Dataset Information

Dysregulation of microRNAs and target genes networks in human abdominal aortic aneurysm tissues.

ABSTRACT:

Background

Abdominal aortic aneurysm (AAA) is a pathological enlargement of infrarenal aorta close to the aortic bifurcation, and it is an important cause of mortality in the elderly. Therefore, the biomarker identification for early diagnosis is of great interest for clinical benefit. It is known that microRNAs (miRNAs) have important roles via target genes regulation in many diseases. This study aimed to identify miRNAs and their target genes involved in the pathogenesis of AAA.

Methods

Tissue samples were obtained from patients who underwent AAA surgery and from organ donors (control group). Quantitative PCR Array was applied to assess 84 genes and 384 miRNAs aiming to identify differentially expressed targets (AAA n = 6, control n = 6), followed by validation in a new cohort (AAA n = 18, control n = 6) by regular qPCR. The functional interaction between validated miRNAs and target genes was performed by the Ingenuity Pathway Analysis (IPA) software.

Results

The screening cohort assessed by PCR array identified 10 genes and 59 miRNAs differentially expressed (≥2-fold change, p<0.05). Among these, IPA identified 5 genes and 9 miRNAs with paired interaction. ALOX5, PTGIS, CX3CL1 genes, and miR-193a-3p, 125b-5p, 150-5p maintained a statistical significance in the validation cohort. IPA analysis based on the validated genes and miRNAs revealed that eicosanoid and metalloproteinase/TIMP synthesis are potentially involved in AAA.

Conclusion

Paired interactions of differentially expressed ALOX5, PTGIS, CX3CL1 genes, and miR-193b-3p, 125b-5p, 150-5p revealed a potentially significant role of the eicosanoid synthesis and metalloproteinase/TIMP pathways in the AAA pathogenesis.

SUBMITTER: Araujo NNF

PROVIDER: S-EPMC6754147 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Dysregulation of microRNAs and target genes networks in human abdominal aortic aneurysm tissues.

Araujo Neire Niara Ferreira de NNF Lin-Wang Hui Tzu HT Germano Juliana de Freitas JF Farsky Pedro Silvio PS Feldman Andre A Rossi Fabio Henrique FH Izukawa Nilo Mitsuru NM Higuchi Maria de Lourdes ML Savioli Neto Felicio F Hirata Mario Hiroyuki MH Bertolami Marcelo Chiara MC

PloS one 20190920 9

<h4>Background</h4>Abdominal aortic aneurysm (AAA) is a pathological enlargement of infrarenal aorta close to the aortic bifurcation, and it is an important cause of mortality in the elderly. Therefore, the biomarker identification for early diagnosis is of great interest for clinical benefit. It is known that microRNAs (miRNAs) have important roles via target genes regulation in many diseases. This study aimed to identify miRNAs and their target genes involved in the pathogenesis of AAA.<h4>Met ...[more]

PMID: 31539405

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Project description:BackgroundAbdominal 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.MethodsThree 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).ResultsIn 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.ConclusionBased 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.

Project description:BackgroundRupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.MethodsIn this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.Results38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.ConclusionWe identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.

Dataset Information

Dysregulation of microRNAs and target genes networks in human abdominal aortic aneurysm tissues.

Background

Methods

Results

Conclusion

Publications

Dysregulation of microRNAs and target genes networks in human abdominal aortic aneurysm tissues.

Similar Datasets

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