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Identification of diagnostic markers for moyamoya disease by combining bulk RNA-sequencing analysis and machine
Yifan Xu1, Bing Chen1, Zhongxiang Guo1
1Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China.
Abstract:
Moyamoya disease (MMD) remains a chronic progressive cerebrovascular disease with unknown etiology. A growing number of reports describe the development of MMD relevant to infection or autoimmune diseases. Identifying biomarkers of MMD is to understand the pathogenesis and development of novel targeted therapy and may be the key to improving the patient's outcome. Here, we analyzed gene expression from two GEO databases. To identify the MMD biomarkers, the weighted gene co-expression network analysis (WGCNA) and the differential expression analyses were conducted to identify 266 key genes. The KEGG and GO analyses were then performed to construct the protein interaction (PPI) network. The three machine-learning algorithms of support vector machine-recursive feature elimination (SVM-RFE), random forest and least absolute shrinkage and selection operator (LASSO) were used to analyze the key genes and take intersection to construct MMD diagnosis based on the four core genes found (ACAN, FREM1, TOP2A and UCHL1), with highly accurate AUCs of 0.805, 0.903, 0.815, 0.826. Gene enrichment analysis illustrated that the MMD samples revealed quite a few differences in pathways like one carbon pool by folate, aminoacyl-tRNA biosynthesis, fat digestion and absorption and fructose and mannose metabolism. In addition, the immune infiltration profile demonstrated that ACAN expression was associated with mast cells resting, FREM1 expression was associated with T cells CD4 naive, TOP2A expression was associated with B cells memory, UCHL1 expression was associated with mast cells activated. Ultimately, the four key genes were verified by qPCR. Taken together, our study analyzed the diagnostic biomarkers and immune infiltration characteristics of MMD, which may shed light on the potential intervention targets of moyamoya disease patients.
Insights
Researchers identified four key genes (ACAN, FREM1, TOP2A, UCHL1) as potential diagnostic biomarkers for moyamoya disease (MMD). This discovery could lead to new targeted therapies for this progressive cerebrovascular condition.
Area of Science:
- Neuroscience
- Genetics
- Immunology
Background:
- Moyamoya disease (MMD) is a progressive cerebrovascular disorder with unknown causes.
- Infections and autoimmune conditions are increasingly linked to MMD development.
- Identifying biomarkers is crucial for understanding MMD pathogenesis and developing targeted therapies.
Purpose of the Study:
- To identify diagnostic biomarkers for moyamoya disease (MMD).
- To explore the immune infiltration characteristics associated with MMD.
- To uncover potential therapeutic targets for MMD.
Main Methods:
- Gene expression data from two GEO databases were analyzed.
- Weighted gene co-expression network analysis (WGCNA) and differential expression analyses identified 266 key genes.
- Machine learning algorithms (SVM-RFE, Random Forest, LASSO) pinpointed four core genes: ACAN, FREM1, TOP2A, and UCHL1.
Main Results:
- Four core genes (ACAN, FREM1, TOP2A, UCHL1) were identified as potential MMD diagnostic biomarkers with high AUC values (0.805-0.903).
- MMD samples showed significant pathway differences in folate metabolism, aminoacyl-tRNA biosynthesis, and lipid/carbohydrate metabolism.
- Specific gene expressions correlated with distinct immune cell types, including mast cells and T cells.
Conclusions:
- The study identified novel diagnostic biomarkers for MMD.
- Distinct immune infiltration profiles were associated with MMD.
- These findings offer potential intervention targets for moyamoya disease patients.
