Background Intracranial aneurysm (IA) is the most common and is the main cause of spontaneous subarachnoid hemorrhage (SAH). The underlying molecular mechanisms for preventing IA progression have not been fully identified. Our research aimed to identify the key genes and critical pathways of IA through gene co-expression networks. Methods Gene Expression Omnibus (GEO) datasets GSE13353, GSE54083 and GSE75436 were used in the study. The genetic data were analyzed by weighted gene co-expression network analysis (WGCNA). Then the clinically significant modules were identified and the differentially expressed genes (DEGs) with the genes were intersected in these modules. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the function or pathway. In addition, the composition of immune cells was analyzed by CIBERSORT algorithm. Finally, the hub genes and key genes were identified by GSE122897. Results A total of 266 DEGs and two modules with clinical significance were identified. The inflammatory response and immune response were identified by GO and KEGG. CCR5, CCL4, CCL20, and FPR3 were the key genes in the module correlated with IA. The proportions of infiltrating immune cells in IA and normal tissues were different, especially in terms of macrophages and mast cells. Conclusion The chemotactic system has been identified as a key pathway of IA, and interacting macrophages may regulate this pathological process.
Background: Gliomas are the most common malignant tumors of the central nervous system, with extremely bad prognoses. Cuproptosis is a novel form of regulated cell death. The impact of cuproptosis-related genes on glioma development has not been reported.Methods: The TCGA, GTEx, and CGGA databases were used to retrieve transcriptomic expression data. We employed Cox’s regressions to determine the associations between clinical factors and cuproptosis-related gene expression. Overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were evaluated using the Kaplan-Meier method. We also used the least absolute shrinkage and selection operator (LASSO) regression technique.Results: The expression levels of all 10 CRGs varied considerably between glioma tumors and healthy tissues. In glioma patients, the levels of CDKN2A, FDX1, DLD, DLAT, LIAS, LIPT1, and PDHA1 were significantly associated with the OS, disease-specific survival, and progression-free interval. We used LASSO Cox’s regression to create a prognostic model; the risk score was (0.882340) *FDX1 expression + (0.141089) *DLD expression + (–0.333875) *LIAS expression + (0.356469) *LIPT1 expression + (–0.123851) *PDHA1 expression. A high-risk score/signature was associated with poor OS (hazard ratio = 3.50, 95% confidence interval 2, –4.55, log-rank p < 0.001). Cox’s regression revealed that the FDX1 level independently predicted prognosis; FDX1 may control immune cell infiltration of the tumor microenvironment.Conclusion: The CRG signature may be prognostic in glioma patients, and the FDX1 level may independently predict glioma prognosis. These data may afford new insights into treatment.
BackgroundIntracranial aneurysm (IA) causes more than 80% of nontraumatic subarachnoid hemorrhages (SAHs). The mechanism of ferroptosis involved in IA formation remains unclear. The roles played by competitive endogenous RNA (ceRNA) regulation networks in many diseases are becoming clearer. The goal of this study was to understand more fully the ferroptosis-related ceRNA regulation network in IA.Materials and methodsTo identify differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and differentially expressed lncRNAs (DELs) across IA and control samples, the GEO datasets GSE122897 and GSE66239 were downloaded and analyzed with the aid of R. Ferroptosis DEGs were discovered by exploring the DEGs of ferroptosis-related genes of the ferroptosis database. Potentially interacting miRNAs and lncRNAs were predicted using miRWalk and StarBase. Enrichment analysis was also performed. We utilized the STRING database and Cytoscape software to identify protein-protein interactions and networks. DAB-enhanced Prussian blue staining was used to detect iron in IA tissues.ResultsIron deposition was evident in IA tissue. In all, 30 ferroptosis DEGs, 5 key DEMs, and 17 key DELs were screened out for constructing a triple regulatory network. According to expression regulation of DELs, DEMs, and DEGs, a hub triple regulatory network was built. As the functions of lncRNAs are determined by their cellular location, PVT1-hsa-miR-4644-SLC39A14 ceRNA and DUXAP8-hsa-miR-378e/378f-SLC2A3 ceRNA networks were constructed.ConclusionCeRNA (PVT1-hsa-miR-4644-SLC39A14 and DUXAP8-hsa-miR-378e/378f-SLC2A3) overexpression networks associated with ferroptosis in IA were established.
PurposeThree dopamine agonists [bromocriptine, cabergoline, and quinagolide (CV)] have been used for hyperprolactinemia treatment for decades. Several studies have reviewed the efficacy and safety of bromocriptine and cabergoline. However, no systematic review or meta-analysis has discussed the efficacy and safety of CV in hyperprolactinemia and prolactinoma treatment.MethodsFive medical databases (PubMed, Web of Science, Embase, Scopus, and Cochrane Library) were searched up to 9 May 2022 to identify studies related to CV and hyperprolactinemia. A meta-analysis was implemented by using a forest plot, funnel plot, sensitivity analysis, meta-regression, and Egger’s test via software R 4.0 and STATA 12.ResultsA total of 1,211 studies were retrieved from the five medical databases, and 33 studies consisting of 827 patients were finally included in the analysis. The pooled proportions of patients with prolactin concentration normalization and tumor reduction (>50%) under CV treatment were 69% and 20%, respectively, with 95% confidence intervals of 61%–76% and 15%–28%, respectively. The pooled proportion of adverse effects was 13%, with a 95% confidence interval of 11%–16%.ConclusionOur study showed that CV is not less effective than cabergoline and bromocriptine in treating hyperprolactinemia, and the side effects were not significant. Hence, this drug could be considered an alternative first-line or rescue treatment in treating hyperprolactinemia in the future.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42022347750.
BackgroundSeveral studies have suggested that anti-silencing function 1 B (ASF1B) can serve as a good potential marker for predicting tumor prognosis. But the values of ASF1B in gliomas have not been elucidated and further confirmation is needed.MethodsTranscriptomic and clinical data were downloaded from The Cancer Genome Atlas database (TCGA), genotypic tissue expression (GTEx), and the Chinese Gliomas Genome Atlas database (CGGA). Univariate and multivariate Cox regression analyses were used to investigate the link between clinical variables and ASF1B. Survival analysis was used to assess the association between ASF1B expression and overall survival (OS). The relationship between ASF1B expression and OS was studied using survival analysis. To investigate the probable function and immunological infiltration, researchers used gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and single-sample GSEA (ssGSEA).ResultsIn glioma tissues, ASF1B expression was considerably higher than in normal tissues. The survival analysis found that increased ASF1B expression was linked with a poor prognosis in glioma patients. ASF1B demonstrated a high diagnostic value in glioma patients, according to a Receiver Operating Characteristic (ROC) analysis. ASF1B was found to be an independent predictive factor for OS in a Cox regression study (HR = 1.573, 95% CI: 1.053–2.350, p = 0.027). GO, KEGG, and GSEA functional enrichment analysis revealed that ASF1B was associated with nuclear division, cell cycle, m-phase, and cell cycle checkpoints. Immuno-infiltration analysis revealed that ASF1B was positively related to Th2 cells, macrophages, and aDC and was negatively related to pDC, TFH, and NK CD56 bright cells.ConclusionA high level of ASF1B mRNA expression was correlated with a poor prognosis in glioma patients in this study, implying that it could be a reliable prognostic biomarker for glioma patients.
Background Gliomas are the most frequent type of central nervous system tumor, accounting for more than 70% of all malignant CNS tumors. Recent research suggests that the hyaluronan-mediated motility receptor (HMMR) could be a novel potential tumor prognostic marker. Furthermore, mounting data has highlighted the important role of ceRNA regulatory networks in a variety of human malignancies. The complexity and behavioural characteristics of HMMR and the ceRNA network in gliomas, on the other hand, remained unknown. Methods Transcriptomic expression data were collected from TCGA, GTEx, GEO, and CGGA database.The relationship between clinical variables and HMMR was analyzed with the univariate and multivariate Cox regression. Kaplan–Meier method was used to assess OS. TCGA data are analyzed and processed, and the correlation results obtained were used to perform GO, GSEA, and ssGSEA. Potentially interacting miRNAs and lncRNAs were predicted by miRWalk and StarBase. Results HMMR was substantially expressed in gliomas tissues compared to normal tissues. Multivariate analysis revealed that high HMMR expression was an independent predictive predictor of OS in TCGA and CGGA. Functional enrichment analysis found that HMMR expression was associated with nuclear division and cell cycle. Base on ssGSEA analysis, The levels of HMMR expression in various types of immune cells differed significantly. Bioinformatics investigation revealed the HEELPAR-hsa-let-7i-5p-RRM2 ceRNA network, which was linked to gliomas prognosis. And through multiple analysis, the good predictive performance of HELLPAR/RRM2 axis for gliomas patients was confirmed. Conclusion This study provides multi-layered and multifaceted evidence for the importance of HMMR and establishes a HMMR-related ceRNA (HEELPAR-hsa-let-7i-5p-RRM2) overexpressed network related to the prognosis of gliomas.
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