scholarly journals High-Resolution Mapping of Genomic Imbalance and Identification of Gene Expression Profiles Associated with Differential Chemotherapy Response in Serous Epithelial Ovarian Cancer

Neoplasia ◽  
2005 ◽  
Vol 7 (6) ◽  
pp. 603-IN20 ◽  
Author(s):  
Marcus Bernardini ◽  
Chung-Hae Lee ◽  
Ben Beheshti ◽  
Mona Prasad ◽  
Monique Albert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
High Resolution ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Chemotherapy Response ◽  
Genomic Imbalance ◽  
High Resolution Mapping ◽  
Resolution Mapping ◽  
Serous Epithelial Ovarian Cancer

scholarly journals Biological Insights into Chemotherapy Resistance in Ovarian Cancer

2019 ◽  
Vol 20 (9) ◽  
pp. 2131 ◽  
Author(s):  
Michelle A. Glasgow ◽  
Peter Argenta ◽  
Juan E. Abrahante ◽  
Mihir Shetty ◽  
Shobhana Talukdar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Rna Extraction ◽  
Chemotherapy Resistance ◽  
Gene Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Before And After ◽  
Tumor Gene Expression ◽  
Tumor Gene

The majority of patients with high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy; however, most will develop chemotherapy resistance. Gene signatures may change with the development of chemotherapy resistance in this population, which is important as it may lead to tailored therapies. The objective of this study was to compare tumor gene expression profiles in patients before and after treatment with neoadjuvant chemotherapy (NACT). Tumor samples were collected from six patients diagnosed with HGSOC before and after administration of NACT. RNA extraction and whole transcriptome sequencing was performed. Differential gene expression, hierarchical clustering, gene set enrichment analysis, and pathway analysis were examined in all of the samples. Tumor samples clustered based on exposure to chemotherapy as opposed to patient source. Pre-NACT samples were enriched for multiple pathways involving cell cycle growth. Post-NACT samples were enriched for drug transport and peroxisome pathways. Molecular subtypes based on the pre-NACT sample (differentiated, mesenchymal, proliferative and immunoreactive) changed in four patients after administration of NACT. Multiple changes in tumor gene expression profiles after exposure to NACT were identified from this pilot study and warrant further attention as they may indicate early changes in the development of chemotherapy resistance.

scholarly journals Independent Test Set Performance in the Prediction of Early Relapse in Ovarian Cancer with Gene Expression Profiles

2005 ◽  
Vol 11 (21) ◽  
pp. 7958-7959 ◽  
Author(s):  
Frank De Smet ◽  
Nathalie L.M.M. Pochet ◽  
Bart L.R. De Moor ◽  
Toon Van Gorp ◽  
Dirk Timmerman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Test Set ◽  
Early Relapse ◽  
Independent Test

Predicting the clinical behavior of ovarian cancer from gene expression profiles

2006 ◽  
Vol 16 (S1) ◽  
pp. 147-151 ◽  
Author(s):  
F. DE SMET ◽  
N.L.M.M. POCHET ◽  
K. ENGELEN ◽  
T. VAN GORP ◽  
P. VAN HUMMELEN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Behavior

scholarly journals Over-expression of the kayopherin 𝛂2 gene (KPNA2) in high-grade serous ovarian cancers.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
High Grade ◽  
Over Expression ◽  
Ovarian Cancers ◽  
Microarray Datasets

Ovarian cancer is the most lethal gynecologic malignancy and 70-80% of ovarian cancers are of the high-grade serous type (1-3). To identify the most significant changes in gene expression in high-grade serous ovarian cancer (HGSC), we compared global gene expression profiles of tumors from patients with HGSC to that of normal ovary using published microarray datasets (4, 5). We found that the nuclear import receptor karyopherin 𝛂2 (KPNA2) (6) was among the genes whose expression changed most significantly when comparing HSGC tumors to the ovary. Karyopherin 𝛂2 may be relevant to the biology of high-grade serous ovarian tumors.

scholarly journals Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 375
Author(s):  
Arianna Consiglio ◽  
Gabriella Casalino ◽  
Giovanna Castellano ◽  
Giorgio Grillo ◽  
Elda Perlino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Genetic Algorithms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Fuzzy Rule ◽  
Expression Data ◽  
Final Model ◽  
Ovarian Diseases ◽  
Rule Systems

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples.

scholarly journals Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 713 ◽  
Author(s):  
Kulbe ◽  
Otto ◽  
Darb-Esfahani ◽  
Lammert ◽  
Abobaker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pelvic Mass ◽  
Aurora Kinase ◽  
Malignant Tumours ◽  
Novel Biomarkers

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery.

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