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Identifying critical genes associated with aneurysmal subarachnoid hemorrhage by weighted gene co-expression network analysis.

ABSTRACT: Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening medical condition with a high mortality and disability rate. aSAH has an unclear pathogenesis, and limited treatment options are available. Here, we aimed to identify critical genes involved in aSAH pathogenesis using peripheral blood gene expression data of 43 patients with aSAH due to ruptured intracranial aneurysms and 18 controls with headache, downloaded from Gene Expression Omnibus. These data were used to construct a co-expression network using weighted gene co-expression network analysis (WGCNA). The biological functions of the hub genes were explored, and critical genes were selected by combining with differentially expressed genes analysis. Fourteen modules were identified by WGCNA. Among those modules, red, blue, brown and cyan modules were closely associated with aSAH. Moreover, 364 hub genes in the significant modules were found to play important roles in aSAH. Biological function analysis suggested that protein biosynthesis-related processes and inflammatory responses-related processes were involved in the pathology of aSAH pathology. Combined with differentially expressed genes analysis and validation in 35 clinical samples, seven gene (CD27, ANXA3, ACSL1, PGLYRP1, ALPL, ARG1, and TPST1) were identified as potential biomarkers for aSAH, and three genes (ANXA3, ALPL, and ARG1) were changed with disease development, that may provide new insights into potential molecular mechanisms for aSAH.

SUBMITTER: Yan Z

PROVIDER: S-EPMC8507255 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Subarachnoid hemorrhage (SAH) is a major cause of death and morbidity worldwide, often due to rupture of intracranial aneurysms (IAs). Immune infiltration and inflammatory activation play key roles in the process of aneurysmal SAH (aSAH). This study aimed to elaborate the immune infiltration and identify related biomarkers both in blood and tissue samples from patients with aSAH. Expression data of aSAH and healthy control samples were obtained from gene expression omnibus (GEO) database. Overall, a blood sample dataset GSE36791 and a tissue sample dataset GSE122897 were included. Differentially expressed genes (DEGs) between aSAH and healthy samples were explored. We applied GO biological and Gene Set Enrichment Analyses (GSEA) processes to access the functional enrichment. Then feature elimination algorithms based on random forest were used to screen and verify the biomarkers of aSAH. We performed three computational algorithms including Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), Microenvironment Cell Populations-counter (MCPcounter), and xcell to evaluate the immune cell infiltration landscape to identify the unique infiltration characteristics associated with rupturing. We found 2,220 DEGs (856 upregulated and 1,364 downregulated) in the original dataset. Functional analysis revealed most of these genes are enriched in immunological process, especially related with neutrophil response. Similar signaling pathway enrichment patterns were observed in tissue sample dataset and ClueGo. Analysis of immune microenvironment infiltration suggested neutrophils were abnormally upregulated in aSAH compared with those in the control group. Key gene SRPK1 was then filtered based on feature elimination algorithms, and transcription factor (TF) ZNF281 is assumed to participate in immunomodulation by regulating expression of SRPK1. Several immunomodulators such as CXCR1 and CXCR2 also appear to be involved in the progression of aSAH. In the present study, we performed a comprehensive stratification and quantification of the immune infiltration status of aSAH. By exploring the potential mechanism for aSAH based on several computational algorithms, key genes including SRPK1 and ZNF281 were filtered. This study may be of benefit to patients who are at high risk of suffering aSAH which allows for early diagnosis and potential therapy.

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Identifying critical genes associated with aneurysmal subarachnoid hemorrhage by weighted gene co-expression network analysis.

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