BackgroundDespite SGK1 has been identified and characterized as a tumor-promoting gene, the functions and underlying mechanisms of SGK1 involved in metastasis regulation have not yet been investigated in cancer.MethodsWe investigated the cellular responses to GSK650394 treatment and SGK1 silencing (or overexpression) in human prostate cancer (PCa) cell lines and PC3 xenografts by wound healing assay, migration and invasion assay, western blotting, immunofluorescence and immunohistochemistry.ResultsIn the present study, we found that SGK1 expression positively correlates with human prostate cancer (PCa) progression and metastasis. We show that SGK1 inhibition significantly attenuates EMT and metastasis both in vitro and in vivo, whereas overexpression of SGK1 dramaticlly promoted the invasion and migration of PCa cells. Our further results suggest that SGK1 inhibition induced antimetastatic effects, at least partially via autophagy-mediated repression of EMT through the downregulation of Snail. Moreover, ectopic expression of SGK1 obviously attenuated the GSK650394-induced autophagy and antimetastatic effects. What’s more, dual inhibition of mTOR and SGK1 enhances autophagy and leads to synergistic antimetastatic effects on PCa cells.ConclusionsTaken together, this study unveils a novel mechanism in which SGK1 functions as a tumor metastasis-promoting gene and highlights how co-targeting SGK1 and autophagy restrains cancer progression due to the amplified antimetastatic effects.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-0743-1) contains supplementary material, which is available to authorized users.

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Gastric cancer (GC) is one of the leading causes of malignancy-associated mortality worldwide. However, the underlying molecular mechanisms of GC are unclear and the prognosis of GC is poor. Therefore, it is important and urgent to explore the underlying mechanisms and screen for novel diagnostic and prognostic biomarkers, as well as therapeutic targets. In the current study, scale-free gene co-expression networks were constructed using weighted gene co-expression network analysis, the potential associations between gene sets and clinical features were investigated, and the hub genes were identified. The gene expression profiles of GSE38749 were downloaded from the Gene Expression Omnibus database. RNA-seq and clinical data for GC from The Cancer Genome Atlas were utilized for verification. Furthermore, the expression of candidate biomarkers in gastric tissues was investigated. Survival analysis was performed using Kaplan-Meier and log-rank test. The predictive role of candidate biomarkers in GC was evaluated using a receiver operator characteristic (ROC) curve. Gene Ontology, gene set enrichment analysis and gene set variation analysis methods were used to interpret the function of candidate biomarkers in GC. A total of 29 modules were identified via the average linkage hierarchical clustering. A significant module consisting of 48 genes associated with clinical traits was found; three genes with high connectivity in the clinical significant module were identified as hub genes. Among them, SLC5A6 and microfibril-associated protein 2 (MFAP2) were negatively associated with the overall survival, and their expression was elevated in GC compared with non-tumor tissues. Additionally, ROC curves indicated that SLC5A6 and MFAP2 showed a good diagnostic power in discriminating cancerous from normal tissues. SLC5A6 and MFAP2 were identified as novel diagnostic and prognostic biomarkers in GC patients; both of these genes were first reported here in connection with GC and deserved further research.

Background: Compared to non-recurrent type, recurrent prostate adenocarcinoma (PCa) is highly fatal, and significantly shortens the survival time of affected patients. Early and accurate laboratory diagnosis is particularly important in identifying patients at high risk of recurrence, necessary for additional systemic intervention. We aimed to develop efficient and accurate diagnostic and prognostic biomarkers for new PCa following radical therapy. Methods: We identified differentially expressed genes (DEGs) and clinicopathological data of PCa patients from Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) repositories. We then uncovered the most relevant clinical traits and genes modules associated with PCa prognosis using the Weighted gene correlation network analysis (WGCNA). Univariate Cox regression analysis and multivariate Cox proportional hazards (Cox-PH) models were performed to identify candidate gene signatures related to Disease-Free Interval (DFI). Data for internal and external cohorts were utilized to test and validate the accuracy and clinical utility of the prognostic models. Results: We constructed and validated an accurate and reliable model for predicting the prognosis of PCa using 5 Gleason score-associated gene signatures (ZNF695, CENPA, TROAP, BIRC5 and KIF20A). The ROC and Kaplan-Meier analysis revealed the model was highly accurate in diagnosing and predicting the recurrence and metastases of PCa. The accuracy of the model was validated using the calibration curves based on internal TCGA cohort and external GEO cohort. Using the model, patients could be prognostically stratified in to various groups including TNM classification and Gleason score. Multivariate analysis revealed the model could independently predict the prognosis of PCa patients and its utility was superior to that of clinicopathological characteristics. In addition, we fund the expression of the 5 gene signatures strongly and positively correlated with tumor purity but negatively correlated with infiltration CD8+ T cells to the tumor microenvironment. Conclusions: A 5 gene signatures can accurately be used in the diagnosis and prediction of PCa prognosis. Thus this can guide the treatment and management prostate adenocarcinoma.

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IntroductionProstate cancer (PCa) is one of the most frequently diagnosed cancers and the leading cause of cancer death in males worldwide. Although prostate-specific antigen (PSA) screening has considerably improved the detection of PCa, it has also led to a dramatic increase in overdiagnosing indolent disease due to its low specificity. This study aimed to develop and validate a multivariate diagnostic model based on the urinary epithelial cell adhesion molecule (EpCAM)-CD9–positive extracellular vesicles (EVs) (uEVEpCAM-CD9) to improve the diagnosis of PCa.MethodsWe investigated the performance of uEVEpCAM-CD9 from urine samples of 193 participants (112 PCa patients, 55 benign prostatic hyperplasia patients, and 26 healthy donors) to diagnose PCa using our laboratory-developed chemiluminescent immunoassay. We applied machine learning to training sets and subsequently evaluated the multivariate diagnostic model based on uEVEpCAM-CD9 in validation sets.ResultsResults showed that uEVEpCAM-CD9 was able to distinguish PCa from controls, and a significant decrease of uEVEpCAM-CD9 was observed after prostatectomy. We further used a training set (N = 116) and constructed an exclusive multivariate diagnostic model based on uEVEpCAM-CD9, PSA, and other clinical parameters, which showed an enhanced diagnostic sensitivity and specificity and performed excellently to diagnose PCa [area under the curve (AUC) = 0.952, P < 0.0001]. When applied to a validation test (N = 77), the model achieved an AUC of 0.947 (P < 0.0001). Moreover, this diagnostic model also exhibited a superior diagnostic performance (AUC = 0.917, P < 0.0001) over PSA (AUC = 0.712, P = 0.0018) at the PSA gray zone.ConclusionsThe multivariate model based on uEVEpCAM-CD9 achieved a notable diagnostic performance to diagnose PCa. In the future, this model may potentially be used to better select patients for prostate transrectal ultrasound (TRUS) biopsy.

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