Overexpressed PTOV1 associates with tumorigenesis and progression of esophageal squamous cell carcinoma

SGK2, 14-3-3, and HUWE1 Cooperate to Control the Localization, Stability, and Function of the Oncoprotein PTOV1

PTOV1 is an oncogenic protein, initially identified in prostate cancer, that promotes proliferation, cell motility, and invasiveness. However, the mechanisms that regulate PTOV1 remain unclear. Here, we identify 14-3-3 as a PTOV1 interactor and show that high levels of 14-3-3 expression, like PTOV1, correlate with prostate cancer progression. We discover an SGK2-mediated phosphorylation of PTOV1 at S36, which is required for 14-3-3 binding. Disruption of the PTOV1-14-3-3 interaction results in an accumulation of PTOV1 in the nucleus and a proteasome-dependent reduction in PTOV1 protein levels. We find that loss of 14-3-3 binding leads to an increase in PTOV1 binding to the E3 ubiquitin ligase HUWE1, which promotes proteasomal degradation of PTOV1. Conversely, our data suggest that 14-3-3 stabilizes PTOV1 protein by sequestering PTOV1 in the cytosol and inhibiting its interaction with HUWE1. Finally, our data suggest that stabilization of the 14-3-3-bound form of PTOV1 promotes PTOV1-mediated expression of cJun, which drives cell-cycle progression in cancer. Together, these data provide a mechanism to understand the regulation of the oncoprotein PTOV1. IMPLICATIONS: These findings identify a potentially targetable mechanism that regulates the oncoprotein PTOV1.

PTOV1 promotes cisplatin-induced chemotherapy resistance by activating the nuclear factor kappa B pathway in ovarian cancer

Chemotherapy resistance is a bottleneck for ovarian cancer treatment; therefore, revealing its regulatory mechanism is critical. In the present study, we found that prostate tumor overexpressed-1 (PTOV1) was upregulated significantly in ovarian cancer cells and tissues. Patients with high PTOV1 levels had a poor outcome. In addition, PTOV1 overexpression increased CDDP (cisplatin) resistance, while PTOV1 knockdown inhibited CDDP resistance, as determined using cell viability assays, apoptosis assays, and an animal model. Mechanistic analysis showed that PTOV1 increased nuclear factor kappa B (NF-κB) pathway activity, reflected by increased nuclear translocation of its p65 subunit and the phosphorylation of inhibitor of nuclear factor kappa-B kinase subunits alpha and beta, which are markers of NF-κB pathway activation. Inhibition of the NF-κB pathway in PTOV1-overexpressing ovarian cancer cells increased CDDP-induced apoptosis, suggesting that PTOV1 promoted chemotherapy resistance by activating the NF-κB pathway. In summary, we identified PTOV1 as a prognostic factor for patients with ovarian cancer. PTOV1 might be a target for inhibition of chemotherapy resistance.

Silencing of Peroxiredoxin 1 Inhibits the Proliferation of Esophageal Cancer Cells and Promotes Apoptosis by Inhibiting the Activity of the PI3K/AKT Pathway

OBJECTIVE

To study the effect of peroxiredoxin 1 (PRDX1) on esophageal squamous carcinoma cells and determine whether it plays a role in regulating the PI3K/AKT signaling pathway.

METHODS

Three esophageal squamous cell carcinoma cell lines (Eca-109, EC9706, and KYSE150) and one normal cell line (human esophageal epithelial cells) were selected. The protein expression of peroxiredoxin 1 (PRDX1) and the activity of the PI3K/AKT pathway were detected via Western blotting. The proliferation ability of cells was detected through the MTT assay and cell clone formation. Apoptosis was detected using flow cytometry. Subsequently, cells were treated with a PI3K/AKT pathway inhibitor and activator, alone or in combination with silencing of PRDX1, and the above indicators were re-tested.

RESULTS

The expression of PRDX1 and activity of PI3K/AKT pathway-associated proteins were higher in esophageal cancer cells than in normal esophageal epithelial cells. Compared with normal human esophageal epithelial cells, the proliferation of the three types of esophageal cancer cells was increased, whereas their level of apoptosis was decreased (p<0.05). In Eca-109 cells (cell line with silenced expression of PRDX1), the expression of PRDX1 was significantly decreased. In contrast to the control group, the proliferation and clonality of cells in the silencing PRDX1 group was decreased, the proportion of apoptotic cells was increased, and the phosphorylation levels of PI3K and AKT were decreased (p<0.05). Compared with the control group, treatment with the inhibitor LY294002 alone significantly inhibited cell proliferation and promoted apoptosis (p<0.05); this effect was similar to that observed in the silencing PRDX1 group.

CONCLUSION

PRDX1 was highly expressed in esophageal cancer cells. Silencing of PRDX1 can inhibit the proliferation of esophageal cancer cells and promote apoptosis. The mechanism involved in this process may be related to the inhibition of the PI3K/AKT signaling pathway.

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data

Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.

High expression of HLA-DQA1 predicts poor outcome in patients with esophageal squamous cell carcinoma in Northern China

BACKGROUND

Our previous studies demonstrate that the major histocompatibility complex (MHC) is associated with the progression of esophageal squamous cell carcinoma (ESCC). HLA-DQA1, which belongs to the MHC Class II family, may be a potential biomarker in ESCC progression. However, the association between HLA-DQA1 and ESCC in high-incidence area of northern China has not been well characterized. The purpose of this study is to investigate the relationship of HLA-DQA1 expression with the progression and prognosis of ESCC.

METHODS

We analyzed the expression profiles of HLA-DQA1 in esophageal cancer (EC) samples in the TCGA database and validated HLA-DQA1 expression by immunohistochemistry, western blotting, and quantitative reverse-transcription polymerase chain reaction in matched EC and normal tissues, respectively. The correlation between HLA-DQA1 expression and clinicopathologic characteristics of ESCC was further analyzed.

RESULT

Immunohistochemical analysis indicated that the expression level of HLA-DQA1 in ESCC tissues was significantly higher than the matched normal tissues (P < .001). HLA-DQA1 mRNA and protein expression were significantly higher in ESCC tissues compared to the matched normal tissues. Patients with family history negative or with tumor sizes >4 cm were associated with higher HLA-DQA1 expression levels. A prognostic significance of HLA-DQA1 was also found by the Log-rank method, in which high expression of HLA-DQA1 was correlated with a shorter overall survival time. The receiver operating characteristic (ROC) curve analysis yielded the area under the ROC curve value of 0.693. Univariate and multivariate analyses also suggest that high expression of HLA-DQA1 is a potential indicator for poor prognosis of ESCC.

CONCLUSIONS

Our results demonstrate that HLA-DQA1 plays an important role in ESCC progression and may be a biomarker for ESCC diagnosis and prognosis, as well as a potential target for the treatment of patients with ESCC.