Hyperlipidaemia microarray datasets
The microarray dataset obtained from patients with HLP (GSE66676) was downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database, which is based on the platform of the GPL6244 Affymetrix Human Gene 1.0 ST Array. Gene expression value matrices were obtained from the original files in CEL format after normalizing the expression values by using RMA methods in R software (version 4.0.0). [15]. Then, the Bioconductor package was used to transform the probe identification numbers (IDs) into gene symbols [16]. When multiple probe IDs corresponded to the same gene, the average expression value was used as the expression value.
Construction of the weighted gene co-expression network
WGCNA is a widely used systems biology method that is usually used to establish a scale-free network based on gene expression data profiles [12]. The co-expression network was constructed by selecting the genes whose variance was greater than all the quartiles of variance. After the sample cluster tree was constructed, cut height = 35 was used to screen the samples for subsequent studies. To ensure the reliability of the results of the network construction, the outlier samples were eliminated, and the samples in cluster 1 were selected to build the sample dendrogram and trait heatmap. The appropriate soft threshold power (soft power = 9) was selected according to the standard scale-free networks, and the adjacency values between all differentially expressed genes were calculated using a power function. Then, the adjacency values were transformed into a topological overlap matrix (TOM), and the corresponding dissimilarity (1-TOM) values were calculated. Module identification was accomplished with the dynamic tree cut method by hierarchically clustering genes using 1-TOM as the distance measure with a minimum size cut-off of 30 and a deep split value of 2 for the resulting dendrogram. To verify the stability of the identified modules, a module preservation function was used to calculate module preservation and quality statistics in the WGCNA package [17].
Identification of the module of interest and functional annotation
Pearson correlation analysis was used to assess the correlations between modules and clinical characteristics to identify biologically meaningful modules. All genes associated with the significant module were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). P < 0.05 was set as the cut-off criterion.
Hub gene analysis
The degree of module membership (MM) was defined as the correlation between the gene expression profile and the module eigengenes (Mes). The degree of gene significance (GS) was defined as the absolute value of the correlation between the gene and external traits. In general, modules with increased MS and GS values among all the identified modules were selected for further analysis of their biological function [18]. The protein-protein interaction (PPI) network of genes in the selected module was constructed by the Search Tool for the Retrieval of Interacting Genes database (version 11.0; http://www.string-db.org) [19] and then visualized using Cytoscape software [20]. Molecular complex detection (MCODE) [21] was used to identify the most valuable clustering module. An MCODE score > 4 was the threshold for inclusion in further analysis. CytoHubba, a Cytoscape plugin, was used to identify hub genes in the PPI network; it provides 11 methods to explore important nodes in biological networks, of which degree has a better performance [22].
Sample verification and diagnostic criteria
A total of 462 (229 males, 49.57%; 233 females, 50.43%) unrelated participants with normal lipid levels and 485 (236 males, 48.66%; 249 females, 51.34%) unrelated subjects with hypercholesterolaemia (HCH, TC > 5.17 mmol/l) and 474 (232 males, 49.16%; 241 females, 50.84%) unrelated participants with hypertriglyceridaemia (HTG, TG > 1.70 mmol/l) were randomly recruited from the Physical Examination Center of the Affiliated Hospital of Guizhou Medical University. The age ranged from 24 to 82 years. There was no difference in age distribution or sex ratio between the control and HCH or HTG groups. Patients suffering from HCH did not have a history of HTG, and patients suffering from HTG did not have a history of HCH. All participants were basically healthy and had no history of myocardial infarction, CAD, type 2 diabetes mellitus (T2DM) or ischaemic stroke. They were not taking any medicines that could alter serum lipid levels. All subjects had signed written informed consent. The research protocol was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University.
Epidemiological analysis
Universally standardized methods and protocols were used to conduct the epidemiological survey [23]. Detailed lifestyle and demographic characteristics were collected with a standard set of questionnaires. Alcohol consumption (0 (non-drinker), < 25 g/day and ≥ 25 g/day) and smoking status (0 (non-smoker), < 20 cigarettes/day and ≥ 20 cigarettes/day) were divided into three different subgroups. Waist circumference, BMI, height, blood pressure and weight were measured as previously described [24].
Biochemical assays
Fasting venous blood samples of 5 ml were collected from each subject. A portion of the sample (2 ml) was placed in a tube and used to measure serum lipid levels. The remaining sample (3 ml) was collected in a glass tube containing anticoagulants (14.70 g/L glucose, 13.20 g/L trisodium citrate, 4.80 g/L citric acid) and utilized to extract deoxyribonucleic acid (DNA). The methods for performing serum ApoA1, HDL-C, ApoB, TG, LDL-C and TC measurements were described in a previous study [25]. All determinations were conducted using an autoanalyser (Type 7170A; Hitachi Ltd., Tokyo, Japan) in the Clinical Science Experiment Center of the Affiliated Hospital of Guizhou Medical University.
Quantitative real-time PCR
Peripheral blood monocytes (PBMCs) were isolated from blood samples with TRIzol reagent, which was used to extract the total RNA that was then reverse-transcribed into cDNA by using the PrimeScript RT reagent kit (Takara Bio, Japan). The obtained cDNA was used as a template for RT-qPCR. Table 1 shows that specific primer sequences, which were designed by Sangon Biotech (Shanghai, China), were used to detect the 2 hub genes. Quantitative RT-PCR was performed using a Taq PCR Master Mix Kit (Takara) on an ABI Prism 7500 sequence-detection system (Applied Biosystems, USA) using RT Reaction Mix in a total volume of 20 μL with the following reaction conditions: pre-denaturation at 95°C for 30 seconds, then 40 cycles of 95°C for 30 seconds and 60°C for 30 seconds.
Table 1 PCR primers for quantitative real-time PCR
Gene
|
Forward primer
|
Reverse primer
|
SQLE
|
TCTGGGGGTTAAGAGCAGTG
|
GTGTCTACACTTACCATCTGTGGC
|
SCD
|
CTTGCGATATGCTGTGGTGC
|
GGCTCCTAGCCTAATCCCCT
|
GAPDH
|
GCAACTAGGATGGTGTGGCT
|
TCCCATTCCCCAGCTCTCATA
|
Diagnostic criteria
The values of serum ApoB (0.80-1.05 g/L), HDL-C (1.16-1.42 mmol/L), ApoA1 (1.20-1.60 g/L), TC (3.10-5.17 mmol/L), TG (0.56-1.70 mmol/L), the ApoA1/ApoB ratio (1.00-2.50) and LDL-C (2.70-3.10 mmol/L) were defined as normal at our Clinical Science Experiment Center. Subjects with TG > 1.70 mmol/L were defined as having hypertriglyceridaemia, and TC > 5.17 mmol/L was defined as having hypercholesterolaemia [26]. Participants with a fasting plasma (blood) glucose value ≥ 7.0 mmol/L were defined as having diabetes [27]. The diagnostic criteria of hypertension [28], obesity, normal weight and overweight were described in our previous study [29].
Statistical analyses
SPSS (Version 22.0) was used to process the research data. The results are presented as the mean ± SD except for TG levels, which are presented as medians and interquartile ranges. The differences in the general characteristics except for TG between HCH/HTG patients and controls were analysed by independent-samples t tests. The Kruskal-Wallis and Mann-Whitney nonparametric tests were used to detect the difference in TG levels between patients with HCH/HTG and controls. The chi-square test was utilized to assess the differences in the proportion of smokers, age distribution and alcohol consumption between patients with HCH/HTG and controls. Heat mapping of the correlation models and bioinformatic analysis were performed in R software (version 4.0.0). A P value < 0.05 was considered to be statistically significant.