Expression of SOCSs in human prostate cancer and their association in prognosis.

Suppressors of cytokine signaling (SOCS) proteins have been identified as negative feedback regulators of cytokine-mediated signaling in various tissues, and demonstrated to play critical roles in tumorigenesis and tumor development of different cancers. The involvement of SOCSs in human prostate cancer (PCa) has not been fully elucidated. Thus, the aim of this study is to investigate the expression patterns and the clinical significance of SOCSs in PCa. The expression changes of SOCSs at mRNA and protein levels in human PCa tissues compared with adjacent benign prostate tissues were, respectively, detected by using real-time quantitative reverse transcriptase-polymerase chain reaction (QRT-PCR) and immunohistochemistry analyses. The associations of SOCSs expression with clinicopathological features and clinical outcome of PCa patients were further statistically analyzed. Among SOCSs, both QRT-PCR and immunohistochemistry analyses found that SOCS2 expression was upregulated (at mRNA level: change ratio = 1.98, P = 0.031; at protein level: 5.12 ± 0.60 vs. 2.68 ± 0.37, P = 0.016) and SOCS6 expression was downregulated (at mRNA level: change ratio = -1.65, P = 0.008; at protein level: 3.03 ± 0.32 vs. 4.0.72 ± 0.39, P = 0.004) in PCa tissues compared with those in non-cancerous prostate tissues. In addition, the upregulation of SOCS2 in PCa tissues was correlated with the lower Gleason score (P < 0.001), the absence of metastasis (P < 0.001) and the negative PSA failure (P = 0.009); the downregulation of SOCS6 tended to be found in PCa tissues with the higher Gleason score (P = 0.016), the advanced pathological stage (P = 0.007), the positive metastasis (P = 0.020), and the positive PSA failure (P = 0.032). Furthermore, both univariate and multivariate analyses showed that the downregulation of SOCS2 was an independent predictor of shorter biochemical recurrence-free survival. Our data offer the convincing evidence for the first time that the dysregulation of SOCS2 and SOCS6 may be associated with the aggressive progression of PCa. SOCS2 may be potential markers for prognosis in PCa patients.

The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks.

BACKGROUND: Prostate cancer (PCa) is the most common malignancy seen in men and the second leading cause of cancer-related death in males. The incidence and mortality associated with PCa has been rapidly increasing in China recently. METHODS: Multiple diagnostic models of human PCa were developed based on Taylor database by combining the artificial neural networks (ANNs) to enhance the ability of PCa diagnosis. Genetic algorithm (GA) is used to select feature genes as numerical encoded parameters that reflect cancer, metastatic, or normal samples. Back propagation (BP) neural network and learning vector quantization (LVQ) neural network were used to build different Cancer/Normal, Primary/Metastatic, and Gleason Grade diagnostic models. RESULTS: The performance of these modeling approaches was evaluated by predictive accuracy (ACC) and area under the receiver operating characteristic curve (AUC). By observing the statistically significant parameters of the three training sets, our Cancer/Normal, Primary/Metastatic, and Gleason Grade models' with ACC and AUC can be drawn (97.33%, 0.9832), (99.17%, 0.9952), and (90.48%, 0.8742), respectively. CONCLUSION: These results indicated that our diagnostic models of human PCa based on Taylor database combining the feature gene expression profiling data and artificial intelligence algorithms might act as a powerful tool for diagnosing PCa. Gleason Grade diagnostic models were used as novel prognostic diagnosis models for biochemical recurrence-free survival and overall survival, which might be helpful in the prognostic diagnosis of PCa in patients.

Increasing of FKBP9 can predict poor prognosis in patients with prostate cancer.

BACKGROUND: FK506 binding protein 9 (FKBP9) has been reported and identified for a long time, but its relationship with cancer is rarely studied. For example, the role of FK506 binding protein 9 in prostate cancer (PCa) is still unclear. Therefore, we decided to detect the expression level of FKBP9 in PCa and explore its clinical significance. METHODS: The expression level of FKBP9 protein was detected by immunohistochemistry. In addition, it was demonstrated by high-throughput sequencing of mRNA levels in the TCGA (cancer genome atlas) dataset of 499 patients. Kaplan-meier analysis and Cox proportional hazard regression model were used to evaluate the relationship between FKBP9 expression and survival in prostate cancer patients. RESULTS: The expression of FKBP9 was localized in the cytoplasm, which in normal prostate tissues was obviously lower than that in PCa tissues (P = 0.001). High expression of FKBP9 was related with lymph node metastasis (P = 0.022) and distant metastasis (P = 0.028). Kaplan-Meier survival analysis revealed that the BCR-free survival of PCa patients with high FKBP9 level was significantly shortened (P=0.041). CONCLUSIONS: FKBP9 may be a cancer promoter that enhances PCa progression, and the level of FKBP9 may be used as an independent precursor of PCa patients.

miR­30c suppresses prostate cancer survival by targeting the ASF/SF2 splicing factor oncoprotein.

Our previous study revealed that microRNA (miR) ­30c represents a potential tumor suppressor gene, the expression of which is associated with decreased oncogenic potential in prostate cancer (PCa) cell lines. However, the functional role and underlying mechanisms of miR­30c in PCa remain to be fully elucidated. Reverse transcription­quantitative polymerase chain reaction and immunohistochemical analysis were used to detect the expression levels of alternative splicing factor/splicing factor 2 (ASF/SF2) in PCa tissues. A luciferase reporter assay was used to investigate whether ASF/SF2 may be a direct target gene of miR­30c. In addition, the effects of miR­30c on the proliferation and apoptosis of PCa cell lines were examined, following transfection with miR­30c mimics. Furthermore, correlation analysis was performed to investigate the relationship between the expression of miR­30c and ASF/SF2 and various clinicopathological parameters of patients with PCa. The present results demonstrated that PCa tissues exhibited higher levels of alternative splicing factor/splicing factor 2 (ASF/SF2), compared with normal tissues. In addition, miR­30c was revealed to targete the 3'­untranslated region of the ASF/SF2 gene, causing a decrease in the mRNA and protein levels of ASF/SF2. Furthermore, miR­30c was reported to decrease cell proliferation, increase the percentage of cells in the G1 cell cycle phase, and promote apoptosis through the inhibition of ASF/SF2. Following correlation analysis using patient samples, the expression of ASF/SF2 was revealed to be tightly correlated with the pathological stage of PCa and biochemical recurrence (BCR). In addition, patients with PCa exhibiting low expression levels of miR­30c and high expression of ASF/SF2 had significantly lower rates of BCR­free survival. In conclusion, the present study suggested that the tumor suppressor miR­30c may be involved in PCa tumorigenesis, possibly via targeting ASF/SF2. The combined analysis of the expression of ASF/SF2 and miR­30c may be a valuable tool for early prediction of BCR in patients with PCa following radical prostatectomy.

Down-regulation of dual-specificity phosphatase 5 predicts poor prognosis of patients with prostate cancer.

Dual-specificity phosphatase 5 (DUSP5), which specifically inactivates the extracellular signal-regulated kinase (ERK) 1/2 within the mitogen-activated protein kinase (MAPK) signaling, has recently been considered to be a tumor suppressor. However, its role in prostate cancer is still elusive. In this study, we performed immunohistochemistry analysis on human tissue microarray (TMA) to detect the DUSP5 protein expression pattern. The results indicated that DUSP5 was down-regulated in the human prostate cancer relative to the adjacent benign tissues (IRS: PCa = 4.29 ± 1.72 versus Benign = 4.89 ± 1.58, P = 0.04). In addition, when we linked the DUSP5 protein levels to the clinicopathological features of the patients, we found that the downregulation of DUSP5 was significantly associated with advanced pathological stage (P = 0.004) and high Gleason score (P = 0.009). Moreover, we attempted to validate these findings and investigate the prognostic value of DUSP5 in a publicly available microarray-based Taylor Dataset. Statistic analysis demonstrated that the downregulation of DUSP5 was closely correlated with high Gleason score (P = 0.011), positive metastasis (P < 0.001) and biochemical recurrence (BCR) (P = 0.016). More importantly, Kaplan-Meier analysis revealed that significant differences between patients with high and low DUSP5 expression level in regard to the BCR-free survival of overall (P = 0.009), non-metastatic (P = 0.006) and patients with Gleason score 7 (P = 0.044). Multivariate analysis by Cox regression indicated that DUSP5 could be an independent predictor for the risk of BCR (HR: 0.41, 95% CI: 0.2-0.82; P = 0.012). In summary, our findings disclose that DUSP5 may be an important tumor suppressor that inhibits the progression of PCa. The downregulation of DUSP5 may accurately predict poor prognosis in PCa patients.

miR-195 Inhibits Tumor Progression by Targeting RPS6KB1 in Human Prostate Cancer.

PURPOSE: To investigate the involvement of hsa-miRNA-195-5p (miR-195) in progression and prognosis of human prostate cancer. EXPERIMENTAL DESIGN: qRT-PCR was performed to detect miR-195 expression in both prostate cancer cell lines and clinical tissue samples. Its clinical significance was statistically analyzed. The roles of miR-195 and its candidate target gene, ribosomal protein S6 kinase, 70 kDa, polypeptide 1 (RPS6KB1) in prostate cancer progression were confirmed on the basis of both in vitro and in vivo systems. RESULTS: miR-195 downregulation in prostate cancer tissues was significantly associated with high Gleason score (P = 0.001), positive metastasis failure (P < 0.001), and biochemical recurrence (BCR, P < 0.001). Survival analysis identified miR-195 as an independent prognostic factor for BCR-free survival of prostate cancer patients (P = 0.022). Then, we confirmed the tumor suppressive role of miR-195 through prostate cancer cell invasion, migration, and apoptosis assays in vitro, along with tumor xenograft growth, angiogenesis, and invasion in vivo according to both gain-of-function and loss-of-function experiments. In addition, RPS6KB1 was identified as a novel direct target of miR-195 through proteomic expression profiling combined with bioinformatic target prediction and luciferase reporter assay. Moreover, the reexpression and knockdown of RPS6KB1 could respectively rescue and imitate the effects induced by miR-195. Importantly, RPS6KB1 expression was closely correlated with aggressive progression and poor prognosis in prostate cancer patients as opposed to miR-195. Furthermore, we identified MMP-9, VEGF, BAD, and E-cadherin as the downstream effectors of miR-195-RPS6KB1 axis. CONCLUSION: The newly identified miR-195-RPS6KB1 axis partially illustrates the molecular mechanism of prostate cancer progression and represents a novel potential therapeutic target for prostate cancer treatment.

Combined overexpression of HIVEP3 and SOX9 predicts unfavorable biochemical recurrence-free survival in patients with prostate cancer.

BACKGROUND: To clarify the involvement of HIVEP3 and SOX9 coexpression in prostate cancer (PCa). METHODS: A small interfering RNA was used to knockdown SOX9 expression in a PCa cell line and to analyze the effects of SOX9 inhibition on the expression of HIVEP3 in vitro. Then, HIVEP3 and SOX9 expression patterns in the human PCa tissues were detected using quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis and immunohistochemistry. RESULTS: We found that the downregulation of SOX9 could inhibit the expression of HIVEP3 in the PCa cells in vitro. In addition, both HIVEP3 and SOX9 messenger RNA expression levels in the PCa tissues were significantly higher than those in the noncancerous prostate tissues (P=0.006 and P<0.001, respectively). Moreover, the immunohistochemical staining scores of HIVEP3 in the PCa tissues with PSA failure were significantly higher than those without (P=0.042); the increased SOX9 protein expression was more frequently found in the PCa tissues with a high Gleason score (P=0.045) and a high clinical stage (P=0.012). The tumors showing the HIVEP3-high/SOX9-high expression more frequently had PSA failure (P=0.024). When the patients with an HIVEP3 overexpression combined with the SOX9 overexpression, this group had a worse biochemical recurrence-free survival (P<0.001). Furthermore, the multivariate analysis showed that the HIVEP3/SOX9 coexpression was an independent predictor of an unfavorable biochemical recurrence-free survival. CONCLUSION: Our data offer the convincing evidence for the first time that a combined analysis of HIVEP3 and SOX9 may help to predict the tumor progression and prognosis of PCa patients. In particular, the overexpression of HIVEP3 in PCa might partly explain the poor prognosis of patients with an upregulation of SOX9.

CC chemokine ligand 18 correlates with malignant progression of prostate cancer.

BACKGROUND AND AIM: CC chemokine ligand 18 (CCL18) promotes malignant behaviors of various human cancer types. However, its involvement in human prostate cancer has not been fully elucidated. The aim of this study was to investigate the role of CCL18 in PCa. METHODS: Expression of CCL18 at mRNA and protein levels was detected using real-time qRT-PCR and immunohistochemistry analysis. We analyzed the associations of CCL18 expression with clinical features of human PCa. The effects of PCa cell migration, invasion, and apoptosis were tested. The efficiency of CCL18 on prostate tumor growth was assessed in a subcutaneous xenograft model. RESULTS: CCL18 expression was upregulated (both P < 0.01) in PCa tissues compared with those in noncancerous prostate tissues. CCL18 upregulation was correlated with high Gleason score (P = 0.034) of patients with PCa. rCCL18 stimulation in PCa cells promoted cell migration and invasion but decreased DU145 cells apoptosis rate. Furthermore, subcutaneous homografts models showed the increased tumor growth and tumor vascularization with the CCL18 stimulation, and the expression of Ki67, PCNA, and CD31 in CCL18 stimulation mice was also significantly increased. CONCLUSIONS: Our data offer the convincing evidence that the upregulation of CCL18 may be involved in the malignant progression of PCa.

An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis.

AIM: Early diagnosis of prostate cancer (PCa), which is a clinically heterogeneous-multifocal disease, is essential to improve the prognosis of patients. However, published PCa diagnostic markers share little overlap and are poorly validated using independent data. Therefore, we here developed an integrative proteomics and interaction network-based classifier by combining the differential protein expression with topological features of human protein interaction networks to enhance the ability of PCa diagnosis. METHODS AND RESULTS: By two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MS using PCa and adjacent benign tissues of prostate, a total of 60 proteins with the differential expression in PCa tissues were identified as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and three hub proteins (PTEN, SFPQ and HDAC1) were chosen. After that, a PCa diagnostic classifier was constructed by support vector machine (SVM) modeling based on the microarray gene expression data of the genes which encode the hub proteins mentioned above. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.96∼90.18%) and area under ROC curve (approximating 1.0). Furthermore, the clinical significance of PTEN, SFPQ and HDAC1 proteins in PCa was validated by both ELISA and immunohistochemistry analyses. More interestingly, PTEN protein was identified as an independent prognostic marker for biochemical recurrence-free survival in PCa patients according to the multivariate analysis by Cox Regression. CONCLUSIONS: Our data indicated that the integrative proteomics and interaction network-based classifier which combines the differential protein expression and topological features of human protein interaction network may be a powerful tool for the diagnosis of PCa. We also identified PTEN protein as a novel prognostic marker for biochemical recurrence-free survival in PCa patients.