Identification of key gene modules for human osteosarcoma by co-expression analysis.
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ABSTRACT: Osteosarcoma is a type of bone cancer casting huge threat to the human health worldwide. Previously, gene expression analyses were performed to identify biomarkers for cancer; however, systemic co-expression analysis for osteosarcoma is still in need. The aim of this study was to construct a gene co-expression network that predicts clusters of candidate genes associated with the pathogenesis of osteosarcoma.Here, we extracted the large scale of datasets from the GEO database. With systematical approaches, we identified the co-expression modules by using weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichments of important modules at GO and KEGG terms.First, seven co-expression modules, which contain different genes, were conducted for 2228 genes in the 22 human osteosarcoma samples. Then, correlation study showed that the hub genes between pairwise modules displayed great differences. Lastly, functional enrichments of the co-expression modules showed that the module 5 enriched in immune response, antigen processing, and presentation, which is in consistence with GO result. Therefore, we speculated that the module 5 may play a key role in the pathogenesis of osteosarcoma.Here, we speculated that genes of the module 5 were the essential genes that were associated to human osteosarcoma. Together, our findings not only provided outline of co-expression gene modules for human osteosarcoma, but also promoted the understanding of these modules at functional aspects.
<h4>Background</h4>Osteosarcoma is a type of bone cancer casting huge threat to the human health worldwide. Previously, gene expression analyses were performed to identify biomarkers for cancer; however, systemic co-expression analysis for osteosarcoma is still in need. The aim of this study was to construct a gene co-expression network that predicts clusters of candidate genes associated with the pathogenesis of osteosarcoma.<h4>Methods</h4>Here, we extracted the large scale of datasets from th ...[more]
Project description:Osteoarthritis (OA) is one of the most prevalent causes of joint disease. However, the pathological mechanisms of OA have remained to be completely elucidated, and further investigation into the underlying mechanisms of OA development and the identification of novel therapeutic targets are urgently required. In the present study, the dataset GSE114007 was downloaded from the Gene Expression Omnibus database. Based on weighted gene co-expression network analysis (WGCNA) and the identification of differentially expressed genes (DEGs), the microarray data were further analyzed to identify hub genes, key transcription factors (TFs) and pivotal signaling pathways involved in the pathogenesis of OA. A total of 1,898 genes were identified to be differentially expressed between OA samples and normal samples. Based on WGCNA, the present study identified 5 hub modules closely associated with OA, and the potential key TFs for hub modules were further explored based on CisTargetX. The results demonstrated that B-Cell Lymphoma 6, Myelin Gene Expression Factor 2, Activating Transcription Factor 3, CCAAT Enhancer Binding Protein ?, Nuclear Factor Interleukin-3-Regulated, FOS Like Antigen-2, FOS-Like Antigen-1, Fos Proto-Oncogene, JunD Proto-Oncogene, Transcription Factor CP2 Like 1, RELA proto-oncogene NF-kB subunit, SRY-box transcription factor 3, V-Ets Avian Erythroblastosis Virus E26 Oncogene Homolog 2, Interferon Regulatory Factor 4 and REL proto-oncogene, NF-kB subunit were the potential key TFs. In addition, osteoclast differentiation, FoxO, MAPK and PI3K/Akt signaling pathways were revealed to be imperative for the pathogenesis of OA, as these 4 pivotal signaling pathways were observed to be tightly linked through 4 key TFs Fos Proto-Oncogene, JUN, JunD Proto-Oncogene and MYC, and 4 DEGs Vascular Endothelial Growth Factor A, Growth Arrest and DNA Damage Inducible ?, Growth Arrest and DNA Damage Inducible ? and Cyclin D1. The present study identified a set of potential key genes and signaling pathways, and provided an important opportunity to advance the current understanding of OA.
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.
Project description:Background: Osteosarcoma (OS) is one of the malignant bone tumors occurring in both human and canine, and in both of them, it is characterized by a high rate of metastasis and poor prognosis. Cross-species analysis reveals previously neglected molecular or signaling pathways involved in the progression of diseases, and dogs are genetically comparable to humans and live in similar environments. Therefore, the aim of this study was to find out OS hub genes through a cross-species analysis. Materials and Methods: All the human and canine OS gene expression data obtained by the Affymetrix platform were collected. After quality assessment and normalization, co-expression network was performed using weighted gene co-expression network analysis (WGCNA). Species-specific modules and consensus modules were identified. Protein-protein interaction (PPI) networks analysis was performed based on consensus gene modules. Then, consensus modules were functionally annotated and correlated with clinical traits. Hub nodes were identified by a subnetwork analysis of PPI network and WGCNA module membership. Modules of interest and hub nodes were validated in an external data set. Results: Three modules for the human network, seven modules for the canine network, and four consensus modules were identified. The consensus module 3 (C3) showed a significant correlation with the metastatic status in the training data set and a significant correlation with metastasis-free survival in the external data set. Cluster of differentiation 86 (CD86) was identified as the hub gene of C3, showing a significant correlation with metastasis-free survival. Conclusion: Genes in C3 play an important role in OS metastasis, whereas CD86 might be a potential molecular biomarker for OS metastasis.
Project description:Adrenocortical carcinoma (ACC) is a rare and aggressive cancer with a high relapse rate and limited treatment options. Therefore, the identification of potential prognostic markers in patients with ACC may improve early detection, survival rates and may additionally provide novel insights into the early detection of recurrence. In the present study, clinical traits and RNA-seq data of 79 patients with ACC were obtained from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis was carried out and 17 distinct co-expression modules were built to examine the association between the modules and the clinical traits. Of the 17 modules, two co-expression modules, which contained 214 and 168 genes, were significantly correlated with two clinical traits, tumor stage and vital status. Functional enrichment analysis was performed on the selected modules. The results showed that one of the modules was primarily enriched in cell division and the other module was enriched in metabolic pathways, suggesting their involvement in tumor progression. Furthermore, cyclin dependent kinase 1 (CDK1) and ubiquitin C (UBC) were identified as hub genes in both modules. Survival analysis revealed that the high expression of the hub genes significantly correlated with the poor survival rate of patients, suggesting that CDK1 and UBC have vital roles in the progression of ACC. In the present study, a co-expression gene module of ACC was provided and the prognostic genes that may serve as new diagnostic markers in the future were defined.
Project description:Breast cancer (BC) is the most common leading cause of cancer-related death in women worldwide. Gene expression profiling analysis for human BCs has been studied previously. However, co-expression analysis for BC cell lines is still devoid to date. The aim of the study was to identify key pathways and hub genes that may serve as a biomarker for BC and uncover potential molecular mechanism using weighted correlation network analysis. We analyzed microarray data of BC cell lines (GSE 48213) listed in the Gene Expression Omnibus database. Gene co-expression networks were used to construct and explore the biological function in hub modules using the weighted correlation network analysis algorithm method. Meanwhile, Gene ontology and KEGG pathway analysis were performed using Cytoscape plug-in ClueGo. The network of the key module was also constructed using Cytoscape. A total of 5000 genes were selected, 28 modules of co-expressed genes were identified from the gene co-expression network, one of which was found to be significantly associated with a subtype of BC lines. Functional enrichment analysis revealed that the brown module was mainly involved in the pathway of the autophagy, spliceosome, and mitophagy, the black module was mainly enriched in the pathway of colorectal cancer and pancreatic cancer, and genes in midnightblue module played critical roles in ribosome and regulation of lipolysis in adipocytes pathway. Three hub genes CBR3, SF3B6, and RHPN1 may play an important role in the development and malignancy of the disease. The findings of the present study could improve our understanding of the molecular pathogenesis of breast cancer.
Project description:BackgroundOsteosarcoma is the most common bone tumor that occurs in children.MethodsTo identify co-expression modules and pathways correlated with osteosarcoma and its clinical characteristics, we performed weighted gene co-expression network analysis (WGCNA) on RNA-seq data of osteosarcoma with 52 samples. Then we performed pathway enrichment analysis on genes from significant modules.ResultsA total of 5471 genes were included in WGCNA, and 16 modules were identified. Module-trait analysis identified that a module involved in microtubule bundle formation, drug metabolism-cytochrome P450, and IL-17 signaling pathway was negatively correlated with osteosarcoma and positively correlated with metastasis; a module involved in DNA replication was positively correlated with osteosarcoma; a module involved in cell junction was positively correlated with metastasis; and a module involved in heparin binding negatively correlated with osteosarcoma. Moreover, expression levels in four of the top ten differentially expressed genes were validated in another independent dataset.ConclusionsOur analysis might provide insight for molecular mechanisms of osteosarcoma.
Project description:Acute myocardial infarction (AMI) seriously threatens human life. In this study we aimed to systemically analyze the function of key gene modules in human platelets in AMI. We used weighted gene co-expression network analysis (WGCNA) to construct a co-expression module, and analyzed the relationship between potential modules and clinical characteristics based on platelet RNA-seq RPKM count reads of 16 ST-segment elevation myocardial infarction (STEMI) patients and 16 non-STEMI (NSTEMI) patients provided by the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed with the DAVID tool. Hub genes were calculated by the Cytohubba package. A total of 3653 genes was selected to construct the co-expression modules. A significant correlation between BMI and the module with color of sky-blue in STEMI. In NSTEMI, there was a significant correlation between the sky blue module and CAD, the Salmon module and HT, and the Cyan module and HT. In STEMI, the Hub genes were mainly enriched in functions related to cell membrane signal transduction including Aqp1, Armcx1, Gsta4, Hist3h2a and Il17re. In NSTEMI, the Hub genes are related mainly to energy metabolism in the sky-blue module including Olr1, Nap1l3, Gfer, Dohh, Crispld1 and Ccdc8b; they are mainly related to extracellular space and calcium binding in the Cyan module, including Clec12b, Chd4, Asgr1, Armcx4, Chid1 and Alkbh7. The hub genes in the Salmon module include Ell3, Aldh1b1, Cavin4, Cabp4, Eif1ay and Dus3l. Our results provide a framework for co-expression gene modules in STEMI and NSTEMI patients, and identify key targets as biomarkers for patients with different subtypes of AMI.
Project description:BACKGROUND:Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma. METHOD:Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients. RESULTS:We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21?+?MED10?+?PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC?=?0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n?=?156). CONCLUSION:We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.
Project description:Tuberculosis (TB) is still a leading cause of death worldwide. Treatments remain unsatisfactory due to an incomplete understanding of the underlying host-pathogen interactions during infection. In the present study, weighted gene co-expression network analysis (WGCNA) was conducted to identify key macrophage modules and hub genes associated with mycobacterial infection. WGCNA was performed combining our own transcriptomic results using Mycobacterium aurum-infected human monocytic macrophages (THP1) with publicly accessible datasets obtained from three types of macrophages infected with seven different mycobacterial strains in various one-to-one combinations. A hierarchical clustering tree of 11,533 genes was built from 198 samples, and 47 distinct modules were revealed. We identified a module, consisting of 226 genes, which represented the common response of host macrophages to different mycobacterial infections that showed significant enrichment in innate immune stimulation, bacterial pattern recognition, and leukocyte chemotaxis. Moreover, by network analysis applied to the 74 genes with the best correlation with mycobacteria infection, we identified the top 10 hub-connecting genes: NAMPT, IRAK2, SOCS3, PTGS2, CCL20, IL1B, ZC3H12A, ABTB2, GFPT2, and ELOVL7. Interestingly, apart from the well-known Toll-like receptor and inflammation-associated genes, other genes may serve as novel TB diagnosis markers and potential therapeutic targets.
Project description:Glioblastoma multiforme (GBM) is the most malignant primary tumour in the central nervous system, but the molecular mechanisms underlying its pathogenesis remain unclear. In this study, data set GSE50161 was used to construct a co-expression network for weighted gene co-expression network analysis. Two modules (dubbed brown and turquoise) were found to have the strongest correlation with GBM. Functional enrichment analysis indicated that the brown module was involved in the cell cycle, DNA replication, and pyrimidine metabolism. The turquoise module was primarily related to circadian rhythm entrainment, glutamatergic synapses, and axonal guidance. Hub genes were screened by survival analysis using The Cancer Genome Atlas and Human Protein Atlas databases and further tested using the GSE4290 and Gene Expression Profiling Interactive Analysis databases. The eight hub genes (NUSAP1, SHCBP1, KNL1, SULT4A1, SLC12A5, NUF2, NAPB, and GARNL3) were verified at both the transcriptional and translational levels, and these gene expression levels were significant based on the World Health Organization classification system. These hub genes may be potential biomarkers and therapeutic targets for the accurate diagnosis and management of GBM.