Computational Investigation of Smooth Muscle Cell Plasticity in Atherosclerosis and Vascular Calcification: Insights from Differential Gene Expression Analysis of Microarray Data

TL;DR

This study uses gene expression data to identify proteins that may serve as markers for smooth muscle cell changes linked to atherosclerosis and vascular calcification.

Contribution

The integration of multiple microarray datasets to identify novel marker proteins for smooth muscle cell dedifferentiation in vascular diseases.

Findings

Twelve potential marker proteins were identified for smooth muscle cell differentiation and dedifferentiation.

Bioinformatics analysis revealed significant gene expression differences across different smooth muscle cell states.

The study demonstrates the feasibility of using bioinformatics to explore smooth muscle cell plasticity in vascular diseases.

Abstract

The dedifferentiation of smooth muscle cells (SMCs) is the main cause of atherosclerosis and vascular calcification. This study integrated the gene expression data of multiple microarrays to identify relevant marker molecules. A total of 72 Gene Expression Omnibus (GEO) samples (GSM) were collected from 10 gene expression data series (GSE) and divided into five groups: non-SMC, SMC, atherosclerotic SMC (SMC-ath), calcified SMC (SMC-calc), and treated SMC (SMC-t). The SMC-t group included synthetic SMCs that had undergone treatment to inhibit proliferation, migration, or inflammation. The gene expression data were merged, normalized, and batch effects were removed before differential gene expression (DGE) analysis was performed via linear models for microarray data (limma) and statistical analysis of metagenomic profiles (STAMPs). The genes with expressions that significantly differed were subsequently subjected to protein-protein interaction (PPI) and functional prediction analyses. In addition, the random forest method was used for classification. Twelve proteins that may be marker molecules for SMC differentiation and dedifferentiation were identified, namely, Proprotein convertase subtilisin/kexin type 1 (PCSK1), Transforming growth factor beta-induced (TGFBI), Complement C1s (C1S), Phosphomannomutase 1 (PMM1), Claudin 7 (CLDN7), Calcium binding and coiled-coil domain 2 (CALCOCO2), SAC3 domain-containing protein 1 (SAC3D1), Natriuretic peptide B (NPPB), Monoamine oxidase A (MAOA), Regulator of the Cell Cycle (RGCC), Alpha-crystallin B Chain (CRYAB), and Alcohol dehydrogenase 1B (ADH1B). Finally, their possible roles in SMCs are discussed. This study highlights the feasibility of bioinformatics analysis for studying SMC dedifferentiation.

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