independent validation sets. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) revealed the main biological effects, and that interleukin-17 was one of several signaling pathways enriched by the three genes. We also constructed a competitive endogenous RNA (CeRNA) network reflecting a potential posttranscriptional regulatory mechanism. Expression of diagnostic markers was validated in vivo and in biospecimens, and our immunohistochemistry (IHC) results confirmed that HMGCS2 gradually decreased during the transformation of UC to colorectal cancer. In conclusion, ENPP1, SLC6A14, and HMGCS2 are associated with macrophages and the progression of UC pathogenesis and have good diagnostic value for patients with UC.http://dx.doi.org/10.1155/2023/4373840 |
| spellingShingle | Shaocheng Hong Hongqian Wang Shixin Chan Jiayi Zhang Bangjie Chen Xiaohan Ma Xi Chen Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning Mediators of Inflammation |
| title | Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning |
| title_full | Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning |
| title_fullStr | Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning |
| title_full_unstemmed | Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning |
| title_short | Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning |
| title_sort | identifying macrophage related genes in ulcerative colitis using weighted coexpression network analysis and machine learning |
| url | http://dx.doi.org/10.1155/2023/4373840 |
| work_keys_str_mv | AT shaochenghong identifyingmacrophagerelatedgenesinulcerativecolitisusingweightedcoexpressionnetworkanalysisandmachinelearning AT hongqianwang identifyingmacrophagerelatedgenesinulcerativecolitisusingweightedcoexpressionnetworkanalysisandmachinelearning AT shixinchan identifyingmacrophagerelatedgenesinulcerativecolitisusingweightedcoexpressionnetworkanalysisandmachinelearning AT jiayizhang identifyingmacrophagerelatedgenesinulcerativecolitisusingweightedcoexpressionnetworkanalysisandmachinelearning AT bangjiechen identifyingmacrophagerelatedgenesinulcerativecolitisusingweightedcoexpressionnetworkanalysisandmachinelearning AT xiaohanma identifyingmacrophagerelatedgenesinulcerativecolitisusingweightedcoexpressionnetworkanalysisandmachinelearning AT xichen identifyingmacrophagerelatedgenesinulcerativecolitisusingweightedcoexpressionnetworkanalysisandmachinelearning |