scholarly journals Microarray expression identification of differentially expressed genes in serous epithelial ovarian cancer compared with bulk normal ovarian tissue and ovarian surface scrapings

2007 ◽  
Author(s):  
Dan Grisaru ◽  
Jan Hauspy ◽  
Mona Prasad ◽  
Monique Albert ◽  
K. Murphy ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Ovarian Tissue ◽  
Differentially Expressed ◽  
Ovarian Surface ◽  
Normal Ovarian Tissue ◽  
Serous Epithelial Ovarian Cancer ◽  
Microarray Expression

scholarly journals An integrated approach for identifying genes associated with chemotherapy resistance in high-grade serous epithelial ovarian cancer

2017 ◽  
Vol 3 (1) ◽  
pp. 31
Author(s):  
Ahmed Hossain ◽  
Gias Uddin Ahsan ◽  
Hayatun Nabi
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Statistical Methods ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Sparse Pca ◽  
Platinum Sensitive ◽  
New Genes ◽  
Platinum Resistant ◽  
Serous Epithelial Ovarian Cancer

<p>Treatment with chemotherapy is important in limiting the intensity of serous epithelial ovarian cancer. However, not all patients are sensitive to platinum chemotherapy corresponding to longer progression-free survival (PFS &gt;8 months). Koti <em>et al.</em>[1] revealed a set of 204 discriminating genes possessing expression levels, which could influence differential chemotherapy response between the platinum-resistant and platinum-sensitive group of patients. They considered Welch two-sample <em>t</em>-test and non-parametric Mann-Whitney U test to identify the differentially expressed genes. However, both the statistical methods turned out to be unsuitable for microarray data. In this paper, we used three alternative statistical methods to select a combined list of genes and compared the genes that were proposed by Koti <em>et al.</em>[1]. Subsequently, we recommended using sparse principal component analysis (sparse PCA) to identify a final list of genes. Sparse PCA incorporates correlation into account among the genes and helps to draw a biologically important gene discovery. We identified 77 differentially expressed genes, which include 11 new genes that can separate the groups of patients who are platinum-resistant and platinum-sensitive to the chemotherapy. The integrative approach can also be effective in another high dimensional dataset to compare between two groups.</p>

Characterization of differentially expressed genes in ovarian cancer by cDNA microarrays

2005 ◽  
Vol 15 (1) ◽  
pp. 50-57
Author(s):  
X. Zhang ◽  
J. Feng ◽  
Y. Cheng ◽  
Y. Yao ◽  
X. Ye ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ovarian Carcinoma ◽  
Differentially Expressed Genes ◽  
Large Scale ◽  
Ovarian Tissue ◽  
Immunohistochemical Analysis ◽  
Cdna Microarrays ◽  
Suppressor Gene ◽  
Differentially Expressed ◽  
Normal Ovarian Tissue

The molecular events leading to the development and progression of ovarian carcinoma are not completely understood. We performed a large-scale survey for the identification of differentially expressed genes between ovarian carcinoma and normal ovarian tissue by using cDNA microarray analysis. We utilized 512 member human novel putative oncogene and tumor suppressor gene cDNA microarrays to study the differences in gene expression between ovarian carcinoma and normal ovarian tissues. Some differentially expressed genes have been further confirmed by immunohistochemical analysis. A total of 39 differentially expressed genes were identified, of which 16 and 23 were specifically expressed in ovarian cancer and normal ovarian tissue, respectively. The comparison of average signal of differentially expressed genes exhibited at least a twofold difference in expression. The differentially expressed genes may be related to the carcinogenesis and progression of the malignant growth. The use of cDNA microarrays allows simultaneous monitor of the expression of many genes, thereby it speeds up the identification of differentially expressed genes. It is essential for further exploration of the mechanisms of the disease.

Molecular Cloning of Differentially Expressed Genes in Human Epithelial Ovarian Cancer

1994 ◽  
Vol 52 (2) ◽  
pp. 247-252 ◽  
Author(s):  
Samuel C. Mok ◽  
Kwong-Kwok Wong ◽  
Raymond K.W. Chan ◽  
Ching C. Lau ◽  
Sai-Wah Tsao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Molecular Cloning ◽  
Differentially Expressed Genes ◽  
Differentially Expressed

scholarly journals CPED1 is differentially expressed in ovarian cancer.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Ovarian Cancer ◽  
Differential Expression ◽  
Ovarian Tissue ◽  
Gynecological Cancer ◽  
Differentially Expressed ◽  
Common Reason ◽  
Normal Ovary ◽  
Cancer Death ◽  
Normal Ovarian Tissue ◽  
Developed World

Ovarian cancer is most common reason for a gynecological cancer death in the developed world (1). There are zero targeted chemotherapies available for the treatment of ovarian cancer. We studied the transcriptomes of tumors from ovarian cancer by comparing them to the transcriptome of normal ovarian tissue using two separate datasets (2, 3). We found that the cadherin-like and PC esterase domain containing 1, CPED1, was among the genes whose expression changed the most between ovarian tumors and the normal ovary. This is the first report of differential expression of CPED1 in ovarian cancer.

Cluster analysis of chemotherapy non-responders for patients with serous epithelial ovarian cancer

2017 ◽  
Vol 145 ◽  
pp. 5-6
Author(s):  
M.E. McDonald ◽  
E.A. Salinas ◽  
A.M. Newtson ◽  
M.J. Goodheart ◽  
K.K. Leslie ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cluster Analysis ◽  
Epithelial Ovarian Cancer ◽  
Serous Epithelial Ovarian Cancer

scholarly journals Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

2018 ◽  
Vol Volume 11 ◽  
pp. 1457-1474 ◽  
Author(s):  
Xiao Yang ◽  
ShaoMing Zhu ◽  
Li Li ◽  
Li Zhang ◽  
Shu Xian ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed

scholarly journals Deletion at 6q24.2-26 predicts longer survival of high-grade serous epithelial ovarian cancer patients

2014 ◽  
Vol 9 (2) ◽  
pp. 422-436 ◽  
Author(s):  
Marta M. Kamieniak ◽  
Daniel Rico ◽  
Roger L. Milne ◽  
Ivan Muñoz-Repeto ◽  
Kristina Ibáñez ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Cancer Patients ◽  
High Grade ◽  
Serous Epithelial Ovarian Cancer

scholarly journals Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches

2020 ◽  
Vol 21 (13) ◽  
pp. 4806
Author(s):  
Razia Zakarya ◽  
Viive M. Howell ◽  
Emily K. Colvin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Genetically Engineered ◽  
The Cancer Genome Atlas ◽  
Genetically Engineered Mouse Models ◽  
Molecular Features ◽  
Somatic Gene ◽  
Cancer Genome Atlas ◽  
Serous Epithelial Ovarian Cancer ◽  
Aggressive Subtype

High-grade serous epithelial ovarian cancer (HGSC) is the most aggressive subtype of epithelial ovarian cancer. The identification of germline and somatic mutations along with genomic information unveiled by The Cancer Genome Atlas (TCGA) and other studies has laid the foundation for establishing preclinical models with high fidelity to the molecular features of HGSC. Notwithstanding such progress, the field of HGSC research still lacks a model that is both robust and widely accessible. In this review, we discuss the recent advancements and utility of HGSC genetically engineered mouse models (GEMMs) to date. Further analysis and critique on alternative approaches to modelling HGSC considers technological advancements in somatic gene editing and modelling prototypic organs, capable of tumorigenesis, on a chip.

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