Identification of Diagnosis and Typological Characteristics Associated with Ferroptosis for Ulcerative Colitis via Bioinformatics and Machine Learning

Weihao Wang; Hao Ma; Xujiao Song; Shanshan Ding

doi:10.2174/0118715303263609231101074056

Identification of Diagnosis and Typological Characteristics Associated with Ferroptosis for Ulcerative Colitis via Bioinformatics and Machine Learning

Endocr Metab Immune Disord Drug Targets. 2023 Nov 10. doi: 10.2174/0118715303263609231101074056. Online ahead of print.

Authors

Weihao Wang¹, Hao Ma¹, Xujiao Song¹, Shanshan Ding²

Affiliations

¹ School of Chemical and Biological Engineering, Yichun University, Yichun 336000, Jiangxi, China.
² Aesthetic Medical School, Yichun University, China.

PMID: 37957905
DOI: 10.2174/0118715303263609231101074056

Abstract

Objective: To investigate and validate ferroptosis genes (FRGs) in ulcerative colitis (UC) for diagnostic, subtype, and biological agent reactivity, with the goal of providing a foundation for the identification of novel therapeutic targets and the rational use of infliximab in clinical practice.

Methods: UC datasets and FRGs were selected from the Gene Expression Omnibus (GEO) and FerrDb databases. WGCNA was used to identify characteristic genes of UC. LASSO and SVM models were used to discover key FRGs in UC. A nomogram was constructed for diagnosing UC using logistic regression (LR), We performed internal and external validation for the model. Furthermore, we constructed a hub-gene-signature prediction model for the effectiveness of infliximab in treating UC and deployed it on the website. Finally, the hub gene-drug interaction networks were constructed.

Results: Nineteen ferroptosis-related genes associated with UC were identified through bioinformatics analysis. FTH1 and GPX4 were two of the down-regulated genes.The seventeen upregulated genes consisted of DUOX1, DUOX2, SOCS1, LPIN1, QSOX1, TRIM21, IDO1, SLC7A11, MUC1, HSPA5, SCD, ACSL3, NOS2, PARP9, PARP14, LCN2, and TRIB2. Five hub genes, including LCN2, QSOX1, MUC1, IDO1, and TRIB2, were acquried via machine learning. The mean auc of internal validation was 0.964 and 0.965 respectively, after using cross-validation and bootstrap in the training set based on the 5 hub-gene diagnostic models. In the external validation set, the AUC reached 0.976 and 0.858. RF model performs best in predicting infliximab effectiveness. In addition, we identified two ferroptosis subtypes. Cluster A mostly overlaps with the high-risk score group, with a hyperinflammatory phenotype.

Conclusions: This research indicated that five hub genes related to ferroptosis might be potential markers in diagnosing and predicting infliximab sensitivity for UC.

Keywords: diagnostic model; ferroptosis; infliximab reactivity; machine learning; ulcerative colitis.