To flourish, cancers greatly depend on their surrounding tumor microenvironment (TME), and cancer-associated fibroblasts (CAFs) in TME are critical for cancer occurrence and progression because of their versatile roles in extracellular matrix remodeling, maintenance of stemness, blood vessel formation, modulation of tumor metabolism, immune response, and promotion of cancer cell proliferation, migration, invasion, and therapeutic resistance. CAFs are highly heterogeneous stromal cells and their crosstalk with cancer cells is mediated by a complex and intricate signaling network consisting of transforming growth factor-beta, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin, mitogen-activated protein kinase, Wnt, Janus kinase/signal transducers and activators of transcription, epidermal growth factor receptor, Hippo, and nuclear factor kappa-light-chain-enhancer of activated B cells, etc., signaling pathways. These signals in CAFs exhibit their own special characteristics during the cancer progression and have the potential to be targeted for anticancer therapy. Therefore, a comprehensive understanding of these signaling cascades in interactions between cancer cells and CAFs is necessary to fully realize the pivotal roles of CAFs in cancers. Herein, in this review, we will summarize the enormous amounts of findings on the signals mediating crosstalk of CAFs with cancer cells and its related targets or trials. Further, we hypothesize three potential targeting strategies, including, namely, epithelial–mesenchymal common targets, sequential target perturbation, and crosstalk-directed signaling targets, paving the way for CAF-directed or host cell-directed antitumor therapy.

ObjectiveUse systematic review methods to quantify the association between prostatitis and prostate cancer, under both fixed and random effects model. Evidence AcquisitionCase control studies of prostate cancer with information on prostatitis history. All studies published between 1990-2012, were collected to calculate a pooled odds ratio. Selection criteria: the selection criteria are as follows: human case control studies; published from May 1990 to July 2012; containing number of prostatitis, and prostate cancer cases. Evidence SynthesisIn total, 20 case control studies were included. A significant association between prostatitis and prostate cancer was found, under both fixed effect model (pooled OR=1.50, 95%CI: 1.39-1.62), and random effects model (OR=1.64, 95%CI: 1.36-1.98). Personal interview based case control studies showed a high level of association (fixed effect model: pooled OR=1.59, 95%CI: 1.47-1.73, random effects model: pooled OR= 1.87, 95%CI: 1.52-2.29), compared with clinical based studies (fixed effect model: pooled OR=1.05, 95%CI: 0.86-1.28, random effects model: pooled OR= 0.98, 95%CI: 0.67-1.45). Additionally, pooled ORs, were calculated for each decade. In a fixed effect model: 1990’s: OR=1.58, 95% CI: 1.35-1.84; 2000’s: OR=1.59, 95% CI: 1.40-1.79; 2010’s: OR=1.37, 95% CI: 1.22-1.56. In a random effects model: 1990’s: OR=1.98, 95% CI: 1.08-3.62; 2000’s: OR=1.64, 95% CI: 1.23-2.19; 2010’s: OR=1.34, 95% CI: 1.03-1.73. Finally a meta-analysis stratified by each country was conducted. In fixed effect models, U.S: pooled OR =1.45, 95%CI: 1.34-1.57; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90. In random effects model, U.S: pooled OR=1.50, 95%CI: 1.25-1.80; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90.CONCLUSIONS: the present meta-analysis provides the statistical evidence that the association between prostatitis and prostate cancer is significant.

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cir-ITCH, a well-known tumor-suppressive circular RNA, plays a critical role in different cancers. However, its expression and functional role in prostate cancer (PCa) are unclear. Herein, we explored the potential mechanism and tumor-inhibiting role of cir-ITCH in PCa. Using reverse transcriptase polymerase chain reaction assay, we analyzed the expression of cir-ITCH in PCa and paired adjacent nontumor tissue samples resected during surgical operation, as well as in two cell lines of human PCa (LNCaP and PC-3) and the immortalized normal prostate epithelial cell line (RWPE-1). Cell viability and migration of PCa cell lines were evaluated using CCK-8 and wound-healing assays. Expression of key proteins of the Wnt/β-catenin and PI3K/AKT/mTOR pathways was detected using western blotting. We found that cir-ITCH expression was typically downregulated in the tissues and cell lines of PCa compared to that in the peritumoral tissue and in RWPE-1 cells, respectively. The results showed that cir-ITCH overexpression significantly inhibits the proliferation, migration, and invasion of human PCa cells and that reciprocal inhibition of expression occurred between cir-ITCH and miR-17. Proteins in the Wnt/β-catenin and PI3K/AKT/mTOR pathways were downregulated by overexpression of cir-ITCH both in androgen receptor-positive LNCaP cells and androgen receptor-negative PC-3 cells. Taken together, these data demonstrated that cir-ITCH plays a tumor-suppressive role in human PCa cells, partly through the Wnt/β-catenin and PI3K/AKT/mTOR pathways. Thus, cir-ITCH may serve as a novel therapeutic target for the treatment of PCa, especially castration-resistant prostate cancer.

Bladder cancer is one of the most common malignant tumors in the urinary system with high mortality and morbidity. Evidence revealed that bergenin could affect the development of cancer. Here, we aimed to investigate the effect of bergenin on bladder cancer progression and its mechanism. The effect of bergenin on cell function was first detected, followed by assessing the changes of the epithelial‐mesenchymal transition (EMT) in bergenin‐treated cells. The effect of bergenin on peroxisome proliferator‐activated receptor γ (PPARγ)/phosphatase and tensin homolog (PTEN)/Akt signal pathway was measured by Western blotting, followed by the rescue experiments. The results showed that bergenin treatment significantly decreased cell viability and increased G1 phase arrest, accompanied by reduced expression of Ki67, cycling D1, and cycling B1 in bladder cancer cells. Apoptosis was induced by bergenin in bladder cancer cells, as evidenced by increased Bax and cleaved caspase 3 protein levels and decreased Bcl‐2 level in bergenin‐treated cells. Meanwhile, the inhibition of the invasion, migration, and EMT was also observed in bergenin‐treated cells. Mechanism studies showed that bergenin treatment could activate PPARγ/PTEN/Akt signal pathway, as evidence by the increased nucleus PPARγ and phosphatase and tensin homolog (PTEN) expression and decreased Akt expression. Moreover, PPARγ inhibitor administration inverted the effects of bergenin on bladder cancer cell function, including the proliferation, apoptosis, invasion, and migration in bladder cancer cells. Our findings revealed that bergenin could inhibit bladder cancer progression via activating the PPARγ/PTEN/Akt signal pathway, indicating that bergenin may be a potential therapeutic medicine for bladder cancer treatment.

Abstract. A popular way to forecast streamflow is to use bias-corrected meteorological forecasts to drive a calibrated hydrological model, but these hydrometeorological approaches suffer from deficiencies over small catchments due to uncertainty in meteorological forecasts and errors from hydrological models, especially over catchments that are regulated by dams and reservoirs. For a cascade reservoir catchment, the discharge from the upstream reservoir contributes to an important part of the streamflow over the downstream areas, which makes it tremendously hard to explore the added value of meteorological forecasts. Here, we integrate meteorological forecasts, land surface hydrological model simulations and machine learning to forecast hourly streamflow over the Yantan catchment, where the streamflow is influenced by both the upstream reservoir water release and the rainfall–runoff processes within the catchment. Evaluation of the hourly streamflow hindcasts during the rainy seasons of 2013–2017 shows that the hydrometeorological ensemble forecast approach reduces probabilistic and deterministic forecast errors by 6 % compared with the traditional ensemble streamflow prediction (ESP) approach during the first 7 d. The deterministic forecast error can be further reduced by 6 % in the first 72 h when combining the hydrometeorological forecasts with the long short-term memory (LSTM) deep learning method. However, the forecast skill for LSTM using only historical observations drops sharply after the first 24 h. This study implies the potential of improving flood forecasts over a cascade reservoir catchment by integrating meteorological forecasts, hydrological modeling and machine learning.