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Beiying Deng,1,2,* Junhai Zhen,3,* Zixuan Xiang,1,2,* Xiangyun Li,1,2 Cheng Tan,1,2 Ying Chen,1 Pengzhan He,1 Jingjing Ma,4 Weiguo Dong1,* 1Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Key Laboratory of Hubei Province for Digestive System Disease, Wuhan, People’s Republic of China; 3Department of General Practice, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 4Department of Geriatric, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Weiguo Dong, Department of Gastroenterology, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuhan, Hubei Province, 430060, People’s Republic of China, Tel +86 027-88041911, Email dongweiguo@whu.edu.cnBackground: Ulcerative colitis (UC) is a debilitating intestinal disorder that imposes a significant burden on those affected. Fatty acid metabolism plays a pivotal role in regulating immune cell function and maintaining internal homeostasis. This study investigates the biological and clinical significance of fatty acid metabolism within the context of UC.Methods: Gene expression profiles from patients with UC and healthy controls were retrieved, enabling the identification of differentially expressed genes (DEGs) specific to UC. These DEGs were then intersected with genes related to fatty acid metabolism, resulting in the identification of differentially expressed fatty acid metabolism-related genes (FAM-DEGs). Machine learning was employed to pinpoint key feature genes from the FAM-DEGs, which were subsequently used to construct a predictive UC model and to uncover molecular subtypes associated with fatty acid metabolism in UC. An animal model of UC was established using 3% dextran sulfate sodium (DSS) administration. Western blot analysis confirmed the expression levels of genes in intestinal tissues.Results: The machine learning analysis identified three pivotal genes—ACAT1, ACOX2, and HADHB—culminating in a highly predictive nomogram. Consensus cluster analysis further categorized 637 UC samples into two distinct subgroups. The molecular subtypes related to fatty acid metabolism in UC exhibited significant differences in gene expression, biological activities, and enrichment pathways. Immune infiltration analysis highlighted elevated expression of two genes (excluding HADHB) in subtype 1, which corresponded with a marked increase in immune cell infiltration within this subtype. Western blot analysis demonstrated that ACAT1, ACOX2, and HADHB expression levels in the DSS group were significantly reduced, paralleling those observed in the normal group.Conclusion: This study highlights the critical role of specific fatty acid metabolism-related genes in UC, emphasizing their potential as targets for therapeutic intervention and shedding light on the underlying mechanisms of UC progression.Keywords: fatty acid metabolism, ulcerative colitis, machine learning, biomarkers, pharmacology, immune infiltrations |
