Expression and Function of Androgen Receptor Coactivator p44/Mep50/WDR77 in Ovarian Cancer

Ligr, Martin; Patwa, Ruzeen; Daniels, Garrett; Pan, Lorraine; Wu, Xinyu; Li, Yirong; Tian, Liantian; Wang, Zhenxing; Xu, Ruliang; Wu, Jingjing; Chen, Fan; Liu, Jinsong; Wei, Jian Jun; Lee, Peng

doi:10.1371/journal.pone.0026250

Cited by 36 publications

(34 citation statements)

References 28 publications

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“…WDR77, also known as p44, was reported to be related to the differentiation and proliferation in prostate epithelium (37). Its differential expression was observed in ovarian cancer (38). However, there is no report associating WDR77 with colon cancer.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data

Yan

Xiong

et al. 2012

Oncology Reports

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Abstract. Colon cancer is the third most common cancer and one of the leading causes of cancer-related death in the world. Therefore, identification of biomarkers with potential in recognizing the biological characteristics is a key problem for early diagnosis of colon cancer patients. In this study, we used a random forest approach to discover biomarkers based on a set of oligonucleotide microarray data of colon cancer. Realtime PCR was used to validate the related expression levels of biomarkers selected by our approach. Furthermore, ROC curves were used to analyze the sensitivity and specificity of each biomarker in both training and test sample sets. Finally, we analyzed the clinical significance of each biomarker based on their differential expression. A single classifier consisting of 4 genes (IL8, WDR77, MYL9 and VIP) was selected by random forests with an average sensitivity and specificity of 83.75 and 76.15%. The differential expression levels of each biomarker was validated by real-time PCR in 48 test colon cancer samples compared to the matched normal tissues. Patients with high expression of IL8 and WDR77, and low expression of MYL9 and VIP had a significantly reduced median survival rate compared to colon cancer patients. The results indicate that our approach can be employed for biomarker identification based on microarray data. These 4 genes identified by our approach have the potential to act as clinical biomarkers for the early diagnosis of colon cancer. IntroductionColon cancer is the third most common cancer, and one of the leading causes of morbidity and mortality in the world (1). According to the United States' statistics released in 2010 the incidence rate of colon has decreased (2). Over the last decade, many studies have proposed various kinds of statistical methods to analyze gene expression patterns and identify new biomarkers for prognostic and/or predictive information in relation to human diseases (3,4). However, most of the early studies applied unsupervised approaches to data-mining and identification of differential gene expressed profiling of certain diseases, such as hierarchical clustering for class discovering, taking an unbiased approach to searching for subgroups in the data (5). Along with the statistical methods extensively penetrated into the field of biomedicine, many supervised clustering analysis and machine learning approaches were adopted to deal with gene expression profiling data and sieved feature genes which contained more information to classify different kinds of diseases or subclasses of the same disease.Various methods of statistics and machine learning, including clustering (6,7), Bayesian algorithms (8), and support vector machines (9), have been proposed to analyze microarray data generated through high-throughput experiments. Over the last few years, the technology of multiclassifier fusion developed substantially, and became very successful in improving the accuracy of certain classifiers. Random forests (RF) (10,11), a tree-based method of classificat...

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Section: Discussionmentioning

confidence: 99%

Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data

Yan

Xiong

et al. 2012

Oncology Reports

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“…The ovarian cancer cells express AR and respond to androgen with increased proliferation and attenuated apoptosis. Over-expression of AR has been found in ovarian cancer, and once activated, it can stimulate ovarian cancer cell invasion (Gogoi et al, 2008;Ligr et al, 2011), indicating targeting AR is also a promising treatment strategy.…”

Section: Mapk and Ovarian Cancermentioning

confidence: 99%

Mitogen-Activated Protein Kinase Signal Transduction in Solid Tumors

Lei

Wang²,

Mei³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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“…Printed in Great Britain associated increases in cell proliferation and invasion are AR mediated (Ligr et al 2011). …”

Section: Endocrine-related Cancermentioning

confidence: 99%

Evidence of androgen action in endometrial and ovarian cancers

Gibson

Simitsidellis

Collins

et al. 2014

Endocrine-Related Cancer

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Endometrial cancer (EC) and ovarian cancer are common gynaecological malignancies. The impact of androgen action in these cancers is poorly understood; however, there is emerging evidence to suggest that targeting androgen signalling may be of therapeutic benefit. Epidemiological evidence suggests that there is an increased risk of EC associated with exposure to elevated levels of androgens, and genetic variants in genes related to both androgen biosynthesis and action are associated with an increased risk of both EC and ovarian cancer. Androgen receptors (ARs) may be a potential therapeutic target in EC due to reported anti-proliferative activities of androgens. By contrast, androgens may promote growth of some ovarian cancers and anti-androgen therapy has been proposed. Introduction of new therapies targeting ARs expressed in EC or ovarian cancer will require a much greater understanding of the impacts of cell context-specific AR-dependent signalling and how ARs can crosstalk with other steroid receptors during progression of disease. This review considers the evidence that androgens may be important in the aetiology of EC and ovarian cancer with discussion of evidence for androgen action in normal and malignant endometrial and ovarian tissue.

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Expression and Function of Androgen Receptor Coactivator p44/Mep50/WDR77 in Ovarian Cancer

Cited by 36 publications

References 28 publications

Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data

Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data

Mitogen-Activated Protein Kinase Signal Transduction in Solid Tumors

Evidence of androgen action in endometrial and ovarian cancers

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