Identification and validation of key genes mediating intracranial aneurysm rupture by weighted correlation network analysis

Identification of differentially expressed autophagy-related genes in cases of intracranial aneurysm: Bioinformatics analysis

OBJECTIVE

Recent research indicates that autophagy is essential for the rupture of intracranial aneurysm (IA). This study aimed to examine and validate potential autophagy-related genes (ARGs) in cases of IA using bioinformatics analysis.

METHODS

Two expression profiles (GSE54083 and GSE75436) were obtained from the Gene Expression Omnibus database. Differentially expressed ARGs (DEARGs) in cases of IA were screened using GSE75436, and enrichment analysis and Protein-Protein Interaction (PPI) networks were used to identify the hub genes and related pathways. Furthermore, a novel predictive diagnostic signature for IA based on the hub genes was constructed. The area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the signature performance in GSE75436.

RESULTS

In total, 75 co-expressed DEARGs were identified in the GSE75436 and GSE54083 dataset (28 upregulated and 47 downregulated genes). Enrichment analysis of DEARGs revealed several enriched terms associated with proteoglycans in cancer and human immunodeficiency virus 1 infection. PPI analysis revealed interactions between these genes. Hub DEARGs included insulin-like growth factor 1, clusters of differentiation 4, cysteine-aspartic acid protease 8, Bcl-2-like protein 11, mouse double mutant 2 homolog, toll-like receptor 4, growth factor receptor-bound protein 2, Jun proto-oncogene, AP-1 transcription factor subunit, hypoxia inducible factor 1 alpha, and erythroblastic oncogene B-2. Notably, the signature showed good performance in distinguishing IA (AUC = 0.87). The sig calibration curves showed good calibration.

CONCLUSION

Bioinformatic analysis identified 75 potential DEARGs in cases of IA. This study revealed that IA is affected by autophagy, which could explain the pathogenesis of IA and aid in its diagnosis and treatment. However, future research with experimental validation is necessary to identify potential DEARGs in cases of IA.

Integrative analysis of multi-omics data to identify three immune-related genes in the formation and progression of intracranial aneurysms

OBJECTIVE AND DESIGN

The prevalence of intracranial aneurysms (IAs) has increased globally. We performed bioinformatics analysis to identify key biomarkers associated with IA formation.

METHODS AND RESULTS

We conducted a comprehensive analysis combined with multi-omics data and methods to identify immune-related genes (IRGs) and immunocytes involved in IAs. Functional enrichment analyses showed enhanced immune responses and suppressed organizations of extracellular matrix (ECM) during aneurysm progression. xCell analyses showed that the abundance of B cells, macrophages, mast cells, and monocytes significantly increased from levels in control to unruptured aneurysms and to ruptured aneurysms. Of 21 IRGs identified by overlapping, a three-gene (CXCR4, S100B, and OSM) model was constructed through LASSO logistic regression. The diagnostic ability of the three biomarkers in discriminating aneurysms from the control samples demonstrated a favorable diagnostic value. Among the three genes, OSM and CXCR4 were up-regulated and hypomethylated in IAs, while S100B was down-regulated and hypermethylated. The expression of the three IRGs was further validated by qRT-PCR and immunohistochemistry and mouse IA model using scRNA-seq analysis.

CONCLUSION

The present study demonstrated heightened immune response and suppressed ECM organization in aneurysm formation and rupture. The three-gene immune-related signature (CCR4, S100B, and OSM) model may facilitate IA diagnosis and prevention.

Transcriptomic Studies on Intracranial Aneurysms

Intracranial aneurysm (IA) is a relatively common vascular malformation of an intracranial artery. In most cases, its presence is asymptomatic, but IA rupture causing subarachnoid hemorrhage is a life-threating condition with very high mortality and disability rates. Despite intensive studies, molecular mechanisms underlying the pathophysiology of IA formation, growth, and rupture remain poorly understood. There are no specific biomarkers of IA presence or rupture. Analysis of expression of mRNA and other RNA types offers a deeper insight into IA pathobiology. Here, we present results of published human studies on IA-focused transcriptomics.

m6A regulator–mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in patients with intracranial aneurysms

Background

The role of epigenetic modulation in immunity is receiving increased recognition—particularly in the context of RNA N6-methyladenosine (m6A) modifications. Nevertheless, it is still uncertain whether m6A methylation plays a role in the onset and progression of intracranial aneurysms (IAs). This study aimed to establish the function of m6A RNA methylation in IA, as well as its correlation with the immunological microenvironment.

Methods

Our study included a total of 97 samples (64 IA, 33 normal) in the training set and 60 samples (44 IA, 16 normal) in the validation set to systematically assess the pattern of RNA modifications mediated by 22 m6A regulators. The effects of m6A modifications on immune microenvironment features, i.e., immune response gene sets, human leukocyte antigen (HLA) genes, and infiltrating immune cells were explored. We employed Lasso, machine learning, and logistic regression for the purpose of identifying an m6A regulator gene signature of IA with external data validation. For the unsupervised clustering analysis of m6A modification patterns in IA, consensus clustering methods were employed. Enrichment analysis was used to assess immune response activity along with other functional pathways. The identification of m6A methylation markers was identified based on a protein–protein interaction network and weighted gene co-expression network analysis.

Results

We identified an m6A regulator signature of IGFBP2, IGFBP1, IGF2BP2, YTHDF3, ALKBH5, RBM15B, LRPPRC, and ELAVL1, which could easily distinguish individuals with IA from healthy individuals. Unsupervised clustering revealed three m6A modification patterns. Gene enrichment analysis illustrated that the tight junction, p53 pathway, and NOTCH signaling pathway varied significantly in m6A modifier patterns. In addition, the three m6A modification patterns showed significant differences in m6A regulator expression, immune microenvironment, and bio-functional pathways. Furthermore, macrophages, activated T cells, and other immune cells were strongly correlated with m6A regulators. Eight m6A indicators were discovered—each with a statistically significant correlation with IA—suggesting their potential as prognostic biological markers.

Conclusion

Our study demonstrates that m6A RNA methylation and the immunological microenvironment are both intricately correlated with the onset and progression of IA. The novel insight into patterns of m6A modification offers a foundation for the development of innovative treatment approaches for IA.

ML323 suppresses the progression of ovarian cancer via regulating USP1-mediated cell cycle

Background: Ubiquitin specific protease 1 (USP1) tightly correlates with poor prognosis of multiple cancers. However, whether USP1 underlies ovarian cancer (OV) progression remains unclarified.

Methods: First, GSEA strategy and WGCNA analysis were used to screen for anti-ovarian cancer drugs and furthern optimal module, respectively. In addition, functional enrichments of module genes were realized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Kaplan-Meier was then employed to analyze the prognostic impact of USP1 expression on OV patients. Cell proliferation and cell cycle assays were used to confirm biological functions of USP1 in the final.

Results: Through the forementioned methods, we obtained five candidate drugs against OV from 353 anticancer drugs, and proposed ML323 as a novel anti-OV drug. As our hypothesized, ML323 significantly inhibited the proliferation of OV cells. Combined with WGCNA and KEGG analysis, the turquoise module was related to ML323, together with cell cycle. USP1 was subsequently identified as a target of ML323 and according to the TCGA database, USP1 negatively correlated with prognosis in OV, and its reduction and ML323-treatment both inhibited the proliferation of OV cells, blocking the S phase of cell cycle in vitro.

Conclusion: Taken together, ML323 exerts its inhibitory effect on the proliferation of OV cells by targeting USP1-regulated cell cycle, providing a therapeutical strategy and potential target against OV.

Identification of Key Exosome Gene Signature in Mediating Coronary Heart Disease by Weighted Gene Correlation Network Analysis

Background

Coronary heart disease (CHD) is the most prevalent disease with an unelucidated pathogenetic mechanism and is mediated by complex molecular interactions of exosomes. Here, we aimed to identify differentially expressed exosome genes for the disease development and prognosis of CHD.

Method

Six CHD samples and 32 normal samples were downloaded from the exoRbase database to identify the candidate genes in the CHD. The differentially expressed genes (DEGs) were identified. And then, weighted gene correlation network analysis (WGCNA) was used to investigate the modules in coexpressed genes between CHD samples and normal samples. DEGs and the module of the WGCNA were intersected to obtain the most relevant exosome genes. After that, the function enrichment analyses and protein-protein interaction network (PPI) were performed for the particular module using STRING and Cytoscape software. Finally, the CIBERSORT algorithm was used to analyze the immune infiltration of exosome genes between CHD samples and normal samples.

Result

We obtain a total of 715 overlapping exosome genes located at the intersection of the DEGs and key modules. The Gene Ontology enrichment of DEGs in the blue module included inflammatory response, neutrophil degranulation, and activation of CHD. In addition, protein-protein networks were constructed, and hub genes were identified, such as LYZ, CAMP, HP, ORM1, and LTF. The immune infiltration profiles varied significantly between normal controls and CHD. Finally, we found that mast cells activated and eosinophils had a positive correlation. B cell memory had a significant negative correlation with B cell naive. Besides, neutrophils and mast cells were significantly increased in CHD patients.

Conclusion

The underlying mechanism may be related to neutrophil degranulation and the immune response. The hub genes and the difference in immune infiltration identified in the present study may provide new insights into the diagnostic and provide candidate targets for CHD.