Screening key genes for abdominal aortic aneurysm based on gene expression omnibus dataset

Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.

Glypican-1 may be a plasma biomarker for predicting the rupture of small intracranial aneurysms

Currently, there are no effective methods for predicting the rupture of asymptomatic small intracranial aneurysms (IA) (<7 mm). In this study the aim was to identify early warning biomarkers in peripheral plasma for predicting IA rupture. Four experimental groups were included: ruptured intracranial aneurysm (RIA), unruptured intracranial aneurysm (UIA), traumatic subarachnoid hemorrhage control (tSAHC), and healthy control (HC) groups. Plasma proteomics of these four groups were detected using iTRAQ combined LC-MS/MS. Differentially expressed proteins (DEPs) were identified in RIA, UIA, tSAHC compared with HC. Target proteins associated with aneurysm rupture were obtained by comparing the DEPs of the RIA and UIA groups after filtering out the DEPs of the tSAHC group. The plasma concentrations of target proteins were validated using enzyme-linked immunosorbent assay (ELISA). The iTRAQ analysis showed a significant increase in plasma GPC1 concentration in the RIA group compared to the UIA group, which was further validated among the IA patients. Logistic regression analysis identified GPC1 as an independent risk factor for predicting aneurysm rupture. The ROC curve indicated that the GPC1 plasma cut-off value for predicting aneurysms rupture was 4.99 ng/ml. GPC1 may be an early warning biomarker for predicting the rupture of small intracranial aneurysms. SIGNIFICANCE: The current management approach for asymptomatic small intracranial aneurysms (<7 mm) is limited to conservative observation and surgical intervention. However, the decision-making process regarding these options poses a dilemma due to weighing their respective advantages and disadvantages. Currently, there is a lack of effective diagnostic methods to predict the rupture of small aneurysms. Therefore, our aim is to identify early warning biomarkers in peripheral plasma that can serve as quantitative detection markers for predicting intracranial aneurysm rupture. In this study, four experimental populations were established: small ruptured intracranial aneurysm (sRIA) group, small unruptured intracranial aneurysm (sUIA) group, traumatic subarachnoid hemorrhage control (tSAHC) group, and healthy control (HC) group. The tSAH group was the control group of spontaneous subarachnoid hemorrhage caused by ruptured aneurysm. Compared with patients with UIA, aneurysm tissue and plasma GPC1 in patients with RIA is significantly higher, and GPC1 may be an early warning biomarker for predicting the rupture of intracranial small aneurysms.

Comprehensive bioinformatics analysis revealed potential key genes and pathways underlying abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a permanent, asymptomatic segmental dilatation of the abdominal aorta, with a high mortality risk upon rupture. Identification of potential key genes and pathways may help to develop curative drugs for AAA. We conducted RNA-seq on abdominal aortic tissues from both AAA patients and normal individuals as a control group. Integrated bioinformatic analysis was subsequently performed to comprehensively reveal potential key genes and pathways. A total of 1148 differential expressed genes (DEGs) (631 up-regulated and 517 down-regulated) were identified in our study. Gene Ontology (GO) analysis revealed enrichment in terms related to extracellular matrix organization, while KEGG analysis indicated enrichment in hematopoietic cell lineage and ECM-receptor interaction. Protein-protein interaction (PPI) network analysis revealed several candidate key genes, and differential expression of 6 key genes (CXCL8, CCL2, PTGS2, SELL, CCR7, and CXCL1) was validated by Gene Expression Omnibus (GEO) datasets. Receiver operating characteristic curve (ROC) analysis demonstrated these genes' high discriminatory ability between AAA and normal tissues. Immunohistochemistry indicated that several key genes were highly expressed in AAA tissues. Single-cell RNA sequencing revealed differential distribution patterns of these identified key genes among various cell types. 26 potential drugs linked to our key genes were found through DGIdb. Overall, our study provides a comprehensive evaluation of potential key genes and pathways in AAA, which could pave the way for the development of curative pharmacological therapies.

Dysregulations of Key Regulators of Angiogenesis and Inflammation in Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is a chronic vascular disease caused by localized weakening and broadening of the abdominal aorta. AAA is a clearly underdiagnosed disease and is burdened with a high mortality rate (65-85%) from AAA rupture. Studies indicate that abnormal regulation of angiogenesis and inflammation contributes to progression and onset of this disease; however, dysregulations in the molecular pathways associated with this disease are not yet fully explained. Therefore, in our study, we aimed to identify dysregulations in the key regulators of angiogenesis and inflammation in patients with AAA in peripheral blood mononuclear cells (using qPCR) and plasma samples (using ELISA). Expression levels of ANGPT1, CXCL8, PDGFA, TGFB1, VEGFB, and VEGFC and plasma levels of TGF-alpha, TGF-beta 1, VEGF-A, and VEGF-C were found to be significantly altered in the AAA group compared to the control subjects without AAA. Associations between analyzed factors and risk factors or biochemical parameters were also explored. Any of the analyzed factors was associated with the size of the aneurysm. The presented study identified dysregulations in key angiogenesis- and inflammation-related factors potentially involved in AAA formation, giving new insight into the molecular pathways involved in the development of this disease and providing candidates for biomarkers that could serve as diagnostic or therapeutic targets.

Identification of two novel lipid metabolism-related long non-coding RNAs (SNHG17 and LINC00837) as potential signatures for osteosarcoma prognosis and precise treatment

OBJECTIVE

Dysregulated lipid metabolism enhances the development and advancement of many cancers, including osteosarcoma (OS); however, the underlying mechanisms are still largely unknown. Therefore, this investigation aimed to elucidate novel potential lipid metabolism-related long non-coding RNAs (lncRNAs) that regulate OS development and provide novel signatures for its prognosis and precise treatment.

MATERIALS AND METHODS

The GEO datasets (GSE12865 and GSE16091) were downloaded and analyzed using R software packages. Immunohistochemistry (IHC) was used to evaluate protein levels in OS tissues while real-time qPCR was used to measure lncRNA levels, and MTT assays were used to assess OS cell viability.

RESULTS

Two lipid metabolism-associated lncRNAs (LM-lncRNAs), small nucleolar RNA host gene 17 (SNHG17) and LINC00837, were identified as efficient and independent prognostic indicators for OS. In addition, further experiments confirmed that SNHG17 and LINC00837 were significantly elevated in OS tissues and cells than para-cancerous counterparts. Knockdown of SNHG17 and LINC00837 synergistically suppressed the viability of OS cells, whereas overexpression of the two lncRNAs promoted OS cell proliferation. Moreover, bioinformatics analysis was conducted to construct six novel SNHG17-microRNA-mRNA competing endogenous RNA (ceRNA) networks, and three lipid metabolism-associated genes (MIF, VDAC2, and CSNK2A2) were found to be abnormally upregulated in OS tissues, suggesting that they were potential effector genes of SNHG17.

CONCLUSION

In summary, SNHG17 and LINC00837 were found to promote OS cell malignancy, suggesting their use as ideal biomarkers for OS prognosis and treatment.

Proteomics and bioinformatics analysis of cardiovascular related proteins in offspring exposed to gestational diabetes mellitus

Introduction

Previous studies have demonstrated that exposed to the initial suboptimal intrauterine environment of gestational diabetes mellitus (GDM) may increase risk of cardiovascular disease in adulthood.

Methods

In order to investigate the underlying mechanisms involved in the increased risk of cardiovascular diseases (CVDs) in the offspring of GDM, we applied a high-throughput proteomics approach to compare the proteomic expression profile of human umbilical vessels of normal and GDM offspring.

Results

A total of significantly different 100 proteins were identified in umbilical vessels from GDM group compared with normal controls, among which 31 proteins were up-regulated, while 69 proteins were down-regulated. Differentially expressed proteins (DEPs) are validated using Western blotting analysis. The analysis of these differently expressed proteins (DEPs) related diseases and functions results, performed by Ingenuity Pathway Analysis (IPA) software. Based on "Diseases and Disorders" analysis, 17 proteins (ACTA2, ADAR, CBFB, DDAH1, FBN1, FGA, FGB, FGG, GLS, GSTM1, HBB, PGM3, PPP1R13L, S100A8, SLC12A4, TPP2, VCAN) were described to be associated with CVD, especially in Anemia, Thrombus and Myocardial infarction. Functional analysis indicated that DEPs involved in many cardiovascular functions, especially in "vasoconstriction of blood vessel" (related DEPs: ACTA2, DDAH1, FBN1, FGA, FGB, and FGG). Upstream regulator analyses of DEPs identifies STAT3 as inhibitor of ACTA2, FGA, FGB, and FGG.

Conclusion

The results of this study indicate that intrauterine hyperglycemia is associated with an elevated risk of cardiovascular risk in the offspring.

Construction of the circRNA-miRNA-mRNA Regulatory Network of an Abdominal Aortic Aneurysm to Explore Its Potential Pathogenesis

Background

Abdominal aortic aneurysm (AAA) is a progressive cardiovascular disease, which is a permanent and localized dilatation of the abdominal aorta with potentially fatal consequence of aortic rupture. Dysregulation of circRNAs is correlated with the development of various pathological events in cardiovascular diseases. However, the function of circRNAs in abdominal aortic aneurysm (AAA) is unknown and remains to be explored. This study is aimed at determining the regulatory mechanisms of circRNAs in AAAs. This study was aimed at exploring the underlying molecular mechanisms of abdominal aortic aneurysms based on the competing endogenous RNA (ceRNA) regulatory hypothesis of circRNA, miRNA, and mRNA.

Methods

The expression profiles of circRNAs (GSE144431), miRNAs (GSE62179), and mRNAs (GSE7084, GSE57691, and GSE47472) in human tissue sample from the aneurysm group and normal group were obtained from the Gene Expression Omnibus database, respectively. The circRNA-miRNA-mRNA network was constructed by using Cytoscape 3.7.2 software; then, the protein-protein interaction (PPI) network was constructed by using the STRING database, and the hub genes were identified by using the cytoHubba plug-in. The circRNA-miRNA-hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in AAAs.

Results

The present study identified 40 differentially expressed circRNAs (DECs) in the GSE144431, 90 differentially expressed miRNAs (DEmiRs) in the GSE62179, and 168 differentially expressed mRNAs (DEGs) with the same direction regulation (130 downregulated and 38 upregulated) in the GSE7084, GSE57691, and GSE47472 datasets identified regarding AAAs. The miRNA response elements (MREs) of three DECs were then predicted. Four overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 17 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of 4 miRNAs with 168 DEGs. Furthermore, the circRNA-miRNA-mRNA network was constructed through 3 circRNAs, 4 miRNAs, and 17 mRNAs, and three hub genes (SOD2, CCR7, and PGRMC1) were identified. Simultaneously, functional enrichment and pathway analysis were performed within genes in the circRNA-miRNA-mRNA network. Three of them (SOD2, CCR7, and PGRMC1) were suggested to be crucial based on functional enrichment, protein-protein interaction, and ceRNA network analysis. Furthermore, the expression of SOD2 and CCR7 may be regulated by hsa_circ_0011449/hsa_circ_0081968/hsa-let-7f-5p; the expression of PGRMC1 may be regulated by hsa_circ_0011449/hsa_circ_0081968-hsa-let-7f-5p/hsa-let-7e-5p.

Conclusion

In conclusion, the ceRNA interaction axis we identified may be an important target for the treatment of abdominal aortic aneurysms. This study provided further understanding of the potential pathogenesis from the perspective of the circRNA-related competitive endogenous RNA network in AAAs.

Bioinformatics Analysis Reveals the Potential Diagnostic Biomarkers for Abdominal Aortic Aneurysm

Objectives: Abdominal aortic aneurysms (AAAs) are associated with high mortality rates. The genes and pathways linked with AAA remain poorly understood. This study aimed to identify key differentially expressed genes (DEGs) linked to the progression of AAA using bioinformatics analysis.

Methods: Gene expression profiles of the GSE47472 and GSE57691 datasets were acquired from the Gene Expression Omnibus (GEO) database. These datasets were merged and normalized using the “sva” R package, and DEGs were identified using the limma package in R. The functions of these DEGs were assessed using Cytoscape software. We analyzed the DEGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein–protein interaction networks were assembled using Cytoscape, and crucial genes were identified using the Cytoscape plugin, molecular complex detection. Data from GSE15729 and GSE24342 were also extracted to verify our findings.

Results: We found that 120 genes were differentially expressed in AAA. Genes associated with inflammatory responses and nuclear-transcribed mRNA catabolic process were clustered in two gene modules in AAA. The hub genes of the two modules were IL6, RPL21, and RPL7A. The expression levels of IL6 correlated positively with RPL7A and negatively with RPL21. The expression of RPL21 and RPL7A was downregulated, whereas that of IL6 was upregulated in AAA.

Conclusions: The expression of RPL21 or RPL7A combined with IL6 has a diagnostic value for AAA. The novel DEGs and pathways identified herein might provide new insights into the underlying molecular mechanisms of AAA.

FRZB as a key molecule in abdominal aortic aneurysm progression affecting vascular integrity

Abdominal aortic aneurysm (AAA), when ruptured, results in high mortality. The identification of molecular pathways involved in AAA progression is required to improve AAA prognosis. The aim of the present study was to assess the key genes for the progression of AAA and their functional role. Genomic and clinical data of three independent cohorts were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (GSE57691, GSE7084, and GSE98278). To develop AAA diagnosis and progression-related differentially expressed genes (DEGs), we used a significance analysis of microarray (SAM). Spearman correlation test and gene set analysis were performed to identify potential enriched pathways for DEGs. Only the Frizzled-related protein (FRZB) gene and chromosome 1 open reading frame 24 (C1orf24) exhibited significant down-regulation in all analyses. With FRZB, the pathways were associated with RHO GTPase and elastin fiber formation. With C1orf24, the pathways were elastic fiber formation, extracellular matrix organization, and cell–cell communication. Since only FRZB was evolutionally conserved in the vertebrates, function of FRZB was validated using zebrafish embryos. Knockdown of frzb remarkably reduced vascular integrity in zebrafish embryos. We believe that FRZB is a key gene involved in AAA initiation and progression affecting vascular integrity.

Identification of crucial genes in abdominal aortic aneurysm by WGCNA

Background

Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vital importance.

Methods

Three expression datasets (GSE7084, GSE47472 and GSE57691) were downloaded from the Gene Expression Omnibus (GEO). These datasets were merged and then normalized using the “sva” R package. Differential expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were conducted. We compared the co-expression patterns between AAA and normal conditions, and hub genes of each functional module were identified. DEGs were mapped to co-expression network under AAA condition and a DEG co-expression network was generated. Crucial genes were identified using molecular complex detection (MCODE) (a plugin in Cytoscape).

Results

In our study, 6 and 10 gene modules were detected for the AAA and normal conditions, respectively, while 143 DEGs were screened. Compared to the normal condition, genes associated with immune response, inflammation and muscle contraction were clustered in three gene modules respectively under the AAA condition; the hub genes of the three modules were MAP4K1, NFIB and HPK1, respectively. A DEG co-expression network with 102 nodes and 303 edges was identified, and a hub gene cluster with 10 genes from the DEG co-expression network was detected. YIPF6, RABGAP1, ANKRD6, GPD1L, PGRMC2, HIGD1A, GMDS, MGP, SLC25A4 and FAM129A were in the cluster. The expression levels of these 10 genes showed potential diagnostic value.

Conclusion

Based on WGCNA, we detected 6 modules under the AAA condition and 10 modules in the normal condition. Hub genes of each module and hub gene clusters of the DEG co-expression network were identified. These genes may act as potential targets for medical therapy and diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these genes in the pathogenesis of AAA.