Research Paper Volume 16, Issue 7 pp 6314—6333
Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke
- 1 Department of Gerontology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
- 2 Department of Neurology, Liaocheng People’s Hospital and Liaocheng Clinical School of Shandong First Medical University, Liaocheng, Shandong Province, China
- 3 Department of Gerontology, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China
- 4 Department of Geriatric Neurology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
- 5 Department of Neurology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong Province, China
- 6 Laboratory of Microvascular Medicine, Medical Research Center, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Jinan, Shandong Province, China
- 7 Department of Neurology, Liaocheng People’s Hospital, Cheeloo College of Medicine, Liaocheng, Shandong Province, China
Received: July 10, 2023 Accepted: March 5, 2024 Published: April 3, 2024
https://doi.org/10.18632/aging.205706How to Cite
Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Coagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to identify diagnostic value of coagulation genes (CGs) in IS and to explore their role in the immune microenvironment of IS.
Methods: Aberrant expressed CGs in IS were input into unsupervised consensus clustering to classify IS subtypes. Meanwhile, key CGs involved in IS were further selected by weighted gene co-expression network analysis (WGCNA) and machine learning methods, including random forest (RF), support vector machine (SVM), generalized linear model (GLM) and extreme-gradient boosting (XGB). The diagnostic performance of key CGs were evaluated by receiver operating characteristic (ROC) curves. At last, quantitative PCR (qPCR) was performed to validate the expressions of key CGs in IS.
Results: IS patients were classified into two subtypes with different immune microenvironments by aberrant expressed CGs. Further WGCNA, machine learning methods and ROC curves identified ACTN1, F5, TLN1, JMJD1C and WAS as potential diagnostic biomarkers of IS. In addition, their expressions were significantly correlated with macrophages, neutrophils and/or T cells. GSEA also revealed that those biomarkers may regulate IS via immune and inflammation. Moreover, qPCR verified the expressions of ACTN1, F5 and JMJD1C in IS.
Conclusions: The current study identified ACTN1, F5 and JMJD1C as novel coagulation-related biomarkers associated with IS immune microenvironment, which enriches our knowledge of coagulation-mediated pathogenesis of IS and sheds light on next-step in vivo and in vitro experiments to elucidate the relevant molecular mechanisms.
Abbreviations
IS: Ischemic stroke; DECRGs: Differentially expressed coagulation related genes; HC: Healthy control; ssGSEA: Single-sample gene set enrichment analysis; WGCNA: Weighted gene co-expression network analysis; RF: Random forest; SVM: Support vector machine; GLM: Generalized linear model; XGB: Extreme-gradient boosting; RT-qPCR: Reverse transcription quantitative real-time PCR; CGs: Coagulation genes; NCBI: National center for biotechnology information; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; PPI: Protein-protein interaction; DEG: Differentially expressed gene; GSVA: Gene set variation analysis; GS: Gene significance; MM: Module membership; ROC: Receiver operating characteristic; AUC: Area under curve; TLN1: Talin 1; PROS1: Protein S; MYH9: Myosin heavy chain 9; F5: Coagulation factor V; F13A1: Coagulation Factor XIII A Chain; ACTN1: Actinin alpha 1; P2RX1: Purinergic receptor P2X 1, ITPK1: Inositol-tetrakisphosphate 1-kinase; WAS: WASP actin nucleation promoting factor; JMJD1C: Jumonji domain containing 1C; BP: Biological process; CC: Cellular component; MF: Molecular function; BBB: Blood-brain barrier.