Intracranial aneurysms

Identification of immune-related molecular markers in intracranial aneurysm (IA) based on machine learning and cytoscape-cytohubba plug-in

BACKGROUND

Intracranial aneurysm (IA) is a common cerebrovascular disease. The immune mechanism of IA is more complicated, and it is unclear so far. Therefore, it is necessary to continue to explore the immune related molecular mechanism of IA.

METHODS

All data were downloaded from the public database. Limma package and ssGSEA algorithm was used to identify differentially expressed mRNAs (DEmRNAs) and analyze immune cell infiltration, respectively. Machine learning and cytoscape-cytohubba plug-in was used to identify key immune types and multicentric DEmRNAs of IA, respectively. Multicentric DEmRNAs related to key immune cells were screened out as key DEmRNAs by Spearman correlation analysis. Diagnostic models, competing endogenous RNA (ceRNA) regulatory network and transcription factor regulatory network were constructed based on key DEmRNAs. Meanwhile, drugs related to key DEmRNAs were screened out based on DGIdb database. The expression of key DEmRNAs was also verified by real time-PCR.

RESULTS

In this study, 7 key DEmRNAs (NRXN1, GRIA2, SLC1A2, SLC17A7, IL6, VEGFA and SYP) associated with key differential immune cell infiltration (CD56bright natural killer cell, Immature B cell and Type 1 T helper cell) were identified. Functional enrichment analysis showed that VEGFA and IL6 may be involved in the regulation of the PI3K-Akt signaling pathway. Moreover, IL6 was also found to be enriched in cytokine-cytokine receptor interaction signaling pathway. In the ceRNA regulatory network, a large number of miRNAs and lncRNAs were found. In the transcription factor regulatory network, the transcription factor SP1 was correlated with VEGFA, SYP and IL6. It is also predicted that drugs related to key DEmRNAs such as CARBOPLATIN, FENTANYL and CILOSTAZOL may contribute to the treatment of IA. In addition, it was also found that SVM and RF models based on key DEmRNAs may be potential markers for diagnosing IA and unruptured intracranial aneurysm (UIA), respectively. The expression trend of key DEmRNAs verified by real-time PCR was consistent with the bioinformatics analysis results.

CONCLUSION

The identification of molecules and pathways in this study provides a theoretical basis for understanding the immune related molecular mechanism of IA. Meanwhile, the drug prediction and diagnosis model construction may also be helpful for clinical diagnosis and management.

Uncovering of Key Pathways and miRNAs for Intracranial Aneurysm Based on Weighted Gene Co-Expression Network Analysis

BACKGROUND

Intracranial aneurysm (IA) is a serious cerebrovascular disease. The identification of key regulatory genes can provide research directions for early diagnosis and treatment of IA.

METHODS

Initially, the miRNA and mRNA data were downloaded from the Gene Expression Omnibus database. Subsequently, the limma package in R was used to screen for differentially expressed genes. In order to investigate the function of the differentially expressed genes, a functional enrichment analysis was performed. Moreover, weighted gene co-expression network analysis (WGCNA) was performed to identify the hub module and hub miRNAs. The correlations between miRNAs and mRNAs were assessed by constructing miRNA-mRNA regulatory networks. In addition, in vitro validation was performed. Finally, diagnostic analysis and electronic expression verification were performed on the GSE122897 dataset.

RESULTS

In the present study, 955 differentially expressed mRNAs (DEmRNAs, 480 with increased and 475 with decreased expression) and 46 differentially expressed miRNAs (DEmiRNAs, 36 with increased and 10 with decreased expression) were identified. WGCNA demonstrated that the yellow module was the hub module. Moreover, 16 hub miRNAs were identified. A total of 1,124 negatively regulated miRNA-mRNA relationship pairs were identified. Functional analysis demonstrated that DEmRNAs in the targeted network were enriched in vascular smooth muscle contraction and focal adhesion pathways. In addition, the area under the curve of 16 hub miRNAs was >0.8. It is implied that 16 hub miRNAs may be used as potential diagnostic biomarkers of IA.

CONCLUSION

Hub miRNAs and key signaling pathways were identified by bioinformatics analysis. This evidence lays the foundation for understanding the underlying molecular mechanisms of IA and provided potential therapeutic targets for the treatment of this disease.