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Large-scale evaluation of SLC18A2 in prostate cancer reveals diagnostic and prognostic biomarker potential at three molecular levels.

ABSTRACT: Limitations of current diagnostic and prognostic tools for prostate cancer (PC) have led to over-diagnosis and over-treatment. Here, we investigate the biomarker potential of the SLC18A2 (VMAT2) gene for PC at three molecular levels. Thus, SLC18A2 promoter methylation was analyzed in 767 malignant and 78 benign radical prostatectomy (RP) samples using methylation-specific qPCR and Illumina 450K methylation microarray data. SLC18A2 transcript levels were assessed in 412 malignant and 45 benign RP samples using RNAseq data. SLC18A2 protein was evaluated by immunohistochemistry in 502 malignant and 305 benign RP samples. Cancer-specificity of molecular changes was tested using Mann-Whitney U tests and/or receiver operating characteristic (ROC) analyses. Log rank, uni- and multivariate Cox regression tests were used for survival analyses. We found that SLC18A2 promoter hypermethylation was highly cancer-specific (area under the curve (AUC): 0.923-0.976) and associated with biochemical recurrence (BCR) after RP in univariate analyses. SLC18A2 transcript levels were reduced in PC and had independent prognostic value for BCR after RP (multivariate HR 0.13, P < 0.05). Likewise, SLC18A2 protein was down-regulated in PC (AUC 0.898) and had independent prognostic value for BCR (multivariate HR 0.51, P < 0.05). Reduced SLC18A2 protein expression was also associated with poor overall survival in univariate analysis (HR 0.29, P < 0.05). Our results highlight SLC18A2 as a new promising methylation marker candidate for PC diagnosis. Furthermore, SLC18A2 expression (RNA and protein) showed promising prognostic potential beyond routine clinicopathological variables. Thus, novel SLC18A2-based molecular tests could have useful future applications for PC detection and identification of high-risk patients.

SUBMITTER: Haldrup C

PROVIDER: S-EPMC5423165 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:The identification of appropriate biomarkers is essential to support important clinical decisions in patients with prostate cancer. The aim of our study was a systematic bioinformatical analysis of the mRNA expression of all genes available for the prostate adenocarcinoma cohort of The Cancer Genome Atlas (TCGA), regarding their potential prognostic and diagnostic role. METHODS:The study cohort comprises 499 patients (TCGA prostate cancer cohort). mRNA expression data were available for approx. 20,000 genes. The bioinformatical statistical pipeline addressed gene expression differences in tumor vs. benign prostate tissue (including gene set enrichment analysis, GSEA) in samples from tumors with different aggressivenesses (Gleason score), as well as prognostic values in multistep survival analyses. RESULTS:Among all genes analyzed, 1754 were significantly downregulated and 1553 genes were significantly upregulated in tumor tissue. In GSEA, 16 of 30 top enriched biological processes were alterations of epigenetic regulation at different levels. Significant correlation with Gleason Score was evident for 8724 genes (range of Pearson r-values 0.09-0.43; all p < 0.05). In univariate Cox regression analyses, mRNA expression of 3571 genes showed statistically significant association with biochemical recurrence-free survival with a range of hazard ratios 0.3-3.8 (p-value 7.4e- 07 to 0.05). Among these, 571 genes were independently associated with biochemical recurrence in multivariate analysis. Access to the full database including results is provided as supplement. CONCLUSIONS:In our systematic analysis we found a big number of genes of potential diagnostic and prognostic value, many of which have not been studied in prostate cancer to date. Due to the comprehensive nature of this analysis and free access to the results, this study represents a reference database for prostate cancer researchers which can be used as a powerful tool for validation purposes and planning of new studies.

Project description:The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91-0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

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Large-scale evaluation of SLC18A2 in prostate cancer reveals diagnostic and prognostic biomarker potential at three molecular levels.

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