Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics.
ABSTRACT: : Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors ) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C) , VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.
Project description:The present study aimed to screen potential genes implicated in epithelial ovarian cancer (EOC) and to further understand the molecular pathogenesis of EOC. In order to do this, datasets GSE14407 (containing 12 human ovarian cancer epithelia samples and 12 normal epithelia samples) and GSE29220 (containing 11 salivary transcriptomes from ovarian cancer patients with serous papillary adenocarcinoma and 11 matched controls) were obtained from the Gene Expression Omnibus. Differentially expressed genes (DEGs) within these datasets were screened using the Linear Models for Microarray Data package, and potential gene functions were predicted by functional and pathway enrichment analyses. Additionally, module analysis of protein-protein interaction networks was performed using MCODE software in Cytoscape. The potential microRNAs (miRNAs/miRs) and transcription factors (TFs) regulating DEGs were also analyzed, and the integrated TF-DEG and miRNA-DEG regulatory networks were visualized with Cytoscape. In total, 31 upregulated DEGs and 64 downregulated DEGs were screened. The upregulated DEGs, such as centromere protein F (CENPF) and ubiquitin like with PHD and ring finger domains 1 (UHRF1), were significantly associated with the cell cycle and were regulated by the TF nuclear transcription factor Y (NF-Y). CENPF was modulated by miR-373, and UHRF1 was regulated by miR-146a. The downregulated DEGs, such as aldehyde dehydrogenase 1 family member A2 (ALDH1A2), were distinctly involved in the response to estrogen stimulus and modulated by tumor protein 53 (TP53); protocadherin 9 (PCDH9) was regulated by TP53, miR-92b-3p and miR-137. The DEGs, including CENPF, UHRF1, ALDH1A2 and PCDH9, and a set of gene regulators, including all NFY genes, TP53, miR-373, miR-146a, miR-92b-3p and miR-137, may be involved in the pathogenesis of EOC.
Project description:Background:Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable. Materials and methods:Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the "limma" package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein-protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples. Results:We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA. Conclusion:In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.
Project description:The purpose of the current study was to explore the carboplatin?induced sequential changes in gene expression and screen out key genes, which were associated with effects of carboplatin on epithelial ovarian cancer (EOC). The microarray dataset GSE13525 was downloaded from the Gene Expression Omnibus database, including 6 EOC cell samples separately treated with carboplatin at 24, 30 and 36 h (case group), and 6 samples treated with phosphate?buffered saline at the same time points (control group). A total of 3 sets of differentially expressed genes (DEGs) were respectively identified in case samples at 24, 30 and 36 h compared with the control group via the Limma package, and separately recorded as DEG?24, DEG?30 and DEG?36. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the overlapped DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery. The protein?protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In addition, the survival curves were drawn to illustrate the association between the expression levels of certain critical genes and the prognosis of EOC. A total of 170, 605 and 1043 DEGs were separately obtained in DEG?24 DEG?30 and DEG?36, and 110 overlaps were identified. The overlaps were enriched in 77 GO terms and 3 KEGG pathways. A total of 152 pairs were involved in the PPI network, and the abnormal expression levels (high or low) of c?Jun and cyclin B1 (CCNB1) would reduce the survival time of patients with EOC. The study indicated that c?Jun and CCNB1 may be the prognostic biomarkers of EOC treated with carboplatin, and certain pathways (such as p53 signaling pathway, cell cycle and mitogen?activation protein kinase signaling pathway) may be involved in carbo-platin?resistant EOC.
Project description:OBJECTIVE:To uncover the potential regulatory mechanisms of the relevant genes that contribute to the prognosis and prevention of multiple myeloma (MM). METHODS:Microarray data (GSE13591) were downloaded, including five plasma cell samples from normal donors and 133 plasma cell samples from MM patients. Differentially expressed genes (DEGs) were identified by Student's t-test. Functional enrichment analysis was performed for DEGs using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Transcription factors and tumor-associated genes were also explored by mapping genes in the TRANSFAC, the tumor suppressor gene (TSGene), and tumor-associated gene (TAG) databases. A protein-protein interaction (PPI) network and PPI subnetworks were constructed by Cytoscape software using the Search Tool for the Retrieval of Interacting Genes (STRING) database. RESULTS:A total of 63 DEGs (42 downregulated, 21 upregulated) were identified. Functional enrichment analysis showed that HLA-DRB1 and VCAM1 might be involved in the positive regulation of immune system processes, and HLA-DRB1 might be related to the intestinal immune network for IgA production pathway. The genes CEBPD, JUND, and ATF3 were identified as transcription factors. The top ten nodal genes in the PPI network were revealed including HLA-DRB1, VCAM1, and TFRC. In addition, genes in the PPI subnetwork, such as HLA-DRB1 and VCAM1, were enriched in the cell adhesion molecules pathway, whereas CD4 and TFRC were both enriched in the hematopoietic cell pathway. CONCLUSION:Several crucial genes correlated to MM were identified, including CD4, HLA-DRB1, TFRC, and VCAM1, which might exert their roles in MM progression via immune-mediated pathways. There might be certain regulatory correlations between HLA-DRB1, CD4, and TFRC.
Project description:OBJECTIVE:To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. METHODS:Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein-protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules. RESULTS:In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle. CONCLUSION:The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine-cytokine receptor interaction pathway.
Project description:Background:Epithelial ovarian cancer (EOC) is a lethal and aggressive gynecological malignancy. Despite recent advances, existing therapies are challenged by a high relapse rate, eventually resulting in disease recurrence and chemoresistance. Emerging evidence indicates that a subpopulation of cells known as cancer stem-like cells (CSLCs) exists with non-tumorigenic cancer cells (non-CSCs) within a bulk tumor and is thought to be responsible for tumor recurrence and drug-resistance. Therefore, identifying the molecular drivers for cancer stem cells (CSCs) is critical for the development of novel therapeutic strategies for the treatment of EOC. Methods:Two gene datasets were downloaded from the Gene Expression Omnibus (GEO) database based on our search criteria. Differentially expressed genes (DEGs) in both datasets were obtained by the GEO2R web tool. Based on log2 (fold change) >2, the top thirteen up-regulated genes and log2 (fold change) < -1.5 top thirteen down-regulated genes were selected, and the association between their expressions and overall survival was analyzed by OncoLnc web tool. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways analysis, and protein-protein interaction (PPI) networks were performed for all the common DEGs found in both datasets. SK-OV-3 cells were cultured in an adherent culture medium and spheroids were generated in suspension culture with CSCs specific medium. RNA from both cell population was extracted to validate the selected DEGs expression by q-PCR. Growth inhibition assay was performed in SK-OV-3 cells after carboplatin treatment. Results:A total of 200 DEGs, 117 up-regulated and 83 down-regulated genes were commonly identified in both datasets. Analysis of pathways and enrichment tests indicated that the extracellular matrix part, cell proliferation, tissue development, and molecular function regulation were enriched in CSCs. Biological pathways such as interferon-alpha/beta signaling, molecules associated with elastic fibers, and synthesis of bile acids and bile salts were significantly enriched in CSCs. Among the top 13 up-regulated and down-regulated genes, MMP1 and PPFIBP1 expression were associated with overall survival. Higher expression of ADM, CXCR4, LGR5, and PTGS2 in carboplatin treated SK-OV-3 cells indicate a potential role in drug resistance. Conclusions:The molecular signature and signaling pathways enriched in ovarian CSCs were identified by bioinformatics analysis. This analysis could provide further research ideas to find the new mechanism and novel potential therapeutic targets for ovarian CSCs.
Project description:Coronary artery disease (CAD) is a major cause of end-stage cardiac disease. Although profound efforts have been made to illuminate the pathogenesis, the molecular mechanisms of CAD remain to be analyzed. To identify the candidate genes in the advancement of CAD, microarray dataset GSE23766 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and pathway and gene ontology (GO) enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Biological General Repository for Interaction Datasets (BioGRID) and Cytoscape. Additionally, target genes-miRNA regulatory network and target genes-TF regulatory network were constructed and analyzed. There were 894 DEGs between male human CAD samples and female human CAD samples, including 456 up regulated genes and 438 down regulated genes. Pathway enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the superpathway of steroid hormone biosynthesis, ABC transporters, oxidative ethanol degradation III and Complement and coagulation cascades. Similarly, geneontology enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the forebrain neuron differentiation, filopodium membrane, platelet degranulation and blood microparticle. In the PPI network and modules (up and down regulated), MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74 and VHL were hub genes. In the target genes-miRNA regulatory network and target genes-TF regulatory network (up and down regulated), TAOK1, KHSRP, HSD17B11 and PAH were target genes. In conclusion, the pathway and GO ontology enriched by DEGs may reveal the molecular mechanism of CAD. Its hub and target genes, MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74, VHL, TAOK1, KHSRP, HSD17B11 and PAH were expected to be new targets for CAD. Our finding provided clues for exploring molecular mechanism and developing new prognostics, diagnostic and therapeutic strategies for CAD.
Project description:Ovarian cancer is refractory in response towards platinum-based chemotherapy, and resistance frequently develops. We attempted to identify the driving pathways in cisplatin-resistant ovarian cancer and develop targeted therapies to overcome this resistance. Using an integrated bioinformatics approach, a GSE15372 database from NCBI's Gene Expression Omnibus database was obtained for identifying differentially expressed genes (DEGs), in which 535 DEGs were found (407 up-regulated and 128 down-regulated) in association with ovarian cancer cisplatin-resistance. Gene ontology and pathway enrichment analyses further found that aberrant activation of EGFR/ErbB2 signaling was the driving event in resistant cells. A network of dysregulated genes was built based on these identified DEGs and protein-protein interaction network, which led to the identification of 7 potential inhibitors based on screening a 77 small molecule natural product library. Sanguinarine, alone and in combination with cisplatin, was found to significantly suppress the proliferation of wt/resistant ovarian cancer cells in vitro and the growth of parental and resistant ovarian xenograft tumors in vivo. Our study suggests that EGFR/ErbB2 activation is one of the driving pathways in developing cisplatin-resistance in ovarian cancer, and that sanguinarine has the potential to be developed as an effective therapy to overcome this therapeutic resistance.
Project description:Epithelial ovarian cancer (EOC) is one of the malignancies in women, which has the highest mortality. However, the microlevel mechanism has not been discussed in detail. The expression profiles GSE27651, GSE38666, GSE40595, and GSE66957 including 188 tumor and 52 nontumor samples were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were filtered using R software, and we performed functional analysis using the clusterProfiler. Cytoscape software, the molecular complex detection plugin and database STRING analyzed DEGs to construct protein-protein interaction network. We identified 116 DEGs including 81 upregulated and 35 downregulated DEGs. Functional analysis revealed that they were significantly enriched in the extracellular region and biosynthesis of amino acids. We next identified four bioactive compounds (vorinostat, LY-294002,trichostatin A, and tanespimycin) based on ConnectivityMap. Then 114 nodes were obtained in protein-protein interaction. The three most relevant modules were detected. In addition, according to degree ? 10, 14 core genes including FOXM1, CXCR4, KPNA2, NANOG, UBE2C, KIF11, ZWINT, CDCA5, DLGAP5, KIF15, MCM2, MELK, SPP1, and TRIP13 were identified. Kaplan-Meier analysis, Oncomine, and Gene Expression Profiling Interactive Analysis showed that overexpression of FOXM1, SPP1, UBE2C, KIF11, ZWINT, CDCA5, UBE2C, and KIF15 was related to bad prognosis of EOC patients. CDCA5, FOXM1, KIF15, MCM2, and ZWINT were associated with stage. Receiver operating characteristic (ROC) curve showed that messenger RNA levels of these five genes exhibited better diagnostic efficiency for normal and tumor tissues. The Human Protein Atlas database was performed. The protein levels of these five genes were significantly higher in tumor tissues compared with normal tissues. Functional enrichment analysis suggested that all the hub genes played crucial roles in citrate cycle tricarboxylic acid cycle. Furthermore, the univariate and multivariate Cox proportional hazards regression showed that ZWINT was independent prognostic indictor among EOC patients. The genes and pathways discovered in the above studies may open a new direction for EOC treatment.
Project description:Ovarian epithelial cancer (OEC) is an often fatal disease with poor prognosis in women with high-stage disease. In contrast, ovarian low malignant potential (LMP) tumors with favorable prognosis behaves as a disease between benign and malignant tumors. The involved genes and pathways between benign-like LMP and aggressive OEC are largely unknown. This study integrated two cohorts profile datasets to investigate the potential key candidate genes and pathways associated with OEC. Gene expression in two datasets (GSE9891 and GSE12172), including 327 OECs and 48 LMP tumors, were analyzed. 559 differentially expressed genes were found to overlap, 251 up-regulated and 308 down-regulated. Subsequently, analysis of gene ontology, signaling pathway enrichment and protein-protein interaction (PPI) network was performed. Gene ontology analysis clustered the up-regulated and down-regulated genes based on significant enrichment. 282 nodes/ differentially expressed genes (DEGs) were identified from DEGs PPI network complex, and two most significant k-clique modules were identified from PPI. In a summary, using integrated bioinformatics analysis, we are able to identify biomarkers potentially significant in the pathogenesis of OEC, which can improve our understanding of the cause and molecular events. These candidate genes and pathways could be used for further confirmation, and lead to better disease diagnose and therapy.