Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Prediction of transcript structure and concentration using RNA-Seq data.

Alternative splicing (AS) is a key post-transcriptional modification that helps in increasing protein diversity. Almost 90% of the protein-coding genes in humans are known to undergo AS and code for different transcripts. Some transcripts are associated with diseases such as breast cancer, lung cancer and glioblastoma. Hence, these transcripts can serve as novel therapeutic and prognostic targets for drug discovery. Herein, we have developed a pipeline, Finding Alternative Splicing Events (FASE), as the R package that includes modules to determine the structure and concentration of transcripts using differential AS. To predict the correct structure of expressed transcripts in given conditions, FASE combines the AS events with the information of exons, introns and junctions using graph theory. The estimated concentration of predicted transcripts is reported as the relative expression in terms of log2CPM. Using FASE, we were able to identify several unique transcripts of EMILIN1 and SLK genes in the TCGA-BRCA data, which were validated using RT-PCR. The experimental study demonstrated consistent results, which signify the high accuracy and precision of the developed methods. In conclusion, the developed pipeline, FASE, can efficiently predict novel transcripts that are missed in general transcript-level differential expression analysis. It can be applied selectively from a single gene to simple or complex genome even in multiple experimental conditions for the identification of differential AS-based biomarkers, prognostic targets and novel therapeutics.

Dynamics and recognition of homeodomain containing protein-DNA complex of IRX4.

Iroquois Homeobox 4 (IRX4) belongs to a family of homeobox TFs having roles in embryogenesis, cell specification, and organ development. Recently, large scale genome-wide association studies and epigenetic studies have highlighted the role of IRX4 and its associated variants in prostate cancer. No studies have investigated and characterized the structural aspect of the IRX4 homeodomain and its potential to bind to DNA. The current study uses sequence analysis, homology modeling, and molecular dynamics simulations to explore IRX4 homeodomain-DNA recognition mechanisms and the role of somatic mutations affecting these interactions. Using publicly available databases, gene expression of IRX4 was found in different tissues, including prostate, heart, skin, vagina, and the protein expression was found in cancer cell lines (HCT166, HEK293), B cells, ascitic fluid, and brain. Sequence conservation of the homeodomain shed light on the importance of N- and C-terminal residues involved in DNA binding. The specificity of IRX4 homodimer bound to consensus human DNA sequence was confirmed by molecular dynamics simulations, representing the role of conserved amino acids including R145, A194, N195, S190, R198, and R199 in binding to DNA. Additional N-terminal residues like T144 and G143 were also found to have specific interactions highlighting the importance of N-terminus of the homeodomain in DNA recognition. Additionally, the effects of somatic mutations, including the conserved Arginine (R145, R198, and R199) residues on DNA binding elucidated the importance of these residues in stabilizing the protein-DNA complex. Secondary structure and hydrogen bonding analysis showed the roles of specific residues (R145, T191, A194, N195, R198, and R199) in maintaining the homogeneity of the structure and its interaction with DNA. The differences in relative binding free energies of all the mutants shed light on the structural modularity of this protein and the dynamics behind protein-DNA interaction. We also have predicted that the C-terminal sequence of the IRX4 homeodomain could act as a potential cell-penetrating peptide, emphasizing the role these small peptides could play in targeting homeobox TFs.

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.

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.

A supervised machine learning approach identifies gene-regulating factor-mediated competing endogenous RNA networks in hormone-dependent cancers.

Competing endogenous RNAs (ceRNAs) have become an emerging topic in cancer research due to their role in gene regulatory networks. To date, traditional ceRNA bioinformatic studies have investigated microRNAs as the only factor regulating gene expression. Growing evidence suggests that genomic (e.g., copy number alteration [CNA]), transcriptomic (e.g., transcription factors [TFs]), and epigenomic (e.g., DNA methylation [DM]) factors can influence ceRNA regulatory networks. Herein, we used the Least absolute shrinkage and selection operator regression, a machine learning approach, to integrate DM, CNA, and TFs data with RNA expression to infer ceRNA networks in cancer risk. The gene-regulating factors-mediated ceRNA networks were identified in four hormone-dependent (HD) cancer types: prostate, breast, colorectal, and endometrial. The shared ceRNAs across HD cancer types were further investigated using survival analysis, functional enrichment analysis, and protein-protein interaction network analysis. We found two (BUB1 and EXO1) and one (RRM2) survival-significant ceRNA(s) shared across breast-colorectal-endometrial and prostate-colorectal-endometrial combinations, respectively. Both BUB1 and BUB1B genes were identified as shared ceRNAs across more than two HD cancers of interest. These genes play a critical role in cell division, spindle-assembly checkpoint signalling, and correct chromosome alignment. Furthermore, shared ceRNAs across multiple HD cancers have been involved in essential cancer pathways such as cell cycle, p53 signalling, and chromosome segregation. Identifying ceRNAs' roles across multiple related cancers will improve our understanding of their shared disease biology. Moreover, it contributes to the knowledge of RNA-mediated cancer pathogenesis.

Analysis of human leukocyte antigen associations in human papillomavirus-positive and -negative head and neck cancer: Comparison with cervical cancer.

BACKGROUND: Although the majority of human papillomavirus (HPV) infections are cleared by the immune system, a small percentage of them progress to develop HPV-driven cancers. Cervical cancer studies highlight that HPV persistence and cancer risk are associated with genetic factors, especially at the human leukocyte antigen (HLA) genes. This study was conducted to investigate such associations in head and neck cancer (HNC). METHODS: In all, 192 patients with HNC and 384 controls were genotyped with the Infinium Global Screening Array (Illumina, Inc). HLA variants were imputed with SNP2HLA, and an association analysis was performed by logistic regression. RESULTS: HPV-positive HNCs were significantly associated with single-nucleotide polymorphisms (SNPs) at DRB1_32660090 (P = 1.728 × 10-6 ) and DRB1_32660116 (P = 1.728 × 10-6 ) and with the amino acid variant DRB1_11_32660115 (P = 1.728 × 10-6 ). None of these associations were observed in the HPV-negative cohort, and this suggested their specificity to convey risk for HPV-associated HNCs. In general, associations observed for HPV-negative HNC were relatively weak, and variants in the HLA-DPA1 region were the strongest among them (P = 4.531 × 10-4 ). Several lead signals reported by previous HNC genome-wide association studies, including SNPs rs3135001 (P = .012), rs1049055 (P = .012), and rs34518860 (P = .029) and allele HLA-DQB1*06 (P = .009), were replicated in the current study. However, these associations were limited to the HPV-positive HNC group. Several cervical cancer-associated HLA variants, including SNPs rs9272143 (P = .002) and rs9271858 (P = .002) and alleles HLA-B-1501 (P = .009) and HLA-B-15 (P = .015), were also exclusively associated with HPV-positive HNC. CONCLUSIONS: HPV-positive HNC risk is associated with distinct HLA variants, and some of them are shared by both cervical cancer and HPV-positive HNC. Human papillomavirus (HPV)-positive head and neck cancer (HNC) risk is associated with distinct human leukocyte antigen variants, and some of them are shared by both cervical cancer and HPV-positive HNC. LAY SUMMARY: Cervical cancer studies highlight that human papillomavirus (HPV)-driven cancer risk is linked with human leukocyte antigen (HLA) polymorphism. Hence, the current study was designed to investigate the HLA associations in HPV-positive and HPV-negative head and neck cancer (HNC) and compare these associations with cervical cancer. Several lead signals reported by previous HNC and cervical genome-wide association studies were replicated in the current study. However, these associations were limited to the HPV-positive HNC group, and this suggests that HPV-positive HNC risk is associated with distinct HLA variants, and some of them are shared by both cervical cancer and HPV-positive HNC.

Dehydroepiandrosterone Sulfate and Colorectal Cancer Risk: A Mendelian Randomization Analysis.

Colorectal cancer is the third most common and second most deadly type of cancer worldwide, with approximately 1.9 million cases and 0.9 million deaths worldwide in 2020. Previous studies have shown that estrogen and testosterone hormones are associated with colorectal cancer risk and mortality. However, the potential effect of their precursor, dehydroepiandrosterone sulfate (DHEAS), on colorectal cancer risk has not been investigated. Therefore, evaluating DHEAS's effect on colorectal cancer will expand our understanding of the hormonal contribution to colorectal cancer risk. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to investigate the causal effect of DHEAS on colorectal cancer. We obtained DHEAS and colorectal cancer genomewide association study (GWAS) summary statistics from the Leipzig Health Atlas and the GWAS catalog and conducted MR analyses using the TwoSampleMR R package. Our results suggest that higher DHEAS levels are causally associated with decreased colorectal cancer risk (odds ratio per unit increase in DHEAS levels z score = 0.70; 95% confidence interval [0.51, 0.96]), which is in line with previous observations in a case-control study of colon cancer. The outcome of this study will be beneficial in developing plasma DHEAS-based biomarkers in colorectal cancer. Further studies should be conducted to interpret the DHEAS-colorectal cancer association among different ancestries and populations.

IsomiR-eQTL: A Cancer-Specific Expression Quantitative Trait Loci Database of miRNAs and Their Isoforms.

The identification of expression quantitative trait loci (eQTL) is an important component in efforts to understand how genetic variants influence disease risk. MicroRNAs (miRNAs) are short noncoding RNA molecules capable of regulating the expression of several genes simultaneously. Recently, several novel isomers of miRNAs (isomiRs) that differ slightly in length and sequence composition compared to their canonical miRNAs have been reported. Here we present isomiR-eQTL, a user-friendly database designed to help researchers find single nucleotide polymorphisms (SNPs) that can impact miRNA (miR-eQTL) and isomiR expression (isomiR-eQTL) in 30 cancer types. The isomiR-eQTL includes a total of 152,671 miR-eQTLs and 2,390,805 isomiR-eQTLs at a false discovery rate (FDR) of 0.05. It also includes 65,733 miR-eQTLs overlapping known cancer-associated loci identified through genome-wide association studies (GWAS). To the best of our knowledge, this is the first study investigating the impact of SNPs on isomiR expression at the genome-wide level. This database may pave the way for researchers toward finding a model for personalised medicine in which miRNAs, isomiRs, and genotypes are utilised.

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.

Marital status and prostate cancer incidence: a pooled analysis of 12 case-control studies from the PRACTICAL consortium.

While being in a committed relationship is associated with a better prostate cancer prognosis, little is known about how marital status relates to its incidence. Social support provided by marriage/relationship could promote a healthy lifestyle and an increased healthcare seeking behavior. We investigated the association between marital status and prostate cancer risk using data from the PRACTICAL Consortium. Pooled analyses were conducted combining 12 case-control studies based on histologically-confirmed incident prostate cancers and controls with information on marital status prior to diagnosis/interview. Marital status was categorized as married/partner, separated/divorced, single, or widowed. Tumours with Gleason scores ≥ 8 defined high-grade cancers, and low-grade otherwise. NCI-SEER's summary stages (local, regional, distant) indicated the extent of the cancer. Logistic regression was used to derive odds ratios (ORs) and 95% confidence intervals (CI) for the association between marital status and prostate cancer risk, adjusting for potential confounders. Overall, 14,760 cases and 12,019 controls contributed to analyses. Compared to men who were married/with a partner, widowed men had an OR of 1.19 (95% CI 1.03-1.35) of prostate cancer, with little difference between low- and high-grade tumours. Risk estimates among widowers were 1.14 (95% CI 0.97-1.34) for local, 1.53 (95% CI 1.22-1.92) for regional, and 1.56 (95% CI 1.05-2.32) for distant stage tumours. Single men had elevated risks of high-grade cancers. Our findings highlight elevated risks of incident prostate cancer among widowers, more often characterized by tumours that had spread beyond the prostate at the time of diagnosis. Social support interventions and closer medical follow-up in this sub-population are warranted.

Design and Characterization of a Cell-Penetrating Peptide Derived from the SOX2 Transcription Factor.

SOX2 is an oncogenic transcription factor overexpressed in nearly half of the basal-like triple-negative breast cancers associated with very poor outcomes. Targeting and inhibiting SOX2 is clinically relevant as high SOX2 mRNA levels are positively correlated with decreased overall survival and progression-free survival in patients affected with breast cancer. Given its key role as a master regulator of cell proliferation, SOX2 represents an important scaffold for the engineering of dominant-negative synthetic DNA-binding domains (DBDs) that act by blocking or interfering with the oncogenic activity of the endogenous transcription factor in cancer cells. We have synthesized an interference peptide (iPep) encompassing a truncated 24 amino acid long C-terminus of SOX2 containing a potential SOX-specific nuclear localization sequence, and the determinants of the binding of SOX2 to the DNA and to its transcription factor binding partners. We found that the resulting peptide (SOX2-iPep) possessed intrinsic cell penetration and promising nuclear localization into breast cancer cells, and decreased cellular proliferation of SOX2 overexpressing cell lines. The novel SOX2-iPep was found to exhibit a random coil conformation predominantly in solution. Molecular dynamics simulations were used to characterize the interactions of both the SOX2 transcription factor and the SOX2-iPep with FGF4-enhancer DNA in the presence of the POU domain of the partner transcription factor OCT4. Predictions of the free energy of binding revealed that the iPep largely retained the binding affinity for DNA of parental SOX2. This work will enable the future engineering of novel dominant interference peptides to transport different therapeutic cargo molecules such as anti-cancer drugs into cells.

Differential roles of protease isoforms in the tumor microenvironment.

Alternative splicing of precursor mRNA is a key mediator of gene expression regulation leading to greater diversity of the proteome in complex organisms. Systematic sequencing of the human genome and transcriptome has led to our understanding of how alternative splicing of critical genes leads to multiple pathological conditions such as cancer. For many years, proteases were known only for their roles as proteolytic enzymes, acting to regulate/process proteins associated with diverse cellular functions. However, the differential expression and altered function of various protease isoforms, such as (i) anti-apoptotic activities, (ii) mediating intercellular adhesion, and (iii) modifying the extracellular matrix, are evidence of their specific contribution towards shaping the tumor microenvironment. Revealing the alternative splicing of protease genes and characterization of their protein products/isoforms with distinct and opposing functions creates a platform to understand how protease isoforms contribute to specific cancer hallmarks. Here, in this review, we address cancer-specific isoforms produced by the alternative splicing of proteases and their distinctive roles in the tumor microenvironment.

Beyond the biomarker role: prostate-specific antigen (PSA) in the prostate cancer microenvironment.

The prostate-specific antigen (PSA) blood test is the accepted biomarker of tumor recurrence. PSA levels in serum correlate with disease progression, though its diagnostic accuracy is questionable. As a result, significant progress has been made in developing modified PSA tests such as PSA velocity, PSA density, 4Kscore, PSA glycoprofiling, Prostate Health Index, and the STHLM3 test. PSA, a serine protease, is secreted from the epithelial cells of the prostate. PSA has been suggested as a molecular target for prostate cancer therapy due to the fact that it is not only active in prostate tissue but also has a pivotal role on prostate cancer signaling pathways including proliferation, invasion, metastasis, angiogenesis, apoptosis, immune response, and tumor microenvironment regulation. Here, we summarize the current standing of PSA in prostate cancer progression as well as its utility in prostate cancer therapeutic approaches with an emphasis on the role of PSA in the tumor microenvironment.

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

Motivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary information: Supplementary data are available at Bioinformatics online.

Emergence of MicroRNAs as Key Players in Cancer Cell Metabolism.

BACKGROUND: Metabolic reprogramming is a hallmark of cancer. MicroRNAs (miRNAs) have been found to regulate cancer metabolism by regulating genes involved in metabolic pathways. Understanding this layer of complexity could lead to the development of novel therapeutic approaches. CONTENT: miRNAs are noncoding RNAs that have been implicated as master regulators of gene expression. Studies have revealed the role of miRNAs in the metabolic reprogramming of tumor cells, with several miRNAs both positively and negatively regulating multiple metabolic genes. The tricarboxylic acid (TCA) cycle, aerobic glycolysis, de novo fatty acid synthesis, and altered autophagy allow tumor cells to survive under adverse conditions. In addition, major signaling molecules, hypoxia-inducible factor, phosphatidylinositol-3 kinase/protein kinase B/mammalian target of rapamycin/phosphatase and tensin homolog, and insulin signaling pathways facilitate metabolic adaptation in tumor cells and are all regulated by miRNAs. Accumulating evidence suggests that miRNA mimics or inhibitors could be used to modulate the activity of miRNAs that drive tumor progression via altering their metabolism. Currently, several clinical trials investigating the role of miRNA-based therapy for cancer have been launched that may lead to novel therapeutic interventions in the future. SUMMARY: In this review, we summarize cancer-related metabolic pathways, including glycolysis, TCA cycle, pentose phosphate pathway, fatty acid metabolism, amino acid metabolism, and other metabolism-related oncogenic signaling pathways, and their regulation by miRNAs that are known to lead to tumorigenesis. Further, we discuss the current state of miRNA therapeutics in the clinic and their future potential.

MicroRNA-3162-5p-Mediated Crosstalk between Kallikrein Family Members Including Prostate-Specific Antigen in Prostate Cancer.

BACKGROUND: MicroRNAs mediate biological processes through preferential binding to the 3' untranslated region (3' UTR) of target genes. Studies have shown their association with prostate cancer (PCa) risk through single-nucleotide polymorphisms (SNPs), known as miRSNPs. In a European cohort, 22 PCa risk-associated miRSNPs have been identified. The most significant miRSNP in the 3' UTR of Kallikrein-related peptidase 3 (KLK3) created a binding site for miR-3162-5p. Here we investigated the miR-3162-5p-KLK interaction and the clinical implication of miR-3162-5p in PCa. METHODS: We tested the role of miR-3162-5p in PCa etiology using IncuCyte live-cell imaging and anchorage-independent growth assays. The effect of miR-3162-5p on KLK and androgen receptor (AR) expression was measured by RT-quantitative (q)PCR and target pulldown assays. KLK3 proteolytic activity was determined by DELFIA® immunoassay. Mass spectrometry identified pathways affected by miR-3162-5p. miR-3162-5p expression was measured in clinical samples using RT-qPCR. RESULTS: miR-3162-5p affected proliferation, migration, and colony formation of LNCaP cells by regulating the expression of KLK2-4 and AR by direct targeting. KLK3 protein expression was regulated by miR-3162-5p consistent with lower KLK3 proteolytic activity observed in LNCaP-conditioned media. KLK/AR pulldown and mass spectrometry analysis showed a potential role of miR-3162-5p in metabolic pathways via KLK/AR and additional targets. Increased miR-3162-5p expression was observed in prostate tumor tissues with higher Gleason grade. CONCLUSIONS: Our study provides an insight into possible involvement of miR-3162-5p in PCa etiology by targeting KLKs and AR. It highlights clinical utility of miR-3162-5p and its interactive axis as a new class of biomarkers and therapeutic targets for PCa.

Prostate Cancer Risk-Associated Single-Nucleotide Polymorphism Affects Prostate-Specific Antigen Glycosylation and Its Function.

BACKGROUND: Genetic association studies have reported single-nucleotide polymorphisms (SNPs) at chromosome 19q13.3 to be associated with prostate cancer (PCa) risk. Recently, the rs61752561 SNP (Asp84Asn substitution) in exon 3 of the kallikrein-related peptidase 3 (KLK3) gene encoding prostate-specific antigen (PSA) was reported to be strongly associated with PCa risk (P = 2.3 × 10-8). However, the biological contribution of the rs61752561 SNP to PCa risk has not been elucidated. METHODS: Recombinant PSA protein variants were generated to assess the SNP-mediated biochemical changes by stability and substrate activity assays. PC3 cell-PSA overexpression models were established to evaluate the effect of the SNP on PCa pathogenesis. Genotype-specific correlation of the SNP with total PSA (tPSA) concentrations and free/total (F/T) PSA ratio were determined from serum samples. RESULTS: Functional analysis showed that the rs61752561 SNP affects PSA stability and structural conformation and creates an extra glycosylation site. This PSA variant had reduced enzymatic activity and the ability to stimulate proliferation and migration of PCa cells. Interestingly, the minor allele is associated with lower tPSA concentrations and high F/T PSA ratio in serum samples, indicating that the amino acid substitution may affect PSA immunoreactivity to the antibodies used in the clinical immunoassays. CONCLUSIONS: The rs61752561 SNP appears to have a potential role in PCa pathogenesis by changing the glycosylation, protein stability, and PSA activity and may also affect the clinically measured F/T PSA ratio. Accounting for these effects on tPSA concentration and F/T PSA ratio may help to improve the accuracy of the current PSA test.

Assessment of miR-98-5p, miR-152-3p, miR-326 and miR-4289 Expression as Biomarker for Prostate Cancer Diagnosis.

Prostate cancer (PCa) is one of the most commonly diagnosed cancers worldwide, accounting for almost 1 in 5 new cancer diagnoses in the US alone. The current non-invasive biomarker prostate specific antigen (PSA) has lately been presented with many limitations, such as low specificity and often associated with over-diagnosis. The dysregulation of miRNAs in cancer has been widely reported and it has often been shown to be specific, sensitive and stable, suggesting miRNAs could be a potential specific biomarker for the disease. Previously, we identified four miRNAs that are significantly upregulated in plasma from PCa patients when compared to healthy controls: miR-98-5p, miR-152-3p, miR-326 and miR-4289. This panel showed high specificity and sensitivity in detecting PCa (area under the curve (AUC) = 0.88). To investigate the specificity of these miRNAs as biomarkers for PCa, we undertook an in depth analysis on these miRNAs in cancer from the existing literature and data. Additionally, we explored their prognostic value found in the literature when available. Most studies showed these miRNAs are downregulated in cancer and this is often associated with cancer progression and poorer overall survival rate. These results suggest our four miRNA signatures could potentially become a specific PCa diagnostic tool of which prognostic potential should also be explored.

Height, selected genetic markers and prostate cancer risk: results from the PRACTICAL consortium.

This corrects the article DOI: 10.1038/bjc.2017.231.