1.1 Sample Collection
From November 1, 2021 to May 9, 2022, data of CAD patients underwent coronary artery bypass grafting (CABG) in the Department of Cardiac Surgery of the First Affiliated Hospital of Xinjiang Medical University (Ürümqi, China) were continuously collected, and their pericoronary adipose tissue and subcutaneous adipose tissue were collected. This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University, and all patients signed informed consent form.
The inclusion criteria were as follows: (1) Age ≥ 18 years old; (2) Patients with confirmed CAD; (3) Standardized completion of coronary computed tomography angiography (CCTA) examination in accordance with the requirements of imaging guidelines issued by the Cardiothoracic Group of the Chinese Medical Association Radiology Branch; (4) CABG within 1 month after CCTA examination.
The exclusion criteria were as follows: (1) Patients with malignant tumors; (2) Patients with hematological diseases (acute leukemia, megaloblastic anemia, aplastic anemia, etc.); (3) Patients with autoimmune diseases; (4) Patients with cardiorenal insufficiency; (5) Image quality of CCTA does not meet the diagnostic requirements.
1.2 Quantitative reverse transcription polymerase chain reaction (RT-qPCR)
After extracting total RNA from pericoronary adipose tissue and subcutaneous adipose tissue using TRIzol reagent (Sigma-Aldrich, St. Louis, MO, USA), it was reversely transcribed to cDNA using the TakaRa kit (Takara Biomedical Technology, Beijing, China), and the RT-qPCR experiment was performed using the instruments QuantStudio 6 and 7 Flex real-time PCR systems (Thermo Fisher Scientific, Waltham, MA, USA), according to the following conditions: denature at 95°C for 10 min, at 95°C for 15 s, and at 60°C for 1 min for 45 cycles. Primers used for RT-qPCR are listed in Table 1. Using β-actin as an internal reference, the relative expression level of mRNA of the gene was analyzed by the 2−∆∆C t method.
Table 1
Primer name | Primer sequence (5'→3') |
β-actin | F: AGAAAATCT GGCACCACACC R: AATGTGAGCAACGCAGCATAATTCG |
Leptin | F: GGCAGGGAAATGGGCAGTGAT G R: AATGTGAGCAACGCAGCATAATTCG |
MCP-1 | F: GGCTGAGACTAACCCAGAAACATCC R: GGGAATGAAGGTGGCTGCTATGAG |
CD31 | F: CAAGGTCAGCAGCATCGTGGTC R: TGGGATGGAGCAGGACAGGTTC |
IL-6 | F: CAAAGAGGCACTGGCAGAAAACAAC R: CCAGGAAAGTCTCCTCATTGAATCC |
PPAR | F: CCACAGTTGATTTCTCCAGCATTTC R: CAGGTTCTACTTTGATCGCACTTTG |
JAK | F: CTGGTAGATGGCTACTTC R: GGCTCATAGAGTAGACAG |
1.3 Chip data acquisition
By searching in the GEO database for gene chips containing CAD patients and healthy individuals, we searched for PVAT gene expression data and obtained its gene chip datasets GSE7638 (No.50, CAD 110) and GSE19339 (GEO, https://www.ncbi.nlm.nih.gov/geo/), based on GPL570 Affymetrix Human Genome U133 Plus 2.0 Array platform. This dataset contains 54 patients in the control group and 114 patients with CAD. Both datasets are public datasets, and annotation files are available from the GEO. DEGs were screened in control and CAD groups. The Affy R package was used to correct the background of the expression value and to normalize the expression data, including conversion of the format of the original data, supplementation of missing values, and the quantile method to standardize data.
1.4 Screening of DEGs
To analyze DEGs in PVAT and normal adipose tissue, R 4.1.0 software was utilized to analyze different gene chip datasets using LIMMA R package (linear models for microarray data) to obtain DEGs in the sample data and to plot heat maps and principal component analysis (PCA) maps. P < 0.05 and fold-change (FC) > 2 were used as the screening criteria for up-regulated DEGs, and P < 0.05 and FC < 0.5 were used as the screening criteria for downregulated DEGs. Subsequently, upregulated and downregulated DEGs were further identified. The DEGs in the GSE7638 and GSE19339 datasets were intersected to obtain common DEGs and to draw a Wayne diagram.
1.5 Functional enrichment analysis of common DEGs
DAVID (http://DAVID.abcc.ncifcrf.gov/) is a bioinformatic database that integrates biological data and analytical tools for identifying GO entries, gene ID conversion, and functional enrichment analysis. Gene Ontology (GO) is a database established by the Gene Ontology Consortium that comprehensively describes the properties of genes and gene products in organisms, including molecular function (MF), biological process (BP), and cellular component (CC)[19]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive database that integrates genomic, chemical, and systemic functional information. One of these databases, known as KEG pathway, is dedicated to store genetic pathway information for different species. In the present study, based on the above-mentioned tools, the enrichment of DEGs was analyzed using the GO and KEGG pathway enrichment analyses, and P < 0.05 was considered statistically significant.
1.6 Protein-protein interaction network
Determining the complete PPI network is important for detecting the molecular mechanisms of PVAT. The STRING database (http://string-db.org) was used to establish a PPI network of DEGs, including nodes and edges, in which each node represents a DEG. Visual analysis was carried out using Cytoscape 3.5.1 software, and the central genes were screened according to the degree of gene.
1.7 Statistical Analysis
SPSS 22.0 software (IBM, Armonk, NY, USA) was used to perform statistical analysis. The Shapiro-Wilk test was utilized to assess normality of continuous variables. Data were expressed as mean ± standard deviation or the median (interquartile range), as appropriate. Paired sample t-test or the Wilcoxon rank-sum test were used to compare pericoronary adipose tissue and subcutaneous adipose tissue, and P < 0.05 indicated the presence of a statistically significant difference.