Tumor Specific Methylome in Chinese High-Grade Serous Ovarian Cancer Characterized by Gene Expression Profile and Tumor Genotype

2019 ◽  
Author(s):  
Fangfang Song ◽  
Lian Li ◽  
Baifeng Zhang ◽  
Yanrui Zhao ◽  
Hong Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
High Grade ◽  
Serous Ovarian Cancer

scholarly journals Tumor specific methylome in Chinese high-grade serous ovarian cancer characterized by gene expression profile and tumor genotype

2020 ◽  
Vol 158 (1) ◽  
pp. 178-187
Author(s):  
Fangfang Song ◽  
Lian Li ◽  
Baifeng Zhang ◽  
Yanrui Zhao ◽  
Hong Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
High Grade ◽  
Serous Ovarian Cancer

scholarly journals Identification of metastasis and prognosis-associated genes for serous ovarian cancer

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Yijun Yang ◽  
Suwan Qi ◽  
Can shi ◽  
Xiao Han ◽  
Juanpeng Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Signaling Pathway ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Tumor Metastasis ◽  
Cox Regression ◽  
Serous Ovarian Cancer ◽  
Hub Genes ◽  
Blue Module

Abstract Serous ovarian cancer is one of the most fatal gynecological tumors with an extremely low 5-year survival rate. Most patients are diagnosed at an advanced stage with wide metastasis. The dysregulation of genes serves an important role in the metastasis progression of ovarian cancer. Differentially expressed genes (DEGs) between primary tumors and metastases of serous ovarian cancer were screened out in the gene expression profile of GSE73168 from Gene Expression Omnibus (GEO). Cytoscape plugin cytoHubba and weighted gene co-expression network analysis (WGCNA) were utilized to select hub genes. Univariate and multivariate Cox regression analyses were used to screen out prognosis-associated genes. Furthermore, the Oncomine validation, prognostic analysis, methylation mechanism, gene set enrichment analysis (GSEA), TIMER database analysis and administration of candidate molecular drugs were conducted for hub genes. Nine hundred and fifty-seven DEGs were identified in the gene expression profile of GSE73168. After using Cytoscape plugin cytoHubba, 83 genes were verified. In co-expression network, the blue module was most closely related to tumor metastasis. Furthermore, the genes in Cytoscape were analyzed, showing that the blue module and screened 17 genes were closely associated with tumor metastasis. Univariate and multivariate Cox regression revealed that the age, stage and STMN2 were independent prognostic factors. The Cancer Genome Atlas (TCGA) suggested that the up-regulated expression of STMN2 was related to poor prognosis of ovarian cancer. Thus, STMN2 was considered as a new key gene after expression validation, survival analysis and TIMER database validation. GSEA confirmed that STMN2 was probably involved in ECM receptor interaction, focal adhesion, TGF beta signaling pathway and MAPK signaling pathway. Furthermore, three candidate small molecule drugs for tumor metastasis (diprophylline, valinomycin and anisomycin) were screened out. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blot showed that STMN2 was highly expressed in ovarian cancer tissue and ovarian cancer cell lines. Further studies are needed to investigate these prognosis-associated genes for new therapy target.

scholarly journals Gene Expression Profile for Predicting Survival in Advanced-Stage Serous Ovarian Cancer Across Two Independent Datasets

PLoS ONE ◽  
2010 ◽  
Vol 5 (3) ◽  
pp. e9615 ◽  
Author(s):  
Kosuke Yoshihara ◽  
Atsushi Tajima ◽  
Tetsuro Yahata ◽  
Shoji Kodama ◽  
Hiroyuki Fujiwara ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Advanced Stage ◽  
Serous Ovarian Cancer

scholarly journals Prognostic gene expression signature for high-grade serous ovarian cancer

2020 ◽  
Vol 31 (9) ◽  
pp. 1240-1250 ◽  
Author(s):  
J. Millstein ◽  
T. Budden ◽  
E.L. Goode ◽  
M.S. Anglesio ◽  
A. Talhouk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Signature ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Signature ◽  
Prognostic Gene

scholarly journals Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

2016 ◽  
Author(s):  
Gregory P. Way ◽  
James Rudd ◽  
Chen Wang ◽  
Habib Hamidi ◽  
Brooke L. Fridley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Conflicts Of Interest ◽  
Population Analysis ◽  
Expression Patterns ◽  
Rocky Mountain ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Bioinformatics Pipeline ◽  
Early Results

AbstractFour gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and non-negative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by including serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.CONFLICTS OF INTERESTThe authors do not declare any conflicts of interest.OTHER PRESENTATIONSAspects of this study were presented at the 2015 AACR Conference and the 2015 Rocky Mountain Bioinformatics Conference.

scholarly journals Gene expression profile (GEP) and survival among patients with advanced ovarian cancer

2018 ◽  
Vol 29 ◽  
pp. viii50
Author(s):  
E. Høgdall ◽  
C.K. Høgdall ◽  
P.-T. Vo ◽  
W. Zhou ◽  
L. Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Advanced Ovarian Cancer

A stromal-associated gene expression signature predicting for survival in a series of patients with advanced high-grade serous ovarian cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5552-5552
Author(s):  
T. Bonome ◽  
G. Samimi ◽  
M. Randonovich ◽  
J. Brady ◽  
S. Ghosh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cox Regression ◽  
Patient Survival ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Data ◽  
Prognostic Signature ◽  
Qrt Pcr ◽  
Microarray Expression

5552 Background: Prognostic gene expression signatures have been derived for undissected serous ovarian epithelial tumors, yet the specific contribution of stromal cells to patient survival has not been addressed. The aim of this study is to identify stromal genes impacting patient survival in the context of serous ovarian cancer. Methods: Expression profiling utilizing Affymetrix U133 Plus 2.0 oligonucleotide arrays was completed for 50 microdissected stromal samples derived from high-grade, late-stage serous tumors displaying a broad spectrum of survival endpoints. A semi-supervised dimension reduction method employing multivariate Cox regression and principal components analysis was applied to the expression data to identify genes associated with patient survival and establish a predictive model. qRT-PCR was employed to validate the microarray expression data. Results: Cox regression analysis identified 267 significant genes. The first 6 principal components of these genes, representing >65% of total variance, entered a multivariate Cox model through which the relative hazard of future patients can be predicted. To confirm our finding, the microarray data underwent leave-one-out validation. The patients were equally divided into low- and high-risk groups and non-parametric Kaplan-Meier analysis and log rank test demonstrated the two groups were significantly different in survival (p = 0.0115). Genes associated with cell survival and migration were identified in the prognostic signature. For validation, qRT-PCR data for all 50 specimens was correlated with microarray expression values for a series of select prognostic genes. Conculsions: In this study, we characterized and validated a stromal dervied prognostic signature associated with poor patient survival. Contained in this novel predictor may be stromal targets suitable for the design of new therapeutic interventions, or use as independent diagnostic markers. No significant financial relationships to disclose.

Sign in / Sign up

Export Citation Format

Share Document