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Background: Prostate cancer (PCa) is the most common malignant male neoplasm in the American male population. Our prior studies have demonstrated that protein phosphatase 1 regulatory subunit 12A (PPP1R12A) could be an efficient prognostic factor in patients with PCa, promoting further investigation. The present study attempted to construct a gene signature based on PPP1R12A and metabolism-related genes to predict the prognosis of PCa patients. Methods: The mRNA expression profiles of 499 tumor and 52 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. We selected differentially expressed PPP1R12A-related genes among these mRNAs. Tandem affinity purification-mass spectrometry was used to identify the proteins that directly interact with PPP1R12A. Gene set enrichment analysis (GSEA) was used to extract metabolism-related genes. Univariate Cox regression analysis and a random survival forest algorithm were used to confirm optimal genes to build a prognostic risk model. Results: We identified a five-gene signature (PPP1R12A , PTGS2 , GGCT , AOX1 , and NT5E) that was associated with PPP1R12A and metabolism in PCa, which effectively predicted disease-free survival (DFS) and biochemical relapse-free survival (BRFS). Moreover, the signature was validated by two internal datasets from TCGA and one external dataset from the Gene Expression Omnibus (GEO). Conclusion: The five-gene signature is an effective potential factor to predict the prognosis of PCa, classifying PCa patients into high- and low-risk groups, which might provide potential novel treatment strategies for these patients. [ABSTRACT FROM AUTHOR]

Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer. [ABSTRACT FROM AUTHOR]

Background: Solute Carrier Family 6 Member 1 (SLC6A1) has been identified as a cancer-promoting gene in various human cancers, such as clear cell renal cell carcinoma and ovarian cancer. However, its roles in prostate cancer (PCa) has not been fully elucidated. The aim of this study was to investigate the expression and clinical significance of SLC6A1 in PCa tissues and its effect on drug resistance to docetaxel in PCa.Methods: Expression patterns of SLC6A1 protein in PCa tissues were examined by immunohistochemistry based on Tissue microarray. Associations of SLC6A1 protein expression with various clinicopathological features and patients' prognosis of PCa were also statistically evaluated based on TCGA data. Roles of SLC6A1 deregulation in prostate carcinogenesis and drug resistance was further determined in vitro and in vivo experiments.Results: Based on TCGA Dataset, SLC6A1 expression was markedly higher in patients with high Gleason score, advanced clinical stage and positive biochemical recurrence than those with control features (all P < 0.05). Both unvariate and multivariate analyses demonstrated that SLC6A1 expression was significantly associated with biochemical recurrence-free survival in PCa patients. In addition, enforced expression of SLC6A1 effectively promoted cell proliferation, migration and invasion of PCa cells in vitro. Moreover, the inhibition of SLC6A1 suppressed the tumor growth in vivo. Additionally, immunohistochemical notches of PCNA and MMP-9 in the low-expression cluster were pointedly lower compared to those of NC group. Finally, the cell viability revealed that the overexpression of SLC6A1 obviously promoted the PCa cell resistant to docetaxel (DTX), and the transplanted tumor in the overexpression group had no significant reduction compared with the untreated group.Conclusions: Our data suggest that SLC6A1 overexpression may be associated with aggressive progression and short biochemical recurrence-free survival of PCa, and may be related to the resistance to docetaxel therapy. [ABSTRACT FROM AUTHOR]

BACKGROUND Autophagy has recently been found to play important roles in tumorigenesis and leucine-rich pentatricopeptide repeat motif-containing protein (LRPPRC) has been identified as an inhibitor that suppresses autophagy and mitophagy and maintains mitochondrial activity. The authors hypothesized that LRPPRC levels can be used as a biomarker for the diagnosis and prognosis of prostate cancer. METHODS Immunochemistry analysis was performed to evaluate the levels of LRPPRC in 112 samples collected from patients with prostate adenocarcinoma (PCa) and 38 samples from patients with benign prostatic hyperplasia (BPH) who were enrolled in hospitals in Guangzhou City, China and were followed for 10 years. RESULTS Significantly higher levels of LRPPRC were found in PCa samples compared with BPH samples. Greater than 75% of patients with PCa demonstrated high levels of LRPPRC whereas only 10% of patients with BPH were found to have similar levels of LRPPRC. The levels of LRPPRC were found to be positively correlated with tumor grade, metastasis, and serum prostate-specific antigen level, but were negatively correlated with hormone therapy sensitivity after 2 years of surgery and overall survival. The association between high levels of LRPPRC and late-stage PCa or hormone therapy insensitivity was confirmed in tissue samples collected from prostate-specific phosphatase and tensin homolog ( PTEN) -/- mice or hormone-dependent and hormone-independent PCa cell lines. CONCLUSIONS LRPPRC levels may be used as an independent biomarker for patients with PCa at a late stage with poor prognosis. Cancer 2014;120:1228-1236. © 2014 American Cancer Society. [ABSTRACT FROM AUTHOR]

This study was aimed at constructing a pyroptosis-related signature for prostate cancer (PCa) and elucidating the prognosis and immune landscape and the sensitivity of immune checkpoint blockade (ICB) therapy in signature-define subgroups of PCa. We identified 22 differentially expressed pyroptosis-related genes in PCa from The Cancer Genome Atlas (TCGA) database. The pyroptosis-related genes could divide PCa patients into two clusters with differences in survival. Seven genes were determined to construct a signature that was confirmed by qRT-PCR to be closely associated with the biological characteristics of malignant PCa. The signature could effectively and independently predict the biochemical recurrence (BCR) of PCa, which was validated in the GSE116918 and GSE21034. We found that patients in the high-risk group were more prone to BCR and closely associated with high-grade and advanced-stage disease progression. Outperforming clinical characteristics and nine published articles, our signature demonstrated excellent predictive performance. The patients in the low-risk group were strongly related to the high infiltration of various immune cells including CD8+ T cells and plasma B cells. Furthermore, the high-risk group with higher TMB levels and expression of immune checkpoints was more likely to benefit from immune checkpoint therapy such as PD-1 and CTLA-4 inhibitors. The sensitivity to chemotherapy, endocrine, and targeted therapy showed significant differences in the two risk groups. Our signature was a novel therapeutic strategy to distinguish the prognosis and guide treatment strategies. [ABSTRACT FROM AUTHOR]