Identifying significant genes and functionally enriched pathways in familial hypercholesterolemia using integrated gene co-expression network analysis

Familial hypercholesterolemia (FH) is a monogenic lipid disorder which promotes atherosclerosis and cardiovascular diseases. Owing to the lack of sufficient published information, this study aims to identify the potential genetic biomarkers for FH by studying the global gene expression profile of blood cells. The microarray expression data of FH patients and controls was analyzed by different computational biology methods like differential expression analysis, protein network mapping, hub gene identification, functional enrichment of biological pathways, and immune cell restriction analysis. Our results showed the dysregulated expression of 115 genes connected to lipid homeostasis, immune responses, cell adhesion molecules, canonical Wnt signaling, mucin type O-glycan biosynthesis pathways in FH patients. The findings from expanded protein interaction network construction with known FH genes and subsequent Gene Ontology (GO) annotations have also supported the above findings, in addition to identifying the involvement of dysregulated thyroid hormone and ErbB signaling pathways in FH patients. The genes like CSNK1A1, JAK3, PLCG2, RALA, and ZEB2 were found to be enriched under all GO annotation categories. The subsequent phenotype ontology results have revealed JAK3I, PLCG2, and ZEB2 as key hub genes contributing to the inflammation underlying cardiovascular and immune response related phenotypes. Immune cell restriction findings show that above three genes are highly expressed by T-follicular helper CD4⁺ T cells, naïve B cells, and monocytes, respectively. These findings not only provide a theoretical basis to understand the role of immune dysregulations underlying the atherosclerosis among FH patients but may also pave the way to develop genomic medicine for cardiovascular diseases.     

Weighted gene co-expression network analysis reveals potential candidate genes affecting drip loss in pork

Drip loss is an essential evaluation indicator for pork quality. It is closely related to other meat quality indicators, including water-holding capacity, water loss rate and pH value at 45 min (pH₁) and 24 h post-mortem (pH₂), and is influenced by environmental and genetic factors and their interactions. We previously conducted differentially expressed gene analysis to identify candidate genes affecting drip loss using eight individuals with extremely high- and low-drip loss selected from 28 purebred Duroc pigs. Using 28 identical samples, in the present study, we performed weighted gene co-expression network analysis with drip loss and drip loss-related traits, including water-holding capacity, water loss rate, pH₁ and pH₂. A total of 25 modules were identified, and five of them correlated with at least two drip loss or drip loss-related traits. After functional enrichment analysis of genes in the five modules, three modules were found to be critical, as their genes were significantly involved in amino acid metabolism, immune response and apoptosis, which have potential relationships with drip loss. Furthermore, we identified five candidate genes affecting drip loss in one critical module, AASS, BCKDHB, ALDH6A1, MUT and MCCC1, as they overlapped with differentially expressed genes detected in our previous study, exhibited protein–protein interactions and had potential biological functions in affecting drip loss according to the literature. The outcomes of the present study enhance our understanding of the molecular mechanisms underlying drip loss and will aid in improving the pork quality.     

Novel key genes in triple-negative breast cancer identified by weighted gene co-expression network analysis

Triple-negative breast cancer (TNBC) is a special subtype of breast cancer (BC) with poor prognosis. Although some molecular mechanisms of TNBC have been elucidated, the efficacy of current treatments is limited. Therefore, it is urgently demanded to screen for novel biomarkers and drug targets for TNBC. In this study, we obtained four independent data sets (GSE76250, GSE31448, GSE43358, and METABRIC) from the Gene Expression Omnibus (GEO) database and the cBioPortal website. In the GSE76250 data set, 890 differentially expressed genes were identified and weighted gene co-expression network analysis was performed based on them. Then, two preserved modules associated with the KI67 score were detected. Gene ontology and pathway enrichment analyses showed genes in the modules participated in some cancer-related biological processes or pathways. Non-SMC condensin I complex subunit G (NCAPG) and ATP-binding cassette subfamily A member 9 (ABCA9) were identified as hub genes of the modules, and the significance of hub genes was validated in the GSE43358 data set. Finally, their prognostic value was assessed by survival analysis. These findings suggested that NCAPG and ABCA9 may be the key genes of TNBC. Moreover, ABCA9 was first reported in TNBC. They deserved further studies.     

Systematic prediction of key genes for ovarian cancer by co-expression network analysis

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R² = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.     

LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA)

BackgroundIschemic stroke (IS) was a significant public health concern and long-chain noncoding RNAs (lncRNAs) were gaining particular importance in stroke biology, however, the potential mechanism of lncRNAs in IS was not fully understood.MethodsIn this study, three diagnosed patients with IS and three controls were selected to establish the lncRNA library. Weighted gene co-expression network analysis (WGCNA) was applied to screen key lncRNA modules associated with IS. The key lncRNAs were identified by module membership (MM) and gene significance (GS). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways and protein-protein interaction (PPI) network method was used to identify the key genes.ResultsA total of 3627 lncRNAs were investigated, followed by an analysis of 17 modules, and only one module was highly associated with the IS. The top 10 lncRNAs were identified based on GS and MM. KEGG pathways analysis revealed the top two pathways of the Human T cell Lymphotropic Virus-1 (HTLV-1) infection and the mTOR signaling pathway might influence the progress of IS. Further, genes meeting the top two degree (AKT1 and MAPK14) were selected as the hub genes in the PPI network.ConclusionTo summarize, this study identified the key pathways and genes, which might serve as biomarkers and targets for precise diagnosis and treatment of IS in the future.     

Incorporating gene co-expression network in identification of cancer prognosis markers

Background  

Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them.