Post genome-wide association studies functional characterization of prostate cancer risk loci

Racial disparity in prostate cancer: an outlook in genetic and molecular landscape

Prostate cancer (PCa) incidence, morbidity, and mortality rates are significantly impacted by racial disparities. Despite innovative therapeutic approaches and advancements in prevention, men of African American (AA) ancestry are at a higher risk of developing PCa and have a more aggressive and metastatic form of the disease at the time of initial PCa diagnosis than other races. Research on PCa has underlined the biological and molecular basis of racial disparity and emphasized the genetic aspect as the fundamental component of racial inequality. Furthermore, the lower enrollment rate, limited access to national-level cancer facilities, and deferred treatment of AA men and other minorities are hurdles in improving the outcomes of PCa patients. This review provides the most up-to-date information on various biological and molecular contributing factors, such as the single nucleotide polymorphisms (SNPs), mutational spectrum, altered chromosomal loci, differential gene expression, transcriptome analysis, epigenetic factors, tumor microenvironment (TME), and immune modulation of PCa racial disparities. This review also highlights future research avenues to explore the underlying biological factors contributing to PCa disparities, particularly in men of African ancestry.

Genome-wide association analysis reveals regulation of at-risk loci by DNA methylation in prostate cancer

Epigenetic changes are potentially important for the ontogeny and progression of tumors but are not usually studied because of the complexity of analyzing transcript regulation resulting from epigenetic alterations. Prostate cancer (PCa) is characterized by variable clinical manifestations and frequently unpredictable outcomes. We performed an expression quantitative trait loci (eQTL) analysis to identify the genomic regions that regulate gene expression in PCa and identified a relationship between DNA methylation and clinical information. Using multi-level information published in The Cancer Genome Atlas, we performed eQTL-based analyses on DNA methylation and gene expression. To better interpret these data, we correlated loci and clinical indexes to identify the important loci for both PCa development and progression. Our data demonstrated that although only a small proportion of genes are regulated via DNA methylation in PCa, these genes are enriched in important cancer-related groups. In addition, single nucleotide polymorphism analysis identified the locations of CpG sites and genes within at-risk loci, including the 19q13.2–q13.43 and 16q22.2–q23.1 loci. Further, an epigenetic association study of clinical indexes detected risk loci and pyrosequencing for site validation. Although DNA methylation-regulated genes across PCa samples are a small proportion, the associated genes play important roles in PCa carcinogenesis.

Germline genetics in localized prostate cancer

PURPOSE OF REVIEW

To provide the reader an understanding of the importance and limitations of prostate cancer (PCa) screening, the heritable component of PCa and the role that germline genetic markers can play in risk-adapted screening and treatment.

RECENT FINDINGS

Despite strong science supporting the association of germline genetic change with PCa risk and outcome, there has been a reluctance to pursue practical application of these technologies. Recent findings suggest that actionable information may now be garnered from this form of testing, which can help men at risk for and with PCa.

SUMMARY

This is an exciting time whereby germline genetic markers can help overcome some of the shortcomings of current PCa screening and treatment paradigms. Understanding their benefit and limitations while keeping the patient's best interest in mind will be the key for the responsible application of these exciting technologies.

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Post-GWAS in prostate cancer: from genetic association to biological contribution

Genome-wide association studies (GWAS) have been successful in deciphering the genetic component of predisposition to many human complex diseases including prostate cancer. Germline variants identified by GWAS progressively unravelled the substantial knowledge gap concerning prostate cancer heritability. With the beginning of the post-GWAS era, more and more studies reveal that, in addition to their value as risk markers, germline variants can exert active roles in prostate oncogenesis. Consequently, current research efforts focus on exploring the biological mechanisms underlying specific susceptibility loci known as causal variants by applying novel and precise analytical methods to available GWAS data. Results obtained from these post-GWAS analyses have highlighted the potential of exploiting prostate cancer risk-associated germline variants to identify new gene networks and signalling pathways involved in prostate tumorigenesis. In this Review, we describe the molecular basis of several important prostate cancer-causal variants with an emphasis on using post-GWAS analysis to gain insight into cancer aetiology. In addition to discussing the current status of post-GWAS studies, we also summarize the main molecular mechanisms of potential causal variants at prostate cancer risk loci and explore the major challenges in moving from association to functional studies and their implication in clinical translation.

An Integrative Genomics Approach for Associating Genome-Wide Association Studies Information With Localized and Metastatic Prostate Cancer Phenotypes

High-throughput genotyping has enabled discovery of genetic variants associated with an increased risk of developing prostate cancer using genome-wide association studies (GWAS). The goal of this study was to associate GWAS information of patients with primary organ–confined and metastatic prostate cancer using gene expression data and to identify molecular networks and biological pathways enriched for genetic susceptibility variants involved in the 2 disease states. The analysis revealed gene signatures for the 2 disease states and a gene signature distinguishing the 2 patient groups. In addition, the analysis revealed molecular networks and biological pathways enriched for genetic susceptibility variants. The discovered pathways include the androgen, apoptosis, and insulinlike growth factor signaling pathways. This analysis established putative functional bridges between GWAS discoveries and the biological pathways involved in primary organ–confined and metastatic prostate cancer.

Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci

BACKGROUND

Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors (TFs) critical to somatic tumorigenesis.

METHODS

All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21 693 controls; independent from discovery), and combined (9627 cases/30 845 controls; including additional individuals).

RESULTS

The PAX8-target gene set was ranked 1/615 in the discovery (PGSEA<0.001; FDR=0.21), 7/615 in the replication (PGSEA=0.004; FDR=0.37), and 1/615 in the combined (PGSEA<0.001; FDR=0.21) studies. Adding other genes reported to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P<10-5 (including six with P<5 × 10-8). The pathway was also associated with differential gene expression after shRNA-mediated silencing of PAX8 in HeyA8 (PGSEA=0.025) and IGROV1 (PGSEA=0.004) SOC cells and several PAX8 targets near SOC risk loci demonstrated in vitro transcriptomic perturbation.

CONCLUSIONS

Putative PAX8 target genes are enriched for common SOC risk variants. This finding from our agnostic evaluation is of particular interest given that PAX8 is well-established as a specific marker for the cell of origin of SOC.

Screening key microRNAs for castration-resistant prostate cancer based on miRNA/mRNA functional synergistic network

High-throughput methods have been used to explore the mechanisms by which androgen-sensitive prostate cancer (ASPC) develops into castration-resistant prostate cancer (CRPC). However, it is difficult to interpret cryptic results by routine experimental methods. In this study, we performed systematic and integrative analysis to detect key miRNAs that contribute to CRPC development. From three DNA microarray datasets, we retrieved 11 outlier microRNAs (miRNAs) that had expression discrepancies between ASPC and CRPC using a specific algorithm. Two of the miRNAs (miR-125b and miR-124) have previously been shown to be related to CRPC. Seven out of the other nine miRNAs were confirmed by quantitative PCR (Q-PCR) analysis. MiR-210, miR-218, miR-346, miR-197, and miR-149 were found to be over-expressed, while miR-122, miR-145, and let-7b were under-expressed in CRPC cell lines. GO and KEGG pathway analyses revealed that miR-218, miR-197, miR-145, miR-122, and let-7b, along with their target genes, were found to be involved in the PI3K and AKT3 signaling network, which is known to contribute to CRPC development. We then chose five miRNAs to verify the accuracy of the analysis. The target genes of each miRNA were altered significantly upon transfection of specific miRNA mimics in the C4–2 CRPC cell line, which was consistent with our pathway analysis results. Finally, we hypothesized that miR-218, miR-145, miR-197, miR-149, miR-122, and let-7b may contribute to the development of CRPC through the influence of Ras, Rho proteins, and the SCF complex. Further investigation is needed to verify the functions of the identified novel pathways in CRPC development.

eQTL analysis links inflammatory bowel disease associated 1q21 locus to ECM1 gene

Genome-wide association studies (GWAS) have been highly successful in inflammatory bowel disease (IBD) with 163 confirmed associations so far. We used expression quantitative trait loci (eQTL) mapping to analyze IBD associated regions for which causative gene from the region is still unknown. First, we performed an extensive literature search and in silico analysis of published GWAS in IBD and eQTL studies and extracted 402 IBD associated SNPs assigned to 208 candidate loci, and 9562 eQTL correlations. When crossing GWA and eQTL data we found that for 50 % of loci there is no eQTL gene, while for 31.2 % we can determine one gene, for 11.1 % two genes and for the remaining 7.7 % three or more genes. Based on that we selected loci with one, two, and three or more eQTL genes and analyzed them in peripheral blood lymphocytes and intestine tissue samples of 606 Slovene patients with IBD and in 449 controls. Association analysis of selected SNPs showed statistical significance for three (rs2631372 and rs1050152 on 5q locus and rs13294 on 1q locus) out of six selected SNPs with at least one phenotype. Furthermore, with eQTL analysis of selected chromosomal regions, we confirmed a link between SNP and gene for four (SLC22A5 on 5q, ECM1 on 1q, ORMDL3 on 17q, and PUS10 on 2p locus) out of five selected regions. For 1q21 loci, we confirmed gene ECM1 as the most plausible gene from this region to be involved in pathogenesis of IBD and thereby contributed new eQTL correlation from this genomic region.

An analysis of the association between prostate cancer risk loci, PSA levels, disease aggressiveness and disease-specific mortality

BACKGROUND

Genome-wide association studies have identified multiple single-nucleotide polymorphsims (SNPs) associated with prostate cancer (PCa). Although these SNPs have been clearly associated with disease risk, their relationship with clinical outcomes is less clear. Our aim was to assess the frequency of known PCa susceptibility alleles within a single institution ascertainment and to correlate risk alleles with disease-specific outcomes.

METHODS

We genotyped 1354 individuals treated for localised PCa between June 1988 and December 2007. Blood samples were prospectively collected and de-identified before being genotyped and matched to phenotypic data. We investigated associations between 61 SNPs and disease-specific end points using multivariable analysis and also determined if SNPs were associated with PSA at diagnosis.

RESULTS

Seven SNPs showed associations on multivariable analysis (P<0.05), rs13385191 with both biochemical recurrence (BR) and castrate metastasis (CM), rs339331 (BR), rs1894292, rs17178655 and rs11067228 (CM), and rs11902236 and rs4857841 PCa-specific mortality. After applying a Bonferroni correction for number of SNPs (P<0.0008), the only persistent significant association was between rs17632542 (KLK3) and PSA levels at diagnosis (P=1.4 × 10(-5)).

CONCLUSIONS

We confirmed that rs17632542 in KLK3 is associated with PSA at diagnosis. No significant association was seen between loci and disease-specific end points when accounting for multiple testing. This provides further evidence that known PCa risk SNPs do not predict likelihood of disease progression.

Discovery and characterization of long intergenic non-coding RNAs (lincRNA) module biomarkers in prostate cancer: an integrative analysis of RNA-Seq data

Background

Prostate cancer (PCa) is a leading cause of cancer-related death of men worldwide. There is an urgent need to develop novel biomarkers for PCa prognosis and diagnosis in the post prostate-specific antigen era. Long intergenic noncoding RNAs (lincRNAs) play essential roles in many physiological processes and can serve as alternative biomarkers for prostate cancer, but there has been no systematic investigation of lincRNAs in PCa yet.

Results

Nine lincRNA co-expression modules were identified from PCa RNA-Seq data. The association between the principle component of each module and the PCa phenotype was examined by calculating the Pearson's correlation coefficients. Three modules (M1, M3, and M5) were found associated with PCa. Two modules (M3 and M5) were significantly enriched with lincRNAs, and one of them, M3, may be used as a lincRNA module-biomarker for PCa diagnosis. This module includes seven essential lincRNAs: TCONS_l2_00001418, TCONS_l2_00008237, TCONS_l2_00011130, TCONS_l2_00013175, TCONS_l2_00022611, TCONS_l2_00022670 and linc-PXN-1. The clustering analysis and microRNA enrichment analysis further confirmed our findings.

Conclusion

The correlation between lincRNAs and protein-coding genes is helpful for further exploration of functional mechanisms of lincRNAs in PCa. This study provides some important insights into the roles of lincRNAs in PCa and suggests a few lincRNAs as candidate biomarkers for PCa diagnosis and prognosis.

Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation

BACKGROUND

Prostate cancer is one of the most widespread cancers in men and is fundamentally a genetic disease. Identifying regulators in cancer using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks (GRNs). Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as cancer subtypes.

METHODS

In our approach, independent gene regulatory networks from each prostate state were reconstructed using one of the current state-of-art reverse engineering approaches. Next, crucial genes involved in this cancer were highlighted by analyzing each network individually and also in comparison with each other.

RESULTS

In this paper, a novel network-based approach was introduced to find critical transcription factors involved in prostate cancer. The results led to detection of 38 essential transcription factors based on hub type variation. Additionally, experimental evidence was found for 29 of them as well as 9 new transcription factors.

CONCLUSION

The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell.

FGF23: mediator of poor prognosis in a sizeable subgroup of patients with castration-resistant prostate cancer presenting with severe hypophosphatemia?

Castration-resistant prostate cancer (CRPC) is an advanced and incurable stage of the second most frequently diagnosed malignancy in men globally. Current treatment options improve survival modestly but eventually fail due to intrinsic or acquired therapeutic resistance. A hypothesis is presented wherein circulating levels of fibroblast growth factor 23 (FGF23), an endocrine member of the fibroblast growth factor family with phosphaturic properties, are proposed as a prognostic and predictive marker to identify CRPC patients with poor prognosis that are amenable to FGF23 antibody therapy (FGF23i) or treatment with fibroblast growth factor receptor inhibitors (FGFRi). With respect to the latter, FGF23 may also serve as a pharmacodynamic marker enabling individualized FGFRi dosing. We recently discovered that the development of severe and sustained hypophosphatemia in CRPC patients undergoing zoledronic acid therapy for bone metastases was associated with markedly worse prognosis compared to patients without or with only mild and transient hypophosphatemia. Severe hypophosphatemia is a typical manifestation of tumor-induced hypophosphatemic osteomalacia (TIO), a paraneoplastic condition mediated by FGF23 overexpression in most instances. While the postulated tumor-promoting role of FGF23 in CRPC or other malignancies has not yet been studied, several lines of evidence suggest that FGF23 may mediate both severe hypophosphatemia (via its endocrine properties) and aggressive CRPC behavior (via autocrine and paracrine activities): (i) FGF23 and the necessary signalling machinery (i.e. members of the fibroblast growth factor receptor [FGFR] family and the essential co-receptor α-KLOTHO [KL]) are highly expressed in a sizeable subgroup of CRPC patients; (ii) FGF/FGFR signalling plays important roles in prostate cancer; (iii) FGF23 can induce its own expression via a positive autocrine feedback loop involving FGFR1; and (iv) this positive feedback loop may be triggered by bone-targeted therapies frequently used for the treatment of CRPC-associated bone metastases. While there is a lack of personalized treatment strategies in the management of CRPC to date, FGF23 targeted therapy has the potential to fill this unmet clinical need in the not-so-distant future. In fact, FGFRi are currently in advanced clinical testing for a number of malignancies such as kidney and lung cancer, but there is a lack of conclusive data on FGFRi therapy in patients selected for FGF/FGFR pathway activation.

Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer

Background

MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.

Methods

The human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.

Results

Using this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.

Conclusions

Our analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

Interdisciplinary dialogue for education, collaboration, and innovation: intelligent Biology and Medicine in and beyond 2013

The 2013 International Conference on Intelligent Biology and Medicine (ICIBM 2013) was held on August 11-13, 2013 in Nashville, Tennessee, USA. The conference included six scientific sessions, two tutorial sessions, one workshop, two poster sessions, and four keynote presentations that covered cutting-edge research topics in bioinformatics, systems biology, computational medicine, and intelligent computing. Here, we present a summary of the conference and an editorial report of the supplements to BMC Genomics and BMC Systems Biology that include 19 research papers selected from ICIBM 2013.