HIF-1αmRNA gene expression levels in improved diagnosis of early stages of prostate cancer

AbstractBackgroundProstate cancer (PC) incidence has risen globally. As there are no current independent biomarkers with high diagnostic efficiency to detect PC, this study was performed to investigate the relative gene expression levels of E2F3 and survivin in the whole blood of PC, benign prostate hyperplasia (BPH), and normal control individuals and to explore their diagnostic value.Material and methodsParticipants of the study were divided into three groups; normal control group (n=25), BPH patients (n=25), and PC patients (n=75). The E2F3 and survivin gene expression levels were assessed using real-time qPCR in addition to the measurement of free and total levels of prostate-specific antigen (PSA) using electrochemiluminescence assays.ResultsSurvivin relative gene expression was over-expressed in PC and BPH patients compared to the normal control group, whereas, E2F3 did not differ significantly among the studied groups. Compared to PSA, E2F3 and survivin mRNA expression levels had lower diagnostic efficacy to differentiate PC from normal and BPH individuals with an area under curve (AUC) of 0.471 and 0.727, respectively. Further, survivin expression level was associated with increased the risk of PC.ConclusionSurvivin and E2F3 relative expression levels in peripheral blood had low diagnostic performance to detect PC and individuals with high survivin expression levels may have higher risk to develop PC.

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Gene expression levels of Casein kinase 1 (CK1) isoforms are correlated to adiponectin levels in adipose tissue and site-specific phosphorylation mediated by CK1 influences multimerization of adiponectin

Zeitschrift für Gastroenterologie ◽

10.1055/s-0038-1669041 ◽

2018 ◽

Vol 56 (08) ◽

pp. e345-e346

Author(s):

P Xu ◽

P Fischer-Posovszky ◽

M Wabitsch ◽

U Knippschild

Keyword(s):

Gene Expression ◽

Adipose Tissue ◽

Casein Kinase ◽

Casein Kinase 1 ◽

Site Specific ◽

Expression Levels ◽

Gene Expression Levels

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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 ◽

10.3390/genes12060854 ◽

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.

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Supplementation of postbiotic RI11 improves antioxidant enzymes activity, upregulated gut barrier genes and reduced cytokines, acute phase proteins and heat shock protein70 gene expression levels in heat-stressed broilers

Poultry Science ◽

10.1016/j.psj.2020.12.011 ◽

2020 ◽

Author(s):

Ali Merzza Humam ◽

Teck Chwen Loh ◽

Hooi Ling Foo ◽

Wan Ibrahim Izuddin ◽

Idrus Zulkifli ◽

...

Keyword(s):

Gene Expression ◽

Antioxidant Enzymes ◽

Heat Shock ◽

Acute Phase ◽

Acute Phase Proteins ◽

Gut Barrier ◽

Expression Levels ◽

Enzymes Activity ◽

Gene Expression Levels

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

Human Genomics ◽

10.1186/s40246-021-00308-5 ◽

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.

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