Regulation of gene expression in ovarian cancer cells by luteinizing hormone receptor expression and activation

Selection signatures in candidate genes and QTL for reproductive traits in Nellore heifers

The identification of selection signature genes may help to detect genomic regions that underwent artificial selection and contributed to phenotypic diversity. The aim of this study, therefore, was to detect selection signatures in candidate genes and quantitative trait locus (QTL) for reproductive traits in a Nellore population being selected for sexual precocity. A total of 2035 Nellore heifers, sourced from breeding programs focused on sexual precocity, were used. Candidate genes and some specific QTL related to reproductive traits were chosen based on published literature and Animal QTL databases, respectively, for investigation whether these regions were affected by selection. Selection signature DNA sequences were detected in the selected regions using the extended haplotype homozygosity (EHH) and relative extended haplotype homozygosity (REHH) methods. From 22,241 single nucleotide polymorphisms (SNPs) located in the candidate genes and QTL, 17,312 SNPs generated 2756 haplotype blocks. A total of 7518 EHH tests were analyzed using haplotypes with a frequency of more than 25%, for which there were 39 tests that were significant for REHH (P<0.01). Selection signature DNA sequences were detected that contained several QTLs for important reproductive traits in cattle, suggesting that reproductive traits may have been affected by selection for sexual precocity in this population. Forty-six genes were located in the selection signature regions, whereas 24 genes participated in important biological processes or pathways that may underlie sexual precocity. These results indicate there are possible molecular mechanisms related to sexual precocity in the Nellore breed.

Association of hCG and LHCGR expression patterns with clinicopathological parameters in ovarian cancer

In addition to its critical role during pregnancy, human chorionic gonadotropin (hCG) has been shown to be expressed by various tumor types. Recent studies have similarly documented the presence of the luteinizing hormone (LH)/hCG receptor (LHCGR) in a variety of nongonadal organs; however, its clinicopathological significance in ovarian cancer remains unclear. The present study used a combination of immunohistochemical, real-time PCR, and western blot analyses to examine hCG and LHCGR expression in normal and cancerous tissues collected from patients with epithelial ovarian cancer (EOC). hCG and LHCGR expression levels were resultantly shown to be significantly increased and decreased in cancerous versus normal (or benign) ovarian tissues, respectively (P < 0.05), and both expression pattern changes were associated with more advanced tumor stages and a higher rate of metastasis. Furthermore, patients with tumors with high or low levels of hCG and LHCGR, respectively, experienced a worse overall survival (OS) rate than those with low hCG or high LHCGR expression levels (P < 0.05). In fact, hCG and LHCGR expression levels were independent prognostic factors of patient OS (P < 0.05) for EOC. Collectively, these findings indicate that hCG and LHCGR expression pattern changes are associated with EOC occurrence and progression. Thus, hCG and LHCGR represent promising potential targets to improve the diagnosis, treatment, and prognosis of patients with EOC.

Identification of candidate biomarkers for epithelial ovarian cancer metastasis using microarray data

Epithelial ovarian cancer (EOC) is a common cancer in women worldwide. The present study assessed effective biomarkers for the prognosis of EOC metastasis. The GSE30587 dataset, containing 9 EOC primary tumor samples and 9 matched omental metastasis samples, was analyzed. Following normalization, the differentially expressed genes (DEGs) between these samples were identified using the limma package for R. Subsequently, pathway enrichment analysis was performed using ClueGO, and a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database. The microRNA (mRNA/miR)-target network was established using the multiMiR package. A set of 272 DEGs was identified in metastatic EOC samples, including 189 upregulated and 83 downregulated genes. Collagen type I α 1 chain (COL1A1), COL1A2, collagen type XI α 1 chain (COL11A1) and thrombospondin (THBS)1 were enriched in the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), focal adhesion and extracellular matrix (ECM)-receptor interaction signaling pathways. THBS1 and tissue inhibitor of metalloproteinase (TIMP)3 were two dominant nodes in the PPI network and were key in the miRNA-target network, being targeted by hsa-miR-1. Multiple DEGs and miRNAs were identified as potential biomarkers for the prognosis of EOC metastasis in the present study, which likely affected metastasis by regulating the PI3K/Akt, ECM-receptor interaction and cell adhesion signaling pathways. In addition, THBS1 and TIMP3 were identified as potential targets of hsa-miR-1.

Identifying disease network perturbations through regression on gene expression and pathway topology analysis

In Systems Biology, network-based approaches have been extensively used to effectively study complex diseases. An important challenge is the detection of network perturbations which disrupt regular biological functions as a result of a disease. In this regard, we introduce a network based pathway analysis method which isolates casual interactions with significant regulatory roles within diseased-perturbed pathways. Specifically, we use gene expression data with Random Forest regression models to assess the interactivity strengths of genes within disease-perturbed networks, using KEGG pathway maps as a source of prior-knowledge pertaining to pathway topology. We deliver as output a network with imprinted perturbations corresponding to the biological phenomena arising in a disease-oriented experiment. The efficacy of our approach is demonstrated on a serous papillary ovarian cancer experiment and results highlight the functional roles of high impact interactions and key gene regulators which cause strong perturbations on pathway networks, in accordance with experimentally validated knowledge from recent literature.

Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer

Accumulating evidence has underscored the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in cancer initiation and progression. In this study, we used an integrative computational method to identify miRNA-mediated ceRNA crosstalk between lncRNAs and mRNAs, and constructed global and progression-related lncRNA-associated ceRNA networks (LCeNETs) in ovarian cancer (OvCa) based on "ceRNA hypothesis". The constructed LCeNETs exhibited small world, modular architecture and high functional specificity for OvCa. Known OvCa-related genes tended to be hubs and occurred preferentially in the functional modules. Ten lncRNA ceRNAs were identified as potential candidates associated with stage progression in OvCa using ceRNA-network driven method. Finally, we developed a ten-lncRNA signature which classified patients into high- and low-risk subgroups with significantly different survival outcomes. Our study will provide novel insight for better understanding of ceRNA-mediated gene regulation in progression of OvCa and facilitate the identification of novel diagnostic and therapeutic lncRNA ceRNAs for OvCa.

Steroidogenesis and early response gene expression in MA-10 Leydig tumor cells following heterologous receptor down-regulation and cellular desensitization

The Leydig tumor cell line, MA-10, expresses the luteinizing hormone receptor, a G protein-coupled receptor that, when activated with luteinizing hormone or chorionic gonadotropin (CG), stimulates cAMP production and subsequent steroidogenesis, notably progesterone. These cells also respond to epidermal growth factor (EGF) and phorbol esters with increased steroid biosynthesis. In order to probe the intracellular pathways along with heterologous receptor down-regulation and cellular desensitization, cells were preincubated with EGF or phorbol esters and then challenged with CG, EGF, dibutryl-cyclic AMP, and a phorbol ester. Relative receptor numbers, steroid biosynthesis, and expression of the early response genes, JUNB and c-FOS, were measured. It was found that in all cases but one receptor down-regulation and decreased progesterone production were closely coupled under the conditions used; the exception involved preincubation of the cells with EGF followed by addition of CG where the CG-mediated stimulation of steroidogenesis was considerably lower than the level of receptor down-regulation. In a number of instances JUNB and c-FOS expression paralleled the decreases in receptor number and progesterone production, while in some cases these early response genes were affected little if at all by the changes in receptor number. This finding may indicate that even low levels of activated signaling kinases, e.g. protein kinase A, protein kinase C, or receptor tyrosine kinase, may suffice to yield good expression of JUNB and c-FOS, or it may suggest alternative pathways for regulating expression of these two early response genes.

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Leydig tumor cells respond to hCG, cAMP, EGF, and phorbol esters with increased steroidogenesis.

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These same agents increase expression of the early response genes JUNB and c-FOS.

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Down-regulation of EGF receptors reduced hCG receptors and steroidogenesis.

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Desensitization of the PKC pathway reduced hCG receptors and steroidogenesis.

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Often expression of JUNB and c-FOS paralleled receptor loss, but not always.

A case study on the potential angiogenic effect of human chorionic gonadotropin hormone in rapid progression and spontaneous regression of metastatic renal cell carcinoma during pregnancy and after surgical abortion

BACKGROUND

Treatment possibilities of metastatic renal cell carcinoma (mRCC) have recently changed dramatically prolonging the overall survival of the patients. This kind of development brings new challenges for the care of mRCC.

CASE PRESENTATION

A 22 year-old female patient with translocation type mRCC, who previously had been treated for nearly 5 years, became pregnant during the treatment break period. Follow-up examinations revealed a dramatic clinical and radiological progression of mRCC in a few weeks therefore the pregnancy was terminated. A few days after surgical abortion, CT examination showed a significant spontaneous regression of the pulmonary metastases, and the volume of the largest manifestation decreased from ca. 30 to 3.5 cm(3) in a week. To understand the possible mechanism of this spectacular regression, estrogen, progesterone and luteinizing hormone receptors (ER, PGR and LHR, respectively) immuno-histochemistry assays were performed on the original surgery samples. Immuno-histochemistry showed negative ER, PGR and positive LHR status suggesting the possible angiogenic effect of human chorionic gonadotropin hormone (hCG) in the background.

CONCLUSION

We hypothesize that pregnancy may play a causal role in the progression of mRCC via the excess amount of hCG, however, more data are necessary to validate the present notions and the predictive role of LHR overexpression.

Potential Therapy for Rheumatoid Arthritis and Sjögren Syndrome With Human Chorionic Gonadotropin

Autoimmune diseases such as rheumatoid arthritis (RA) and Sjögren syndrome (SS) ameliorate during pregnancy, through dampening (immunotolerance) of the maternal immune system which protects the fetus from rejection. A large number of studies have shown that human chorionic gonadotropin (hCG) contributes to this tolerance. Studies on animal models have reaffirmed that hCG treatment mimics the benefits of pregnancy. Based on the scientific evidence, randomized clinical trials comparing hCG with current therapies and/or placebo are recommended for RA, SS, and for other autoimmune diseases such as, type 1 diabetes and ankylosing spondylitis, which also get better during pregnancy and hCG treatment seems to help.

Full text clustering and relationship network analysis of biomedical publications

Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

Neonatal exposure to 17α-ethynyl estradiol affects ovarian gene expression and disrupts reproductive cycles in female rats

Neonatal exposure to synthetic estrogen causes delayed reproductive dysfunction in female rats. Exposure to 17α-ethynyl estradiol (EE, low: 20 and high: 2000 μg/kg) induced an abnormal estrous cycle during PND171-190 in low-dose and PND126-145 in high-dose group. At PND90 within normal estrous cycle, high-dose animals showed lack of LH surge and low of ovarian hormones in serum level. Gene expression analysis demonstrated that level of mRNA encoding luteinizing hormone/chorionic gonadotropin receptor (LHCGR) was higher in EE-treated ovaries than in control ovaries, and LHCGR protein colocalized with apoptosis-related proteins in the interstitial area of the ovary. At PND1, ovarian LHCGR mRNA levels were higher in EE-treated rats than in control rats, and direct induction of LHCGR expression by EE was observed in vitro. Our results indicate that neonatal exposure to EE induces irregular LHCGR expression in the immature ovary, which may influence the occurrence of delayed reproductive dysfunction in adult animals.

Integrating microRNAs into the complexity of gonadotropin signaling networks

Follicle-stimulating hormone (FSH) is a master endocrine regulator of mammalian reproductive functions. Hence, it is used to stimulate folliculogenesis in assisted reproductive technologies (ART), both in women and in breeding animals. However, the side effects that hormone administration induces in some instances jeopardize the success of ART. Similarly, the luteinizing hormone (LH) is also of paramount importance in the reproductive function because it regulates steroidogenesis and the LH surge is a pre-requisite to ovulation. Gaining knowledge as extensive as possible on gonadotropin-induced biological responses could certainly lead to precise selection of their effects in vivo by the use of selective agonists at the hormone receptors. Hence, over the years, numerous groups have contributed to decipher the cellular events induced by FSH and LH in their gonadal target cells. Although little is known on the effect of gonadotropins on microRNA expression so far, recent data have highlighted that a microRNA regulatory network is likely to superimpose on the signaling protein network. No doubt that this will dramatically alter our current understanding of the gonadotropin-induced signaling networks. This is the topic of this review to present this additional level of complexity within the gonadotropin signaling network, in the context of recent findings on the microRNA machinery in the gonad.

Microarray-based transcriptome profiling of ovarian cancer cells

Transcriptome profiling is a powerful method for monitoring genes and their expression levels under a variety of conditions. Completion of the human genome and advances in high-throughput gene microarray instrumentation enables one to collect large amounts of data in a relatively short time. The challenge then becomes that of data analysis to identify patterns in expression changes and, from there, to relate the observed changes to functional compartments and pathways in cells, tissues, and organisms. Using cultured human ovarian cancer cells as an experimental model cellular system, we describe approaches that are used in analysis of the transcriptome, focusing on those genes encoding proteins and microRNAs. Coupled with other approaches described herein, one can also use the transcriptome to identify potential serum biomarkers, thus providing direction to what usually is a laborious search for low abundance proteins.

Gonadotropin signalling in epithelial ovarian cancer

Ovarian cancer is the most lethal of all gynecologic malignancies, although its aetiology remains poorly understood. A role for the gonadotropins, follicle-stimulating hormone (FSH) and luteinising hormone (LH), has been implicated in a variety of different aspects of ovarian cancer tumorigenesis, including cellular proliferation, migration and invasion. This review focuses on the latest advances in knowledge concerning signalling pathways and functional consequences of gonadotropin action, including changes in protein-, miRNA- and gene expression, in epithelial ovarian cancer cells.