scholarly journals Angiogenic mRNA and microRNA Gene Expression Signature Predicts a Novel Subtype of Serous Ovarian Cancer

PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e30269 ◽  
Author(s):  
Stefan Bentink ◽  
Benjamin Haibe-Kains ◽  
Thomas Risch ◽  
Jian-Bing Fan ◽  
Michelle S. Hirsch ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Signature ◽  
Serous Ovarian Cancer ◽  
Expression Signature ◽  
Microrna Gene

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 Large-scale integrated analysis of ovarian cancer tumors and cell lines identifies an individualized gene expression signature for predicting response to platinum-based chemotherapy

2019 ◽  
Vol 10 (9) ◽  
Author(s):  
Jie Sun ◽  
Siqi Bao ◽  
Dandan Xu ◽  
Yan Zhang ◽  
Jianzhong Su ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cell Lines ◽  
Large Scale ◽  
Gene Expression Signature ◽  
Integrated Analysis ◽  
Chemotherapy Response ◽  
Expression Signature ◽  
Platinum Based Chemotherapy ◽  
Response To Chemotherapy

Abstract Heterogeneity in chemotherapeutic response is directly associated with prognosis and disease recurrence in patients with ovarian cancer (OvCa). Despite the significant clinical need, a credible gene signature for predicting response to platinum-based chemotherapy and for guiding the selection of personalized chemotherapy regimens has not yet been identified. The present study used an integrated approach involving both OvCa tumors and cell lines to identify an individualized gene expression signature, denoted as IndividCRS, consisting of 16 robust chemotherapy-responsive genes for predicting intrinsic or acquired chemotherapy response in the meta-discovery dataset. The robust performance of this signature was subsequently validated in 25 independent tumor datasets comprising 2215 patients and one independent cell line dataset, across different technical platforms. The IndividCRS was significantly correlated with the response to platinum therapy and predicted the improved outcome. Moreover, the IndividCRS correlated with homologous recombination deficiency (HRD) and was also capable of discriminating HR-deficient tumors with or without platinum-sensitivity for guiding HRD-targeted clinical trials. Our results reveal the universality and simplicity of the IndividCRS as a promising individualized genomic tool to rapidly monitor response to chemotherapy and predict the outcome of patients with OvCa.

scholarly journals MicroRNA Gene Expression Signature Driven by miR-9 Overexpression in Ovarian Clear Cell Carcinoma

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162584 ◽  
Author(s):  
Nozomu Yanaihara ◽  
Yukiko Noguchi ◽  
Misato Saito ◽  
Masataka Takenaka ◽  
Satoshi Takakura ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Gene Expression Signature ◽  
Ovarian Clear Cell Carcinoma ◽  
Expression Signature ◽  
Microrna Gene

Use of predictive gene expression signature from advanced-stage serous papillary ovarian cancer to identify biologically relevant molecular targets for chemoresponse

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5500-5500
Author(s):  
L. Ozbun ◽  
T. Bonome ◽  
M. Radonovich ◽  
C. Pise-Masison ◽  
J. Brady ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cell Lines ◽  
Ovarian Cancer Cell ◽  
Gene Expression Signature ◽  
Advanced Stage ◽  
Expression Signature ◽  
Qrt Pcr ◽  
Ovarian Cancer Cell Lines ◽  
Cisplatin Sensitivity

5500 Background: The aim of our study was to develop and validate a gene expression signature predictive for chemoresponse in advanced stage serous papillary ovarian cancer. Methods: Gene expression profiling was performed on 52 chemonaive, microdissected advanced stage, high-grade papillary serous ovarian cancers using Affymetrix whole-genome microarrays. Patient samples were grouped based on chemoresponse. 19 nonresponders were refractory to chemotherapy, 14 responders relapsing 6 months were considered chemosensitive. Each group was divided into training/validation sets. To generate a predictive gene signature, class prediction algorithms were applied to genes differentially expressed between chemosensitive/resistant or chemosensitive/refractory tumors (p<0.001) using leave-one-out cross-validation. Array validation was performed by qRT-PCR. Select genes underwent biological validation in a series of ovarian cancer cell lines. Results: 31 genes predictive for resistance and 105 genes predictive for refractory to chemotherapy were identified. Percentages of arrays accurately predicted in independent validation sets were 90% (9/10) for resistant and 92% (12/13) for refractory gene signatures. Correlations between microarray/qRT-PCR data were robust for both resistant (17/23 genes) and refractory gene signatures (25/34 genes). Data mining of the predictive signatures using PathwayStudio software identified several biological processes (collagen regulation, apoptosis, cell survival, and DNA repair) implicated in conferring resistance to chemotherapy. We transiently transfected RNAi molecules to silence several signature genes and determine their contribution to taxol/cisplatin sensitivity in a series ofl ovarian cancer cell lines. Preliminary data showed DUSP1 gene expression knockdown potentiated cisplatin sensitivity in SKOV3/OVCA429 cell lines, while POLH knockdown potentiated cisplatin sensitivity in OVCA429/OVCA420 cell lines. Conclusions: A gene expression signature predicts for chemoresponse in ovarian cancers, and has identified novel targets of biological/therapeutic interest. No significant financial relationships to disclose.

scholarly journals A 12-Gene Genomic Instability Signature Predicts Clinical Outcomes in Multiple Cancer Types

2010 ◽  
Vol 25 (4) ◽  
pp. 219-228 ◽  
Author(s):  
Rama K.R. Mettu ◽  
Ying-Wooi Wan ◽  
Jens K. Habermann ◽  
Thomas Ried ◽  
Nancy Lan Guo
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Ovarian Cancer ◽  
Genomic Instability ◽  
Gene Expression Signature ◽  
Small Cell ◽  
Small Cell Lung ◽  
Multiple Cancer ◽  
Expression Signature ◽  
Cancer Types

Background and aims Genomic instability, as reflected in specific chromosomal aneuploidies and variation in the nuclear DNA content, is a defining feature of human carcinomas. It is solidly established that the degree of genomic instability influences clinical outcome. We have recently identified a 12-gene expression signature that discerned genomically stable from unstable breast carcinomas. This gene expression signature was also useful to predict, with high accuracy, the clinical course in independent multiple published breast cancer cohorts. From a biological point of view, this result confirmed the central role of genomic instability for a tumor's ability to adapt to external challenges and selective pressure, and hence for continued survival fitness. This prompted us to investigate whether this genomic instability signature could also predict clinical outcome in other cancer types of epithelial origin, including colorectal tumors, non-small cell lung carcinomas, and ovarian cancer. Results The results show that the gene expression signature that defines genomic instability and poor outcome in breast cancer contributes significantly more accurate (p<0.05 compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. The 12-gene genomic instability signature stratified patients into high- and low-risk groups with distinct postoperative survival in three non-small cell lung cancer cohorts (n=637) in KaplanMeier analyses (log-rank p<0.05). It predicted recurrence in colon cancer patients (n=92) with an overall accuracy greater than 69% (p=0.04) in cross-cohort validation. It quantified relapse-free survival in ovarian cancer (n=124; log-rank p<0.05). Functional pathway analysis revealed interactions between the 12 signature genes and well-known cancer hallmarks. Conclusion The degree of genomic instability has diagnostic and prognostic implications. It is tempting to speculate that pursuing genomic instability therapeutically could provide entry points for a target that is unique to cancer cells.

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