Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Machine Learning* ; Colitis, Ulcerative*/genetics ; Colitis, Ulcerative*/diagnosis; Humans ; Algorithms ; Gene Expression Profiling/methods ; Transcriptome ; Interleukin-1 Receptor-Associated Kinases/genetics ; Male ; Female ; Lipocalin-2/genetics ; Case-Control Studies ; Biomarkers ; Adult

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.
(© 2024. The Author(s).)

Clin Exp Gastroenterol. 2022 Feb 11;15:5-25. (PMID: 35185343)
Cancers (Basel). 2022 Oct 05;14(19):. (PMID: 36230801)
Arthritis Rheumatol. 2022 Jan;74(1):81-91. (PMID: 34114357)
J Clin Bioinforma. 2012 Nov 21;2(1):20. (PMID: 23171526)
N Engl J Med. 1991 Jan 10;324(2):84-8. (PMID: 1984188)
Diagnostics (Basel). 2023 Sep 13;13(18):. (PMID: 37761298)
Lancet Gastroenterol Hepatol. 2021 Jul;6(7):589-595. (PMID: 34019798)
Gut Liver. 2024 Feb 22;:. (PMID: 38384179)
BMC Bioinformatics. 2013 Jan 16;14:7. (PMID: 23323831)
Cells. 2020 Jul 09;9(7):. (PMID: 32659925)
Infect Immun. 2021 Aug 16;89(9):e0001421. (PMID: 33526559)
Biology (Basel). 2023 Jul 13;12(7):. (PMID: 37508427)
Cell Mol Gastroenterol Hepatol. 2016 Jul;2(4):482-498.e6. (PMID: 27458605)
Nat Rev Immunol. 2014 May;14(5):329-42. (PMID: 24751956)
Clin Gastroenterol Hepatol. 2020 May;18(6):1261-1267. (PMID: 31778805)
Adv Med Sci. 2023 Sep;68(2):386-395. (PMID: 37813048)
Sci Rep. 2021 Apr 27;11(1):9010. (PMID: 33907256)
J Inflamm (Lond). 2020 Apr 21;17:15. (PMID: 32336953)
J Crohns Colitis. 2019 May 27;13(6):772-784. (PMID: 30715224)
Front Immunol. 2022 Aug 05;13:972107. (PMID: 35990650)
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. (PMID: 16199517)
Clin Gastroenterol Hepatol. 2012 Jun;10(6):639-45. (PMID: 22289873)
J Can Assoc Gastroenterol. 2019 Feb;2(Suppl 1):S1-S5. (PMID: 31294380)
Cells. 2021 Mar 06;10(3):. (PMID: 33800865)
JAMA. 2023 Sep 12;330(10):951-965. (PMID: 37698559)
Indian J Dermatol. 2016 May-Jun;61(3):251-60. (PMID: 27293244)
Cell Rep. 2017 Jan 3;18(1):248-262. (PMID: 28052254)
Gastroenterology. 2022 Jun;162(7):2104-2106. (PMID: 35122762)
Annu Rev Nutr. 2017 Aug 21;37:103-130. (PMID: 28628361)
Psychol Med. 2022 Jan;52(1):57-67. (PMID: 32524918)
Clin Gastroenterol Hepatol. 2021 Jun;19(6):1117-1138.e19. (PMID: 32801010)
Gastroenterol Clin North Am. 2020 Dec;49(4):643-654. (PMID: 33121686)
Gastroenterology. 2012 Jan;142(1):46-54.e42; quiz e30. (PMID: 22001864)
Semin Cancer Biol. 2020 Aug;64:51-60. (PMID: 31112753)
J Pathol. 2014 Jan;232(2):112-20. (PMID: 24122796)
PLoS Pathog. 2009 Oct;5(10):e1000622. (PMID: 19834550)
Lancet. 2018 Dec 23;390(10114):2769-2778. (PMID: 29050646)
Science. 2005 Jun 10;308(5728):1635-8. (PMID: 15831718)
Gut. 2022 Apr;71(4):676-685. (PMID: 33980610)

WK2022ZF03 Key Research Project of Wan-nan Medical College

Keywords: Diagnostic model; Gene set enrichment analysis; Immunocytes infiltration; Machine learning algorithms; Ulcerative colitis

EC 2.7.11.1 (Interleukin-1 Receptor-Associated Kinases)
0 (Lipocalin-2)
EC 2.7.11.1 (IRAK3 protein, human)
0 (Biomarkers)
0 (LCN2 protein, human)

Date Created: 20240701 Date Completed: 20240702 Latest Revision: 20240704

20240704

PMC11217275

10.1038/s41598-024-65481-8

38951638