ArticlePeerJ2022
Identification of potential biomarkers of vascular calcification using bioinformatics analysis and validation
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 5 citations in OpenAlex.
- Deciphering the molecular landscape of aortic aging: a meta-analysis of bulk RNA sequencing studies in mice.GeroScience · 2026Article
- Identification of hub targets involved in carotid atherosclerosis through bioinformatics and machine learning approaches.BMC cardiovascular disorders · 2026Article
- Association of chaperone-mediated autophagy with the mechanisms of vascular calcification in diabetic nephropathy.Renal failure · 2025Article
- A miRNA signature linked to high lipoprotein (a) and coronary calcification in familial hypercholesterolaemia.Computational and structural biotechnology journal · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- Identification of Prognostic and Immune Characteristics of Two Lung Adenocarcinoma Subtypes Based on TRPV Channel Family Genes.The Journal of membrane biology · 2024Article
- Impact of Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitors on Arterial Stiffness and Vascular Aging-What Do We Know So Far? (A Narrative Review).Life (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Vascular calcification (VC) is the most widespread pathological change in diseases of the vascular system. However, we know poorly about the molecular mechanisms and effective therapeutic approaches of VC. Methods: The VC dataset, GSE146638, was downloaded from the Gene Expression Omnibus (GEO) database. Using the edgeR package to screen Differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to find pathways affecting VC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the DEGs. Meanwhile, using the String database and Cytoscape software to construct protein-protein interaction (PPI) networks and identify hub genes with the highest module scores. Correlation analysis was performed for hub genes. Receiver operating characteristic (ROC) curves, expression level analysis, GSEA, and subcellular localization were performed for each hub gene. Expression of hub genes in normal and calcified vascular tissues was verified by quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry (IHC) experiments. The hub gene-related miRNA-mRNA and TF-mRNA networks were constructed and functionally enriched for analysis. Finally, the DGIdb database was utilized to search for alternative drugs targeting VC hub genes. Results: By comparing the genes with normal vessels, there were 64 DEGs in mildly calcified vessels and 650 DEGs in severely calcified vessels. Spp1, Sost, Col1a1, Fn1, and Ibsp were central in the progression of the entire VC by the MCODE plug-in. These hub genes are primarily enriched in ossification, extracellular matrix, and ECM-receptor interactions. Expression level results showed that Spp1, Sost, Ibsp, and Fn1 were significantly highly expressed in VC, and Col1a1 was incredibly low. RT-qPCR and IHC validation results were consistent with bioinformatic analysis. We found multiple pathways of hub genes acting in VC and identified 16 targeting drugs. Conclusions: This study perfected the molecular regulatory mechanism of VC. Our results indicated that Spp1, Sost, Col1a1, Fn1, and Ibsp could be potential novel biomarkers for VC and promising therapeutic targets.
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