Despite 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. We 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. In 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. Taken 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.

Introduction: Prostate 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) (uEV_EpCAM-CD9) to improve the diagnosis of PCa.Methods: We investigated the performance of uEV_EpCAM-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 uEV_EpCAM-CD9 in validation sets.Results: Results showed that uEV_EpCAM-CD9 was able to distinguish PCa from controls, and a significant decrease of uEV_EpCAM-CD9 was observed after prostatectomy. We further used a training set (N = 116) and constructed an exclusive multivariate diagnostic model based on uEV_EpCAM-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.Conclusions: The multivariate model based on uEV_EpCAM-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.

Food intake affects body homeostasis and significantly changes circulating cell-free DNA (cfDNA). However, the source and elimination of postprandial cfDNA is difficult to trace, and it is unknown whether these changes can be revealed by cfDNA fragmentomics based on liquid biopsy. We performed shallow whole-genome sequencing of 30 plasma samples from 10 healthy individuals at fasting and postprandial (30-min and 2-h time points). We assessed the effect of postprandial states on cfDNA fragment size distribution and utilized deconvolutional analysis of end motifs to determine the potential roles of DNA nucleases in cfDNA fragmentation. We correlated the fragmentation index (defined as the ratio of short-to-long fragments) with gene expression to estimate the relative contribution of various cellular and tissue sources to cfDNA. Compared to the fasting state, we observed a significant increase in short cfDNA fragments (70–150 bp) and a decrease in long fragments (151–250 bp) at the 30-minute postprandial state, followed by an inverse trend two hours later. Deconvolutional analysis of cfDNA end motifs showed that DNASE1L3 activity decreased at the 30-minute postprandial state, while DNASE1 and DFFB activities increased at the 2-hour postprandial state. We found that the expression of genes related to cellular metabolism and immune responses was upregulated at the postprandial state. Meanwhile, the contribution of cells and tissues involved in metabolic and immune progress to circulating plasma cfDNA was increased. The fragmentation of cfDNA is considerably influenced by postprandial states, highlighting the significance of taking postprandial effects into account when evaluating cfDNA as a biomarker. Furthermore, our study reveals the potential application of cfDNA fragmentation features in monitoring metabolic and immune status changes.

Nucleic acid hybridization is crucial in target recognition with respect to in vitro and in vivo nucleic acid biosensing. Conventional linear probes and molecular beacons encounter challenges in multiplexing and specific recognition of intractable nucleic acids. Advances in nucleic acid nanotechnologies have resulted in a set of novel structural probes: junction probes (JPs), which make full use of the advantages of specificity, stability, programmability and predictability of Watson–Crick base pairing. In recent years, junction probes have been regularly implemented in constructing systems related to biosensing, synthetic biology and gene regulation. Herein, we summarize the latest advances in JP designs as potential nucleic acid biosensing systems and their expansive applications, and provide some general guidelines for developing JP based sensing strategies for implementation of such systems.