Identifying effective diagnostic biomarkers for childhood cerebral malaria in Africa integrating coexpression analysis with machine learning algorithm

European Journal of Medical Research

Research

Jia-Xin Li¹  Wan-Zhe Liao²  Ze-Min Huang³  Xin Yin⁴  Xu-Guang Guo⁵  Shi Ouyang⁶  Bing Gu⁷ 

[1] Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China;Department of Clinical Medicine, The First Clinical School of Guangzhou Medical University, 511436, Guangzhou, China;Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China;Department of Clinical Medicine, The Nanshan College of Guangzhou Medical University, 511436, Guangzhou, China;Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China;Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, 511436, Guangzhou, China;Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China;Department of Pediatrics, The Pediatrics School of Guangzhou Medical University, 511436, Guangzhou, China;Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China;Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China;Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China;Department of Infectious Disease, The Fifth Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China;Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510000, Guangzhou, China;

关键词: Cerebral malaria;    WGCNA;    Machine learning;    Neutrophil;    Blood–brain barrier (BBB);   

DOI  :  10.1186/s40001-022-00980-w

 received in 2022-07-29, accepted in 2022-12-30,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundCerebral malaria (CM) is a manifestation of malaria caused by plasmodium infection. It has a high mortality rate and severe neurological sequelae, existing a significant research gap and requiring further study at the molecular level.MethodsWe downloaded the GSE117613 dataset from the Gene Expression Omnibus (GEO) database to determine the differentially expressed genes (DEGs) between the CM group and the control group. Weighted gene coexpression network analysis (WGCNA) was applied to select the module and hub genes most relevant to CM. The common genes of the key module and DEGs were selected to perform further analysis. The least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) were applied to screen and verify the diagnostic markers of CM. Eventually, the hub genes were validated in the external dataset. Gene set enrichment analysis (GSEA) was applied to investigate the possible roles of the hub genes.ResultsThe GO and KEGG results showed that DEGs were enriched in some neutrophil-mediated pathways and associated with some lumen structures. Combining LASSO and the SVM-RFE algorithms, LEF1 and IRAK3 were identified as potential hub genes in CM. Through the GSEA enrichment results, we found that LEF1 and IRAK3 participated in maintaining the integrity of the blood–brain barrier (BBB), which contributed to improving the prognosis of CM.ConclusionsThis study may help illustrate the pathophysiology of CM at the molecular level. LEF1 and IRAK3 can be used as diagnostic biomarkers, providing new insight into the diagnosis and prognosis prediction in pediatric CM.

【 授权许可】

CC BY   
© The Author(s) 2023

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