Dataset Information

LMTK2 as Potential Biomarker for Stratification between Clinically Insignificant and Clinically Significant Prostate Cancer.

ABSTRACT: A set of prostate tumors tend to grow slowly and do not require active treatment. Therefore, stratification between patients with clinically significant and clinically insignificant prostate cancer (PC) remains a vital issue to avoid overtreatment. Fast development of genetic technologies accelerated development of next-generation molecular tools for reliable PC diagnosis. The aim of this study is to evaluate the diagnostic value of molecular biomarkers (CRISP3, LMTK2, and MSMB) for separation of PC cases from benign prostatic changes and more specifically for identification of clinically significant PC from all pool of PC cases in patients with rising PSA levels. Patients (n?=?200) who had rising PSA (PSA II) after negative transrectal systematic prostate biopsy due to elevated PSA (PSA I) were eligible to the study. In addition to PSA concentration, PSA density was calculated for each patient. Gene expression level was measured in peripheral blood samples of cases applying RT-PCR, while MSMB (-57?C/T) polymorphism was identified by pyrosequencing. LMTK2 and MSMB significantly differentiated control group from both BPD and PC groups. MSMB expression tended to increase from the major alleles of the CC genotype to the minor alleles of the TT genotype. PSA density was the only clinical characteristic that significantly differentiated clinically significant PC from clinically insignificant PC. Therefore, LMTK2 expression and PSA density were significantly distinguished between clinically significant PC and clinically insignificant PC. PSA density rather than PSA can differentiate PC from the benign prostate disease and, in combination with LMTK2, assist in stratification between clinically insignificant and clinically significant PC.

SUBMITTER: Vezelis A

PROVIDER: S-EPMC7803409 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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LMTK2 as Potential Biomarker for Stratification between Clinically Insignificant and Clinically Significant Prostate Cancer.

Vezelis Alvydas A Simiene Julija J Dabkeviciene Daiva D Kincius Marius M Ulys Albertas A Suziedelis Kestutis K Jarmalaite Sonata S Jankevicius Feliksas F

Journal of oncology 20210105

A set of prostate tumors tend to grow slowly and do not require active treatment. Therefore, stratification between patients with clinically significant and clinically insignificant prostate cancer (PC) remains a vital issue to avoid overtreatment. Fast development of genetic technologies accelerated development of next-generation molecular tools for reliable PC diagnosis. The aim of this study is to evaluate the diagnostic value of molecular biomarkers (CRISP3, LMTK2, and MSMB) for separation o ...[more]

PMID: 33488712

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Project description:Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa). Methods: This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group (n = 54) and an internal validation group (n = 22) from one center along with an external independent validation group (n = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit. Results: The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa (P < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. Conclusions: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.

Project description:BackgroundWith the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.MethodsWe retrospectively collected the clinical indicators of patients with TPSA at 4-20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.ResultsAmong the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4-20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS (p = 0.004) and that for the model based on the PHI derivatives (p = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS (p = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives (p = 0.061) in diagnostic accuracy. For PCa in TPSA 4-20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4-20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.ConclusionsThe models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.

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Dataset Information

LMTK2 as Potential Biomarker for Stratification between Clinically Insignificant and Clinically Significant Prostate Cancer.

Publications

LMTK2 as Potential Biomarker for Stratification between Clinically Insignificant and Clinically Significant Prostate Cancer.

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