scholarly journals Genes for anti-Müllerian hormone and androgen receptor are underexpressed in human cumulus cells surrounding morphologically highly graded oocytes

2019 ◽  
Vol 7 ◽  
pp. 205031211986513 ◽  
Author(s):  
Sanja Dević Pavlić ◽  
Tamara Tramišak Milaković ◽  
Linda Panić Horvat ◽  
Kristina Čavlović ◽  
Hrvoje Vlašić ◽  
...  
Keyword(s):  
Gene Expression ◽  
Androgen Receptor ◽  
Follicular Fluid ◽  
Intracytoplasmic Sperm Injection ◽  
Cumulus Cells ◽  
Reproductive Technology ◽  
Expression Levels ◽  
Gene Expression Levels ◽  
Injection Procedure

Objectives: The aim of this study was to investigate the expression of genes crucial for the quality of the oocyte and whether expression levels of these genes in cumulus cells can be biological markers for the quality of the oocyte, zygote or embryo, or even for achievement of pregnancy after the assisted reproductive technology procedure. We examined the expression profile of the anti-Müllerian hormone (AMH) gene and its respective receptors: anti-Müllerian hormone receptor type 2 (AMHR2), follicle-stimulating hormone receptor (FSHR) and androgen receptor (AR) in cumulus cells (CCs) surrounding the oocyte, as well as AMH concentrations in follicular fluid of the associated follicle. The obtained gene expression levels were correlated with the morphological quality of the associated oocyte, zygote and embryo as well as with assisted reproductive technology outcome following the intracytoplasmic sperm injection procedure. Methods: This study involved 129 cumulus cells and 35 follicular fluid samples, taken from 58 patients undergoing the intracytoplasmic sperm injection procedure. Oocytes, zygotes and embryos were assessed for morphological quality. The relative gene expression of AMH, AMHR2, FSHR and AR was calculated using the delta–delta Ct method. Anti-Müllerian hormone concentrations in follicular fluids were measured by enzyme-linked immunosorbent assay. Results: The results yielded suggest a relationship between AMH, AR and oocyte morphology: AMH and AR gene expression levels in CCs surrounding morphologically optimal oocytes were significantly lower than in CCs surrounding oocytes with suboptimal morphology (p = 0.011 and p = 0.008, respectively). Statistically significant positive correlation was found between mRNA expression levels of AMH and FSHR (p < 0.001), AMH and AR (p = 0.001), AMHR2 and FSHR (p < 0.001), AMHR2 and AR (p < 0.001), as well as between FSHR and AR (p < 0.001). Conclusion: Assessed results point to AMH and AR relation with oocyte maturity, but not with its fertilization potential, or with embryo quality.

CALM2 gene expression levels in the cumulus cells as a marker for chromosomal abnormalities in the embryo in in vitro fertilization programs

2016 ◽  
Vol 10_2016 ◽  
pp. 64-72
Author(s):  
Safronova N.A. Safronova ◽  
Kalinina E.A. Kalinina ◽  
Donnikov A.E. Donnikov ◽  
Burmenskaya O.V. Burmenskaya ◽  
Makarova N.P. Makarova ◽  
...  
Keyword(s):  
Gene Expression ◽  
In Vitro Fertilization ◽  
Chromosomal Abnormalities ◽  
Cumulus Cells ◽  
Vitro Fertilization ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals Integrated Analysis to Study the Relationship between Tumor-Associated Selenoproteins: Focus on Prostate Cancer

2020 ◽  
Vol 21 (18) ◽  
pp. 6694
Author(s):  
Francesca Capone ◽  
Andrea Polo ◽  
Angela Sorice ◽  
Alfredo Budillon ◽  
Susan Costantini
Keyword(s):  
Gene Expression ◽  
Patient Outcome ◽  
Androgen Receptor ◽  
Integrated Analysis ◽  
Cancer Type ◽  
Bioinformatics Analyses ◽  
Human Interactome ◽  
Expression Levels ◽  
Network Analyses ◽  
Gene Expression Levels

Selenoproteins are proteins that contain selenium within selenocysteine residues. To date, twenty-five mammalian selenoproteins have been identified; however, the functions of nearly half of these selenoproteins are unknown. Although alterations in selenoprotein expression and function have been suggested to play a role in cancer development and progression, few detailed studies have been carried out in this field. Network analyses and data mining of publicly available datasets on gene expression levels in different cancers, and the correlations with patient outcome, represent important tools to study the correlation between selenoproteins and other proteins present in the human interactome, and to determine whether altered selenoprotein expression is cancer type-specific, and/or correlated with cancer patient prognosis. Therefore, in the present study, we used bioinformatics approaches to (i) build up the network of interactions between twenty-five selenoproteins and identify the most inter-correlated proteins/genes, which are named HUB nodes; and (ii) analyze the correlation between selenoprotein gene expression and patient outcome in ten solid tumors. Then, considering the need to confirm by experimental approaches the correlations suggested by the bioinformatics analyses, we decided to evaluate the gene expression levels of the twenty-five selenoproteins and six HUB nodes in androgen receptor-positive (22RV1 and LNCaP) and androgen receptor–negative (DU145 and PC3) cell lines, compared to human nontransformed, and differentiated, prostate epithelial cells (EPN) by RT-qPCR analysis. This analysis confirmed that the combined evaluation of some selenoproteins and HUB nodes could have prognostic value and may improve patient outcome predictions.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 92
Author(s):  
Joon Seon Lee ◽  
Lexuan Gao ◽  
Laura Melissa Guzman ◽  
Loren H. Rieseberg
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Mixed Model ◽  
Agricultural Land ◽  
Alcohol Fermentation ◽  
Expression Levels ◽  
Flooding Stress ◽  
Genome Wide ◽  
And Control ◽  
Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

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