Screening of key immune - related gene in Parkinson ' s disease based on WGCNA and machine learning

Abstract

Objectives: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning.

Methods: This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn.

Results: A total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4⁺ T cells, NK cells, CD8⁺ T cells, and B cells were higher in the samples of Parkinson's disease patients, while resting NK cells and resting CD4⁺ T cells were significantly infiltrated in the samples of Parkinson's disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation.

Conclusions: This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson's disease.

Keywords: ANK1 gene; Parkinson’s disease; immunity; machine learning; weighted gene co-expression network analysis.

图1

最优软阈值分析图 Figure 1 Optimal soft threshold analysis diagram A: Ordinate is the intra-module connectivity or correlation of each gene within the module, also known as the internal connectivity of the gene (R ²=0.85). B: Ordinate is the mean connectivity (249.07), the degree of correlation between the gene and other genes in the network.

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基因模块聚类分析 Figure 2 Gene module cluster analysis

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基因模块与帕金森病患病相关性热图 Figure 3 Heat map of correlation between gene modules and Parkinson’s disease status

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帕金森病相关与免疫相关基因筛选韦恩图 Figure 4 Venn diagram of Parkinson’s disease-related and immune-related gene screening

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GO富集生物学功能 Figure 5 GO enrichment biological function GO: Gene ontology.

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GO富集细胞成分 Figure 6 GO enriched cell component GO: Gene ontology.

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GO富集分子功能 Figure 7 GO enriched molecular function GO: Gene ontology; RAGE: Receptor for advanced glycation end products.

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KEGG通路分析 Figure 8 KEGG pathway analysis KEGG: Kyoto Encyclopedia of Genes and Genomes.

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免疫细胞浸润分析 Figure 9 Immune cell infiltration analysis A: Infiltration levels of common immune cells in different samples. GSM184354 to GSM184362, GSM503950 to GSM503957, and GSM1192691 to GSM1192698 are normal samples; GSM184363 to GSM184378, GSM503958 to GSM503967, and GSM1192704 to GSM1192718 are disease samples. B: Heatmap of correlations between different types of immune cells. C: Infiltration results of different types of immune cells in normal samples and Parkinson’s disease patients.

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绿松石色模块基因差异表达分析图 Figure 10 Differential expression analysis chart of turquoise module genes

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LASSO回归 Figure 11 LASSO regression A: The abscissa is the logarithm of lambda. The larger the lambda, the more model coefficients tend to zero. A total of 11 genes are screened out according to the number of lines. B: The abscissa is the number of non-zero coefficients in the model. As lambda increases, the number of non-zero coefficients in the model decreases, and the optimal solution is 11 genes.

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随机森林特征重要度 Figure 12 Random forest feature importance CD47: Cluster of differentiation 47; HSPA6: Heat shock protein family A (HSP70) member 6; ICAM2: Intercellular adhesion molecule 2; GPR27: G protein-coupled receptor 27; MICB: MHC class I polypeptide-related sequence B; SIRPA: Signal regulatory protein alpha; MICA: MHC class I polypeptide-related sequence A; ANK1: Ankyrin 1; EMP3: Epithelial membrane protein 3; CXCR2: C-X-C chemokine receptor 2; TIMM13: Translocase of inner mitochondrial membrane 13 homolog; L1CAM: L1 cell adhesion molecule; APOBEC3A: Apolipoprotein B mRNA editing enzyme catalytic subunit 3a; BZW2: Basic leucine zipper and W2 domains 2; PDGFRL: Platelet-derived growth factor receptor-like; CCNA1: Cell cycle and apoptosis regulator 1; S100A9: S100 calcium binding protein A9; METRNL: Meteorin like, glial cell differentiation regulator; SIGLEC10: Sialic acid binding Ig like lectin 10; HLA-E: Human leukocyte antigen E; LncMSE: Long non-coding RNA mean squared error.

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韦恩图显示4种方法共同筛选得到的基因 Figure 13 Venn diagram showing intersected gene screening by 4 methods together LASSO: Least absolute shrinkage and selection operator; RF: Random forest; SVM: Support vector machine; Limma: Linear models for microarray data.

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ANK1 下游蛋白质互作网络及介数中心度排序 Figure 14 ANK1 downstream protein interaction network and betweenness centrality ranking A: A total of 11 downstream proteins translated and expressed by the ANK1 gene. B: By constructing a protein interaction network diagram, the connections between various proteins are revealed. ANK1: Ankyrin 1; ANK2: Ankyrin 2; OBSCN: Obscurin; TTN: Titin; CD44: Cluster of differentiation 44; ITGAM: Integrin subunit alpha M; SPTB: Spectrin, beta, erythrocytic; NFASC: Neurofascin; SPTA1: Spectrin, alpha, erythrocytic 1; RHAG: Rh-associated glycoprotein; SLC4A1: Solute carrier family 4 member 1.