Association of transcript levels of 10 established or candidate-biomarker gene targets with cancerous versus non-cancerous prostate tissue from radical prostatectomy specimens

Objectives: The benefits of PSA (prostate specific antigen)-testing in prostate cancer remain controversial with a consequential need for validation of additional biomarkers. We used highly standardized reverse-transcription (RT)-PCR assays to compare transcript levels of 10 candidate cancer marker genes - BMP6, FGF-8b, KLK2, KLK3, KLK4, KLK15, MSMB, PCA3, PSCA and Trpm8 - in carefully ascertained non-cancerous versus cancerous prostate tissue from patients with clinically localized prostate cancer treated by radical prostatectomy. Design and methods: Total RNA was isolated from fresh frozen prostate tissue procured immediately after resection from two separate areas in each of 87 radical prostatectomy specimens. Subsequent histopathological assessment classified 86 samples as cancerous and 88 as histologically benign prostate tissue. Variation in total RNA recovery was accounted for by using external and internal standards and enabled us to measure transcript levels by RT-PCR in a highly quantitative manner. Results: Of the ten genes, there were significantly higher levels only of one of the less abundant transcripts, PCA3, in cancerous versus non-cancerous prostate tissue whereas PSCA mRNA levels were significantly lower in cancerous versus histologically benign tissue. Advanced pathologic stage was associated with significantly higher expression of KLK15 and PCA3 mRNAs. Median transcript levels of the most abundantly expressed genes (i.e. MSMB, KLK3, KLK4 and KLK2) in prostate tissue were up to 10(5)-fold higher than those of other gene targets. Conclusions: PCA3 expression was associated with advanced pathological stage but the magnitude of overexpression of PCA3 in cancerous versus non-cancerous prostate tissue was modest compared to previously reported data. (C) 2013 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved

Cancer-associated Changes in the Expression of TMPRSS2-ERG, PCA3, and SPINK1 in Histologically Benign Tissue From Cancerous vs Noncancerous Prostatectomy Specimens.

To investigate whether messenger ribonucleic acid (mRNA) expression of TMPRSS2-ERG fusion gene, a suggested prostate cancer (PCa) biomarker, was specific to cancerous lesions alone and to study the expression of SPINK1 and PCA3 mRNAs in the same cohort to also explore the proposed mutual exclusivity of TMPRSS2-ERG and SPINK1 expression

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

BackgroundDetection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005–0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection.ResultHere, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method.ConclusionsOur comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.</div

RNA Polymerase III Subunit POLR3G Regulates Specific Subsets of PolyA+ and SmallRNA Transcriptomes and Splicing in Human Pluripotent Stem Cells

POLR3G is expressed at high levels in human pluripotent stem cells (hPSCs) and is required for maintenance of stem cell state through mechanisms not known in detail. To explore how POLR3G regulates stem cell state, we carried out deep-sequencing analysis of polyA+ and smallRNA transcriptomes present in hPSCs and regulated in POLR3G-dependent manner. Our data reveal that POLR3G regulates a specific subset of the hPSC transcriptome, including multiple transcript types, such as protein-coding genes, long intervening non-coding RNAs, microRNAs and small nucleolar RNAs, and affects RNA splicing. The primary function of POLR3G is in the maintenance rather than repression of transcription. The majority of POLR3G polyA+ transcriptome is regulated during differentiation, and the key pluripotency factors bind to the promoters of at least 30% of the POLR3G-regulated transcripts. Among the direct targets of POLR3G, POLG is potentially important in sustaining stem cell status in a POLR3G-dependent manner.</p

RNA Polymerase III Subunit POLR3G Regulates Specific Subsets of PolyA+ and SmallRNA Transcriptomes and Splicing in Human Pluripotent Stem Cells

POLR3G is expressed at high levels in human pluripotent stem cells (hPSCs) and is required for maintenance of stem cell state through mechanisms not known in detail. To explore how POLR3G regulates stem cell state, we carried out deep-sequencing analysis of polyA+ and smallRNA transcriptomes present in hPSCs and regulated in POLR3G-dependent manner. Our data reveal that POLR3G regulates a specific subset of the hPSC transcriptome, including multiple transcript types, such as protein-coding genes, long intervening non-coding RNAs, microRNAs and small nucleolar RNAs, and affects RNA splicing. The primary function of POLR3G is in the maintenance rather than repression of transcription. The majority of POLR3G polyA+ transcriptome is regulated during differentiation, and the key pluripotency factors bind to the promoters of at least 30% of the POLR3G-regulated transcripts. Among the direct targets of POLR3G, POLG is potentially important in sustaining stem cell status in a POLR3G-dependent manner.Peer reviewe