NMD and microRNA expression profiling of the HPCX1 locus reveal MAGEC1 as a candidate prostate cancer predisposition gene

Exploration of Extracellular Vesicle miRNAs, Targeted mRNAs and Pathways in Prostate Cancer: Relation to Disease Status and Progression

Simple Summary

Prostate cancer lacks non-invasive specific biomarkers for aggressive disease. Urinary extracellular vesicles (uEV) could provide such markers; however, due to technical challenges, little is known regarding the pathogenesis pathways reflected in uEV. We performed a miRNA, target mRNA and pathway study focused on uEV, exploring the differences between cancer (1) status groups (Gleason score) and (2) progression groups. The uEV provided a surprisingly comprehensive presentation of differentially expressed miRNAs, target mRNAs and pathogenesis pathways. The miRNAs associated with prostate cancer status or progression were mostly unique, but still targeted overlapping sets of signalling, resistance, hormonal and immune pathways. Interestingly, mRNA targets of the key miRNAs (miR-892a, miR-223-3p, miR-146a-5p) were widely expressed in both uEV and plasma EV from PCa patients. The study thus suggests that uEV carry a vast presentation of PCa status and progression-linked RNAs that are worth further exploration in large personalized medicine trials.

Abstract

Background: Prostate cancer (PCa) lacks non-invasive specific biomarkers for aggressive disease. We studied the potential of urinary extracellular vesicles (uEV) as a liquid PCa biopsy by focusing on the micro RNA (miRNA) cargo, target messenger RNA (mRNA) and pathway analysis. Methods: We subjected uEV samples from 31 PCa patients (pre-prostatectomy) to miRNA sequencing and matched uEV and plasma EV (pEV) from three PCa patients to mRNA sequencing. EV quality control was performed by electron microscopy, Western blotting and particle and RNA analysis. We compared miRNA expression based on PCa status (Gleason Score) and progression (post-prostatectomy follow-up) and confirmed selected miRNAs by quantitative PCR. Expression of target mRNAs was mapped in matched EV. Results: Quality control showed typical small uEV, pEV, RNA and EV-protein marker enriched samples. Comparisons between PCa groups revealed mostly unique differentially expressed miRNAs. However, they targeted comprehensive and largely overlapping sets of cancer and progression-associated signalling, resistance, hormonal and immune pathways. Quantitative PCR confirmed changes in miR-892a (Gleason Score 7 vs. ≥8), miR-223-3p (progression vs. no progression) and miR-146a-5p (both comparisons). Their target mRNAs were expressed widely in PCa EV. Conclusions: PCa status and progression-linked RNAs in uEV are worth exploration in large personalized medicine trials.

Untouchable genes in the human genome: Identifying ideal targets for cancer treatment

BACKGROUND

Usually, genes with a higher-than-expected number of somatic mutations in tumor samples are assumed to be cancer related. We identified genes with a fewer-than-expected number of somatic mutations - "untouchable genes".

METHODS

To predict the expected number of somatic mutations, we used a linear regression model with the number of mutations in the gene as an outcome, and gene characteristics, including gene size, nucleotide composition, level of evolutionary conservation, expression level and others, as predictors. Analysis of residuals from the regression model was used to compare the observed and predicted number of mutations.

RESULTS

We have identified 19 genes with a less-than-expected number of loss-off-function (nonsense, frameshift or pathogenic missense) mutations - i.e., untouchable genes. The number of silent or neutral missense mutations in untouchable genes was equal or higher than the expected number. Many mucins, including MUC16, MUC17, MUC6, MUC5AC, MUC5B, and MUC12, are untouchable. We hypothesized that untouchable mucins help tumor cells to avoid immune response by providing a protective coat that prevents direct contact between effector immune cells, e.g., cytotoxic T-cells, and tumor cells. Survival analysis of available TCGA data demonstrated that overall survival of patients with low (below the median) expression of untouchable mucins was better compared to patients with high expression of untouchable mucins. Aside from mucins, we have identified a number of other untouchable genes.

CONCLUSIONS

Untouchable genes may be ideal targets for cancer treatment since suppression of untouchable genes is expected to inhibit survival of tumor cells.

Integrated Analysis Reveals together miR-182, miR-200c and miR-221 Can Help in the Diagnosis of Prostate Cancer

Research has shown that microRNAs are promising biomarkers that can be used to promote a more accurate diagnosis of cancer. In this study, we developed an integrated multi-step selection process to analyze available high-throughput datasets to obtain information on microRNAs as cancer biomarkers. Applying this approach to the microRNA expression profiles of prostate cancer and the datasets in The Cancer Genome Atlas Data Portal, we identified miRNA-182, miRNA-200c and miRNA-221 as possible biomarkers for prostate cancer. The associations between the expressions of these three microRNAs with clinical parameters as well as their diagnostic capability were studied. Several online databases were used to predict the target genes of these three microRNAs, and the results were confirmed by significant statistical correlations. Comparing with the other 18 types of cancers listed in The Cancer Genome Atlas Data Portal, we found that the combination of both miRNA-182 and miRNA-200c being up-regulated and miRNA-221 being down-regulated only happens in prostate cancer. This provides a unique biological characteristic for prostate cancer that can potentially be used for diagnosis based on tissue testing. In addition, our study also revealed that these three microRNAs are associated with the pathological status of prostate cancer.

The CO-Regulation Database (CORD): a tool to identify coordinately expressed genes

Background

Meta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene.

Results

Using the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (CDKN2A), vimentin (VIM), MyoD (MYOD1). CDKN2A, VIM, and MYOD1 all displayed gene correlations consistent with known interacting genes.

Conclusions

We developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at http://cord-db.org.

Exclusion of the 750-kb genetically unstable region at Xq27 as a candidate locus for prostate malignancy in HPCX1-linked families

Several linkage studies provided evidence for the presence of the hereditary prostate cancer locus, HPCX1, at Xq27-q28. The strongest linkage peak of prostate cancer overlies a variable region of ~750 kb at Xq27 enriched by segmental duplications (SDs), suggesting that the predisposition to prostate cancer may be a genomic disorder caused by recombinational interaction between SDs. The large size of SDs and their sequence similarity make it difficult to examine this region for possible rearrangements using standard methods. To overcome this problem, direct isolation of a set of genomic segments by in vivo recombination in yeast (a TAR cloning technique) was used to perform a mutational analysis of the 750 kb region in X-linked families. We did not detect disease-specific rearrangements within this region. In addition, transcriptome and computational analyses were performed to search for nonannotated genes within the Xq27 region, which may be associated with genetic predisposition to prostate cancer. Two candidate genes were identified, one of which is a novel gene termed SPANXL that represents a highly diverged member of the SPANX gene family, and the previously described CDR1 gene that is expressed at a high level in both normal and malignant prostate cells, and mapped 210 kb of upstream the SPANX gene cluster. No disease-specific alterations were identified in these genes. Our results exclude the 750-kb genetically unstable region at Xq27 as a candidate locus for prostate malignancy. Adjacent regions appear to be the most likely candidates to identify the elusive HPCX1 locus.

The application of nonsense-mediated mRNA decay inhibition to the identification of breast cancer susceptibility genes

BACKGROUND

Identification of novel, highly penetrant, breast cancer susceptibility genes will require the application of additional strategies beyond that of traditional linkage and candidate gene approaches. Approximately one-third of inherited genetic diseases, including breast cancer susceptibility, are caused by frameshift or nonsense mutations that truncate the protein product 1. Transcripts harbouring premature termination codons are selectively and rapidly degraded by the nonsense-mediated mRNA decay (NMD) pathway. Blocking the NMD pathway in any given cell will stabilise these mutant transcripts, which can then be detected using gene expression microarrays. This technique, known as gene identification by nonsense-mediated mRNA decay inhibition (GINI), has proved successful in identifying sporadic nonsense mutations involved in many different cancer types. However, the approach has not yet been applied to identify germline mutations involved in breast cancer. We therefore attempted to use GINI on lymphoblastoid cell lines (LCLs) from multiple-case, non- BRCA1/2 breast cancer families in order to identify additional high-risk breast cancer susceptibility genes.

METHODS

We applied GINI to a total of 24 LCLs, established from breast-cancer affected and unaffected women from three multiple-case non-BRCA1/2 breast cancer families. We then used Illumina gene expression microarrays to identify transcripts stabilised by the NMD inhibition.

RESULTS

The expression profiling identified a total of eight candidate genes from these three families. One gene, PPARGC1A, was a candidate in two separate families. We performed semi-quantitative real-time reverse transcriptase PCR of all candidate genes but only PPARGC1A showed successful validation by being stabilised in individuals with breast cancer but not in many unaffected members of the same family. Sanger sequencing of all coding and splice site regions of PPARGC1A did not reveal any protein truncating mutations. Haplotype analysis using short tandem repeat microsatellite markers did not indicate the presence of a haplotype around PPARGC1A which segregated with disease in the family.

CONCLUSIONS

The application of the GINI method to LCLs to identify transcripts harbouring germline truncating mutations is challenging due to a number of factors related to cell type, microarray sensitivity and variations in NMD efficiency.