Biomarkers For Prostate Cancer Prognosis Research Articles

An anoikis-related gene signature for prediction of the prognosis in prostate cancer.

This study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs). In this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa. Based on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer. This signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa.

Detection of Prostate Cancer Biomarker PCA3 by Using Aptasensors.

Prostate cancer cells have very high PCA3 messenger RNA levels, which turns them into one of the new biomarkers for prostate cancer prognosis and diagnosis. Our goal here is to develop a new aptasensor to detect PCA3 release by the cancer cell. DNA hairpin containing PCA3 aptamer was thiolated, conjugated to methylene blue (MB) redox probe, and immobilized on gold electrode through self-assembly to detect label-free cancer cells. Our data have evidenced stable and sensitive sensors presenting a wide linear detection range (0-150ng/mL). In addition, monitoring PCA3 released by different types of prostate cells can provide in-depth knowledge about prostate cancer dynamics; therefore, it is a powerful platform for earlier clinical diagnostic. The released PCA3 can vary depending on the type of adopted prostate cells. PCA3 release was monitored in a group of cells for 2 h; it showed significantly higher expression in both LNCaP and PC-3 cells. This strategy provides a unique and simple methodology to achieve more sensitive and specific PCA3 detection; thus, it emerged as a promising tool for early cost-effective diagnosis.

MicroRNAs as biomarkers for prostate cancer prognosis: a systematic review and a systematic reanalysis of public data

BackgroundReliable prognostic biomarkers to distinguish indolent from aggressive prostate cancer (PCa) are lacking. Many studies investigated microRNAs (miRs) as PCa prognostic biomarkers, often reporting inconsistent findings. We present a systematic review of these; also systematic reanalysis of public miR-profile datasets to identify tissue-derived miRs prognostic of biochemical recurrence (BCR) in patients undergoing radical prostatectomy.MethodsIndependent PubMed searches were performed for relevant articles from January 2007 to December 2019. For the review, 128 studies were included. Pooled-hazard-ratios (HRs) for miRs in multiple studies were calculated using a random-effects model (REM). For the reanalysis, five studies were included and Cox proportional-hazard models, testing miR association with BCR, performed for miRs profiled in all.ResultsSystematic review identified 120 miRs as prognostic. Five (let-7b-5p, miR-145-5p, miR152-3p, miR-195-5p, miR-224-5p) were consistently associated with progression in multiple cohorts/studies. In the reanalysis, ten (let-7a-5p, miR-148a-3p, miR-203a-3p, miR-26b-5p, miR30a-3p, miR-30c-5p, miR-30e-3p, miR-374a-5p, miR-425-3p, miR-582-5p) were significantly prognostic of BCR. Of these, miR-148a-3p (HR = 0.80/95% CI = 0.68-0.94) and miR-582-5p (HR = 0.73/95% CI = 0.61-0.87) were also reported in prior publication(s) in the review.ConclusionsFifteen miRs were consistently associated with disease progression in multiple publications or datasets. Further research into their biological roles is warranted to support investigations into their performance as prognostic PCa biomarkers.

Advances in Prognostic Methylation Biomarkers for Prostate Cancer.

Simple SummaryProstate cancer is a major cause of cancer-related death in men worldwide. There is an urgent clinical need for improved prognostic biomarkers to better predict the likely outcome and course of the disease and thus inform the clinical management of these patients. Currently, clinically recognised prognostic markers lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised, intermediate grade prostate cancer. Thus, there is major interest in identifying new molecular biomarkers to complement existing standard clinicopathological markers. DNA methylation is a frequent alteration in the cancer genome and offers potential as a reliable and robust biomarker. In this review, we provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis. We highlight advances in this field that have enabled the discovery of novel prognostic genes and discuss the potential of methylation biomarkers for noninvasive liquid-biopsy testing.There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.

FAM46B Promotes Apoptosis and Inhibits Glycolysis of Prostate Cancer Through Inhibition of the MYC-LDHA Axis.

ObjectiveIncreased dependence on glycolysis is a known element of cancer. This study was designed to examine critical glycolysis components including transcription factor MYC and its downstream target lactate dehydrogenase A (LDHA), potential upstream regulators of glycolysis such as family with sequence similarity 46 member B (FAM46B), and the impact of the abundance of these proteins on apoptosis and glycolysis in prostate cancer.Materials and MethodsA total of 70 primary prostate cancer patient samples were compared to normal tissues for FAM46B and LDHA expression and the corresponding patients’ survival was monitored for 60 months. Prostate cancer cell lines were employed for protein expression manipulation, glucose uptake and LDH assays, and apoptosis measurements. A xenograft mouse model was used to quantify the role of FAM46B and LDHA on tumor growth in vivo.ResultsFAM46B expression was reduced in prostate tumor tissue compared to normal tissue and prostate cancer patients who expressed low amounts of FAM46B had shortened average lifespans compared to those who expressed higher amounts of FAM46B (p=0.008). FAM46B overexpression reduced glucose uptake, decreased LDH activity, and induced apoptosis in prostate cancer cell lines while FAM46B shRNA increased MYC levels in a non-malignant prostate cell line (P69). Conversely, forced expression of LDHA in LNCaP cells produced an increase in glycolysis markers with a corresponding decrease in apoptosis. FAM46B-overexpressing xenografts had starkly blunted growth which was restored with co-overexpression of LDHA.ConclusionFAM46B plays a central role in regulating glycolysis and apoptosis in prostate cancer and operates through the regulation of LDHA via MYC. FAM46B’s keystone status in prostate cancer makes it a potential, robust biomarker for prostate cancer prognosis and a promising therapeutic target.

Increased expression of miR-153 predicts poor prognosis for patients with prostate cancer.

Deregulation of miR-153 has recently been observed in several common human cancer, while miR-153 serves an oncogene or tumor suppressive role in different cancer types. Previously, miR-153 has been identified to be overexpressed in prostate cancer. miR-153 played an important role in promoting proliferation of human prostate cancer cells and presented a novel mechanism of microRNA-mediated direct suppression of phosphatase and tensin homolog (PTEN) expression in prostate cancer cells. Until now, little is known about the clinical significance of miR-153 expression in prostate cancer.The miR-153 expression in 143 pairs of prostate cancer and adjacent non-cancerous prostate tissues was measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis. Student t test was conducted for intergroup comparison. Pearson correlation test was used for correlation analysis. Survival curves were carried out by the Kaplan–Meier method and evaluated using the log-rank test. Multivariable Cox proportional hazard risk regression model was performed to screen the independent factor affected the prognosis of prostate cancer patients.qRT-PCR analysis showed that the expression of miR-153 was significantly increased in the prostate cancer tissues in comparison with the adjacent noncancerous prostate tissues (P < .001). The high expression of miR-153 in prostate cancer tissues is closely correlated with aggressive clinical pathological parameters such as lymph node metastasis (P = .001); bone metastasis (P < .001); Gleason score (P < .001); and tumor-node-metastasis (TNM) stage (P < .001). Prostate cancer patients with a high expression of miR-153 had an evidently lower 5-year overall survival as compared with those with a low expression of miR-153 (P = .019). Notably, the multivariate Cox regression analysis indicated that miR-153 expression was an independent factor for predicting the 5-year overall survival of prostate cancer patients (hazard ratio [HR] = 2.481, 95% confidence interval [CI]: 1.582–10.727; P = .018).Our study demonstrated that high miR-153 expression was significantly associated with a poor overall survival independently of other factors in prostate cancer. Therefore, miR-153 may be an available biomarker for prostate cancer prognosis.

Elevated expression of PTCD3 correlates with tumor progression and predicts poor prognosis in patients with prostate cancer.

Pentatricopeptide repeat domain protein 3 (PTCD3) is a mitochondrial RNA-binding protein that serves a role in mitochondrial translation. PTCD3 was originally reported as an oncogene that is involved in breast cancer and lymphoma. However, the expression and function of PTCD3 in prostate cancer (PCa) are unknown. Therefore, the aim of the present study was to investigate the expression of PTCD3 and its clinical significance in PCa. Immunohistochemistry and dataset analyses revealed that PTCD3 protein expression levels were enhanced in human PCa tissues and mouse PCa models. PTCD3 expression levels were positively correlated with advanced PCa pathological grade and clinical stage. Additionally, PTCD3 mRNA expression was positively correlated with tissue malignancy, high Gleason score and distant metastasis in The Cancer Genome Atlas dataset. Kaplan-Meier analysis revealed that high PTCD3 levels can predict the increased biochemical recurrence (BCR)-free survival in all patients with or without metastasis. The overexpression of PTCD3 could be used as an independent prognostic marker of poor BCR-free survival. Immunofluorescence and western blot analysis in human PCa cell lines further confirmed that PTCD3 levels were associated with the hormone independence of PCa. Therefore, the present study revealed that PTCD3 levels may serve as a novel biomarker for PCa prognosis.

Overexpression of NIMA-related kinase 2 is associated with progression and poor prognosis of prostate cancer.

BackgroundThe NIMA-related kinase 2 (NEK2) is a serine/threonine kinase that is involved in regulation of centrosome duplication and spindle assembly during mitosis. Dysregulation of these processes causes chromosome instability and aneuploidy, which are hallmark changes of many solid tumors. However, whether aberrant expression of NEK2 is associated with outcome of prostate cancer (PCa) patients remains to be determined.MethodsExpression of NEK2 in human PCa cells and primary PCa tissues was assessed by quantitative RT-PCR. Expression of NEK2 in human PCa cells was depleted with siRNA. Effects of the depletion on cell proliferation, survival, and tumorigenicity were assessed both in vitro with cell cultures and in vivo with subcutaneous implantation of xenografts. In silico analyses of the online Taylor dataset were carried out to determine whether the expression level of NEK2 correlated with the clinicopathological characteristics of prostate cancer.ResultsCompared with benign human prostatic epithelial cells and tissues, the expression of NEK2 was elevated in human PCa cells and primary PCa tissues. Depleting NEK2 expression inhibited human PCa cell proliferation in vitro and xenograft growth in vivo. Expression level of NEK2 in PCa positively correlated with the Gleason score and pathologic stage of the patient.ConclusionThe results suggest that overexpression of NEK2 has the potential to serve as a biomarker for PCa prognosis. Further validation with large sample pool is warrant.

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

BackgroundProstate cancer (PCa) is a leading cause of cancer-related death of men worldwide. There is an urgent need to develop novel biomarkers for PCa prognosis and diagnosis in the post prostate-specific antigen era. Long intergenic noncoding RNAs (lincRNAs) play essential roles in many physiological processes and can serve as alternative biomarkers for prostate cancer, but there has been no systematic investigation of lincRNAs in PCa yet.ResultsNine lincRNA co-expression modules were identified from PCa RNA-Seq data. The association between the principle component of each module and the PCa phenotype was examined by calculating the Pearson's correlation coefficients. Three modules (M1, M3, and M5) were found associated with PCa. Two modules (M3 and M5) were significantly enriched with lincRNAs, and one of them, M3, may be used as a lincRNA module-biomarker for PCa diagnosis. This module includes seven essential lincRNAs: TCONS_l2_00001418, TCONS_l2_00008237, TCONS_l2_00011130, TCONS_l2_00013175, TCONS_l2_00022611, TCONS_l2_00022670 and linc-PXN-1. The clustering analysis and microRNA enrichment analysis further confirmed our findings.ConclusionThe correlation between lincRNAs and protein-coding genes is helpful for further exploration of functional mechanisms of lincRNAs in PCa. This study provides some important insights into the roles of lincRNAs in PCa and suggests a few lincRNAs as candidate biomarkers for PCa diagnosis and prognosis.

Identification of miR-187 and miR-182 as Biomarkers of Early Diagnosis and Prognosis in Patients with Prostate Cancer Treated with Radical Prostatectomy

miRNAs are noncoding RNAs that negatively regulate target mRNA gene expression. Aberrant miRNA expression is associated with prostate cancer pathogenesis. We identified miRNAs as potential biomarkers for prostate cancer diagnosis and prognosis. Total RNA was obtained from 10 normal prostate and 50 prostate cancer samples, and analyzed using the GeneChip® miRNA 2.0 Array. At a median followup of 92 months (range 2 to 189) an independent cohort of 273 paraffin embedded prostate cancer samples was used for validation by quantitative reverse transcriptase-polymerase chain reaction. Another 92 urine samples from patients undergoing prostate biopsy were evaluated for these miRNAs. miR-182 and 187, the miRNAs most differentially expressed between normal and tumor tissue, were selected for further validation. miR-187 inversely correlated with cT (p = 0.125) and pT (p = 0.0002) stages, Gleason score (p = 0.003) and TMPRSS2-ERG status (p = 0.003). The log rank test showed associations of miR-182 with biochemical (p = 0.026) and clinical (p = 0.043) progression-free survival, as also noted on multivariate analysis. A significant independent improvement in the definition of risk of progression was achieved by combining miR-182 expression with Gleason score (p <0.0001). miR-187 detection in urine provided an independent predictive value for positive biopsy. A prediction model including serum prostate specific antigen, urine PCA3 and miR-187 provided 88.6% sensitivity and 50% specificity (AUC 0.711, p = 0.001). Results show that miR-182 and 187 are promising biomarkers for prostate cancer prognosis to identify patients at risk for progression and for diagnosis to improve the predictive capability of existing biomarkers.

MicroRNA-224 inhibits progression of human prostate cancer by downregulating TRIB1

Our previous microarray data showed that microRNA-224 (miR-224) was downregulated in human prostate cancer (PCa) tissues compared with adjacent benign tissues. However, the underlying mechanisms by which miR-224 is involved in PCa remain unclear. In this study, we identified TRIB1 as a target gene of miR-224. Forced expression of miR-224 suppressed PCa cell proliferation, invasion and migration, and promoted cell apoptosis by downregulating TRIB1. Moreover, the expression level of miR-224 in PCa tissues was negatively correlated with that of TRIB1. miR-224 downregulation was frequently found in PCa tissues with metastasis, higher PSA level and clinical stage, whereas TRIB1 upregulation was significantly associated with metastasis. Both miR-224 downregulation and TRIB1 upregulation were significantly associated with poor biochemical recurrence-free survival of patients with PCa. In conclusion, these findings reveal that the aberrant expression of miR-224 and TRIB1 may promote PCa progression and have potentials to serve as novel biomarkers for PCa prognosis.

Novel tools for prostate cancer prognosis, diagnosis, and follow-up.

Prostate-specific antigen (PSA) is the main diagnostic tool when it comes to prostate cancer but it possesses serious limitations. Therefore, there is an urgent need for more sensitive and specific biomarkers for prostate cancer prognosis and patient follow-up. Recent advances led to the discovery of many novel diagnostic/prognostic techniques and provided us with many worthwhile candidates. This paper briefly reviews the most promising biomarkers with respect to their implementation in screening, early detection, diagnostic confirmation, prognosis, and prediction of therapeutic response or monitoring disease and recurrence; and their use as possible therapeutic targets. This review also examines the possible future directions in the field of prostate cancer marker research.

Prostate cancer – a biomarker perspective

Despite early detection and reduced risk of death, prostate cancer still remains the second leading cause of cancer death in American men. There is currently no cure for advanced prostate cancer. The multistage, stochastic and highly heterogeneous nature of prostate cancer, coupled with genetic and epigenetic alterations that occur during disease progression and response to therapy, represent fundamental challenges in our quest to understand and control this complex and prevalent disease. Recent advances in drug development and breakthroughs in omics technologies have renewed our efforts to identify novel biomarkers for prostate cancer prognosis, prediction, and therapeutic response monitoring. In this perspective article, we overview the current status and highlight future prospects of biomarkers for prostate cancer, a disease that affects millions of men worldwide.

Macrophage Inhibitory Cytokine 1: A New Prognostic Marker in Prostate Cancer

High serum levels of macrophage inhibitory cytokine 1 (MIC-1) are strongly associated with metastatic prostate cancer, suggesting MIC-1 is a biomarker for prostate cancer prognosis. We conducted a prospective cohort study of 1,442 Swedish men with a pathologically verified diagnosis of prostate cancer between 2001 and 2003. Blood was drawn either pretreatment (n = 431) or posttreatment (n = 1,011) and cases were followed for a mean time of 4.9 years (range, 0.1-6.8 years). MIC-1 serum levels independently predicted poor cancer-specific survival with an almost 3-fold higher cancer death rate in patients with serum levels in the highest quartile compared with men with serum levels in the lowest quartile (adjusted hazard ratio, 2.98; 95% confidence interval, 1.82-4.68). Pretreatment MIC-1 levels revealed an even stronger association with disease outcome with an 8-fold higher death rate in the highest compared with the lowest category (adjusted hazard ratio, 7.98; 95% confidence interval, 1.73-36.86). Among patients considered to have localized disease, MIC-1 significantly increased the discriminative capacity between indolent and lethal prostate cancer compared with the established prognostic markers clinical stage, pathologic grade, and prostate-specific antigen level (P = 0.016). A sequence variant in the MIC-1 gene was associated with decreased MIC-1 serum levels (P = 0.002) and decreased prostate cancer mortality (P = 0.003), suggesting a causative role of MIC-1 in prostate cancer prognosis. Serum MIC-1 concentration is a novel biomarker capable of predicting prostate cancer prognosis.