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

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.

Elucidating key immunological biomarkers and immune microenvironment dynamics in aging-related intracranial aneurysm through integrated multi-omics analysis

BACKGROUND

The exact cause of intracranial aneurysms (IA) is still unclear. However, pro-inflammatory factors are known to contribute to IA progression. The specific changes in the immune microenvironment of IAs remain largely unexplored.

METHODS

This study analyzed single-cell sequencing data from a male mouse model of brain aneurysm, focusing on samples before and after elastase-induced Willis aneurysms. The data helped identify eight distinct cell subpopulations: fibroblasts, macrophages, NK cells, endothelial cells, B cells, granulocytes, and monocytes. The study also involved bulk RNA sequencing of 97 IA samples, utilizing ssGSEA and CIBERSORT algorithms for analysis. Intercellular communication among these cells was inferred to understand the immune dynamics in IA.

RESULTS

The study found that fibroblasts and macrophages are predominant in various disease states of IA. Notably, the onset of IA was marked by a significant increase in fibroblasts and a decrease in macrophages. There was a marked increase in cellular interactions, especially involving macrophages, at the onset of the disease. Through enrichment analysis, 12 potential immunogenic biomarkers were identified. Of these, Rgs1 emerged as a critical molecule in IA formation, confirmed through secondary validation in a single-cell sequencing dataset.

CONCLUSION

This comprehensive analysis of immune cell composition and intercellular communication in IA tissues highlights the significant roles of macrophages and the molecule Rgs1. These findings shed light on the physiological and pathological conditions of IA, offering new insights into its immune microenvironment.

Analysis and validation of potential ICD-related biomarkers in development of myopia using machine learning

PURPOSE

We aimed to identify and verify potential biomarkers in the development of myopia associated with immunogenic cell death (ICD).

METHODS

We download high myopia (HM) dataset GSE136701 from Gene Expression Omnibus. Differentially expressed genes in HM were identified to overlapped with ICD-related genes. Least absolute shrinkage and selection operator were used to select the Hub genes. Furthermore, the correlation between the hub genes and immune infiltration, immune response activities, and hub genes Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis was investigated using Spearman's rank correlation. Prediction of the miRNAs upstream of the Hub genes was based on the TargetScan database. We used guinea pig lens-induced myopia model's scleral tissues performed quantitative real-time polymerase chain reaction.

RESULTS

We identified overlapped with ICD-related genes (LY96, IL1A, IL33, and AGER) and two genes (LY96 and AGER) as hub genes. Single sample gene set enrichment analysis and Spearman's rank correlation revealed that hub gene expression levels in HM were significantly correlated with the infiltration percentages of CD56dim natural killer cells, macrophages, immature B cells, and the immune response activities of APC co-stimulation and Kyoto Encyclopedia of Genes and Genomes pathways, such as terpenoid backbone biosynthesis, aminoacyl-trna biosynthesis, Huntington's disease, oxidative phosphorylation; there were a few additional signaling pathways compared to normal samples. Additionally, several miRNA were predicted as upstream regulators of LY96 and AGER. LY96 was identified as a significantly differentially expressed biomarker in myopia guinea pig's scleral tissues, as verified by qPCR.

CONCLUSION

LY96 was identified and verified as a ICD-related potential myopia biomarker. Molecular mechanisms or pathways involved in myopia development by LY96 requires further research.

An immunogenic cell death-related gene expression signature in predicting prognosis of pancreatic ductal adenocarcinoma

BACKGROUND

Immunogenic cell death (ICD) has been identified as regulated cell death, which is sufficient to activate the adaptive immune response. This study aimed to research ICD-related genes and create a gene model to predict pancreatic ductal adenocarcinoma (PAAD) patients' prognosis.

METHODS

The RNA sequencing and clinical data were downloaded from the TGCA and GEO databases. The PAAD samples were classified into two subtypes based on the expression levels of ICD-related genes using consensus clustering. Based on the differentially expressed genes (DEGs), a prognostic scoring model was constructed using LASSO regression and Cox regression, and the scoring model was used to predict the prognosis of PAAD patients. Moreover, colony formation assay was performed to confirm the prognostic value of those genes.

RESULTS

We identified two ICD cluster by consensus clustering, and found that the the ICD-high group was closely associated with immune-hot phenotype, favorable clinical outcomes. We established an ICD-related prognostic model which can predict the prognosis of pancreatic ductal adenocarcinoma. Moreover, depletion of NT5E, ATG5, FOXP3, and IFNG inhibited the colony formation ability of pancreatic cancer cell.

CONCLUSION

We identified a novel classification for PAAD based on the expression of ICD-related genes, which may provide a potential strategy for therapeutics against PAAD.

Enhanced therapeutic efficacy of doxorubicin against multidrug-resistant breast cancer with reduced cardiotoxicity

Doxorubicin (DOX), a commonly used anti-cancer drug, is limited by its cardiotoxicity and multidrug resistance (MDR) of tumor cells. Epigallocatechin gallate (EGCG), a natural antioxidant component, can effectively reduce the cardiotoxicity of DOX. Meanwhile, EGCG can inhibit the expression of P-glycoprotein (P-gp) and reverse the MDR of tumor cells. In this study, DOX is connected with low molecular weight polyethyleneimine (PEI) via hydrazone bond to get the pH-sensitive PEI-DOX, which is then combined with EGCG to prevent the cardiotoxicity of DOX and reverse the MDR of cancer cells. In addition, folic acid (FA) modified polyethylene glycol (PEG) (PEG-FA) is added to get the targeted system PEI-DOX/EGCG/FA. The MDR reversal and targeting ability of PEI-DOX/EGCG/FA is performed by cytotoxicity and in vivo anti-tumor activity on multidrug resistant MCF-7 cells (MCF-7/ADR). Additionally, we investigate the anti-drug resistant mechanism by Western Blot. The ability of EGCG to reduce DOX cardiotoxicity is confirmed by cardiotoxicity assay. In conclusion, PEI-DOX/EGCG/FA can inhibit the expression of P-gp and reverse the MDR in tumor cells. It also shows the ability of remove oxygen free radicals effectively to prevent the cardiotoxicity of DOX.

A Machine Learning-Based Classification of Immunogenic Cell Death Regulators and Characterisation of Immune Microenvironment in Acute Ischemic Stroke

Immunogenic cell death (ICD) regulators exert a crucial part in quite a few in numerous biological processes. This study aimed to determine the function and diagnostic value of ICD regulators in acute ischemic stroke (AIS). 31 significant ICD regulators were identified from the gene expression omnibus (GEO) database in this work (the combination of the GSE16561 dataset and the GSE37587 dataset in the comparison of non-AIS and AIS patients). The random forest model was applied and 15 potential ICD regulators were screened to forecast the probability of AIS. A nomogram, on the basis of 11 latent ICD regulators, was performed. The resolution curve analysis indicated that patients can gain benefits from the nomogram. The consensus clustering approach was applied, and AIS patients were divided into 2 ICD clusters (cluster A and cluster B) based on the identified key ICD regulatory factors. To quantify the ICD pattern, 181 ICD-related dissimilarly expressed genes (DEGs) were selected for further investigation. The expression levels of NFKB1, NFKB2, and PARP1 were greater in gene cluster A than in gene cluster B. In conclusion, ICD regulators exerted a crucial part in the progress of AIS. The investigation made by us on ICD patterns perhaps informs prospective immunotherapeutic methods for AIS.

Comprehensive analysis of mitochondrial dysfunction and necroptosis in intracranial aneurysms from the perspective of predictive, preventative, and personalized medicine

Mitochondrial dysfunction and necroptosis are closely associated, and play vital roles in the medical strategy of multiple cardiovascular diseases. However, their implications in intracranial aneurysms (IAs) remain unclear. In this study, we aimed to explore whether mitochondrial dysfunction and necroptosis could be identified as valuable starting points for predictive, preventive, and personalized medicine for IAs. The transcriptional profiles of 75 IAs and 37 control samples were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis, and least absolute shrinkage and selection operator (LASSO) regression were used to screen key genes. The ssGSEA algorithm was performed to establish phenotype scores. The correlation between mitochondrial dysfunction and necroptosis was evaluated using functional enrichment crossover, phenotype score correlation, immune infiltration, and interaction network construction. The IA diagnostic values of key genes were identified using machine learning. Finally, we performed the single-cell sequencing (scRNA-seq) analysis to explore mitochondrial dysfunction and necroptosis at the cellular level. In total, 42 IA-mitochondrial DEGs and 15 IA-necroptosis DEGs were identified. Screening revealed seven  key genes invovled in mitochondrial dysfunction (KMO, HADH, BAX, AADAT, SDSL, PYCR1, and MAOA) and five genes involved in necroptosis (IL1B, CAMK2G, STAT1, NLRP3, and BAX). Machine learning confirmed the high diagnostic value of these key genes for IA. The IA samples showed  higher expression of mitochondrial dysfunction and necroptosis. Mitochondrial dysfunction and necroptosis exhibited a close association. Furthermore, scRNA-seq indicated that mitochondrial dysfunction and necroptosis were preferentially up-regulated in monocytes/macrophages and vascular smooth muscle cells (VSMCs) within IA lesions. In conclusion, mitochondria-induced necroptosis was involved in IA formation, and was mainly up-regulated in monocytes/macrophages and VSMCs within IA lesions. Mitochondria-induced necroptosis may be a novel potential target for diagnosis, prevention, and treatment of IA.

DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data

BACKGROUND

Intracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify potential biomarkers and therapeutic interventions.

METHODS

We employed a comprehensive bioinformatics investigation of DNA methylation in IA, utilizing a transcriptomics-based methodology that encompassed 100 machine learning algorithms, genome-wide association studies (GWAS), Mendelian randomization (MR), and summary-data-based Mendelian randomization (SMR). Our sophisticated analytical strategy allowed for a systematic assessment of differentially methylated genes and their implications on the onset, progression, and rupture of IA.

RESULTS

We identified DNA methylation-related genes (MRGs) and associated molecular pathways, and the MR and SMR analyses provided evidence for potential causal links between the observed DNA methylation events and IA predisposition.

CONCLUSION

These insights not only augment our understanding of the molecular underpinnings of IA but also underscore potential novel biomarkers and therapeutic avenues. Although our study faces inherent limitations and hurdles, it represents a groundbreaking initiative in deciphering the intricate relationship between genetic, epigenetic, and environmental factors implicated in IA pathogenesis.

Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer

Background

Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of this study was to identify key biomarkers of GC and to elucidate GC-associated immune cell infiltration and related pathways.

Methods

Gene microarray data associated with GC were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia, Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to identify pivotal genes for GC and to assess the diagnostic accuracy of GC hub markers using the subjects' working characteristic curves. In addition, the infiltration levels of 28 immune cells in GC and their interrelationship with hub markers were analyzed using ssGSEA. And further validated by RT-qPCR.

Results

A total of 133 DEGs were identified. The biological functions and signaling pathways closely associated with GC were inflammatory and immune processes. Nine expression modules were obtained by WGCNA, with the pink module having the highest correlation with GC; 13 crossover genes were obtained by combining DEGs. Subsequently, the LASSO algorithm and validation set verification analysis were used to finally identify three hub genes as potential biomarkers of GC. In the immune cell infiltration analysis, infiltration of activated CD4 T cell, macrophages, regulatory T cells and plasmacytoid dendritic cells was more significant in GC. The validation part demonstrated that three hub genes were expressed at lower levels in the gastric cancer cells.

Conclusion

The use of WGCNA combined with the LASSO algorithm to identify hub biomarkers closely related to GC can help to elucidate the molecular mechanism of GC development and is important for finding new immunotherapeutic targets and disease prevention.

Immunogenic cell death-related classifications guide prognosis and immunotherapy in osteosarcoma

Immunogenic cell death (ICD) is a form of cell death that stimulates the immune system to produce an immune response by releasing tumour-associated antigens and tumour-specific antigens and is considered to play an important role in tumour immunotherapy. In the present study, we identified two ICD-related subtypes in osteosarcoma (OS) by consensus clustering. The ICD-low subtype was associated with favourable clinical outcomes, abundant immune cell infiltration, and high activity of immune response signalling. We also established and validated an ICD-related prognostic model, which could not only be used to predict the overall survival of OS patients but was also found to be closely related to the tumour immune microenvironment of OS patients. Overall, we established a new classification system for OS based on ICD-related genes, which can be used to predict the prognosis of OS patients and to select appropriate immunotherapy drugs.

Identification of immunogenic cell death-related gene classification patterns and immune infiltration characterization in ischemic stroke based on machine learning

Ischemic stroke (IS) accounts for more than 80% of strokes and is one of the leading causes of death and disability in the world. Due to the narrow time window for treatment and the frequent occurrence of severe bleeding, patients benefit less from early intravenous thrombolytic drug therapy. Therefore, there is an urgent need to explore the molecular mechanisms poststroke to drive the development of new therapeutic approaches. Immunogenic cell death (ICD) is a type of regulatory cell death (RCD) that is sufficient to activate the adaptive immune response of immunocompetent hosts. Although there is growing evidence that ICD regulation of immune responses and immune responses plays an important role in the development of IS, the role of ICD in the pathogenesis of IS has rarely been explored. In this study, we systematically evaluated ICD-related genes in IS. The expression profiles of ICD-related genes in IS and normal control samples were systematically explored. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis of IS samples using ICD differentially expressed genes. The results showed that IS patients could be classified into two clusters and that the immune infiltration profile was altered in different clusters. In addition, we performed machine learning to screen nine signature genes that can be used to predict the occurrence of disease. We also constructed nomogram models based on the nine risk genes (CASP1, CASP8, ENTPD1, FOXP3, HSP90AA1, IFNA1, IL1R1, MYD88, and NT5E) and explored the immune infiltration correlation, gene-miRNA, and gene-TF regulatory network of the nine risk genes. Our study may provide a valuable reference for further elucidation of the pathogenesis of IS and provide directions for drug screening, personalized therapy, and immunotherapy for IS.

Analysis of co-expression gene network associated with intracranial aneurysm and type 2 diabetes mellitus

To screen for common target genes in intracranial aneurysms (IA) and type 2 diabetes mellitus (T2DM), construct a common transcriptional regulatory network to predict clusters of candidate genes involved in the pathogenesis of T2DM and IA, and identify the common neurovascular markers and pathways in T2DM causing IA. Microarray datasets (GSE55650, GSE25462, GSE26969, GSE75436, and GSE13353) from the GEO database were analyzed in this research. Screening of the IA and the T2DM datasets yielded a total of 126 DEGs, among which 78 were upregulated and 138 were downregulated. Functional enrichment analysis revealed that these DEGs were enriched for a total of 68 GO pathways, including extracellular matrix composition, coagulation regulation, hemostasis regulation, and collagen fiber composition pathways. We also constructed transcriptional regulatory networks, and identified key transcription factors involved in both the conditions. Univariate logistic regression analysis showed that ARNTL2 and STAT1 were significantly associated with the development of T2DM and IA, acting as the common neurovascular markers for both the diseases. In cellular experiments, hyperglycemic microenvironments exhibited upregulated STAT1 expression. STAT1 may be involved in the pathogenesis of IA in T2DM patients. Being the common neurovascular markers, STAT1 may acts as novel therapeutic targets for the treatment of IA and T2DM.