Transcriptome-Based Dissection of Intracranial Aneurysms Unveils an “Immuno-Thermal” Microenvironment and Defines a Pathological Feature-Derived Gene Signature for Risk Estimation

Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis

Background

The use of artificial intelligence (AI) technology has been growing in the management of intracranial aneurysms (IAs). This study aims to conduct a bibliometric analysis of researches on intracranial aneurysm management with artificial intelligence technology (IAMWAIT) to gain insights into global research trends and potential future directions.

Methods

A comprehensive search of articles and reviews related to IAMWAIT, published from January 1, 1900 to July 20, 2023, was conducted using the Web of Science Core Collection (WoWCC).Visualizations of the bibliometric analysis were generated utilizing WPS Office, Scimago Graphica, VOSviewer, CiteSpace, and R.

Results

A total of 277 papers were included in the study. China emerged as the most prolific country in terms of publications, institutions, cooperating countries, and prolific authors. The United States garnered the highest number of total citations, institutions with the highest citations/H index, cooperating countries (n=9), and 3 of the top 10 cited papers. Both the total number of papers and the citation count exhibited a positive and significant correlation with the gross domestic product (GDP) of countries. The journal with the highest publication frequency was Frontiers in Neurology, while Stroke recorded the highest number of citations, H-index, and impact factor (IF). Areas of primary interest in IAMWAIT, leveraging AI technology, included rupture risk assessment/prediction, computer-assisted diagnosis, outcome prediction, hemodynamics, and laboratory research of IAs.

Conclusions

IAMWAIT is an active area of research that has undergone rapid development in recent years. Future endeavors should focus on broader application of AI algorithms in various sub-fields of IAMWAIT to better suit the real world.

Identification and Development of Synovial B-Cell-Related Genes Diagnostic Signature for Rheumatoid Arthritis

Background

The aim of the study was to investigate the landscape of B-cell-related gene expression profiling in rheumatoid arthritis (RA) synovium and explore the biological and clinical significance of these genes in RA.

Methods

Expression profiling of synovial biopsies from subjects with 152 RA patients, 22 osteoarthritis (OA) patients, and 28 healthy controls was downloaded from the Gene Expression Omnibus database. Single-sample gene set enrichment analysis (ssGSEA) was performed to evaluate the abundance of infiltrated immune cells, and the results were validated using immunohistochemical staining. GSEA was employed to decipher differences in B-cell-related biological pathways. B-cell-related differential expression genes (BRDEGs) were screened, and BRDEGs-based model was developed by machine learning algorithms and evaluated by an external validation set and clinical RA cohort, then biological functions were further analyzed.

Results

High levels of immune cell infiltration and B-cell-related pathway activation were revealed in RA synovium. BRDEGs were screened, and three key molecular markers consisting of FAS, GPR183, and TFRC were identified. The diagnosis model was established, and these gene markers have good discriminative ability for RA. Molecular pathological evaluation confirmed RA patients with high-risk scores presented higher levels of B-cell activation and RA characteristics. In addition, a competitive endogenous RNA network was established to elucidate the molecular mechanisms of the posttranscriptional network.

Conclusions

We described the B-cell-related molecular landscape of RA synovium and constructed a molecular diagnostic model in RA. The three genes FAS, GPR183, and TFRC may be potential targets for clinical diagnosis and immunoregulatory therapy of RA.

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.

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.

An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning

Background

Immunogenic Cell Death (ICD) is a novel way to regulate cell death and can sufficiently activate adaptive immune responses. Its role in immunity is still emerging. However, the involvement of ICD in Intracranial Aneurysms (IA) remains unclear. This study aimed to identify biomarkers associated with ICDs and determine the relationship between them and the immune microenvironment during the onset and progression of IA

Methods

The IA gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IA were identified and the effects of the ICD on immune microenvironment signatures were studied. Techniques like Lasso, Bayes, DT, FDA, GBM, NNET, RG, SVM, LR, and multivariate analysis were used to identify the ICD gene signatures in IA. A consensus clustering algorithm was used for conducting the unsupervised cluster analysis of the ICD patterns in IA. Furthermore, enrichment analysis was carried out for investigating the various immune responses and other functional pathways. Along with functional annotation, the weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network and module construction, identification of the hub gene, and co-expression analysis were also carried out.

Results

The above techniques were used for establishing the ICD gene signatures of HMGB1, HMGN1, IL33, BCL2, HSPA4, PANX1, TLR9, CLEC7A, and NLRP3 that could easily distinguish IA from normal samples. The unsupervised cluster analysis helped in identifying three ICD gene patterns in different datasets. Gene enrichment analysis revealed that the IA samples showed many differences in pathways such as the cytokine-cytokine receptor interaction, regulation of actin cytoskeleton, chemokine signaling pathway, NOD-like receptor signaling pathway, viral protein interaction with the cytokines and cytokine receptors, and a few other signaling pathways compared to normal samples. In addition, the three ICD modification modes showed obvious differences in their immune microenvironment and the biological function pathways. Eight ICD-regulators were identified and showed meaningful associations with IA, suggesting they could severe as potential prognostic biomarkers.

Conclusions

A new gene signature for IA based on ICD features was created. This signature shows that the ICD pattern and the immune microenvironment are closely related to IA and provide a basis for optimizing risk monitoring, clinical decision-making, and developing novel treatment strategies for patients with IA.

Six macrophage-associated genes in synovium constitute a novel diagnostic signature for osteoarthritis

Background

Synovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA.

Methods

The OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein–protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models.

Results

OA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms.

Conclusions

We developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy.