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Identifying effective diagnostic biomarkers for childhood cerebral malaria in Africa integrating coexpression
Jia-Xin Li1,2, Wan-Zhe Liao1,3, Ze-Min Huang1,4
1Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
Background:
Cerebral 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.
Methods:
We 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.
Results:
The 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.
Conclusions:
This 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.
Insights
Cerebral malaria (CM) is a severe complication of malaria. Researchers identified LEF1 and IRAK3 genes as potential biomarkers for diagnosing and predicting outcomes in pediatric CM patients.
Area of Science:
- Genomics
- Molecular Biology
- Neuroscience
Background:
- Cerebral malaria (CM) is a severe manifestation of Plasmodium infection with high mortality and neurological deficits.
- Significant knowledge gaps exist regarding CM's molecular pathophysiology.
- Further molecular-level investigations are crucial for understanding and treating CM.
Purpose of the Study:
- To identify molecular players involved in cerebral malaria (CM) pathogenesis.
- To discover potential diagnostic biomarkers for CM.
- To elucidate the molecular mechanisms underlying CM and its neurological sequelae.
Main Methods:
- Differential gene expression analysis of the GSE117613 dataset.
- Weighted gene co-expression network analysis (WGCNA) to identify key gene modules and hub genes.
- Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for marker screening and validation.
- Gene Set Enrichment Analysis (GSEA) to explore the functional roles of identified genes.
Main Results:
- Differentially expressed genes (DEGs) were enriched in neutrophil-mediated pathways and lumen-associated structures.
- LEF1 and IRAK3 were identified as potential hub genes associated with CM using LASSO and SVM-RFE.
- GSEA indicated that LEF1 and IRAK3 are involved in maintaining blood-brain barrier (BBB) integrity, potentially improving CM prognosis.
Conclusions:
- LEF1 and IRAK3 show promise as diagnostic biomarkers for pediatric cerebral malaria (CM).
- These genes may offer new insights into CM diagnosis and prognosis.
- The study contributes to understanding CM pathophysiology at the molecular level.
