Qingqing Zheng,1,2,* Li Wang,1,2,* Yu Zhang,3,* Jun Peng,4,* Jianhong Hou,4,* Hui Wang,1,2 Yazhe Ma,5 Peiren Tang,1,2 Ying Li,1,2 Huan Li,1,2 Yun Chen,4 Jie Li,6 Yang Chen1,2,7 

1Department of Pathology, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, 650032, People’s Republic of China; 2Department of Pathology, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of China; 3Department of Gastroenterology, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, 650032, People’s Republic of China; 4Department of Surgery, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, 650032, People’s Republic of China; 5Yunnan Arrhythmia Research Center, Division of Cardiology, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, 650032, People’s Republic of China; 6Academy of Biomedical Engineering, Kunming Medical University, Kunming, Yunnan, 650500, People’s Republic of China; 7Yunnan Provincial Laboratory of Clinical Virology, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, 650032, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yang Chen, The First People’s Hospital of Yunnan Province, Kunming, 650032, People’s Republic of China, Email cy_koasde@163.com Jie Li, Academy of Biomedical Engineering, Kunming Medical University, Kunming, Yunnan, 650500, People’s Republic of China, Email lijie@kmmu.edu.cn

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease hallmarked by dysregulated immune responses. Current treatments often show limited efficacy, highlighting the need for novel diagnostic and therapeutic approaches.
Methods: RNA-Seq data from 495 UC patients and 320 controls (training dataset) and 389 UC patients and 209 controls (testing dataset) were analyzed. Immune cell infiltration was assessed via the ImmuCellAI algorithm, while differential expression analysis and WGCNA were performed to identify key immune-related genes. Moreover, machine learning models, including Random Forest and Best Subset Selection, were used to construct and validate an optimal diagnostic framework. Lastly, the findings were further corroborated using immunohistochemistry conducted on tissue samples from UC patients and controls.
Results: Thirteen immune cell types, including B cells, macrophages, and naive CD4+ T cells, were identified as significantly altered in UC. Likewise, cytokines such as IL-10, TGF-β, RORγ, and IL-21 exhibited abnormal expression patterns in UC tissues. WGCNA identified three immune cell-associated gene modules, among which the MEblue, MEturquoise, and MEgrey modules were highly correlated with aberrant immune cells. Additionally, machine learning models identified 99 candidate genes, from which an optimal diagnostic model comprising eight crucial genes (GATA2, IL8, LAT, NOLC1, SMARCA5, SMC3, STX10, ZMIZ1) was constructed, achieving an AUC of 0.964 in the training dataset, 0.926 in the internal test dataset, and 0.884 in the independent test dataset. Functional enrichment analysis revealed associations with inflammatory and immune-regulatory pathways, highlighting their biological relevance. Moreover, the identified eight genes hold translational potential for clinical diagnostics and may serve as a foundation for future precision-targeted therapies in UC.
Conclusion: This study highlights alterations in the immune microenvironment in UC and presents an accurate eight-gene diagnostic model, offering the potential for early detection and novel therapeutic targets.
Plain Language Summary: What is Already Known: Previous studies have established that UC involves immune dysregulation, including impaired intestinal barrier function, immune cell infiltration, and alterations in cytokine expression. Conventional treatments primarily focus on anti-inflammatory strategies but are often limited by relapses and a lack of durability.
What is new here: This study identifies a distinct pattern of immune cell dysregulation in UC patients, involving abnormalities in macrophages, neutrophils, and T-cell subsets. It employs machine learning algorithms to construct diagnostic models, including an optimal 8-gene model (GATA2, IL8, LAT, NOLC1, SMARCA5, SMC3, STX10, ZMIZ1), which demonstrates high predictive performance (AUC of 0.964 in training datasets and 0.884 in testing datasets). Functional validation confirmed the abnormal expression of cytokines associated with the immune imbalance in UC.
How can this study help patient care: This research benefits clinicians, researchers, and pharmaceutical developers by providing insights into the immunopathogenesis of UC. It highlights potential diagnostic biomarkers and therapeutic targets, aiding in the development of precision medicine approaches for UC management.

Keywords: ulcerative colitis, immune microenvironment, machine learning, diagnostic biomarkers