Identification of differentially expressed genes of blood leukocytes for Schizophrenia

Abstract

Background: Schizophrenia (SCZ) is a severe neurodevelopmental disorder with brain dysfunction. This study aimed to use bioinformatic analysis to identify candidate blood biomarkers for SCZ.

Methods: The study collected peripheral blood leukocyte samples of 9 SCZ patients and 20 healthy controls for RNA sequencing analysis. Bioinformatic analyses included differentially expressed genes (DEGs) analysis, pathway enrichment analysis, and weighted gene co-expression network analysis (WGCNA).

Results: This study identified 1,205 statistically significant DEGs, of which 623 genes were upregulated and 582 genes were downregulated. Functional enrichment analysis showed that DEGs were mainly enriched in cell chemotaxis, cell surface, and serine peptidase activity, as well as involved in Natural killer cell-mediated cytotoxicity. WGCNA identified 16 gene co-expression modules, and five modules were significantly correlated with SCZ (p < 0.05). There were 106 upregulated genes and 90 downregulated genes in the five modules. The top ten genes sorted by the Degree algorithm were RPS28, BRD4, FUS, PABPC1, PCBP1, PCBP2, RPL27A, RPS21, RAG1, and RPL27. RAG1 and the other nine genes belonged to the turquoise and pink module respectively. Pathway enrichment analysis indicated that these 10 genes were mainly involved in processes such as Ribosome, cytoplasmic translation, RNA binding, and protein binding.

Conclusion: This study finds that the gene functions in key modules and related enrichment pathways may help to elucidate the molecular pathogenesis of SCZ, and the potential of key genes to become blood biomarkers for SCZ warrants further validation.

Keywords: RNA sequencing; Schizophrenia; WGCNA; bioinformatics; gene expression.

FIGURE 1

Differentially expressed genes between SCZ and health control blood samples. (A) The volcano plots. Red denotes the upregulated genes and blue denotes the downregulated genes. (B) The heatmaps of differentially expressed genes. T = SCZ cases, C = health controls.

FIGURE 2

Identification of co-expressed modules by WGCNA. (A) Cluster dendrogram of all genes. The x-axis represents the gene modules, the y-axis represents the network distance with values closer to zero indicating higher gene expression similarity. (B) Heatmap plot of 400 selected co-expressed genes. Each row and column corresponds to a gene, light color denotes low topological overlap, and progressively darker red denotes higher topological overlap. Darker squares along the diagonal correspond to modules.

FIGURE 3

Identification of the relationship of modules and disease status by WGCNA. (A) Dendrogram of Module Eigengene (ME) and heatmap of the adjacencies of modules. Each row and column in the heatmap corresponds to one module eigengene (labeled by color) or SCZ. In the heatmap, blue represents low adjacency (negative correlation), while red represents high adjacency (positive correlation). (B) Correlation of modules and disease status. Red represents a positive correlation with disease status, and blue represents a negative correlation with disease status.

FIGURE 4

Expression levels of all genes in the 5 key modules and the corresponding ME expression values of key modules versus the same sequenced samples. Red represents upregulated genes and green represents downregulated genes.

FIGURE 5

GO enrichment analysis for all genes in key modules. (A) GO terms of biological process (BP). (B) GO terms of molecular function (MF). (C) GO terms of cellular component (CC).

FIGURE 6

Scatterplots of gene significance (GS) for disease status versus module membership (MM) in five modules. The x-axis represents the correlation between gene expression within the module and MEs, the y-axis represents the Pearson correlation coefficient between gene expression within the module and disease status.