Association of imputed prostate cancer transcriptome with disease risk reveals novel mechanisms

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

Role of genetic variations and protein expression of β-Microsemino protein in intrauterine insemination outcome of unexplained infertile men: A case-control study

Background

Intrauterine insemination (IUI) is often the first-line treatment for unexplained infertility. β -Microsemino protein (MSMB) is an abundant protein in seminal plasma that has an inhibitory effect on spontaneous acrosome reaction.

Objective

The present study aimed to evaluate MSMB gene variations and protein expression on IUI success rate in unexplained infertile men.

Materials and Methods

A case-control study was performed on 100 unexplained infertile Iranian men referred to the Royan Institute, Tehran, Iran for IUI (50 men with IUI positive result [IUI+], and 50 men with IUI negative result [IUI-]). Couples with female infertility factors (such as hormonal disorders, infrequent menstrual period, abnormality in uterus, fallopian tubes, or ovaries) and men with infections of the male accessory glands, hypogonadotropic hypogonadism, clinical varicocele, retractile testis, genital trauma, drug use, or concurrent hormonal treatment Y chromosome microdeletions, and abnormal karyotype were excluded from the study. The polymerase chain reaction sequencing was performed for the promoter and the coding regions of MSMB functional domains. To study the protein expression, the total protein of sperm was extracted, and sandwich enzyme-linked immunosorbent assay was performed.

Results

4 variations were detected (rs12770171, rs10993994, rs2075894, and rs4517463). None of them showed significant differences between the IUI+ and IUI- groups. The mean value of protein expression did not show any differences between the groups.

Conclusion

In conclusion, there is no association between genetic variations of promoter and coding regions of MSMB functional domains as well as its expression with IUI success in unexplained infertile men.

Role of HOXA1-4 in the development of genetic and malignant diseases

The HOXA genes, belonging to the HOX family, encompass 11 members (HOXA1-11) and exert critical functions in early embryonic development, as well as various adult processes. Furthermore, dysregulation of HOXA genes is implicated in genetic diseases, heart disease, and various cancers. In this comprehensive overview, we primarily focused on the HOXA1-4 genes and their associated functions and diseases. Emphasis was placed on elucidating the impact of abnormal expression of these genes and highlighting their significance in maintaining optimal health and their involvement in the development of genetic and malignant diseases. Furthermore, we delved into their regulatory mechanisms, functional roles, and underlying biology and explored the therapeutic potential of targeting HOXA1-4 genes for the treatment of malignancies. Additionally, we explored the utility of HOXA1-4 genes as biomarkers for monitoring cancer recurrence and metastasis.

Kallikrein-related peptidase 10 predicts prognosis and mediates tumor immunomodulation in colorectal cancer

The incidence and mortality rates of colorectal cancer (CRC) have significantly increased in recent years. It has been shown that early diagnosis of CRC improves the five-year survival of patients compared to late diagnosis, as patients with stage I disease have a five-year survival rate as high as 90 %. Through bioinformatics analysis, we identified Kallikrein 10 (KLK10), a member of the Kallikrein family, as a reliable predictor of CRC progression, particularly in patients with early-stage CRC. Furthermore, single-cell analysis revealed that KLK10 was highly expressed in tumor and partial immune cells. Analysis of the biological functions of KLK10 using the Kyoto encyclopedia of genes and genomes and gene ontology indicated that KLK10 plays a role in the proliferation and differentiation of cancer cells, along with the maintenance of tumor function and immune regulation, explicitly by T cells and macrophages. EdU cell proliferation staining, plate clone formation assay, and cell scratch assay demonstrated that KLK10 inhibition by siRNA affected the proliferation and migration of CRC cells. Cell cycle detection by flow cytometry demonstrated that KLK10 inhibition led to cell cycle arrest in the G1 phase. In addition, the proportion of M1 and M2 macrophages in 45 tumor specimens was analyzed by immunohistochemistry, the proportion of CD4+ T cells and CD8+ T cells in plasma was identified by flow cytometry, and their correlation with KLK10 was analyzed. The effects of KLK10 on T cells and macrophages were verified in independent cell experiments. The results revealed that KLK10 also activates CD4+ T cells, mediating M2-type macrophage polarization.

Profiling the Somatic Mutational Landscape of Breast Tumors from Hispanic/Latina Women Reveals Conserved and Unique Characteristics

Somatic mutational profiling is increasingly being used to identify potential targets for breast cancer. However, limited tumor-sequencing data from Hispanic/Latinas (H/L) are available to guide treatment. To address this gap, we performed whole-exome sequencing (WES) and RNA sequencing on 146 tumors and WES of matched germline DNA from 140 H/L women in California. Tumor intrinsic subtype, somatic mutations, copy-number alterations, and expression profiles of the tumors were characterized and compared with data from tumors of non-Hispanic White (White) women in The Cancer Genome Atlas (TCGA). Eight genes were significantly mutated in the H/L tumors including PIK3CA, TP53, GATA3, MAP3K1, CDH1, CBFB, PTEN, and RUNX1; the prevalence of mutations in these genes was similar to that observed in White women in TCGA. Four previously reported Catalogue of Somatic Mutations in Cancer (COSMIC) mutation signatures (1, 2, 3, 13) were found in the H/L dataset, along with signature 16 that has not been previously reported in other breast cancer datasets. Recurrent amplifications were observed in breast cancer drivers including MYC, FGFR1, CCND1, and ERBB2, as well as a recurrent amplification in 17q11.2 associated with high KIAA0100 gene expression that has been implicated in breast cancer aggressiveness. In conclusion, this study identified a higher prevalence of COSMIC signature 16 and a recurrent copy-number amplification affecting expression of KIAA0100 in breast tumors from H/L compared with White women. These results highlight the necessity of studying underrepresented populations.

SIGNIFICANCE

Comprehensive characterization of genomic and transcriptomic alterations in breast tumors from Hispanic/Latina patients reveals distinct genetic alterations and signatures, demonstrating the importance of inclusive studies to ensure equitable care for patients. See related commentary by Schmit et al., p. 2443.

A Splicing Transcriptome-Wide Association Study Identifies Candidate Altered Splicing for Prostate Cancer Risk

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy.

Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients

BACKGROUND

Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse.

RESULTS

Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models-NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used.

CONCLUSIONS

Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.

Ancestry: How researchers use it and what they mean by it

Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term "ancestry" is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered. Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles. Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many-including geneticists-ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups. Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a "biological" construct, and it does not represent a "safe haven" for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

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.

FAM57A (Family with Sequence Similarity 57 Member A) Is a Cell-Density-Regulated Protein and Promotes the Proliferation and Migration of Cervical Cancer Cells

The FAM57A (family with sequence similarity 57 member A) gene is controversially discussed to possess pro- or anti-tumorigenic potential. Here, we analyze the regulation of cellular FAM57A protein levels and study the functional role of FAM57A in HPV-positive cervical cancer cells. We find that FAM57A protein expression strongly depends on cell density, with FAM57A being readily detectable at low cell density, but undetectable at high cell density. This regulation occurs post-transcriptionally and is not mirrored by corresponding changes at the RNA level. We further show that FAM57A protein levels are highly increased in cervical cancer cells cultivated at hypoxia compared to normoxia and provide evidence that FAM57A is a hypoxia-responsive gene under control of the α-subunit of the HIF-1 (hypoxia-inducible factor-1) transcription factor. Yet, the strong relative increase of FAM57A protein levels in hypoxic cells is predominantly cell-density-dependent and occurs post-transcriptionally. Other anti-proliferative effectors besides hypoxia, such as silencing of HPV E6/E7 oncogene expression in cervical cancer cells, also result in an increase of FAM57A levels compared to untreated cells. Functional analyses reveal that FAM57A repression leads to pronounced anti-proliferative as well as anti-migratory effects in cervical cancer cells. Taken together, these results provide insights into the regulation of FAM57A protein levels and reveal that they underlie a tight cell-density-dependent control. Moreover, they identify FAM57A as a critical determinant for the phenotype of cervical cancer cells, which promotes their proliferation and migration capacities.

Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

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.

Analysis of a large prostate cancer family identifies novel and recurrent gene fusion events providing evidence for inherited predisposition

There is strong interest in the characterisation of gene fusions and their use to enhance clinical practices in prostate cancer (PrCa). Significantly, ~50% of prostate tumours harbour a gene fusion. Inherited factors are thought to predispose to these events but, to date, only one study has investigated gene fusions in a familial context. Here, we examined the prevalence and diversity of gene fusions in 14 tumours from a single large PrCa family, PcTas9, using the TruSight® RNA Fusion Panel and Sanger sequencing validation. These fusions were then explored in The Cancer Genome Atlas (TCGA) PrCa data set (n = 494). Overall, 64.3% of PcTas9 tumours harboured a gene fusion, including known erythroblast transformation-specific (ETS) fusions involving ERG and ETV1, and two novel gene fusions, C19orf48:ETV4 and RYBP:FOXP1. Although 3' ETS genes were overexpressed in PcTas9 and TCGA tumour samples, 3' fusion of FOXP1 did not appear to alter its expression. In addition, PcTas9 fusion carriers were more likely to have lower-grade disease than noncarriers (p = 0.02). Likewise, TCGA tumours with high-grade disease were less likely to harbour fusions (p = 0.03). Our study further implicates an inherited predisposition to PrCa gene fusion events, which are associated with less aggressive tumours. This knowledge could lead to clinical strategies to predict men at risk for fusion-positive PrCa and, thus, identify patients who are more or less at risk of aggressive disease and/or responsive to particular therapies.

Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models

Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.

A genome-wide association study in a large community-based cohort identifies multiple loci associated with susceptibility to bacterial and viral infections

There is limited data on host-specific genetic determinants of susceptibility to bacterial and viral infections. Genome-wide association studies using large population cohorts can be a first step towards identifying patients prone to infectious diseases and targets for new therapies. Genetic variants associated with clinically relevant entities of bacterial and viral infections (e.g., abdominal infections, respiratory infections, and sepsis) in 337,484 participants of the UK Biobank cohort were explored by genome-wide association analyses. Cases (n = 81,179) were identified based on ICD-10 diagnosis codes of hospital inpatient and death registries. Functional annotation was performed using gene expression (eQTL) data. Fifty-seven unique genome-wide significant loci were found, many of which are novel in the context of infectious diseases. Some of the detected genetic variants were previously reported associated with infectious, inflammatory, autoimmune, and malignant diseases or key components of the immune system (e.g., white blood cells, cytokines). Fine mapping of the HLA region revealed significant associations with HLA-DQA1, HLA-DRB1, and HLA-DRB4 locus alleles. PPP1R14A showed strong colocalization with abdominal infections and gene expression in sigmoid and transverse colon, suggesting causality. Shared significant loci across infections and non-infectious phenotypes in the UK Biobank cohort were found, suggesting associations for example between SNPs identified for abdominal infections and CRP, rheumatoid arthritis, and diabetes mellitus. We report multiple loci associated with bacterial and viral infections. A better understanding of the genetic determinants of bacterial and viral infections can be useful to identify patients at risk and in the development of new drugs.

Allele-specific epigenetic activity in prostate cancer and normal prostate tissue implicates prostate cancer risk mechanisms

Genome-wide association studies (GWASs) of prostate cancer have identified >250 significant risk loci, but the causal variants and mechanisms for these loci remain largely unknown. Here, we sought to identify and characterize risk-harboring regulatory elements by integrating epigenomes from primary prostate tumor and normal tissues of 27 individuals across the H3K27ac, H3K4me3, and H3K4me2 histone marks and FOXA1 and HOXB13 transcription factors. We identified 7,371 peaks with significant allele specificity (allele-specific quantitative trait locus [asQTL] peaks). Showcasing their relevance to prostate cancer risk, H3K27ac T-asQTL peaks were the single annotation most enriched for prostate cancer GWAS heritability (40×), significantly higher than corresponding non-asQTL H3K27ac peaks (14×) or coding regions (14×). Surprisingly, fine-mapped GWAS risk variants were most significantly enriched for asQTL peaks observed in tumors, including asQTL peaks that were differentially imbalanced with respect to tumor-normal states. These data pinpointed putative causal regulatory elements at 20 GWAS loci, of which 11 were detected only in the tumor samples. More broadly, tumor-specific asQTLs were enriched for expression QTLs in benign tissues as well as accessible regions found in stem cells, supporting a hypothesis where some germline variants become reactivated during or after transformation and can be captured by epigenomic profiling of the tumor. Our study demonstrates the power of allele specificity in chromatin signals to uncover GWAS mechanisms, highlights the relevance of tumor-specific regulation in the context of cancer risk, and prioritizes multiple loci for experimental follow-up.

Polygenic Risk Scores in Prostate Cancer Risk Assessment and Screening

More than 40% of the risk of developing prostate cancer (PCa) is from genetic factors. Genome-wide association studies have led to the discovery of more than 140 variants associated with PCa risk. Polygenic risk scores (PRS) generated using these variants show promise in identifying individuals at much higher (and lower) lifetime risk than the average man. PCa PRS also improve the predictive value of prostate-specific antigen screening, may inform the age for starting PCa screening, and are informative for development of more aggressive tumors. Despite the promise, few clinical trials have evaluated the benefit of PCa PRS for clinical care.

A Large-Scale Association Study Detects Novel Rare Variants, Risk Genes, Functional Elements, and Polygenic Architecture of Prostate Cancer Susceptibility

To identify rare variants associated with prostate cancer susceptibility and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we conducted an integrated study of prostate cancer genetic etiology in two cohorts using custom genotyping microarrays, large imputation reference panels, and functional annotation approaches. Specifically, 11,984 men (6,196 prostate cancer cases and 5,788 controls) of European ancestry from Northern California Kaiser Permanente were genotyped and meta-analyzed with 196,269 men of European ancestry (7,917 prostate cancer cases and 188,352 controls) from the UK Biobank. Three novel loci, including two rare variants (European ancestry minor allele frequency < 0.01, at 3p21.31 and 8p12), were significant genome wide in a meta-analysis. Gene-based rare variant tests implicated a known prostate cancer gene (HOXB13), as well as a novel candidate gene (ILDR1), which encodes a receptor highly expressed in prostate tissue and is related to the B7/CD28 family of T-cell immune checkpoint markers. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants at HOXB13 and other loci, reflecting their evolutionary history. In addition, a polygenic risk score (PRS) of 188 prostate cancer variants was strongly associated with risk (90th vs. 40th-60th percentile OR = 2.62, P = 2.55 × 10-191). Many of the 188 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a 6-fold difference in log-probability of androgen receptor binding at the variant rs2680708 (17q22). Rare variant and PRS associations, with concomitant functional interpretation of risk mechanisms, can help clarify the full genetic architecture of prostate cancer and other complex traits. SIGNIFICANCE: This study maps the biological relationships between diverse risk factors for prostate cancer, integrating different functional datasets to interpret and model genome-wide data from over 200,000 men with and without prostate cancer.See related commentary by Lachance, p. 1637.

Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease

Prostate cancer (PrCa) is a heterogeneous disease, which presents in individual patients across a diverse phenotypic spectrum ranging from indolent to fatal forms. No robust biomarkers are currently available to enable routine screening for PrCa or to distinguish clinically significant forms, therefore late stage identification of advanced disease and overdiagnosis plus overtreatment of insignificant disease both remain areas of concern in healthcare provision. PrCa has a substantial heritable component, and technological advances since the completion of the Human Genome Project have facilitated improved identification of inherited genetic factors influencing susceptibility to development of the disease within families and populations. These genetic markers hold promise to enable improved understanding of the biological mechanisms underpinning PrCa development, facilitate genetically informed PrCa screening programmes and guide appropriate treatment provision. However, insight remains largely lacking regarding many aspects of their manifestation; especially in relation to genes associated with aggressive phenotypes, risk factors in non-European populations and appropriate approaches to enable accurate stratification of higher and lower risk individuals. This review discusses the methodology used in the elucidation of genetic loci, genes and individual causal variants responsible for modulating PrCa susceptibility; the current state of understanding of the allelic spectrum contributing to PrCa risk; and prospective future translational applications of these discoveries in the developing eras of genomics and personalised medicine.

Iron: An Essential Element of Cancer Metabolism

Cancer cells undergo considerable metabolic changes to foster uncontrolled proliferation in a hostile environment characterized by nutrient deprivation, poor vascularization and immune infiltration. While metabolic reprogramming has been recognized as a hallmark of cancer, the role of micronutrients in shaping these adaptations remains scarcely investigated. In particular, the broad electron-transferring abilities of iron make it a versatile cofactor that is involved in a myriad of biochemical reactions vital to cellular homeostasis, including cell respiration and DNA replication. In cancer patients, systemic iron metabolism is commonly altered. Moreover, cancer cells deploy diverse mechanisms to increase iron bioavailability to fuel tumor growth. Although iron itself can readily participate in redox reactions enabling vital processes, its reactivity also gives rise to reactive oxygen species (ROS). Hence, cancer cells further rely on antioxidant mechanisms to withstand such stress. The present review provides an overview of the common alterations of iron metabolism occurring in cancer and the mechanisms through which iron promotes tumor growth.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

Pathway Analysis of Genes Identified through Post-GWAS to Underpin Prostate Cancer Aetiology

Understanding the functional role of risk regions identified by genome-wide association studies (GWAS) has made considerable recent progress and is referred to as the post-GWAS era. Annotation of functional variants to the genes, including cis or trans and understanding their biological pathway/gene network enrichments, is expected to give rich dividends by elucidating the mechanisms underlying prostate cancer. To this aim, we compiled and analysed currently available post-GWAS data that is validated through further studies in prostate cancer, to investigate molecular biological pathways enriched for assigned functional genes. In total, about 100 canonical pathways were significantly, at false discovery rate (FDR) < 0.05), enriched in assigned genes using different algorithms. The results have highlighted some well-known cancer signalling pathways, antigen presentation processes and enrichment in cell growth and development gene networks, suggesting risk loci may exert their functional effect on prostate cancer by acting through multiple gene sets and pathways. Additional upstream analysis of the involved genes identified critical transcription factors such as HDAC1 and STAT5A. We also investigated the common genes between post-GWAS and three well-annotated gene expression datasets to endeavour to uncover the main genes involved in prostate cancer development/progression. Post-GWAS generated knowledge of gene networks and pathways, although continuously evolving, if analysed further and targeted appropriately, will have an important impact on clinical management of the disease.

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.

Dual-color magnetic-quantum dot nanobeads as versatile fluorescent probes in test strip for simultaneous point-of-care detection of free and complexed prostate-specific antigen

Simultaneous detection of free and complexed prostate-specific antigen (f-PSA and c-PSA) is critical to the prostate cancer (PCa) diagnostic accuracy for clinical samples with PSA values in the diagnostic gray zone between 4 and 10 ng mL^-1. Herein, red and green magnetic-quantum dot nanobeads (MQBs) with superior magnetic property and high luminescence were fabricated via polyethyleneimine-mediated electrostatic adsorption of numerous quantum dots onto superparamagnetic Fe₃O₄ magnetic cores, and were conjugated with f-PSA antibody and c-PSA antibody, respectively, as versatile fluorescent probes in test strip for immune recognition, magnetic enrichment, and simultaneous detection of f-PSA and c-PSA analytes in complex biological matrix with t-PSA antibody on the test line. A low-cost and portable smartphone readout device with an application was also developed for the imaging of dual-color test strips and data processing. This assay can simultaneously detect f-PSA and c-PSA with the limits of detection of 0.009 ng mL^-1 and 0.087 ng mL^-1, respectively. Clinical serum samples of PCa and benign prostatic hyperplasia patients were evaluated to confirm the clinical feasibility. The results suggest that the proposed dual-color MQBs-based fluorescent lateral flow immunoassay is a promising point-of-care diagnostics technique for the accurate diagnosis of PCa even in resource-limited settings.