Identification of key genes associated with sepsis patients infected by staphylococcus aureus through weighted gene co-expression network analysis.
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SUBMITTER: Wu H
PROVIDER: S-EPMC8748107 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGram-negative sepsis is closely related to the immune response, involving collaborative efforts of different immune cells. However, the mechanisms underlying immune cell regulation in gram-negative sepsis remain unclear. Therefore, this study investigated the potential regulatory mechanisms and identified the key genes related to immune cells in gram-negative sepsis.MethodsThe RNA-sequencing data for gram-negative sepsis samples and normal samples were collected from the Gene Expression Omnibus (GEO) dataset GSE9960. CIBERSORT was performed to analyze the proportion of 22 types of immune cells in gram-negative sepsis and normal samples. Weighted gene co-expression network analysis (WGCNA) was used to determine the networks that are associated with the differentially distributed immune cells in the two groups. Differentially expressed genes were identified using the limma package. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to ascertain hub gene signatures. The gene interaction network of hub gene signatures was determined by ingenuity pathway analysis. Furthermore, the expression levels of the key genes were verified using quantitative real-time polymerase chain reaction (qRT-PCR).ResultsCIBERSORT analysis showed that the proportions of plasma cells, resting CD4+ memory T cells, M1 macrophages, and eosinophils were significantly different between gram-negative sepsis and normal samples. WGCNA identified 1,100 genes in the most relevant modules associated with these immune cells. In addition, 87 differentially expressed genes were identified. By overlapping the genes found in the WGCNA and the differentially expressed genes, a total of 46 genes related to immune cells were identified. Integrative analysis of LASSO and SVM-RFE identified NLR family CARD domain-containing 4 (NLRC4) and ral guanine nucleotide dissociation stimulator like 4 (RGL4) as key gene signatures related to immune cells in gram-negative sepsis. The qRT-PCR results demonstrated that both NLRC4 and RGL4 were upregulated in peripheral blood mononuclear cells (PBMCs) from patients with sepsis.ConclusionsThis investigation provides novel insights into the molecular mechanisms of immune cells involved in the pathogenesis of gram-negative sepsis. NLRC4 and RGL4 were identified as key gene signatures related to immune cells and may act as potential diagnostic biomarkers for gram-negative sepsis.
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Project description:Laryngeal squamous cell carcinoma (LSCC) is the most common malignant tumor in the head and neck, and can seriously affect the daily life of patients. To study the mechanisms of LSCC, the microarray of GSE51958 was analyzed in the present study. GSE51958 was downloaded from Gene Expression Omnibus, and included a collection of LSCC tissue samples and matched adjacent non-cancerous tissue samples from 10 patients. Differentially-expressed genes (DEGs) were identified using limma package. Next, a weighted co-expression network was constructed for the DEGs by WGCNA package in R. Modules of the weighted co-expression network were obtained through constructing a hierarchical clustering tree using the hybrid dynamic shear tree method. Using the clusterProfiler package, the potential functions of DEGs in the modules correlated with LSCC were predicted by pathway enrichment analysis. In total, 959 DEGs were screened from the LSCC samples compared with the adjacent non-cancerous samples, including 553 upregulated and 406 downregulated genes. The appointed black, brown, gray, pink and yellow modules were screened for the DEGs in the weighted co-expression network. For the DEGs in the brown and yellow modules, the enriched pathways were cytokine-cytokine receptor interaction and metabolic pathways, respectively. The DEGs in the pink module were involved in the majority of pathways. With high connectivity degrees in the pink module, TPX2, microtubule-associated (TPX2; degree, 25), minichromosome maintenance complex component 2 (MCM2; degree, 25), ubiquitin-like with PHD and ring finger domains 1 (UHRF1; degree, 22), cyclin-dependent kinase 2 (CDK2; degree, 20) and protein regulator of cytokinesis 1 (PRC1; degree, 20) may be involved in LSCC. Overall, In conclusion, from the integrated bioinformatics analysis of genes that may be associated with LSCC, 959 DEGs were obtained from LSCC samples compared with adjacent non-cancerous samples, and TPX2, MCM2, UHRF1, CDK2 and PRC1 were found to hold a possible association with the disease.
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Project description:BackgroundFat deposition is an important economic consideration in pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) is effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low) and to identify the core genes in each of these modules.ResultsWe used RNA sequences generated in different pig tissues to construct a gene expression matrix consisting of 12,862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA and the number of genes in these modules ranged from 39 to 3,363. Four co-expression modules were significantly correlated with backfat thickness. A total of 16 genes (RAD9A, IGF2R, SCAP, TCAP, SMYD1, PFKM, DGAT1, GPS2, IGF1, MAPK8, FABP, FABP5, LEPR, UCP3, APOF, and FASN) were associated with fat deposition.ConclusionsRAD9A, TCAP, SMYD1, PFKM, GPS2, and APOF were the key genes in the four modules based on the degree of gene connectivity. Combining these results with those from differential gene analysis, SMYD1 and PFKM were proposed as strong candidate genes for body size traits. This study explored the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms underlying porcine fat deposition.
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Project description:BackgroundCalcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear.MethodsThe gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by 'limma' and 'weighted gene co-expression network analysis (WGCNA)' package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein-protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation.ResultsWe identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively.ConclusionsThis study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.
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Project description:BackgroundPulpitis is a common disease mainly caused by bacteria. Conventional approaches of diagnosing the state of dental pulp are mainly based on clinical symptoms, thereby harbor deficiencies. The accurate and rapid diagnosis of pulpitis is important for choosing the suitable therapy. The study aimed to identify pulpits related key genes by integrating micro-array data analysis and systems biology network-based methods such as weighted gene co-expression network analysis (WGCNA).MethodsThe micro-array data of 13 inflamed pulp and 11 normal pulp were acquired from Gene Expression Omnibus (GEO). WGCNA was utilized to establish a genetic network and categorize genes into diverse modules. Hub genes in the most associated module to pulpitis were screened out using high module group members (MM) methods. Pulpitis model in rat was constructed and iRoot BP plus was applied to cap pulp. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of hub genes.ResultsWGCNA was established and genes were categorized into 22 modules. The darkgrey module had the highest correlation with pulpitis among them. A total of 5 hub genes (HMOX1, LOX, ACTG1, STAT3, GNB5) were identified. RT-qPCR proved the differences in expression levels of HMOX1, LOX, ACTG1, STAT3, GNB5 in inflamed dental pulp. Pulp capping reversed the expression level of HMOX1, LOX, ACTG1.ConclusionThe study was the first to produce a holistic view of pulpitis, screen out and validate hub genes involved in pulpitis using WGCNA method. Pulp capping using iRoot BP plus could reverse partial hub genes.
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Project description:Background: Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU). Material and Method: EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram. Results: We obtained EC and EU-associated microarray datasets GSE7305 and GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets. Conclusion: Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.
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Project description:BackgroundChronic obstructive pulmonary disease (COPD) is prevalent mainly in older adults, especially those who are smokers. It appears to be regulated by multiple genes, but there is some degree of familial clustering. The evidence to date suggests that COPD-associated biomarkers are largely inadequate for disease diagnosis, so we conducted a comprehensive search for more specific genetic markers.MethodsWe used 3 datasets from the Gene Expression Omnibus (GEO) database. By investigating the biological information [i.e., Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and weighted gene co-expression network analysis (WGCNA)], we filtered out 8 differentially expressed genes (DEGs) and validated the transcript levels of those hub genes in 16HBE cell lines, THP-1 cell lines and lung tissue of COPD patients.ResultsThe 8 hub genes comprised amyloid precursor protein (APP), fibronectin 1, insulin-like growth factor 1 (IGF1), β-actin, capping actin protein of muscle Z-line subunit alpha 2, secreted phosphoprotein 1 (SPP1), catalase (CAT), and colony stimulating factor 2 (CSF2) were selected from among the DEGs. Cigarette smoke extract-stimulated 16HBE cells were found to highly express SPP1, CSF2, and IGF1. In addition, IGF1 levels were increased and IGF1 and APP levels were decreased in CSE-stimulated THP-1 cells. SPP1 and FN1 showed increased expression levels in lung tissue of COPD patients, but the opposite held for APP and CAT.ConclusionsWe identified 8 hub genes of COPD based on GO, KEGG and WGCNA, which have provided insights into the pathophysiological mechanisms of COPD.
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Project description:Colorectal cancer (CRC) has been one of the most common malignancies worldwide, which tends to get worse for the growth and aging of the population and westernized lifestyle. However, there is no effective treatment due to the complexity of its etiology. Hence, the pathogenic mechanisms remain to be clearly defined. In the present study, we adopted an advanced analytical method-Weighted Gene Co-expression Network Analysis (WGCNA) to identify the key gene modules and hub genes associated with CRC. In total, five gene co-expression modules were highly associated with CRC, of which, one gene module correlated with CRC significantly positive (R = 0.88). Functional enrichment analysis of genes in primary gene module found metabolic pathways, which might be a potentially important pathway involved in CRC. Further, we identified and verified some hub genes positively correlated with CRC by using Cytoscape software and UALCAN databases, including PAICS, ATR, AASDHPPT, DDX18, NUP107 and TOMM6. The present study discovered key gene modules and hub genes associated with CRC, which provide references to understand the pathogenesis of CRC and may be novel candidate target genes of CRC.
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