A 23-Gene Classifier urine test for prostate cancer prognosis.
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ABSTRACT:
SUBMITTER: Guo J
PROVIDER: S-EPMC7919118 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature
Publications
A 23-Gene Classifier urine test for prostate cancer prognosis.
Guo Jinan J Johnson Heather H Zhang Xuhui X Feng Xiaoyan X Zhang Heqiu H Simoulis Athanasios A Wu Alan Hb AH Xia Taolin T Li Fei F Tan Wanlong W Johnson Allan A Dizeyi Nishtman N Abrahamsson Per-Anders PA Kenner Lukas L Chen Lingwu L Zhong Wanmei W Xiao Kefeng K Persson Jenny L JL Zou Chang C
Clinical and translational medicine 20210301 3
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Project description:ObjectivesTo develop a risk classifier using urine-derived extracellular vesicle RNA (UEV-RNA) capable of providing diagnostic information of disease status prior to biopsy, and prognostic information for men on active surveillance (AS).Patients and methodsPost-digital rectal examination UEV-RNA expression profiles from urine (n = 535, multiple centres) were interrogated with a curated NanoString panel. A LASSO-based Continuation-Ratio model was built to generate four Prostate-Urine-Risk (PUR) signatures for predicting the probability of normal tissue (PUR-1), D'Amico Low-risk (PUR-2), Intermediate-risk (PUR-3), and High-risk (PUR-4) PCa. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub-cohort (n = 87) for prognostic evaluation.ResultsEach PUR signature was significantly associated with its corresponding clinical category (p<0.001). PUR-4 status predicted the presence of clinically significant Intermediate or High-risk disease, AUC = 0.77 (95% CI: 0.70-0.84). Application of PUR provided a net benefit over current clinical practice. In an AS sub-cohort (n=87), groups defined by PUR status and proportion of PUR-4 had a significant association with time to progression (p<0.001; IQR HR = 2.86, 95% CI:1.83-4.47). PUR-4, when utilised continuously, dichotomised patient groups with differential progression rates of 10% and 60% five years post-urine collection (p<0.001, HR = 8.23, 95% CI:3.26-20.81).ConclusionUEV-RNA can provide diagnostic information of aggressive PCa prior to biopsy, and prognostic information for men on AS. PUR represents a new & versatile biomarker that could result in substantial alterations to current treatment of PCa patients. This article is protected by copyright. All rights reserved.
| S-EPMC6851983 | biostudies-literature
Project description:Background: Prostate cancer (PCa) is one of the most common cancers in males around the globe, and about one-third of patients with localized PCa will experience biochemical recurrence (BCR) after radical prostatectomy or radiation therapy. Reportedly, a proportion of patients with BCR had a poor prognosis. Cumulative studies have shown that RNA modifications participate in the cancer-related transcriptome, but the role of pseudouridylation occurring in lncRNAs in PCa remains opaque. Methods: Spearman correlation analysis and univariate Cox regression were utilized to determine pseudouridylation-related lncRNAs with prognostic value in PCa. Prognostic pseudouridylation-related lncRNAs were included in the LASSO (least absolute shrinkage and selection operator) regression algorithm to develop a predictive model. KM (Kaplan-Meier) survival analysis and ROC (receiver operating characteristic) curves were applied to validate the constructed model. A battery of biological cell assays was conducted to confirm the cancer-promoting effects of RP11-468E2.5 in the model. Results: A classifier containing five pseudouridine-related lncRNAs was developed to stratify PCa patients on BCR and named the "ψ-lnc score." KM survival analysis showed patients in the high ψ-lnc score group experienced BCR more than those in the low ψ-lnc score group. ROC curves demonstrated that ψ-lnc score outperformed other clinical indicators in BCR prediction. An external dataset, GSE54460, was utilized to validate the predictive model's efficacy and authenticity. A ceRNA (competitive endogenous RNA) network was constructed to explore the model's potential molecular functions and was annotated through GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses. RP11-468E2.5 was picked for further investigation, including pan-cancer analysis and experimental validation. Preliminarily, RP11-468E2.5 was confirmed as a tumor promoter. Conclusion: We provide some evidence that pseudouridylation in lncRNA played a role in the development of PCa and propose a novel prognostic classifier for clinical practice.
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Project description:Splicing factors (SFs) are proteins that control the alternative splicing (AS) of RNAs, which have been recognized as new cancer hallmarks. Their dysregulation has been found to be involved in many biological processes of cancer, such as carcinogenesis, proliferation, metastasis and senescence. Dysregulation of SFs has been demonstrated to contribute to the progression of prostate cancer (PCa). However, a comprehensive analysis of the prognosis value of SFs in PCa is limited. In this work, we systematically analysed 393 SFs to deeply characterize the expression patterns, clinical relevance and biological functions of SFs in PCa. We identified 53 survival-related SFs that can stratify PCa into two de nove molecular subtypes with distinct mRNA expression and AS-event expression patterns and displayed significant differences in pathway activity and clinical outcomes. An SF-based classifier was established using LASSO-COX regression with six key SFs (BCAS1, LSM3, DHX16, NOVA2, RBM47 and SNRPN), which showed promising prognosis-prediction performance with a receiver operating characteristic (ROC) >0.700 in both the training and testing datasets, as well as in three external PCa cohorts (DKFZ, GSE70769 and GSE21035). CRISPR/CAS9 screening data and cell-level functional analysis suggested that LSM3 and DHX16 are essential factors for the proliferation and cell cycle progression in PCa cells. This study proposes that SFs and AS events are potential multidimensional biomarkers for the diagnosis, prognosis and treatment of PCa.
| S-EPMC10494302 | biostudies-literature
Project description:BackgroundProstate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM.MethodsAn advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score.ResultsAn algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing.ConclusionsThe novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.
| S-EPMC11860222 | biostudies-literature
Project description:BackgroundHeterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies.MethodsUsing a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy.ResultsThe 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963-0.929) in the retrospective cohort (n = 614), AUC of 0.901 (0.929-0.873) in the prospective cohort (n = 396), and AUC of 0.936 (0.956-0.916) in the large combination cohort (n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors (p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980-0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947-0.909) in the combination cohort (n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy.ConclusionsThe 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.
| S-EPMC7706045 | biostudies-literature
Project description:We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years' following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05-8.70), N3 vs. N0 hazard ratio: 4.32 (1.41-13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95-6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.
| S-EPMC7711061 | biostudies-literature
Project description:Colorectal cancer (CRC) patients frequently experience disease recurrence and distant metastasis. This study aimed to identify prognostic indicators, including individual responses to chemotherapy, in CRC patients. RNA-seq data was generated using 54 samples (normal colon, primary CRC, and liver metastases) from 18 CRC patients and genes associated with CRC aggressiveness were identified. A risk score based on these genes was developed and validated in four independent CRC patient cohorts (n = 1063). Diverse statistical methods were applied to validate the risk scoring system, including a generalized linear model likelihood ratio test, Kaplan-Meier curves, a log-rank test, and the Cox model. TREM1 and CTGF were identified as two activated regulators associated with CRC aggressiveness. A risk score based on 19 genes regulated by TREM1 or CTGF activation (TCA19) was a significant prognostic indicator. In multivariate and subset analyses based on pathological staging, TCA19 was an independent risk factor (HR = 1.894, 95% CI = 1.227-2.809, P = 0.002). Subset stratification in stage III patients revealed that TCA19 had prognostic potential and identified patients who would benefit from adjuvant chemotherapy, regardless of age. The TCA19 predictor represents a novel diagnostic tool for identifying high-risk CRC patients and possibly predicting the response to adjuvant chemotherapy.
| S-EPMC5528589 | biostudies-literature
Project description:Background:Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). Methods:Voided urine samples (N = 725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients (N = 399). In the discovery phase, seven selected genes from the literature (CDH13, CFTR, NID2, SALL3, TMEFF2, TWIST1, and VIM2) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). Results:A three-gene methylation classifier containing CFTR, SALL3, and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. Conclusions:The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented.
| S-EPMC5975622 | biostudies-literature
Project description:To construct a urine extracellular vesicle long non-coding RNA (lncRNA) classifier that can detect high-grade prostate cancer (PCa) of grade group 2 or greater and estimate the risk of progression during active surveillance, we identify high-grade PCa-specific lncRNAs by combined analyses of cohorts from TAHSY, TCGA, and the GEO database. We develop and validate a 3-lncRNA diagnostic model (Clnc, being made of AC015987.1, CTD-2589M5.4, RP11-363E6.3) that can detect high-grade PCa. Clnc shows higher accuracy than prostate cancer antigen 3 (PCA3), multiparametric magnetic resonance imaging (mpMRI), and two risk calculators (Prostate Cancer Prevention Trial [PCPT]-RC 2.0 and European Randomized Study of Screening for Prostate Cancer [ERSPC]-RC) in the training cohort (n = 350), two independent cohorts (n = 232; n = 251), and TCGA cohort (n = 499). In the prospective active surveillance cohort (n = 182), Clnc at diagnosis remains a powerful independent predictor for overall active surveillance progression. Thus, Clnc is a potential biomarker for high-grade PCa and can also serve as a biomarker for improved selection of candidates for active surveillance.
| S-EPMC10591064 | biostudies-literature
Project description:Background: A 23-gene classifier has been developed based on gene expression profiles of Taiwanese luminal-like breast cancer. We aim to stratify risk of relapse and identify patients who may benefit from adjuvant chemotherapy based on genetic model among distinct clinical risk groups. Methods: There were 248 luminal (hormone receptor-positive and human epidermal growth factor receptor II-negative) breast cancer patients with 23-gene classifier results. Using the modified Adjuvant! Online definition, clinical high/low-risk groups were tabulated with the genetic model. The primary endpoint was a recurrence-free interval (RFI) at 5 years. Results: There was a significant difference between the high/low-risk groups defined by the 23-gene classifier for the 5-year prognosis of recurrence (16 recurrences in high-risk and 3 recurrences in low-risk; log-rank test: p < 0.0001). Among the clinically high-risk group, the 5-year RFI of high risk defined by the 23-gene classifier was significantly higher than that of the low-risk group (15 recurrences in high-risk and 2 recurrences in low-risk; log-rank test: p < 0.0001). Conclusion: This study showed that 23-gene classifier can be used to stratify clinically high-risk patients into distinct survival patterns based on genomic risks and displays the potentiality to guide adjuvant chemotherapy. The 23-gene classifier can provide a better estimation of breast cancer prognosis which can help physicians make a better treatment decision.
| S-EPMC9776657 | biostudies-literature