Dataset Information

Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.

ABSTRACT: Tumor recurrence occurs in more than 70% of ovarian cancer patients, and the majority eventually becomes refractory to treatments. Ovarian Cancer Stem Cells (OCSCs) are believed to be responsible for the tumor relapse and drug resistance. Therefore, eliminating ovarian CSCs is important to improve the prognosis of ovarian cancer patients. However, there is a lack of effective drugs to eliminate OCSCs because the core signaling pathways regulating OCSCs remain unclear. Also it is often hard for biologists to identify a few testable targets and infer driver signaling pathways regulating CSCs from a large number of differentially expression genes in an unbiased manner. In this study, we propose a straightforward and integrative analysis to identify potential core signaling pathways of OCSCs by integrating transcriptome data of OCSCs isolated based on two distinctive markers, ALDH and side population, with regulatory network (Transcription Factor (TF) and Target Interactome) and signaling pathways. We first identify the common activated TFs in two OCSC populations integrating the gene expression and TF-target Interactome; and then uncover up-stream signaling cascades regulating the activated TFs. In specific, 22 activated TFs are identified. Through literature search validation, 15 of them have been reported in association with cancer stem cells. Additionally, 10 TFs are found in the KEGG signaling pathways, and their up-stream signaling cascades are extracted, which also provide potential treatment targets. Moreover, 40 FDA approved drugs are identified to target on the up-stream signaling cascades, and 15 of them have been reported in literatures in cancer stem cell treatment. In conclusion, the proposed approach can uncover the activated up-stream signaling, activated TFs and up-regulated target genes that constitute the potential core signaling pathways of ovarian CSC. Also drugs and drug combinations targeting on the core signaling pathways might be able to eliminate OCSCs. The proposed approach can also be applied for identifying potential activated signaling pathways of other types of cancers.

SUBMITTER: Zhang T

PROVIDER: S-EPMC5933740 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.

Zhang Tianyu T Xu Jielin J Deng Siyuan S Zhou Fengqi F Li Jin J Zhang Liwei L Li Lang L Wang Qi-En QE Li Fuhai F

PloS one 20180503 5

Tumor recurrence occurs in more than 70% of ovarian cancer patients, and the majority eventually becomes refractory to treatments. Ovarian Cancer Stem Cells (OCSCs) are believed to be responsible for the tumor relapse and drug resistance. Therefore, eliminating ovarian CSCs is important to improve the prognosis of ovarian cancer patients. However, there is a lack of effective drugs to eliminate OCSCs because the core signaling pathways regulating OCSCs remain unclear. Also it is often hard for b ...[more]

PMID: 29723215

Similar Datasets

Project description:BackgroundOvarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis.MethodsThe data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein-protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan-Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes.ResultsIn total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database.ConclusionTaken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

Project description:The main purpose of the present study was to recognize the integrative genomics analysis of hub genes and their relationship with prognosis and signaling pathways in esophageal squamous cell carcinoma (ESCC). The mRNA gene expression profile data of GSE38129 were downloaded from the Gene Expression Omnibus database, which included 30 ESCC and 30 normal tissue samples. The differentially expressed genes (DEGs) between ESCC and normal samples were identified using the GEO2R tool. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify the functions and related pathways of the genes. The protein?protein interaction (PPI) network of these DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes and visualized with a molecular complex detection plug?in via Cytoscape. The top five important modules were selected from the PPI network. A total of 928 DEGs, including ephrin?A1 (EFNA1), collagen type IV ?1 (COL4A1), C?X?C chemokine receptor 2 (CXCR2), adrenoreceptor ?2 (ADRB2), P2RY14, BUB1B, cyclin A2 (CCNA2), checkpoint kinase 1 (CHEK1), TTK, pituitary tumor transforming gene 1 (PTTG1) and COL5A1, including 498 upregulated genes, were mainly enriched in the 'cell cycle', 'DNA replication' and 'mitotic nuclear division', whereas 430 downregulated genes were enriched in 'oxidation?reduction process', 'xenobiotic metabolic process' and 'cell?cell adhesion'. The KEGG analysis revealed that 'ECM?receptor interaction', 'cell cycle' and 'p53 signaling pathway' were the most relevant pathways. According to the degree of connectivity and adjusted P?value, eight core genes were selected, among which those with the highest correlation were CHEK1, BUB1B, PTTG1, COL4A1 and CXCR2. Gene Expression Profiling Interactive Analysis in The Cancer Genome Atlas database for overall survival (OS) was applied among these genes and revealed that EFNA1 and COL4A1 were significantly associated with a short OS in 182 patients. Immunohistochemical results revealed that the expression of PTTG1 in esophageal carcinoma tissues was higher than that in normal tissues. Therefore, these genes may serve as crucial predictors for the prognosis of ESCC.

Project description:BACKGROUND:Epithelial ovarian cancer is one of the most ?severe public health threats in women. Since it is still challenging to screen in early stages, identification of core genes that play an essential role in epithelial ovarian cancer initiation and progression is of vital importance. RESULTS:Seven gene expression datasets (GSE6008, GSE18520, GSE26712, GSE27651, GSE29450, GSE36668, and GSE52037) containing 396 ovarian cancer samples and 54 healthy control samples were analyzed to identify the significant differentially expressed genes (DEGs). We identified 563 DEGs, including 245 upregulated and 318 downregulated genes. Enrichment analysis based on the gene ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that the upregulated genes were significantly enriched in cell division, cell cycle, tight junction, and oocyte meiosis, while the downregulated genes were associated with response to endogenous stimuli, complement and coagulation cascades, the cGMP-PKG signaling pathway, and serotonergic synapse. Two significant modules were identified from a protein-protein interaction network by using the Molecular Complex Detection (MCODE) software. Moreover, 12 hub genes with degree centrality more than 29 were selected from the protein-protein interaction network, and module analysis illustrated that these 12 hub genes belong to module 1. Furthermore, Kaplan-Meier analysis for overall survival indicated that 9 of these hub genes was correlated with poor prognosis of epithelial ovarian cancer patients. CONCLUSION:The present study systematically validates the results of previous studies and fills the gap regarding a large-scale meta-analysis in the field of epithelial ovarian cancer. Furthermore, hub genes that could be used as a novel biomarkers to facilitate early diagnosis and therapeutic approaches are evaluated, providing compelling evidence for future genomic-based individualized treatment of epithelial ovarian cancer.

Dataset Information

Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.

Publications

Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets