Evidence map›Paper›PMID 38335213›Full record

ArticlePloS one2024

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes.

Chang Sheng, Qin Zeng, Weihua Huang, Mingmei Liao, Pu Yang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Chang ShengDepartment of Vascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Qin ZengNational Health Commission Key Laboratory of Nanobiological Technology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Weihua HuangDepartment of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Mingmei LiaoNational Health Commission Key Laboratory of Nanobiological Technology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Pu YangDepartment of Vascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID 0000-0003-1370-9847
Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Aortic Aneurysm, AbdominalAortic RuptureAgedAortaBiomarkersHumansPrognosisRisk FactorsBiomarkers

Identifiers

PMID38335213
PMCPMC10857568
OpenAlexW4391677702

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.