Identification of Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA)

Intervertebral disc degeneration (IDD) is a major cause of lower back pain. However, to date, the molecular mechanism of the IDD remains unclear. Gene expression profiles and clinical traits were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, weighted gene coexpression network analysis (WGCNA) was used to screen IDD-related genes. Moreover, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine (SVM) algorithms were used to identify characteristic genes. Furthermore, we further investigated the immune landscape by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and the correlations between key characteristic genes and infiltrating immune cells. Finally, a competing endogenous RNA (ceRNA) network was established to show the regulatory mechanisms of characteristic genes. A total of 2458 genes were identified by WGCNA, and 48 of them were disordered. After overlapping the genes obtained by LASSO and SVM-RFE algorithms, genes including LINC01347, ASAP1-IT1, lnc-SEPT7L-1, B3GNT8, CHRNB3, CLEC4F, LOC102724000, SERINC2, and LOC102723649 were identified as characteristic genes of IDD. Moreover, differential analysis further identified ASAP1-IT1 and SERINC2 as key characteristic genes. Furthermore, we found that the expression of both ASAP1-IT1 and SERINC2 was related to the proportions of T cells gamma delta and Neutrophils. Finally, a ceRNA network was established to show the regulatory mechanisms of ASAP1-IT1 and SERINC2. In conclusion, the present study identified ASAP1-IT1 and SERINC2 as the key characteristic genes of IDD through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of IDD.

Identification of Hub Genes of Keloid Fibroblasts by Coexpression Network Analysis and Degree Algorithm

Background. Keloid is a benign dermal tumor characterized by abnormal proliferation and invasion of fibroblasts. The establishment of biomarkers is essential for the diagnosis and treatment of keloids. Methods. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Differential expressed genes (DEGs) in GSE145725 and genes in significant modules were integrated to identify overlapping key genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed, as well as protein-protein interaction (PPI) network construction for hub gene screening. Results. Using the R package of WGCNA, 22 coexpression modules consisting of different genes were identified from the top 5,000 genes with maximum mean absolute deviation in 19 human fibroblast samples. Blue-green and yellow modules were identified as the most important modules, where genes overlapping with DEGs were identified as key genes. We identified the most critical functions and pathways as extracellular structure organization, vascular smooth muscle contraction, and the cGMP-PKG signaling pathway. Hub genes from key genes as BMP4, MSX1, HAND2, TBX2, SIX1, IRX1, EDN1, DLX5, MEF2C, and DLX2 were identified. Conclusion. The blue-green and yellow modules may play an important role in the pathogenesis of keloid. 10 hub genes were identified as potential biomarkers and therapeutic targets for keloid.

ceRNA Network and Functional Enrichment Analysis of Preeclampsia by Weighted Gene Coexpression Network Analysis

Background. Preeclampsia (PE) is a multisystemic syndrome which has short- and long-term risk to mothers and children and has pluralistic etiology. Objective. This study is aimed at constructing a competitive endogenous RNA (ceRNA) network for pathways most related to PE using a data mining strategy based on weighted gene coexpression network analysis (WGCNA). Methods. We focused on pathways involving hypoxia, angiogenesis, and epithelial mesenchymal transition according to the gene set variation analysis (GSVA) scores. The gene sets of these three pathways were enriched by gene set enrichment analysis (GSEA). WGCNA was used to study the underlying molecular mechanisms of the three pathways in the pathogenesis of PE by analyzing the relationship among pathways and genes. The soft threshold power (β) and topological overlap matrix allowed us to obtain 15 modules, among which the red module was chosen for the downstream analysis. We chose 10 hub genes that satisfied ∣ log 2 Fold Change ∣ > 2 and had a higher degree of connectivity within the module. These candidate genes were subsequently confirmed to have higher gene significance and module membership in the red module. Coexpression networks were established for the hub genes to unfold the connection between the genes in the red module and PE. Finally, ceRNA networks were constructed to further clarify the underlying molecular mechanism involved in the occurrence of PE. 56 circRNAs, 17 lncRNAs, and 20 miRNAs participated in the regulation of the hub genes. Coagulation factor II thrombin receptor (F2R) and lumican (LUM) were considered the most relevant genes, and ceRNA networks of them were constructed. Conclusion. The microarray data mining process based on bioinformatics methods constructed lncRNA and miRNA networks for ten hub genes that were closely related to PE and focused on ceRNAs of F2R and LUM finally. The results of our study may provide insight into the mechanisms underlying PE occurrence.

Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

Background Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes. Methods RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log2 Fold change (FC)|> 1 and adjusted p value < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA. Results In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA. Conclusion FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

Weighted Gene Coexpression Network Analysis in Mouse Livers following Ischemia-Reperfusion and Extensive Hepatectomy

In mouse models, the recovery of liver volume is mainly mediated by the proliferation of hepatocytes after partial hepatectomy that is commonly accompanied with ischemia-reperfusion. The identification of differently expressed genes in liver following partial hepatectomy benefits the better understanding of the molecular mechanisms during liver regeneration (LR) with appliable clinical significance. Briefly, studying different gene expression patterns in liver tissues collected from the mice group that survived through extensive hepatectomy will be of huge critical importance in LR than those collected from the mice group that survived through appropriate hepatectomy. In this study, we performed the weighted gene coexpression network analysis (WGCNA) to address the central candidate genes and to construct the free-scale gene coexpression networks using the identified dynamic different expressive genes in liver specimens from the mice with 85% hepatectomy (20% for seven-day survial rate) and 50% hepatectomy (100% for seven-day survial rate under ischemia-reperfusion condition compared with the sham group control mice). The WGCNA combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses pinpointed out the apparent distinguished importance of three gene expression modules: the blue module for apoptotic process, the turquoise module for lipid metabolism, and the green module for fatty acid metabolic process in LR following extensive hepatectomy. WGCNA analysis and protein-protein interaction (PPI) network construction highlighted FAM175B, OGT, and PDE3B were the potential three hub genes in the previously mentioned three modules. This work may help to provide new clues to the future fundamental study and treatment strategy for LR following liver injury and hepatectomy.

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

Background The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT. Methods Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database. Results In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT. Conclusions These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

Weighted Gene Coexpression Network Analysis of Key Genes Controlling Cancer Stem Cell Characteristics by Mrnasi in Colon Adenocarcinoma

Background: Cancer stem cells are hidden tumor cells with the potential for self-renewal and multi-differentiation, which are the principal cause of tumor heterogeneity, metastasis and drug resistance. However, the key genes that maintain cell stemness of colon adenocarcinoma (COAD) are still unknown. Our study was designed to identify key genes associated with the stemness of COAD cell. Methods: RNA sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Weighted gene coexpression network analysis (WGCNA) was used to recognize key genes based on mRNAsi. Next, the gene expression data downloaded from Gene Expression Omnibus (GEO), Oncomine, and Gene Expression Profiling Integrative Analysis (GEPIA) were used to demonstrate the expression of key genes.Results: The mRNAsi score of COAD tissue was obviously superior than that of normal tissue. N stages, M stages and overall survival were highly related to mRNAsi. WGCNA showed that the green module was positively related to mRNAsi. We obtained 27 genes closely related to each other at the transcriptional and protein levels. The main biological functions of key genes were organelle fission, chromosomal region, and protein serine/threonine kinase activity. The enriched signaling pathway were cell cycle, progesterone−mediated oocyte maturation, human T−cell leukemia virus 1 infection, oocyte meiosis, DNA replication, cellular senescence, viral carcinogenesis, and p53 signaling pathway. The key genes were highly expressed in COAD and various cancers, which was validated multiple databases. Conclusions: 27 key genes were well correlated with maintaining stemness of COAD, which were significantly related to cell cycle events. The key genes may be therapeutic targets for inhibiting the properties of COAD stem cells.

Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis

Diffuse large B-cell lymphoma (DLBC) is a kind of tumor with rapid progress and poor prognosis. Therefore, it is necessary to explore new biomarkers or therapeutic targets to assist in diagnosis or treatment. This study is aimed at screening hub genes by weighted gene coexpression network analysis (WGCNA) and exploring the significance of overall survival (OS) in DLBC patients. Statistical data using WGCNA to analyze mRNA expression in DLBC patients came from The Cancer Genome Atlas (TCGA) dataset. After analyzing with clinical information, the biological functions of hub genes were detected. Survival analysis, Cox regression detection, and correlation analysis of the hub genes were carried out. The potential function of the hub gene related to prognosis was predicted by gene set enrichment analysis (GSEA). The results showed that APOE, CTSD, LGALS2, and TMEM176B expression in normal tissues was significantly higher than that in cancer tissues ( P < 0.01 ). Survival analysis showed that patients with high APOE and CTSD were associated with better OS ( P < 0.01 ). APOE and CTSD genes were mainly enriched in the regulation of ROS and oxidative stress. The two hub genes related to the prognosis of DLBC were identified and verified based on WGCNA. Survival analysis showed that the overexpression of APOE and CTSD in DLBC might be beneficial to the prognosis. These findings identified vital pathways and genes that may become new therapeutic targets and contribute to prognostic indicators.

Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene’s expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 ( p = 0.004 ). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

Proteomics analysis of the gut–brain axis in a gut microbiota-dysbiosis model of depression

Major depressive disorder (MDD) is a serious mental illness. Increasing evidence from both animal and human studies suggested that the gut microbiota might be involved in the onset of depression via the gut–brain axis. However, the mechanism in depression remains unclear. To explore the protein changes of the gut–brain axis modulated by gut microbiota, germ-free mice were transplanted with gut microbiota from MDD patients to induce depression-like behaviors. Behavioral tests were performed following fecal microbiota transplantation. A quantitative proteomics approach was used to examine changes in protein expression in the prefrontal cortex (PFC), liver, cecum, and serum. Then differential protein analysis and weighted gene coexpression network analysis were used to identify microbiota-related protein modules. Our results suggested that gut microbiota induced the alteration of protein expression levels in multiple tissues of the gut–brain axis in mice with depression-like phenotype, and these changes of the PFC and liver were model specific compared to chronic stress models. Gene ontology enrichment analysis revealed that the protein changes of the gut–brain axis were involved in a variety of biological functions, including metabolic process and inflammatory response, in which energy metabolism is the core change of the protein network. Our data provide clues for future studies in the gut–brain axis on protein level and deepen the understanding of how gut microbiota cause depression-like behaviors.