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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Multiple Sclerosis and Neuroimmunology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1475582
This article is part of the Research Topic The diagnostic difficulties of immune-mediated neuropathies View all 9 articles

Integrative Multi-Omics Approach Using Random Forest and Artificial Neural Network Models for Early Diagnosis and Immune Infiltration Characterization in Ischemic Stroke

Provisionally accepted
Ling Lin Ling Lin 1*Chunmao Guo Chunmao Guo 1Hanna Jin Hanna Jin 1Haixiong Huang Haixiong Huang 1Fan Luo Fan Luo 2Ying Wang Ying Wang 3Dongqi Li Dongqi Li 1Yuanxin Zhang Yuanxin Zhang 1Yuqian Xu Yuqian Xu 1Chanyan Zhu Chanyan Zhu 1Fengshan Zeng Fengshan Zeng 1Huahua He Huahua He 1Jie Chen Jie Chen 2Wei Zhang Wei Zhang 4,5Wenlin Yu Wenlin Yu 1
  • 1 Huizhou Hospital of Traditional Chinese Medicine, Huizhou, China
  • 2 Shaanxi Provincial Hospital of Traditional Chinese Medicine, Xi'an, China
  • 3 Gem Flower Changqing Staff Hospital, Xi'an city, China
  • 4 Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
  • 5 School of Basic Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu Province, China

The final, formatted version of the article will be published soon.

    Background: Ischemic stroke (IS) is a significant global health issue, causing high rates of morbidity, mortality, and disability. Since conventional Diagnosis methods for IS have several shortcomings, It is critical to create new Diagnosis models in order to enhance existing Diagnosis approaches.We utilized gene expression data from the Gene Expression Omnibus (GEO) databases GSE16561 and GSE22255 to identify differentially expressed genes (DEGs) associated with IS. DEGs analysis using the Limma package, as well as GO and KEGG enrichment analyses, were performed. Furthermore, PPI networks were constructed using DEGs from the String database, and Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification. Use the GSE58294 dataset to evaluate the effectiveness of the scoring model on healthy controls and ischemic stroke samples. The effectiveness of the scoring model was evaluated through AUC analysis, and CIBERSORT analysis was conducted to estimate the immune landscape and explore the correlation between gene expression and immune cell infiltration.Results: A total of 26 significant DEGs associated with IS were identified. Metascape analysis revealed enriched biological processes and pathways related to IS. Ten key DEGs (ARG1, DUSP1, F13A1, NFIL3, CCR7, ADM, PTGS2, ID3, FAIM3, HLA-DQB1) were selected using Random Forest and artificial neural network models. The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy. Additionally, the analysis of the immune landscape demonstrated elevated immune-related networks in IS patients compared to healthy controls.The study uncovers the involvement of specific genes and immune cells in the pathogenesis of IS, suggesting their importance in understanding and potentially targeting the disease.

    Keywords: ischemic stroke, Differentially expressed genes, random forest, artificial neural network, Diagnosis model

    Received: 04 Aug 2024; Accepted: 14 Nov 2024.

    Copyright: © 2024 Lin, Guo, Jin, Huang, Luo, Wang, Li, Zhang, Xu, Zhu, Zeng, He, Chen, Zhang and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ling Lin, Huizhou Hospital of Traditional Chinese Medicine, Huizhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.