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

piRNA PROPER Suppresses DUSP1 Translation by Targeting N6-Methyladenosine-Mediated RNA Circularization to Promote Oncogenesis of Prostate Cancer

Genetic and epigenetic alterations occur in many physiological and pathological processes. The existing knowledge regarding the association of PIWI-interacting RNAs (piRNAs) and their genetic variants on risk and progression of prostate cancer (PCa) is limited. In this study, three genome-wide association study datasets are combined, including 85,707 PCa cases and 166,247 controls, to uncover genetic variants in piRNAs. Functional investigations involved manipulating piRNA expression in cellular and mouse models to study its oncogenetic role in PCa. A specific genetic variant, rs17201241 is identified, associated with increased expression of PROPER (piRNA overexpressed in prostate cancer) in tumors and are located within the gene, conferring an increased risk and malignant progression of PCa. Mechanistically, PROPER coupled with YTHDF2 to recognize N6-methyladenosine (m6A) and facilitated RNA-binding protein interactions between EIF2S3 at 5'-untranslated region (UTR) and YTHDF2/YBX3 at 3'-UTR to promote DUSP1 circularization. This m6A-dependent mRNA-looping pattern enhanced DUSP1 degradation and inhibited DUSP1 translation, ultimately reducing DUSP1 expression and promoting PCa metastasis via the p38 mitogen-activated protein kinase (MAPK) signaling pathway. Inhibition of PROPER expression using antagoPROPER effectively suppressed xenograft growth, suggesting its potential as a therapeutic target. Thus, targeting piRNA PROPER-mediated genetic and epigenetic fine control is a promising strategy for the concurrent prevention and treatment of PCa.

Clinical and Functional Analyses of an African-ancestry Gain-of-function HOXB13 Variant Implicated in Aggressive Prostate Cancer

BACKGROUND

Recent reports have uncovered a HOXB13 variant (X285K) predisposing to prostate cancer in men of West African ancestry. The clinical relevance and protein function associated with this inherited variant are unknown.

OBJECTIVE

To determine the clinical relevance of HOXB13 (X285K) in comparison with HOXB13 (G84E) and BRCA2 pathogenic/likely pathogenic (P/LP) variants, and to elucidate the oncogenic mechanisms of the X285K protein.

DESIGN, SETTING, AND PARTICIPANTS

Real-world data were collected from 21,393 men with prostate cancer undergoing genetic testing from 2019 to 2022, and in vitro cell-line models were established for the evaluation of oncogenic functions associated with the X285K protein.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Genetic testing results were compared among patient groups according to self-reported race/ethnicity, Gleason scores, and American Joint Committee on Cancer stages using the exact test. Oncogenic functions of X285K were evaluated by RNA sequencing, chromatin immunoprecipitation sequencing, and Western blot analyses.

RESULTS AND LIMITATIONS

HOXB13 (X285K) was significantly enriched in self-reported Black (1.01%) versus White (0.01%) patients. We observed a trend of more aggressive disease in the HOXB13 (X285K) and BRCA2 P/LP carriers than in the HOXB13 (G84E) carriers. Replacement of the wild-type HOXB13 protein with the X285K protein resulted in a gain of an E2F/MYC signature, validated by the elevated expression of cyclin B1 and c-Myc, without affecting the androgen response signature. Elevated expression of cyclin B1 and c-Myc was explained by enhanced binding of the X285K protein to the promoters and enhancers of these genes. The limitations of the study are the lack of complete clinical outcome data for all patients studied and the use of a single cell line in the functional analysis.

CONCLUSIONS

HOXB13 (X285K) is significantly enriched in self-reported Black patients, and X285K carriers detected in the real-world clinical setting have aggressive prostate cancer features similar to the BRCA2 carriers. Functional studies revealed a unique gain-of-function oncogenic mechanism of X285K protein in regulating E2F/MYC signatures.

PATIENT SUMMARY

The HOXB13 (X285K) variant is clinically and functionally linked to aggressive prostate cancer, supporting genetic testing for X285K in Black men and early disease screening of carriers of this variant.

Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells

While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.

Combined SNPs sequencing and allele specific proteomics capture reveal functional causality underpinning the 2p25 prostate cancer susceptibility locus

Genome wide association studies (GWASs) have identified numerous risk loci associated with prostate cancer, yet unraveling their functional significance remains elusive. Leveraging our high-throughput SNPs-seq method, we pinpointed rs4519489 within the multi-ancestry GWAS-discovered 2p25 locus as a potential functional SNP due to its significant allelic differences in protein binding. Here, we conduct a comprehensive analysis of rs4519489 and its associated gene, NOL10, employing diverse cohort data and experimental models. Clinical findings reveal a synergistic effect between rs4519489 genotype and NOL10 expression on prostate cancer prognosis and severity. Through unbiased proteomics screening, we reveal that the risk allele A of rs4519489 exhibits enhanced binding to USF1, a novel oncogenic transcription factor (TF) implicated in prostate cancer progression and prognosis, resulting in elevated NOL10 expression. Furthermore, we elucidate that NOL10 regulates cell cycle pathways, fostering prostate cancer progression. The concurrent expression of NOL10 and USF1 correlates with aggressive prostate cancer characteristics and poorer prognosis. Collectively, our study offers a robust strategy for functional SNP screening and TF identification through high-throughput SNPs-seq and unbiased proteomics, highlighting the rs4519489-USF1-NOL10 regulatory axis as a promising biomarker or therapeutic target for clinical diagnosis and treatment of prostate cancer.

Big data and artificial intelligence in cancer research

The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.

LINC01226 promotes gastric cancer progression through enhancing cytoplasm-to-nucleus translocation of STIP1 and stabilizing β-catenin protein

Gastric cancer (GC) remains one of the most common malignances and the leading cause of cancer-related mortality worldwide. Although the critical role of several long non-coding RNAs (lncRNAs) transcribed from several GC-risk loci has been established, we still know little about the biological significance of these lncRNAs at most gene loci and how they play in cell signaling. In the present study, we identified a novel oncogenic lncRNA LINC01226 transcribed from the 1p35.2 GC-risk locus. LINC01226 shows markedly higher expression levels in GC specimens compared with those in normal tissues. High expression of LINC01226 is evidently correlated with worse prognosis of GC cases. In line with these, oncogenic LINC01226 promotes proliferation, migration and metastasis of GC cells ex vivo and in vivo. Importantly, LINC01226 binds to STIP1 protein, leads to disassembly of the STIP1-HSP90 complex, elevates interactions between HSP90 and β-catenin, stabilizes β-catenin protein, activates the Wnt/β-catenin signaling and, thereby, promote GC progression. Together, our findings uncovered a novel layer regulating the Wnt signaling in cancers and uncovers a new epigenetic mode of GC tumorigenesis. These discoveries also shed new light on the importance of functional lncRNAs as innovative therapeutic targets through precisely controlling protein-protein interactions in cancers.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Genomic and Phenotypic Biomarkers for Precision Medicine Guidance in Advanced Prostate Cancer

OPINION STATEMENT

Prostate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen.

GATA2 co-opts TGFβ1/SMAD4 oncogenic signaling and inherited variants at 6q22 to modulate prostate cancer progression

BACKGROUND

Aberrant somatic genomic alteration including copy number amplification is a hallmark of cancer genomes. We previously profiled genomic landscapes of prostate cancer (PCa), yet the underlying causal genes with prognostic potential has not been defined. It remains unclear how a somatic genomic event cooperates with inherited germline variants contribute to cancer predisposition and progression.

METHODS

We applied integrated genomic and clinical data, experimental models and bioinformatic analysis to identify GATA2 as a highly prevalent metastasis-associated genomic amplification in PCa. Biological roles of GATA2 in PCa metastasis was determined in vitro and in vivo. Global chromatin co-occupancy and co-regulation of GATA2 and SMAD4 was investigated by coimmunoprecipitation, ChIP-seq and RNA-seq assays. Tumor cellular assays, qRT-PCR, western blot, ChIP, luciferase assays and CRISPR-Cas9 editing methods were performed to mechanistically understand the cooperation of GATA2 with SMAD4 in promoting TGFβ1 and AR signaling and mediating inherited PCa risk and progression.

RESULTS

In this study, by integrated genomics and experimental analysis, we identified GATA2 as a prevalent metastasis-associated genomic amplification to transcriptionally augment its own expression in PCa. Functional experiments demonstrated that GATA2 physically interacted and cooperated with SMAD4 for genome-wide chromatin co-occupancy and co-regulation of PCa genes and metastasis pathways like TGFβ signaling. Mechanistically, GATA2 was cooperative with SMAD4 to enhance TGFβ and AR signaling pathways, and activated the expression of TGFβ1 via directly binding to a distal enhancer of TGFβ1. Strinkingly, GATA2 and SMAD4 globally mediated inherited PCa risk and formed a transcriptional complex with HOXB13 at the PCa risk-associated rs339331/6q22 enhancer, leading to increased expression of the PCa susceptibility gene RFX6.

CONCLUSIONS

Our study prioritizes causal genomic amplification genes with prognostic values in PCa and reveals the pivotal roles of GATA2 in transcriptionally activating the expression of its own and TGFβ1, thereby co-opting to TGFβ1/SMAD4 signaling and RFX6 at 6q22 to modulate PCa predisposition and progression.

Combined CRISPRi and proteomics screening reveal a cohesin-CTCF-bound allele contributing to increased expression of RUVBL1 and prostate cancer progression

Genome-wide association studies along with expression quantitative trait locus (eQTL) mapping have identified hundreds of single-nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PCa), yet functional characterization of these risk loci remains challenging. To screen for potential regulatory SNPs, we designed a CRISPRi library containing 9,133 guide RNAs (gRNAs) to cover 2,166 candidate SNP loci implicated in PCa and identified 117 SNPs that could regulate 90 genes for PCa cell growth advantage. Among these, rs60464856 was covered by multiple gRNAs significantly depleted in screening (FDR < 0.05). Pooled SNP association analysis in the PRACTICAL and FinnGen cohorts showed significantly higher PCa risk for the rs60464856 G allele (p value = 1.2 × 10-16 and 3.2 × 10-7, respectively). Subsequent eQTL analysis revealed that the G allele is associated with increased RUVBL1 expression in multiple datasets. Further CRISPRi and xCas9 base editing confirmed that the rs60464856 G allele leads to elevated RUVBL1 expression. Furthermore, SILAC-based proteomic analysis demonstrated allelic binding of cohesin subunits at the rs60464856 region, where the HiC dataset showed consistent chromatin interactions in prostate cell lines. RUVBL1 depletion inhibited PCa cell proliferation and tumor growth in a xenograft mouse model. Gene-set enrichment analysis suggested an association of RUVBL1 expression with cell-cycle-related pathways. Increased expression of RUVBL1 and activation of cell-cycle pathways were correlated with poor PCa survival in TCGA datasets. Our CRISPRi screening prioritized about one hundred regulatory SNPs essential for prostate cell proliferation. In combination with proteomics and functional studies, we characterized the mechanistic role of rs60464856 and RUVBL1 in PCa progression.

The roles of proteases in prostate cancer

Since the proposition of the pro-invasive activity of proteolytic enzymes over 70 years ago, several roles for proteases in cancer progression have been established. About half of the 473 active human proteases are expressed in the prostate and many of the most well-characterized members of this enzyme family are regulated by androgens, hormones essential for development of prostate cancer. Most notably, several kallikrein-related peptidases, including KLK3 (prostate-specific antigen, PSA), the most well-known prostate cancer marker, and type II transmembrane serine proteases, such as TMPRSS2 and matriptase, have been extensively studied and found to promote prostate cancer progression. Recent findings also suggest a critical role for proteases in the development of advanced and aggressive castration-resistant prostate cancer (CRPC). Perhaps the most intriguing evidence for this role comes from studies showing that the protease-activated transmembrane proteins, Notch and CDCP1, are associated with the development of CRPC. Here, we review the roles of proteases in prostate cancer, with a special focus on their regulation by androgens.

Computational Assessment of the Expression-modulating Potential for Non-coding Variants

Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.

Combined CRISPRi and proteomics screening reveal a cohesin-CTCF-bound allele contributing to increased expression of RUVBL1 and prostate cancer progression

Genome-wide association studies along with expression quantitative trait loci (eQTL) mapping have identified hundreds of single nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PCa), yet functional characterization of these risk loci remains challenging. To screen for potential regulatory SNPs, we designed a CRISPRi library containing 9133 guide RNAs (gRNAs) to target 2,166 candidate SNP sites implicated in PCa and identified 117 SNPs that could regulate 90 genes for PCa cell growth advantage. Among these, rs60464856 was covered by multiple gRNAs significantly depleted in the screening (FDR<0.05). Pooled SNP association analysis in the PRACTICAL and FinnGen cohorts showed significantly higher PCa risk for the rs60464856 G allele (pvalue=1.2E-16 and 3.2E-7). Subsequent eQTL analysis revealed that the G allele is associated with increased RUVBL1 expression in multiple datasets. Further CRISPRi and xCas9 base editing proved the rs60464856 G allele leading to an elevated RUVBL1 expression. Furthermore, SILAC-based proteomic analysis demonstrated allelic binding of cohesin subunits at the rs60464856 region, where HiC dataset showed consistent chromatin interactions in prostate cell lines. RUVBL1 depletion inhibited PCa cell proliferation and tumor growth in xenograft mouse model. Gene set enrichment analysis suggested an association of RUVBL1 expression with cell-cycle-related pathways. An increased expression of RUVBL1 and activations of cell-cycle pathways were correlated with poor PCa survival in TCGA datasets. Together, our CRISPRi screening prioritized about one hundred regulatory SNPs essential for prostate cell proliferation. In combination with proteomics and functional studies, we characterized the mechanistic role of rs60464856 and RUVBL1 in PCa progression.

Zn2+-interference and H2S-mediated gas therapy based on ZnS-tannic acid nanoparticles synergistic enhancement of cell apoptosis for specific treatment of prostate cancer

Zn²⁺ and H₂S are essential to maintain normal prostate function, and sometimes can evolve into weapons to attack and destroy prostate cancer (PCa) cells. Nevertheless, how to achieve the targeted and effective release of Zn²⁺ and H₂S, and reverse the concentration distribution within PCa tumor cells still highly challenging. Herein, combined with these pathological characteristics of prostate, we proposed a tumor microenvironment (TME) responsive Zn²⁺-interference and H₂S-mediated gas synergistic therapy strategy based on a nanoplatform of tannic acid (TA) modified zinc sulfide nanoparticles (ZnS@TA) for the specific treatment of PCa. Once the constructed pH-responsive ZnS@TA internalized by cancer cells, it would instantaneously decomposed in acidic TME, and explosively release excess Zn²⁺ and H₂S exceeding the cell self-regulation threshold. Meanwhile, the in situ produced Zn²⁺ and H₂S synergistic enhancement of cell apoptosis, which is evidenced to increase levels of Bax and Bax/Bcl-2 ratio, release of Cytochrome c in cancer cells, contributing to inhibit the growth of tumor. Moreover, the TA in cooperation with Zn²⁺ specifically limits the migration and invasion of PCa cells. Both in vitro and in vivo results demonstrate that the Zn²⁺-interference in combination with H₂S-mediated gas therapy achieves an excellent anti-tumor performance. Overall, this nanotheranostic synergistic therapy provides a promising direction for exploring new strategies for cancer treatment based on specific tumor pathological characteristics, and provides a new vision for promoting practical cancer therapy.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Identification of ZDHHC1 as a Pyroptosis Inducer and Potential Target in the Establishment of Pyroptosis-Related Signature in Localized Prostate Cancer

Pyroptosis or cellular inflammatory necrosis is a programmed cell death kind. Accumulating evidence shows that pyroptosis plays a crucial role in the invasion, metastasis, and proliferation of tumor cells, thus affecting the prognosis of tumors and therapeutic effects. Prostate cancer (PCa), a common malignancy among men, is associated with inflammation. Pathophysiological effects of pyroptosis on tumor development and progression, as well as the mediation of PCa, are known, but its effects on the potential prognosis for PCa warrant in-depth investigation. Herein, we built a risk model of six pyroptosis-related genes and verified their predictive abilities for prognostic and therapeutic effects. Higher risk scores indicated a higher probability of biochemical recurrence (BCR), higher immune infiltration, and worsened clinicopathological features. To derive scientific and reliable predictions for BCR in patients having PCa, the findings of the current study were verified in the Gene Expression Omnibus (GEO) cohort following evaluation in The Cancer Genome Atlas (TCGA) dataset. Additionally, after evaluating the six genes in the model, ZDHHC1 was found to be an important component. Its antitumor role was further assessed through in vivo and in vitro experiments, and its promoting effect on pyroptosis was further evaluated and verified. The above results provided a new perspective for further studies on pyroptosis and its clinical utility for PCa.

Prostate Cancer Transcriptomic Regulation by the Interplay of Germline Risk Alleles, Somatic Mutations, and 3D Genomic Architecture

Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline-somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation.

SIGNIFICANCE

Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline-somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711.

Epigenetics and genetics of hepatoblastoma: Linkage and treatment

Hepatoblastoma is a malignant embryonal tumor with multiple differentiation modes and is the clearest liver malignancy in children. However, little is known about genetic and epigenetic events in Hepatoblastoma. Increased research has recently demonstrated, unique genetic and epigenetic events in Hepatoblastoma, providing insights into its origin and precise treatment. Some genetic disorders and congenital factors are associated with the risk of Hepatoblastoma development, such as the Beckwith-Wiedemann syndrome, Familial Adenomatous polyposis, and Hemihypertrophy. Epigenetic modifications such as DNA modifications, histone modifications, and non-coding RNA regulation are also essential in the development of Hepatoblastoma. Herein, we reviewed genetic and epigenetic events in Hepatoblastoma, focusing on the relationship between these events and cancer susceptibility, tumor growth, and prognosis. By deciphering the genetic and epigenetic associations in Hepatoblastoma, tumor pathogenesis can be clarified, and guide the development of new anti-cancer drugs and prevention strategies.

A comparative mRNA- and miRNA transcriptomics reveals novel molecular signatures associated with metastatic prostate cancers

Background: Prostate cancer (PC) is a fatally aggressive urogenital cancer killing millions of men, globally. Thus, this study aims to identify key miRNAs, target genes, and drug targets associated with prostate cancer metastasis.

Methods: The miRNA and mRNA expression datasets of 148 prostate tissue biopsies (39 tumours and 109 normal tissues), were analysed by differential gene expression analysis, protein interactome mapping, biological pathway analysis, miRNA-mRNA networking, drug target analysis, and survival curve analysis.

Results: The dysregulated expression of 53 miRNAs and their 250 target genes involved in Hedgehog, ErbB, and cAMP signalling pathways connected to cell growth, migration, and proliferation of prostate cancer cells was detected. The subsequent miRNA-mRNA network and expression status analysis have helped us in narrowing down their number to 3 hub miRNAs (hsa-miR-455-3p, hsa-miR-548c-3p, and hsa-miR-582-5p) and 9 hub genes (NFIB, DICER1, GSK3B, DCAF7, FGFR1OP, ABHD2, NACC2, NR3C1, and FGF2). Further investigations with different systems biology methods have prioritized NR3C1, ABHD2, and GSK3B as potential genes involved in prostate cancer metastasis owing to their high mutation load and expression status. Interestingly, down regulation of NR3C1 seems to improve the prostate cancer patient survival rate beyond 150 months. The NR3C1, ABHD2, and GSK3B genes are predicted to be targeted by hsa-miR-582-5p, besides some antibodies, PROTACs and inhibitory molecules.

Conclusion: This study identified key miRNAs (miR-548c-3p and miR-582-5p) and target genes (NR3C1, ABHD2, and GSK3B) as potential biomarkers for metastatic prostate cancers from large-scale gene expression data using systems biology approaches.

Risk Allele Frequency Analysis and Risk Prediction of Single-Nucleotide Polymorphisms for Prostate Cancer

The incidence of prostate cancer (PCa) varies by ethnicity. This study aimed to provide insights into the genetic cause of PCa, which can result in differences in incidence among individuals of diverse ancestry. We collected data on PCa-associated single-nucleotide polymorphisms (SNPs) from a genome-wide association study catalog. Fisher’s exact tests were used to analyze the significance of enrichment or depletion of the effect on the allele at a given SNP. A network analysis was performed based on PCa-related SNPs that showed significant differences among ethnicities. The SNP-based polygenic risk score (PRS) was calculated, and its correlation with PCa incidence was evaluated. European, African, and East Asian populations had different heatmap patterns. Calculated PRS from the allele frequencies of PCa was the highest among Africans, followed by Europeans, and was the lowest among East Asians. PRS was positively correlated with the incidence and mortality of PCa. Network analysis revealed that AR, CDKN1B, and MAD1L1 are genes related to ethnic differences in PCa. The incidence and mortality of PCa showed a strong correlation with PRS according to ethnicity, which may suggest the effect of genetic factors, such as the AR gene, on PCa pathogenesis.

Prioritized candidate causal haplotype blocks in plant genome-wide association studies

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Genome-wide association studies (GWAS) are commonly used in human and plant studies to identify genetic variants responsible for the phenotype of interest and provide foundations for studying disease mechanisms and crop improvement. Most GWAS models are developed and optimized using human datasets. However, the difference between human and plant datasets essentially limits their applications in plant studies, especially when mapping complex traits such as drought resistance and yield. In this study, we present a novel GWAS method, HapFM, tailored for plant datasets to overcome the difficulties of many conventional GWAS methods. HapFM resulted in higher statistical power than conventional GWAS methods for mapping complex traits in our simulation and real dataset analyses. In addition, HapFM reduced the mapping interval by prioritizing candidate causal regions in the genome, which benefits the downstream experimental studies. Last but not least, HapFM can incorporate biological annotations to increase statistical power further. Overall, HapFM balances statistical power, result interpretability, and downstream experimental verifiability.

Identification of Candidate mRNA Isoforms for Prostate Cancer-Risk SNPs Utilizing Iso-eQTL and sQTL Methods

Single nucleotide polymorphisms (SNPs) impacting the alternative splicing (AS) process (sQTLs) or isoform expression (iso-eQTL) are implicated as important cancer regulatory elements. To find the sQTL and iso-eQTL, we retrieved prostate cancer (PrCa) tissue RNA-seq and genotype data originating from 385 PrCa European patients from The Cancer Genome Atlas. We conducted RNA-seq analysis with isoform-based and splice event-based approaches. The MatrixEQTL was used to identify PrCa-associated sQTLs and iso-eQTLs. The overlap between sQTL and iso-eQTL with GWAS loci and those that are differentially expressed between cancer and normal tissue were identified. The cis-acting associations (FDR < 0.05) for PrCa-risk SNPs identified 42, 123, and 90 PrCa-associated cassette exons, intron retention, and mRNA isoforms belonging to 25, 95, and 83 genes, respectively; while assessment of trans-acting association (FDR < 0.05) yielded 59, 65, and 196 PrCa-associated cassette exons, intron retention and mRNA isoforms belonging to 35, 55, and 181 genes, respectively. The results suggest that functional PrCa-associated SNPs can play a role in PrCa genesis by making an important contribution to the dysregulation of AS and, consequently, impacting the expression of the mRNA isoforms.

Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.

Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer

Precision oncology is an absolute need today due to the emergence of treatment resistance and heterogeneity among cancerous profiles. Target-propelled cancer therapy is one of the treasures of precision oncology which has come together with substantial medical accomplishment. Prostate cancer is one of the most common cancers in males, with tremendous biological heterogeneity in molecular and clinical behavior. The spectrum of molecular abnormalities and varying clinical patterns in prostate cancer suggest substantial heterogeneity among different profiles. To identify novel therapeutic targets and precise biomarkers implicated with prostate cancer, we performed a state-of-the-art bioinformatics study, beginning with analyzing high-throughput genomic datasets from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) suggests a set of five dysregulated hub genes (MAF, STAT6, SOX2, FOXO1, and WNT3A) that played crucial roles in biological pathways associated with prostate cancer progression. We found overexpressed STAT6 and SOX2 and proposed them as candidate biomarkers and potential targets in prostate cancer. Furthermore, the alteration frequencies in STAT6 and SOX2 and their impact on the patients' survival were explored through the cBioPortal platform. The Kaplan-Meier survival analysis suggested that the alterations in the candidate genes were linked to the decreased overall survival of the patients. Altogether, the results signify that STAT6 and SOX2 and their genomic alterations can be explored in therapeutic interventions of prostate cancer for precision oncology, utilizing early diagnosis and target-propelled therapy.

Correlation between Genomic Variants and Worldwide Epidemiology of Prostate Cancer

Prostate cancer (PCa) incidence and mortality vary across territories and populations. This can be explained by the genetic factor of this disease. This article aims to correlate the epidemiological data, worldwide incidence, and mortality of PCa with single-nucleotide polymorphisms (SNPs) associated with the susceptibility and severity of this neoplasm in different populations. Eighty-four genetic variants associated with prostate cancer susceptibility were selected from the literature through genome association studies (GWAS). Allele frequencies were obtained from the 1000 Genomes Project, and epidemiological data were obtained from Surveillance, Epidemiology, and End Results (SEER). The PCa incidence, mortality rates, and allele frequencies of variants were evaluated by Pearson’s correlation. Our study demonstrated that 12 SNPs (rs2961144, rs1048169, rs7000448, rs4430796, rs2066827, rs12500426, rs6983267, rs11649743, rs2075110, rs114798100, rs855723, and rs2075109) were correlated with epidemiological data in different ethnic groups. Ten SNPs (rs2961144, rs1048169, rs7000448, rs4430796, rs2066827, rs12500426, rs11649743, rs2075110, rs114798100, and rs2075109) were positively correlated with the mortality rate. Seven SNPs (rs1048169, rs2961144, rs7000448, rs4430796, rs2066827, rs12500426, and rs114798100) were positively correlated with incidence. Positive correlations of incidence and mortality rates were more frequent in the African population. The genetic variants investigated here are likely to predispose to PCa and could play a role in its progression and aggressiveness. This genetic study demonstrated here is promising for implementing personalized strategies to screen for prostate cancer in diverse populations.

Novel role of prostate cancer risk variant rs7247241 on PPP1R14A isoform transition through allelic TF binding and CpG methylation

Although previous studies identified numerous single nucleotide polymorphisms (SNPs) and their target genes predisposed to prostate cancer (PrCa) risks, SNP-related splicing associations are rarely reported. In this study, we applied distance-based sQTL analysis (sQTLseekeR) using RNA-seq and SNP genotype data from benign prostate tissue (n = 467) and identified significant associations in 3344 SNP-transcript pairs (P ≤ 0.05) at PrCa risk loci. We characterized a common SNP (rs7247241) and its target gene (PPP1R14A) located in chr19q13, an sQTL with risk allele T associated with upregulation of long isoform (P = 9.99E-7). We confirmed the associations in both TCGA (P = 2.42E-24) and GTEX prostate cohorts (P = 9.08E-78). To functionally characterize this SNP, we performed chromatin immunoprecipitation qPCR and confirmed stronger CTCF and PLAGL2 binding in rs7247241 C than T allele. We found that CTCF binding enrichment was negatively associated with methylation level at the SNP site in human cell lines (r = -0.58). Bisulfite sequencing showed consistent association of rs7247241-T allele with nearby sequence CpG hypermethylation in prostate cell lines and tissues. Moreover, the methylation level at CpG sites nearest to the CTCF binding and first exon splice-in (ψ) of PPP1R14A was significantly associated with aggressive phenotype in the TCGA PrCa cohort. Meanwhile, the long isoform of the gene also promoted cell proliferation. Taken together, with the most updated gene annotations, we reported a set of sQTL associated with multiple traits related to human prostate diseases and revealed a unique role of PrCa risk SNP rs7247241 on PPP1R14A isoform transition.

Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer

INTRODUCTION

Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer.

AREAS COVERED

The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.

EXPERT OPINION

In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.

Identification of UAP1L1 as a critical factor for prostate cancer and underlying molecular mechanism in tumorigenicity

Background

Prostate cancer is the second most common cancer in men, and some new target genes are needed to predict the risk of prostate cancer progression and the treatment.

Methods

In this study, the effects of UAP1L1 (UAP1-like-1) on prostate cancer were investigated by detecting the proliferation, migration, invasion and apoptosis of prostate cancer cells in vitro using MTT, wound healing, Transwell and flow cytometry assay, and the tumor growth in vivo. The downstream genes and pathways of UAP1L1 were explored using Ingenuity Pathway Analysis (IPA), and screened by qRT-PCR and western blot. The effects of CDCA8 on prostate cancer cells were also verified in vitro, which was through detecting the change of proliferation, migration, invasion and apoptosis of prostate cancer cells after CDCA8 knockdown.

Results

The results indicated that UAP1L1 promoted the proliferation, migration and invasion of prostate cancer cells, which was inhibited by downregulating CDCA8. Furthermore, the promotion of CDCA8 knockdown on cell apoptosis was reduced when UAP1L1 was simultaneously overexpressed.

Conclusions

In conclusion, the results in this study revealed that UAP1L1 promoted the progression of prostate cancer through the downstream gene CDCA8.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03291-0.

Multiple haplotype-based analyses provide genetic and evolutionary insights into tomato fruit weight and composition

Improving fruit quality traits such as metabolic composition remains a challenge for tomato breeders. To better understand the genetic architecture of these traits and decipher the demographic history of the loci controlling tomato quality traits, we applied an innovative approach using multiple haplotype-based analyses, aiming to test the potentials of haplotype based study in association and genomic prediction studies. We performed and compared haplotype vs SNP-based associations (hapQTL) with multi-locus mixed model (MLMM), focusing on tomato fruit weight and metabolite contents (i.e. sugars, organic acids and amino acids). Using a panel of 163 tomato accessions genotyped with 5995 SNPs, we detected a total of 784 haplotype blocks, with an average size of haplotype blocks ~58 kb. A total of 108 significant associations for 26 traits were detected thanks to Haplotype/SNP-based Bayes models. Haplotype-based Bayes model (97 associations) outperformed SNP-based Bayes model (50 associations) and MLMM (53 associations) in identifying marker-trait associations as well as in genomic prediction (especially for those traits with moderate to low heritability). To decipher the demographic history, we identified 24 positive selective sweeps using the integrated haplotype score (iHS). Most of the significant associations for tomato quality traits were located within selective sweeps (54.63% and 71.7% in hapQTL and MLMM models, respectively). Promising candidate genes were identified controlling tomato fruit weight and metabolite contents. We thus demonstrated the benefits of using haplotypes for evolutionary and genetic studies, providing novel insights into tomato quality improvement and breeding history.

Long Non-Coding RNAs at the Chromosomal Risk Loci Identified by Prostate and Breast Cancer GWAS

Long non-coding RNAs (lncRNAs) are emerging as key players in a variety of cellular processes. Deregulation of the lncRNAs has been implicated in prostate and breast cancers. Recently, germline genetic variations associated with cancer risk have been correlated with lncRNA expression and/or function. In addition, single nucleotide polymorphisms (SNPs) at well-characterized cancer-associated lncRNAs have been analyzed for their association with cancer risk. These SNPs may occur within the lncRNA transcripts or spanning regions that may alter the structure, function, and expression of these lncRNA molecules and contribute to cancer progression and may have potential as therapeutic targets for cancer treatment. Additionally, some of these lncRNA have a tissue-specific expression profile, suggesting them as biomarkers for specific cancers. In this review, we highlight some of the cancer risk-associated SNPs that modulated lncRNAs with a potential role in prostate and breast cancers and speculate on how these lncRNAs may contribute to cancer development.

Complexity against current cancer research - are we on the wrong track?

Cancer genetics has led to major discoveries, including proto-oncogene and tumor-suppressor concepts, and cancer genomics generated concepts like driver and passenger genes, revealed tumor heterogeneity and clonal evolution. Reconstructing trajectories of tumorigenesis using spatial and single-cell genomics is possible. Patient stratification and prognostic parameters have been improved. Yet, despite these advances, successful translation into targeted therapies has been scarce and mostly limited to kinase inhibitors. Here, we argue that current cancer research may be on the wrong track, by considering cancer more as a "monogenic" disease, trying to extract common information from thousands of patients, while not properly considering complexity and individual diversity. We suggest to empower a systems cancer approach which reconstructs the information network that has been altered by the tumorigenic events, to analyze hierarchies and predict (druggable) key nodes that could interfere with/block the aberrant information transfer. We also argue that the inter-individual variability between patients of similar cohorts is too high to extract common polygenic network information from large numbers of patients and argue in favor of an individualized approach. The analysis we propose would require a structured multinational and multidisciplinary effort, in which clinicians, and cancer, developmental, cell and computational biologists together with mathematicians and informaticians develop dynamic regulatory networks which integrate the entire information transfer in and between cells and organs in (patho)physiological conditions, revealing hierarchies and available drugs to interfere with key regulators. Based on this blueprint, the altered information transfer in individual cancers could be modeled and possible targeted (combo)therapies proposed. This article is protected by copyright. All rights reserved.

Dinucleotide tag-based parallel reporter gene assay method enables efficient identification of regulatory mutations

BACKGROUND

The causal single nucleotide polymorphisms (SNPs) leading to increased cancer predisposition mainly function as gene regulatory elements, the evaluation of which largely relies on the parallel reporter gene assay system. However, the common DNA barcodes used in parallel reporter gene assay systems typically because nucleotide composition bias, and many barcodes must be allocated for each sequence to reduce the bias effect.

MAIN METHODS AND MAJOR RESULTS

Here, a versatile dinucleotide-tag reporter system (DiR) that enables parallel analysis of regulatory elements with minimized bias based on next-generation sequencing is described. The DiR system is more robust than the classical luciferase assay method, particularly for the investigation of moderate-level regulatory elements. The authors applied the DiR-seq assay in the functional evaluation of SNPs with prostate cancer risk and nominated two and six regulatory SNPs in PC-3 and LNCaP cells, respectively.

CONCLUSIONS AND IMPLICATIONS

The DiR system has great potential to advance the functional study of SNPs associated with polygenic disease risks.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

Large Multicohort Study Reveals a Prostate Cancer Susceptibility Allele at 5p15 Regulating TERT via Androgen Signaling-Orchestrated Chromatin Binding of E2F1 and MYC

Aberrant telomerase reverse transcriptase (TERT) expression is crucial for tumor survival and cancer cells escaping apoptosis. Multiple TERT-locus variants at 5p15 have been discovered in association with cancer risk, yet the underlying mechanisms and clinical impacts remain unclear. Here, our association studies showed that the TERT promoter variant rs2853669 confers a risk of prostate cancer (PCa) in different ethnic groups. Further functional investigation revealed that the allele-specific binding of MYC and E2F1 at TERT promoter variant rs2853669 associates with elevated level of TERT in PCa. Mechanistically, androgen stimulations promoted the binding of MYC to allele T of rs2853669, thereby activating TERT, whereas hormone deprivations enhanced E2F1 binding at allele C of rs2853669, thus upregulating TERT expression. Notably, E2F1 could cooperate with AR signaling to regulate MYC expression. Clinical data demonstrated synergistic effects of MYC/E2F1/TERT expression or with the TT and CC genotype of rs2853669 on PCa prognosis and severity. Strikingly, single-nucleotide editing assays showed that the CC genotype of rs2853669 obviously promotes epithelial-mesenchymal transition (EMT) and the development of castration-resistant PCa (CRPC), confirmed by unbiased global transcriptome profiling. Our findings thus provided compelling evidence for understanding the roles of noncoding variations coordinated with androgen signaling and oncogenic transcription factors in mis-regulating TERT expression and driving PCa.

Functional Screenings Identify Regulatory Variants Associated with Breast Cancer Susceptibility

Genome-wide association studies (GWAS) have identified more than 2000 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, most of which are located in the non-coding region. However, the causal SNPs functioning as gene regulatory elements still remain largely undisclosed. Here, we applied a Dinucleotide Parallel Reporter sequencing (DiR-seq) assay to evaluate 288 breast cancer risk SNPs in nine different breast cancer cell lines. Further multi-omics analysis with the ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing), DNase-seq (DNase I hypersensitive sites sequencing) and histone modification ChIP-seq (Chromatin Immunoprecipitation sequencing) nominated seven functional SNPs in breast cancer cells. Functional investigations show that rs4808611 affects breast cancer progression by altering the gene expression of NR2F6. For the other site, rs2236007, the alteration promotes the binding of the suppressive transcription factor EGR1 and results in the downregulation of PAX9 expression. The downregulated expression of PAX9 causes cancer malignancies and is associated with the poor prognosis of breast cancer patients. Our findings contribute to defining the functional risk SNPs and the related genes for breast cancer risk prediction.

Parallel Reporter Assays Identify Altered Regulatory Role of rs684232 in Leading to Prostate Cancer Predisposition

Functional characterization of cancer risk-associated single nucleotide polymorphism (SNP) identified by genome-wide association studies (GWAS) has become a big challenge. To identify the regulatory risk SNPs that can lead to transcriptional misregulation, we performed parallel reporter gene assays with both alleles of 213 prostate cancer risk-associated GWAS SNPs in 22Rv1 cells. We disclosed 32 regulatory SNPs that exhibited different regulatory activities with two alleles. For one of the regulatory SNPs, rs684232, we found that the variation altered chromatin binding of transcription factor FOXA1 on the DNA region and led to aberrant gene expression of VPS53, FAM57A, and GEMIN4, which play vital roles in prostate cancer malignancy. Our findings reveal the roles and underlying mechanism of rs684232 in prostate cancer progression and hold great promise in benefiting prostate cancer patients with prognostic prediction and target therapies.

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

A Case-Based Clinical Approach to the Investigation, Management and Screening of Families with BRCA2 Related Prostate Cancer

BRCA2 is the most commonly implicated DNA damage repair gene associated with inherited prostate cancer. BRCA2 deficient prostate cancer typically presents at a younger age, is more poorly differentiated, and is associated with worse survival outcomes than non-BRCA2 associated prostate cancer. Despite these unfavourable prognostic implications, poly-ADP ribose polymerase inhibitors and platinum-based chemotherapy have been identified as potent targeted therapeutic agents towards BRCA1/2 deficient cancer cells. This review article explores the literature surrounding BRCA2-related prostate cancer through a familial clinical scenario. The investigation, diagnosis and management of BRCA2 deficient prostate cancer will be explored, alongside the implications of the identification of a germline pathogenic BRCA2 variant within a family, cascade screening and prostate cancer surveillance in unaffected male BRCA2 carriers. A greater understanding of the molecular pathogenesis of DNA damage repair gene deficient prostate cancer, coupled with new treatment paradigms and widened access to both somatic and germline genetic analysis for prostate cancer patients and their families will hopefully enable the robust implementation of high quality evidence-based clinical pathways for both the management and identification of BRCA2 deficient prostate cancer and improved screening, early detection and prevention strategies for individuals at increased genetic risk of prostate cancer.

The oncological relevance of fragile sites in cancer

Recent developments in sequencing the cancer genome have provided the first in-depth mapping of structural variants (SV) across 38 tumour types. Sixteen signatures of structural variants have been proposed which broadly characterise the variation seen across cancer types. One signature shows increased duplications and deletions at fragile sites, with little association with the typical DNA repair defects. We discuss how, for many of these fragile sites, the clinical impacts are yet to be explored. One example is NAALADL2, one of the most frequently altered fragile sites in the cancer genome. The copy-number variations (CNVs) which occur at fragile sites, such as NAALADL2, may span many genes without typical DNA repair defects and could have a large impact on cell signalling.

In this Perspective, Simpson, Pye, and Whitaker discuss recent research identifying structural genomic variants in human cancers with a particular focus on deletions and duplications at genomic fragile sites. They argue that tumours with predominantly fragile site structural variants represent a distinct mutational signature that warrants further research.

Epidemiology and genomics of prostate cancer in Asian men

Prostate cancer is a global health problem, but incidence varies considerably across different continents. Asia is traditionally considered a low-incidence area, but the incidence and mortality of prostate cancer have rapidly increased across the continent. Substantial differences in epidemiological features have been observed among different Asian regions, and incidence, as well as mortality-to-incidence ratio, is associated with the human development index. Prostate cancer mortality decreased in Japan and Israel from 2007 to 2016, but mortality has increased in Thailand, Kyrgyzstan and Uzbekistan over the same period. Genomic analyses have shown a low prevalence of ERG oncoprotein in the East Asian population, alongside a low rate of PTEN loss, high CHD1 enrichments and high FOXA1 alterations. Contributions from single-nucleotide polymorphisms to prostate cancer risk vary with ethnicity, but germline mutation rates of DNA damage repair genes in metastatic prostate cancer are comparable in Chinese and white patients from the USA and UK. Pharmacogenomic features of testosterone metabolism might contribute to disparities seen in the response to androgen deprivation between East Asian men and white American and European men. Overall, considerable diversity in epidemiology and genomics of prostate cancer across Asia defines disease characteristics in these populations, but studies in this area are under-represented in the literature. Taking into account this intracontinental and intercontinental heterogeneity, translational studies are required in order to develop ethnicity-specific treatment strategies.

Age-Related Changes in Zinc, Copper and Selenium Levels in the Human Prostate

Pathophysiological changes in the prostate gland-benign prostatic hyperplasia (BPH) and prostatic adenocarcinoma (PCa)-are closely related to the age of men. In the prostate gland, zinc is of particular importance for its proper functioning, especially with regard to the effects of hormonal disorders. The aim of this study was to evaluate zinc, copper and selenium concentrations in different parts of the prostate gland in relation to age and the nature of pathological changes. Zinc and copper were determined by the AAS method and selenium by the spectrofluorometric method. The concentration of zinc in the central part of the prostate increases with age, and in patients over 36 years it is twice as high as in the peripheral part, where no increase in the level of this element was observed with the age of patients. The above data confirm a possible influence of zinc on the formation of PCa (located mostly in the peripheral part of the prostate, with low levels of zinc) and BPH in the central part where the levels of this element are the highest. The results apparently confirm the disturbed homeostasis of zinc and other essential elements in the etiology of BPH and PCa.

Meta-Analyses of Splicing and Expression Quantitative Trait Loci Identified Susceptibility Genes of Glioma

Background

The functions of most glioma risk alleles are unknown. Very few studies had evaluated expression quantitative trait loci (eQTL), and insights of susceptibility genes were limited due to scarcity of available brain tissues. Moreover, no prior study had examined the effect of glioma risk alleles on alternative RNA splicing.

Objective

This study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both cis eQTL and sQTL.

Methods

We first evaluated eQTLs and sQTLs of the CommonMind Consortium (CMC) and Genotype-Tissue Expression Project (GTEx) using genotyping, or whole-genome sequencing and RNA-seq data. Alternative splicing events were characterized using an annotation-free method that detected intron excision events. Then, we conducted meta-analyses by pooling the eQTL and sQTL results of CMC and GTEx using the inverse variance-weighted model. Afterward, we integrated QTL meta-analysis results (Q < 0.05) with the Glioma International Case Control Study (GICC) GWAS meta-analysis (case:12,496, control:18,190), using a summary statistics-based mendelian randomization (SMR) method.

Results

Between CMC and GTEx, we combined the QTL data of 354 unique individuals of European ancestry. SMR analyses revealed 15 eQTLs in 11 loci and 32 sQTLs in 9 loci relevant to glioma risk. Two loci only harbored sQTLs (1q44 and 16p13.3). In seven loci, both eQTL and sQTL coexisted (2q33.3, 7p11.2, 11q23.3 15q24.2, 16p12.1, 20q13.33, and 22q13.1), but the target genes were different for five of these seven loci. Three eQTL loci (9p21.3, 20q13.33, and 22q13.1) and 4 sQTL loci (11q23.3, 16p13.3, 16q12.1, and 20q13.33) harbored multiple target genes. Eight target genes of sQTLs (C2orf80, SEC61G, TMEM25, PHLDB1, RP11-161M6.2, HEATR3, RTEL1-TNFRSF6B, and LIME1) had multiple alternatively spliced transcripts.

Conclusion

Our study revealed that the regulation of transcriptome by glioma risk alleles is complex, with the potential for eQTL and sQTL jointly affecting gliomagenesis in risk loci. QTLs of many loci involved multiple target genes, some of which were specific to alternative splicing. Therefore, quantitative trait loci that evaluate only total gene expression will miss many important target genes.

NLRC5 promotes transcription of BTN3A1-3 genes and Vγ9Vδ2 T cell-mediated killing

BTN3A molecules—BTN3A1 in particular—emerged as important mediators of Vγ9Vδ2 T cell activation by phosphoantigens. These metabolites can originate from infections, e.g. with Mycobacterium tuberculosis, or by alterations in cellular metabolism. Despite the growing interest in the BTN3A genes and their high expression in immune cells and various cancers, little is known about their transcriptional regulation. Here we show that these genes are induced by NLRC5, a regulator of MHC class I gene transcription, through an atypical regulatory motif found in their promoters. Accordingly, a robust correlation between NLRC5 and BTN3A gene expression was found in healthy, in M. tuberculosis-infected donors' blood cells, and in primary tumors. Moreover, forcing NLRC5 expression promoted Vγ9Vδ2 T-cell-mediated killing of tumor cells in a BTN3A-dependent manner. Altogether, these findings indicate that NLRC5 regulates the expression of BTN3A genes and hence open opportunities to modulate antimicrobial and anticancer immunity.

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BTN3A promoters contain a unique regulatory motif occupied by overexpressed NLRC5

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NLRC5 and BTN3A mRNA levels correlate in healthy and diseased cells

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NLRC5 overexpression increases susceptibility to Vγ9Vδ2 T-cell-mediated elimination

Clinical characteristics and primary management of patients diagnosed with prostate cancer between 2015 and 2019 at the Uganda Cancer Institute

Background

Prostate cancer is the second most common cancer among men in Uganda, with over 2086 incident cases in 2018. This study’s objective was to report the clinical characteristics and primary management of men diagnosed with prostate cancer at the Uganda Cancer Institute from 1^st January 2015 to 31^st December 2019.

Methods

Records from all men diagnosed with Prostate cancer at the Uganda Cancer Institute from 1^st January 2015 to 31^st December 2019 were reviewed. Clinical characteristics and primary treatment were recorded. Risk categorization was done using the European Society for Medical Oncology prostate cancer risk group classification.

Results

A total of 874 medical records for men diagnosed with prostate cancer was retrieved. The median age was 70 years (interquartile range 64–77). In this study, 501 (57.32%) patients had localized disease. Among patients with localized disease, 2 (0.23%) were classified as low-risk, 5 (0.53%) as intermediate-risk, and 494 (56.52%) as high-risk. Three hundred seventy-three (373) patients had metastatic disease at diagnosis. Among patients with distant metastases, the most common site of metastases was bone 143 (16.36%), followed by spinal cord 54 (6.18%), abdomen 22 (2.52%), and lungs 14 (1.60%). Regarding the primary treatment options majority of the patients were on chemotherapy 384(43.94%) followed by hormonal therapy 336 (38.44%) and radiotherapy 127 (14.53%).

Conclusion

The majority of the patients diagnosed with prostate cancer at the Uganda Cancer Institute presented with advanced disease. The primary treatments were mostly chemotherapy, hormonal therapy, and radiotherapy. There is a need to improve prostate cancer screening in regional health care facilities and the communities to enhance early detection and management of prostate cancer.

Tumor somatic mutations also existing as germline polymorphisms may help to identify functional SNPs from genome-wide association studies

We hypothesized that a joint analysis of cancer risk-associated single-nucleotide polymorphism (SNP) and somatic mutations in tumor samples can predict functional and potentially causal SNPs from GWASs. We used mutations reported in the Catalog of Somatic Mutations in Cancer (COSMIC). Confirmed somatic mutations were subdivided into two groups: (1) mutations reported as SNPs, which we call mutational/SNPs and (2) somatic mutations that are not reported as SNPs, which we call mutational/noSNPs. It is generally accepted that the number of times a somatic mutation is reported in COSMIC correlates with its selective advantage to tumors, with more frequently reported mutations being more functional and providing a stronger selective advantage to the tumor cell. We found that mutations reported ≥10 times in COSMIC-frequent mutational/SNPs (fmSNPs) are likely to be functional. We identified 12 cancer risk-associated SNPs reported in the Catalog of published GWASs at least 10 times as confirmed somatic mutations and therefore deemed to be functional. Additionally, we have identified 42 SNPs that are tightly linked (R2 ≥ 0.8) to SNPs reported in the Catalog of published GWASs as cancer risk associated and that are also reported as fmSNPs. As a result, 54 candidate functional/potentially causal cancer risk associated SNPs were identified. We found that fmSNPs are more likely to be located in evolutionarily conserved regions compared with cancer risk associated SNPs that are not fmSNPs. We also found that fmSNPs also underwent positive selection, which can explain why they exist as population polymorphisms.

Pioneer of prostate cancer: past, present and the future of FOXA1

Prostate cancer is the most commonly diagnosed non-cutaneous cancers in North American men. While androgen deprivation has remained as the cornerstone of prostate cancer treatment, resistance ensues leading to lethal disease. Forkhead box A1 (FOXA1) encodes a pioneer factor that induces open chromatin conformation to allow the binding of other transcription factors. Through direct interactions with the Androgen Receptor (AR), FOXA1 helps to shape AR signaling that drives the growth and survival of normal prostate and prostate cancer cells. FOXA1 also possesses an AR-independent role of regulating epithelial-to-mesenchymal transition (EMT). In prostate cancer, mutations converge onto the coding sequence and cis-regulatory elements (CREs) of FOXA1, leading to functional alterations. In addition, FOXA1 activity in prostate cancer can be modulated post-translationally through various mechanisms such as LSD1-mediated protein demethylation. In this review, we describe the latest discoveries related to the function and regulation of FOXA1 in prostate cancer, pointing to their relevance to guide future clinical interventions.

Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network

Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filtering and prioritizing of candidates, we fine-mapped two new risk variants, rs2370964 and rs3093720, on chromosome 3 and 17, respectively. Further analysis revealed that these polymorphisms are associated with the expression level of CX3CR1 and TNFAIP1, and affect the transcription factor binding sites for CTCF, NFATc1 and NR3C1. Our results may provide new insights for ALS pathogenesis, and the proposed research methodology can be applied for other complex diseases as well.

Hereditary Predisposition to Prostate Cancer: From Genetics to Clinical Implications

Prostate cancer (PrCa) ranks among the top five cancers for both incidence and mortality worldwide. A significant proportion of PrCa susceptibility has been attributed to inherited predisposition, with 10-20% of cases expected to occur in a hereditary/familial context. Advances in DNA sequencing technologies have uncovered several moderate- to high-penetrance PrCa susceptibility genes, most of which have previously been related to known hereditary cancer syndromes, namely the hereditary breast and ovarian cancer (BRCA1, BRCA2, ATM, CHEK2, and PALB2) and Lynch syndrome (MLH1, MSH2, MSH6, and PMS2) genes. Additional candidate genes have also been suggested, but further evidence is needed to include them in routine genetic testing. Recommendations based on clinical features, family history, and ethnicity have been established for more cost-efficient genetic testing of patients and families who may be at an increased risk of developing PrCa. The identification of alterations in PrCa predisposing genes may help to inform screening strategies, as well as treatment options, in the metastatic setting. This review provides an overview of the genetic basis underlying hereditary predisposition to PrCa, the current genetic screening recommendations, and the implications for clinical management of the disease.

Genetic Analysis Reveals a Significant Contribution of CES1 to Prostate Cancer Progression in Taiwanese Men

The genes that influence prostate cancer progression remain largely unknown. Since the carboxylesterase gene family plays a crucial role in xenobiotic metabolism and lipid/cholesterol homeostasis, we hypothesize that genetic variants in carboxylesterase genes may influence clinical outcomes for prostate cancer patients. A total of 478 (36 genotyped and 442 imputed) single nucleotide polymorphisms (SNPs) in five genes of the carboxylesterase family were assessed in terms of their associations with biochemical recurrence (BCR)-free survival in 643 Taiwanese patients with prostate cancer who underwent radical prostatectomy. The strongest association signal was shown in CES1 (P = 9.64×10^-4 for genotyped SNP rs8192935 and P = 8.96 × 10^-5 for imputed SNP rs8192950). After multiple test correction and adjustment for clinical covariates, CES1 rs8192935 (P = 9.67 × 10^-4) and rs8192950 (P = 9.34 × 10^-5) remained significant. These SNPs were correlated with CES1 expression levels, which in turn were associated with prostate cancer aggressiveness. Furthermore, our meta-analysis, including eight studies, indicated that a high CES1 expression predicted better outcomes among prostate cancer patients (hazard ratio 0.82, 95% confidence interval 0.70-0.97, P = 0.02). In conclusion, our findings suggest that CES1 rs8192935 and rs8192950 are associated with BCR and that CES1 plays a tumor suppressive role in prostate cancer.