PAX5/ITGAX Contributed to the Progression of Atherosclerosis by Regulation of B Differentiation via TNF-α Signaling Pathway

ITGAX promotes gastric cancer progression via epithelial-mesenchymal transition pathway

Gastric cancer is the fifth most common cancer and the fourth leading cause of cancer-related deaths worldwide, accounting for nearly 800,000 fatalities annually. ITGAX (Integrin alpha X) is closely associated with immune cells, such as macrophages and dendritic cells. Its involvement in gastric cancer was identified through an analysis of The Gene Expression Omnibus (GEO) database, which highlighted ITGAX as one of four key gastric cancer-related genes. Our study demonstrates that ITGAX expression is significantly elevated in tumor tissues compared to normal tissues and is positively correlated with clinical prognosis in gastric cancer patients from the GEO database. Moreover, ITGAX enhanced cell proliferation, invasion, and tumorigenic capacity in mouse models. Furthermore, we explored the underlying role of ITGAX using Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction networks (PPI) analysis. Our findings reveal that ITGAX promotes gastric cancer progression by driving epithelial-mesenchymal transition pathway (EMT), suggesting its potential as a biomarker for early diagnosis and prognosis in gastric cancer.

Identification of key genes for atherosclerosis in different arterial beds

Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

Identification of mitophagy and ferroptosis-related hub genes associated with intracerebral haemorrhage through bioinformatics analysis

BACKGROUND

Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete.

AIM

This study aims to identify shared ICH genes for both processes.

METHODS

ICH differentially expressed mitophagy and ferroptosis-related genes (DEMFRGs) were sourced from the GEO database and literature. Enrichment analysis elucidated functions. Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. Immune microenvironment variances were assessed with MCP and TIMER. Potential small molecules for ICH were forecasted via CMap database.

RESULTS

64 DEMFRGs and ten hub genes potentially involved in various processes like ferroptosis, TNF signalling pathway, MAPK signalling pathway, and NF-kappa B signalling pathway were discovered. Several miRNAs were identified as shared targets of hub genes. The ICH group showed increased infiltration of monocytic lineage and myeloid dendritic cells compared to the Healthy group. Ten potential small molecule drugs (e.g. Zebularine, TWS-119, CG-930) were predicted via CMap.

CONCLUSION

Several shared genes between mitophagy and ferroptosis potentially drive ICH progression via TNF, MAPK, and NF-kappa B pathways. These results offer valuable insights for further exploring the connection between mitophagy, ferroptosis, and ICH.

Identification of molecular signatures in acute myocardial infarction based on integrative analysis of proteomics and transcriptomics

BACKGROUND

Myocardial infarction (MI) is one of the most serious cardiovascular diseases, characterized by a rapid and irreversible decline in myocardial function. Early detection of patients with MI and prolonging the optimal therapeutic window of acute myocardial infarction (AMI) are particularly important. This study aimed to identify the diagnostic biomarkers and novel therapeutic targets for acute myocardial infarction.

METHOD

We generated the AMI mouse models by ligating the proximal left anterior descending coronary artery. Six time points-Sham, AMI 10-min, 1-h, 6-h, 24-h, and 72-h-were chosen to examine the molecular changes that occur during the AMI process. RNA-seq and DIA-MS were performed on the infarcted left ventricular tissues of AMI mice at each time point. Co-expression pattern genes were screened from myocardial infarction samples at different time points by time-series analysis. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to examine these genes. Using the Interactive Gene/Protein Retrieval Tool (STRING) database, the protein-protein interaction network (PPI) was constructed and the hub genes were identified. In order to evaluate the diagnostic value of hub genes, a receiver operating characteristic (ROC) curve was constructed. An independent data set, GSE163772, confirmed the diagnostic value of hub genes further.

RESULT

We obtained the expression profiles at different time points after the occurrence of heart failure through high-throughput sequencing, and found 167 genes with similar expression patterns through time series analysis. The immune response and immune-related pathways had the greatest enrichment of these genes. Among them, Itgb2 Syk, Tlr4, Tlr2, Itgax, and Lcp2 may play key roles as hub genes. Combined with the results of proteomic analysis, it was found that the expression of Coro1a in both omics increased with time. The results of external validation showed that TLR2, ITGAX, and LCP2 had good predictive ability for AMI diagnosis.

CONCLUSION

Itgb2, Syk, Tlr4, Tlr2, Itgax, Lcp2 and Coro1a are considered to be the seven key genes significantly associated with AMI. Our results may provide potential targets for the prevention of adverse ventricular remodeling and the treatment of AMI.