Identification of potential key pathways, genes and circulating markers in the development of intracranial aneurysm based on weighted gene co-expression network analysis

Abstract

Background

Intracranial aneurysm (IA) is a disease resulted from weak brain control, characterized by local expansion or dilation of brain artery. This study aimed to construct a gene co-expression network by Weighted Gene Correlation Network Analysis (WGCNA) to explore the potential key pathways and genes for the development of IA.

Method

Six IA-related gene expression data sets were downloaded from the Gene Expression Omnibus (GEO) database for identifying differentially expressed genes (DEGs). WGCNA was used to identify modules associated with IA. Functional enrichment analysis was used to explore the potential biological functions. ROC analysis was used to find markers for predicting IA.

Results

Purple, greenyellow and yellow modules were significantly associated with unruptured intracranial aneurysms, while blue and turquoise modules were significantly associated with ruptured intracranial aneurysms. Functional modules significantly related to IA were enriched in Ribosome, Glutathione metabolism, cAMP signalling pathway, Lysosome, Glycosaminoglycan degradation and other pathways. CD163, FCEREG, FPR1, ITGAM, NLRC4, PDG, and TYROBP were up-regulated ruptured intracranial aneurysms and serum, these genes were potential circulating markers for predicting IA rupture.

Conclusions

Potential IA-related key pathways, genes and circulating markers were identified for predicting IA rupture by WGCNA analysis.

Keywords:

• Intracranial aneurysm
• key pathways
• hub genes
• circulating markers
• ruptured intracranial aneurysm
• unruptured intracranial aneurysm

Acknowledgement

The authors would like to thank the Doc Chenxi Liu, the Biomedic Technology Co., Ltd and Life-Ontology Biological Technology Co., Ltd for assisting with bioinformatics analysis.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated during and analyzed during the current study are available in the NCBI-GEO Dataset repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6551; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13353, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26969, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75436, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106520, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36791]

Author contributions

Guojia Du designed the study and wrote the paper. Dangmurenjiafu Geng and Kai Zhou and Yandong Fan managed the literature searches and analyses. Riqing Su, Qingjiu Zhou, Bo Liu and Serick Duysenbi wrote the first draft of the manuscript. All authors contributed to and approved the final manuscript.

Additional information

Funding

This study was funded by grants of National Natural Science Foundation Programme of China (NSFC Programme) [81760222 and 31360209].