A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

Genome-Wide CRISPR-Cas9 Screening and Identification of Potential Genes Promoting Prostate Cancer Growth and Metastasis.

OBJECTIVE: Identification and validation of genes that functionally account for the growth and metastasis of prostate cancer. METHODS: DU145-KO cell line was constructed by transfecting DU145 cells with lentivirus packaged with the genome-wide knock-out library. The DU145-KO cells were transplanted into the armpits of immunocompromised Nu/Nu mice, followed by the tissue collection from the lung at week 3 (early lung tissue) or week 7 (late lung tissue with micro-metastasis), as well as from primary tumor site at week 7 (late primary tumor) after inoculation. Lung metastasis was retrieved at various time points for DNA sequencing analysis to identify enriched sgRNAs, thus candidate genes/miRNAs. Further bioinformatics analysis and limited functional validation studies were carried out. RESULTS: DU145-KO cells promoted the formation of transplanted tumors in mice and promoted the growth and metastasis of primary tumors, compared to the controls (DU145-NC cells). The analysis of sequence data showed that the abundance of sgRNAs significantly changed in the primary tumor and micro-metastasis site. Fifteen target genes (C1QTNF9B, FAM229A, hsa-mir-3929, KRT23, TARS2, CRADD, GRIK4, PLA2G15, LOXL1, SLITRK6, CDC42EP5, SLC2A4, PTGDS, MYL9 and ACOX2 for the enriched sgRNAs) have been selected for experimental validation, which showed that knock-out of any of these genes led to the enhanced potential of invasion and metastasis of DU145 cells. CONCLUSION: Genome-wide CRISPR-Cas9 knock-out screening technology combined with highthroughput sequencing analysis identified genes that potentially relate to prostate tumor invasion and metastasis. Analysis of these genes provided insights into biological pathways relevant to the disease and disclosed innovative markers for diagnosis or prognosis as well as potential targets for therapy.

Tumor Suppressor Role and Clinical Significance of the FEV Gene in Prostate Cancer.

Background: In our previous research, we developed a 32-gene risk index model that may be utilized as a robust prognostic method for predicting prostate cancer (PCa) recurrence after surgery. Among the 32 genes, the Fifth Ewing Variant (FEV) gene was one of the top downregulated genes in relapsed PCa. However, current understanding of the FEV gene and its involvement in PCa is limited. Methods: FEV mRNA expression was analyzed and correlated to clinical outcomes in PCa patients who underwent prostatectomy at the Massachusetts General Hospital. Specimens from tissue microarray (TMA) including 102 prostate cancer patients were analysis for the expression of FEV. Meanwhile, FEV expression profiles were also assessed in PCa cell lines and in BPH-1 prostate epithelial cells using western blotting and quantitative reverse transcription-PCR (qRT-PCR). Furthermore, we transfected LNCaP and PC-3 cells with either an empty vector or full-length FEV gene and performed in vitro cell functional assays. The part FEV plays in tumor xenograft growth was also assessed in vivo. Results: Of the 191 patients included in this study base on the DASL dataset, 77 (40.3%) and 24 (13.6%), respectively, developed prostate-specific antigen (PSA) relapse and metastasis postradical prostatectomy. Significant FEV downregulation was observed in PCa patients showing PSA failure and metastasis. The protein expression of FEV was significantly negatively correlated with the Gleason score and pathological stage in prostate cancer tissues. Similarly, FEV expression significantly decreased in all PCa cell lines relative to BPH-1 (all P < 0.05). Functional assays revealed that FEV expression markedly inhibited PCa cell growth, migration, and invasion, which in turn significantly repressed the growth of tumor xenografts in vivo. Conclusion: The results of this study suggest an association between downregulated FEV expression and PSA relapse in PCa patients. In addition, FEV may act as a tumor suppressor in PCa.

Comprehensive Evaluation of Machine Learning Models and Gene Expression Signatures for Prostate Cancer Prognosis Using Large Population Cohorts.

Overtreatment remains a pervasive problem in prostate cancer management due to the highly variable and often indolent course of disease. Molecular signatures derived from gene expression profiling have played critical roles in guiding prostate cancer treatment decisions. Many gene expression signatures have been developed to improve the risk stratification of prostate cancer and some of them have already been applied to clinical practice. However, no comprehensive evaluation has been performed to compare the performance of these signatures. In this study, we conducted a systematic and unbiased evaluation of 15 machine learning (ML) algorithms and 30 published prostate cancer gene expression-based prognostic signatures leveraging 10 transcriptomics datasets with 1,558 primary patients with prostate cancer from public data repositories. This analysis revealed that survival analysis models outperformed binary classification models for risk assessment, and the performance of the survival analysis methods-Cox model regularized with ridge penalty (Cox-Ridge) and partial least squares (PLS) regression for Cox model (Cox-PLS)-were generally more robust than the other methods. Based on the Cox-Ridge algorithm, several top prognostic signatures displayed comparable or even better performance than commercial panels. These findings will facilitate the identification of existing prognostic signatures that are promising for further validation in prospective studies and promote the development of robust prognostic models to guide clinical decision-making. Moreover, this study provides a valuable data resource from large primary prostate cancer cohorts, which can be used to develop, validate, and evaluate novel statistical methodologies and molecular signatures to improve prostate cancer management. SIGNIFICANCE: This systematic evaluation of 15 machine learning algorithms and 30 published gene expression signatures for the prognosis of prostate cancer will assist clinical decision-making.

Mannose inhibits the growth of prostate cancer through a mitochondrial mechanism.

The limited treatment options for advanced prostate cancer (PCa) lead to the urgent need to discover new anticancer drugs. Mannose, an isomer of glucose, has been reported to have an anticancer effect on various tumors. However, the anticancer effect of mannose in PCa remains unclear. In this study, we demonstrated that mannose inhibits the proliferation and promotes the apoptosis of PCa cells in vitro, and mannose was observed to have an anticancer effect in mice without harming their health. Accumulation of intracellular mannose simultaneously decreased the mitochondrial membrane potential, increased mitochondrial and cellular reactive oxygen species (ROS) levels, and reduced adenosine triphosphate (ATP) production in PCa cells. Mannose treatment of PCa cells induced changes in mitochondrial morphology, caused dysregulated expression of the fission protein, such as fission, mitochondrial 1 (FIS1), and enhanced the expression of proapoptotic factors, such as BCL2-associated X (Bax) and BCL2-antagonist/killer 1 (Bak). Furthermore, lower expression of mannose phosphate isomerase (MPI), the key enzyme in mannose metabolism, indicated poorer prognosis in PCa patients, and downregulation of MPI expression in PCa cells enhanced the anticancer effect of mannose. This study reveals the anticancer effect of mannose in PCa and its clinical significance in PCa patients.

CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer.

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Novel immune-related signature for risk stratification and prognosis in prostatic adenocarcinoma.

A substantial proportion of prostatic adenocarcinoma (PRAD) patients experience biochemical failure (BCF) after radical prostatectomy (RP). The immune microenvironment plays a vital role in carcinogenesis and the development of PRAD. This study aimed to identify a novel immune-related gene (IRG)-based signature for risk stratification and prognosis of BCF in PRAD. Weighted gene coexpression network analysis was carried out to identify a BCF-related module in a discovery cohort of patients who underwent RP at the Massachusetts General Hospital. The median follow-up time was 70.32 months. Random forest and multivariate stepwise Cox regression analyses were used to identify an IRG-based signature from the specific module. Risk plot analyses, Kaplan-Meier curves, receiver operating characteristic curves, univariate and multivariate Cox regression analyses, stratified analysis, and Harrell's concordance index were used to assess the prognostic value and predictive accuracy of the IRG-based signature in the internal discovery cohort; The Cancer Genome Atlas database was used as a validation cohort. Tumor immune estimation resource database analysis and CIBERSORT algorithm were used to assess the immunophenotype of PRAD. A novel IRG-based signature was identified from the specific module. Five IRGs (BUB1B, NDN, NID1, COL4A6, and FLRT2) were verified as components of the risk signature. The IRG-based signature showed good prognostic value and predictive accuracy in both the discovery and validation cohorts. Infiltrations of various immune cells were significantly different between low-risk and high-risk groups in PRAD. We identified a novel IRG-based signature that could function as an index for assessing tumor immune status and risk stratification in PRAD.

[Corrigendum] miR­505 suppresses prostate cancer progression by targeting NRCAM.

Subsequently to the publication of this article, one of the corresponding authors, Dr Wei­De Zhong, has realized that the information presented in the box for correspondence for him was incorrect. Although Dr Zhong is correctly shown as having three affiliation addresses in the paper, the address affiliation listed first on the paper should have been presented as the address for correspondence, not the second one. Therefore, the authors' affiliation information should have appeared as follows (changes are highlighted in bold): Correspondence to: Dr Wei­ De Zhong, Guangdong Provincial Institute of Nephrology, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China. Dr Zhong deeply regrets his oversight in this regard, and apologizes for any inconvenience caused. [the original article was published in Oncology Reports 42: 991-1004, 2019; DOI: 10.3892/or.2019.7231].

Functional classification of prostate cancer­associated miRNAs through CRISPR/Cas9­mediated gene knockout.

The aim of the present study was to use the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR­associated (Cas) 9­mediated gene knockout technology for the rapid classification of the differential function of micro (mi)RNAs screened using miRNA expression profiling by microarray. The rational design of single guide RNAs for the CRISPR/Cas9 system was verified to function in human LNCaP cells with rapid and efficient target gene editing. miRNA (miR)­205, miR­221, miR­222, miR­30c, miR­224, miR­455­3p, miR­23b and miR­505 were downregulated in patients with prostate cancer (PCa) and were experimentally validated to function as tumor suppressors in prostate cancer cells, affecting tumor proliferation, invasion and aerobic glycolysis. In addition, the data of the present study suggested that miR­663a and mfiR­1225­5p were upregulated in prostate cancer tissues and cell proliferation of miR­663a and miR­1225­5p knockout PCa cells was significantly lower compared with miR­NC cells. Furthermore, knockout of miR­1225­5p and miR­663a significantly decreased the lactate production in LNCaP cells in vitro. In conclusion, the present study offered a simple and efficient method for rapidly classifying miRNA function by applying CRISPR/Cas9 in LNCaP cells. The present study suggested, for the first time to the best of the authors' knowledge, that the aberrant expression of miR­663a and miR­1225­5p may be involved with the progression of prostate cancer, implying their potential as candidate markers for this type of cancer. However, the precise role of miR­663a and miR­1225­5p in accelerating the development of prostate cancer and promoting tumor progression remains to be elucidated.

Clinicopathological characteristics of localized prostate cancer in younger men aged ≤ 50 years treated with radical prostatectomy in the PSA era: A systematic review and meta-analysis.

OBJECTIVES: With the rapid increase in younger age prostate cancer (PCa) patients, the impact of younger age on decision-making for PCa treatment needs to be revaluated in the new era. MATERIALS AND METHODS: A systematic literature search was performed using PubMed, EMBASE, and Web of Science up to October 2019 to identify the eligible radical prostatectomy (RP) studies focusing on understanding the impact of age on clinicopathological features and oncological prognosis in patients with localized PCa in PSA era. Meta-analyses were conducted using available hazard ratios (HRs) from both univariate and multivariate analyses. RESULTS: Twenty-six studies including 391 068 patients with RP treatments from the PSA era were included. Of these studies, age of 50 years old (age50) is the most commonly used cut-off age to separate the younger patient group (including either age < 50 or age ≤ 50) from the older patient group. In these studies, the incidence of younger patients varied between 2.6% and 16.6% with a median of 8.3%. Younger patients consistently showed more favorable clinicopathological features correlated with better BCR prognosis. Meta-analyses showed a 1.38-fold improved BCR survival of younger patients in multivariate analysis. Among the high-risk PCa patients, younger age was independently associated with worse oncological outcomes in multivariate analyses. CONCLUSION: In this study, we found younger age correlated with favorable clinicopathological characteristics and better BCR prognosis in low- to intermediate-risk patients. In high-risk group patients, younger patients often showed significantly worse oncological outcomes. Our study results suggest that age 50 could be used as a practical cut-off age to separate younger age patients from older age PCa patients.

Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes.

Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire chromosomes because the majority of the haplotypic allele combinations has been preserved. Therefore, compared with the current diploid-based phasing methods, using haploid genomic data of single gametes may substantially reduce the complexity in inferring the donor's chromosomal haplotypes. In this study, we developed the first easy-to-use R package, Hapi, for inferring chromosome-length haplotypes of individual diploid genomes with only a few gametes. Hapi outperformed other phasing methods when analyzing both simulated and real single gamete cell sequencing data sets. The results also suggested that chromosome-scale haplotypes may be inferred by using as few as three gametes, which has pushed the boundary to its possible limit. The single gamete cell sequencing technology allied with the cost-effective Hapi method will make large-scale haplotype-based genetic studies feasible and affordable, promoting the use of haplotype data in a wide range of research.

ARNT-dependent CCR8 reprogrammed LDH isoform expression correlates with poor clinical outcomes of prostate cancer.

Lactate dehydrogenase isozyme (LDH) is a tetramer constituted of two isoforms, LDHA and LDHB, the expression of which is associated with cell metabolism and cancer progression. Our previous study reveals that CC-chemokine ligand-18 (CCL18) is involved in progression of prostate cancer (PCa).This study aims to investigate how CCL18 regulates LDH isoform expression, and therefore, contributes to PCa progression. The data revealed that the expression of LDHA was upregulated and LDHB was downregulated in PCa cells by CCL18 at both messenger RNA and protein levels. The depletion of CCR8 reduced the ability of CCL18 to promote the proliferation, migration, and lactate production of PCa cells. Depletion of a CCR8 regulated transcription factor, ARNT, significantly reduced the expression of LDHA. In addition, The Cancer Genome Atlas dataset analyses revealed a positive correlation between CCR8 and ARNT expression. Two dimension difference gel electrophoresis revealed that the LDHA/LDHB ratio was increased in the prostatic fluid of patients with PCa and PCa tissues. Furthermore, increased LDHA/LDHB ratio was associated with poor clinical outcomes of patients with PCa. Together, our results indicate that the CCR8 pathway programs LDH isoform expression in an ARNT dependent manner and that the ratio of LDHA/LDHB has the potential to serve as biomarkers for PCa diagnosis and prognosis.

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.

Upregulation of Holliday junction recognition protein predicts poor prognosis and biochemical recurrence in patients with prostate cancer.

Abnormal expression of Holliday junction recognition protein (HJURP) in several types of tumor cells plays a vital role in the formation and progression of tumors. Few studies have investigated the role of HJURP in prostate cancer (PCa). The aim of this study was to analyze the expression levels of HJURP in PCa and to establish the association with clinicopathological data. Reverse transcription quantitative polymerase chain reaction and immunohistochemical analysis were used to detect the expression levels of HJURP in benign and PCa prostate tissues. The Taylor dataset was statistically analyzed to determine if HJURP expression levels were associated with PCa clinicopathological data. HJURP was overexpressed in PCa tissues compared with benign prostate tissues. Statistical analysis of the Taylor dataset indicated that upregulation of HJURP was significantly associated with positive prostate-specific antigen (PSA) levels (P=0.004), high Gleason score (P=0.005), advanced pathological stage (P=0.007), metastasis (P<0.001) and PSA failure (P<0.001). Higher HJURP mRNA expression levels were significantly associated with shorter biochemical recurrence (BCR)-free survival (P<0.001). To the best of our knowledge, this study is the first report of HJURP upregulation in PCa tissues. Upregulation of HJURP may predict BCR-free survival and HJURP may be an oncogene that impacts the prognosis of patients with PCa.

Correction to: MicroRNA-30d promotes angiogenesis and tumor growth via MYPT1/c-JUN/VEGFA pathway and predicts aggressive outcome in prostate cancer.

After publication of the article [1], the author reported that this article contained some errors.

Offsetting Expression Profiles of Prognostic Markers in Prostate Tumor vs. Its Microenvironment.

Diagnosis of the presence of tumors and subsequent prognosis based on tumor microenvironment becomes more clinically practical because tumor-adjacent tissues are easy to collect and they are more genetically homogeneous. The purpose of this study was to identify new prognostic markers in prostate stroma that are near the tumor. We have demonstrated the prognostic features of FGFR1, FRS2, S6K1, LDHB, MYPT1, and P-LDHA in prostate tumors using tissue microarrays (TMAs) which consist of 241 patient samples from Massachusetts General Hospital (MGH). In this study, we investigated these six markers in the tumor microenvironment using an Aperio Imagescope system in the same TMAs. The joint prognostic power of markers was further evaluated and classified using a new algorithm named Weighted Dichotomizing. The classifier was verified via rigorous 10-fold cross validation. Statistical analysis of the protein expression indicated that in tumor-adjacent stroma FGFR1 and MYPT1 were significantly correlated with patient outcomes and LDHB showed the outcome-association tendency. More interestingly, these correlations were completely opposite regarding tumor tissue as previously reported. The results suggest that prognostic testing should utilize either tumor-enriched tissue or stroma with distinct signature profiles rather than using mixture of both tissue types. The new classifier based on stroma tissue has potential value in the clinical management of prostate cancer patients.

miR­505 suppresses prostate cancer progression by targeting NRCAM.

Previous researchers have demonstrated that microRNA­505 (miR­505) is negatively correlated with progression in various malignancies. However, the detailed function and molecular mechanisms of miR­505 have yet to be completely elucidated in prostate cancer (PCa). The present study initially identified the potential role of miR­505 in PCa using in vitro experiments, and demonstrated that restoration of miR­505 inhibited proliferation, invasion and migration, yet induced cell cycle arrest and promoted apoptosis in PCa cells. The present study also demonstrated that the expression of neuron­glial­related cell adhesion molecule (NRCAM) was markedly upregulated in PCa cells when compared with benign prostate epithelium. A luciferase reporter assay demonstrated that miR­505 directly targeted NRCAM in PCa cells. In addition, NRCAM stimulation antagonized the inhibitory effects of miR­505 on the proliferation, migration, and invasion of PCa cells. Furthermore, lower levels of miR­505 and higher levels of NRCAM may serve as a predictor of worse biochemical recurrence­free survival or disease­free survival in patients with PCa. In conclusion, the present study revealed the inhibitory effects of miR­505 on PCa tumorigenesis, which potentially occur by targeting NRCAM. The combined analysis of NRCAM and miR­505 may predict disease progression in patients with PCa following radical prostatectomy.

HMGCS2 functions as a tumor suppressor and has a prognostic impact in prostate cancer.

BACKGROUND: Accumulating studies reported that 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) may function as either an oncogene or a tumor suppressor in various human cancers. However, its involvement in prostate cancer (PCa) remains unknown. Therefore, the aim of this study was to investigate the clinical significance of HMGCS2 expression and its functions in PCa. METHODS: Expression levels of HMGCS2 mRNA and protein were detected by quantitative Polymerase Chain Reaction (qPCR), Western blot and immunohistochemistry, respectively. Associations of HMGCS2 expression with various clinicopathological features and patients' prognosis of PCa were statistically evaluated. Roles of HMGCS2 dysregulation in cell proliferation, invasion and migration of PCa cell lines were also determined. RESULTS: HMGCS2 protein expression was significantly reduced in PCa tissues compared to adjacent benign prostate tissues at protein levels (P < 0.05). Clinically, low HMGCS2 mRNA expression was dramatically associated with high Gleason score (GS) and pathological grade, as well as the presence of distant metastasis of PCa patients. In addition, PCa patients with low HMGCS2 mRNA expression more frequently had shorter disease-free survival and biochemical recurrence-free survival (all P < 0.05). HMGCS2 expression was identified as an independent factor to predict both disease-free and biochemical recurrence-free survivals of PCa patients. Moreover, loss-of-function experiments demonstrated that HMGCS2 knockdown-expression promotes cell proliferation, colony formation, invasion and migration of PCa cells in vitro and lower the apoptotic rate of PCa cells in vitro. CONCLUSIONS: Our data indicate that HMGCS2 may be capable of predicting the risk of biochemical recurrence in PCa patients after radical prostatectomy and functions as a tumor suppressor in PCa cancer, implying its related pathway potential as a drug candidate in anti-PCa therapy.