Literature DB >> 29511883

Validation of a 10-gene molecular signature for predicting biochemical recurrence and clinical metastasis in localized prostate cancer.

Hatem Abou-Ouf1,2, Mohammed Alshalalfa3, Mandeep Takhar3, Nicholas Erho3, Bryan Donnelly4, Elai Davicioni3, R Jeffrey Karnes5, Tarek A Bismar6,7,8,9,10.   

Abstract

PURPOSE: To validate a previously characterized 10-gene signature in prostate cancer with implication to distinguish aggressive and indolent disease within low and intermediate patients' risk groups.
METHODS: A case-control study design used to select 545 patients from the Mayo clinic tumor registry who underwent radical prostatectomy. A training set from this cohort (n = 359) was used to build a 10-gene model, based on high-dimensional discriminant analysis (HDDA10) to predict several endpoints of clinical patients' outcome. An independent set (n = 219) from the same institution was used as validation set.
RESULTS: HDDA10 showed significant performance for predicting metastasis (Mets) (AUC 0.68, p = 6.4E - 6) and biochemical recurrence (BCR) (AUC = 0.65, p = 0.003) in the validation set outperforming Gleason grade grouping (GG) for BCR (AUC 0.57, p = 0.03) and with comparable performance for Mets endpoint (GG AUC 0.66, p = 8.1E - 5). HDDA10 prognostic significance was superior to any clinical-pathological parameter within GG2 + 3 (GS7) patients achieving an AUC of 0.74 (p = 0.0037) for BCR compared to Gleason pattern 4 (AUC 0.64) (p = 0.015) and AUC for Mets of 0.68 versus AUC of 0.65 for Gleason pattern 4 (p = 0.01). HDDA10 remained significant for both BCR and Mets in multivariate analysis, suggesting that it can be used to increase accuracy in stratifying patients eligible for active surveillance.
CONCLUSION: HDDA10 is of added value to GG and other clinical-pathological parameters in predicting BCR and Mets endpoint, especially in the low to intermediate patients' risk groups. HDDA10 prognostic value should be further validated prospectively in stratifying patients specifically in low to intermediate GS (GG1-2), such as active surveillance programs.

Entities:  

Keywords:  Biochemical recurrence; Biomarkers; Clinical metastasis; Genetic classifier; Gleason score; Grade grouping; Prognosis; Prostate cancer

Mesh:

Year:  2018        PMID: 29511883     DOI: 10.1007/s00432-018-2615-7

Source DB:  PubMed          Journal:  J Cancer Res Clin Oncol        ISSN: 0171-5216            Impact factor:   4.553


  30 in total

1.  Improved biomarkers for prostate cancer: a definite need.

Authors:  H Ballentine Carter; William B Isaacs
Journal:  J Natl Cancer Inst       Date:  2004-06-02       Impact factor: 13.506

2.  Unsupervised detection of genes of influence in lung cancer using biological networks.

Authors:  Anna Goldenberg; Sara Mostafavi; Gerald Quon; Paul C Boutros; Quaid D Morris
Journal:  Bioinformatics       Date:  2011-09-28       Impact factor: 6.937

3.  Active surveillance for low-risk prostate cancer in African American men: a multi-institutional experience.

Authors:  Brian D Odom; M C Mir; Scott Hughes; Cedric Senechal; Alexis Santy; Remi Eyraud; Andrew J Stephenson; Kelly Ylitalo; Ranko Miocinovic
Journal:  Urology       Date:  2013-11-26       Impact factor: 2.649

4.  Defining aggressive prostate cancer using a 12-gene model.

Authors:  Tarek A Bismar; Francesca Demichelis; Alberto Riva; Robert Kim; Sooryanarayana Varambally; Le He; Jeff Kutok; Jonathan C Aster; Jeffery Tang; Rainer Kuefer; Matthias D Hofer; Phillip G Febbo; Arul M Chinnaiyan; Mark A Rubin
Journal:  Neoplasia       Date:  2006-01       Impact factor: 5.715

5.  Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population.

Authors:  R Jeffrey Karnes; Eric J Bergstralh; Elai Davicioni; Mercedeh Ghadessi; Christine Buerki; Anirban P Mitra; Anamaria Crisan; Nicholas Erho; Ismael A Vergara; Lucia L Lam; Rachel Carlson; Darby J S Thompson; Zaid Haddad; Benedikt Zimmermann; Thomas Sierocinski; Timothy J Triche; Thomas Kollmeyer; Karla V Ballman; Peter C Black; George G Klee; Robert B Jenkins
Journal:  J Urol       Date:  2013-06-11       Impact factor: 7.450

6.  Comparative validation of nomograms predicting clinically insignificant prostate cancer.

Authors:  Viacheslav Iremashvili; Mark S Soloway; Lisét Pelaez; Daniel L Rosenberg; Murugesan Manoharan
Journal:  Urology       Date:  2013-04-03       Impact factor: 2.649

7.  Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy.

Authors:  Nicholas Erho; Anamaria Crisan; Ismael A Vergara; Anirban P Mitra; Mercedeh Ghadessi; Christine Buerki; Eric J Bergstralh; Thomas Kollmeyer; Stephanie Fink; Zaid Haddad; Benedikt Zimmermann; Thomas Sierocinski; Karla V Ballman; Timothy J Triche; Peter C Black; R Jeffrey Karnes; George Klee; Elai Davicioni; Robert B Jenkins
Journal:  PLoS One       Date:  2013-06-24       Impact factor: 3.240

8.  Molecular characterisation of ERG, ETV1 and PTEN gene loci identifies patients at low and high risk of death from prostate cancer.

Authors:  A H M Reid; G Attard; L Ambroisine; G Fisher; G Kovacs; D Brewer; J Clark; P Flohr; S Edwards; D M Berney; C S Foster; A Fletcher; W L Gerald; H Møller; V E Reuter; P T Scardino; J Cuzick; J S de Bono; C S Cooper
Journal:  Br J Cancer       Date:  2010-01-26       Impact factor: 7.640

9.  Duplication of the fusion of TMPRSS2 to ERG sequences identifies fatal human prostate cancer.

Authors:  G Attard; J Clark; L Ambroisine; G Fisher; G Kovacs; P Flohr; D Berney; C S Foster; A Fletcher; W L Gerald; H Moller; V Reuter; J S De Bono; P Scardino; J Cuzick; C S Cooper
Journal:  Oncogene       Date:  2007-07-16       Impact factor: 9.867

10.  MUC-1 gene is associated with prostate cancer death: a 20-year follow-up of a population-based study in Sweden.

Authors:  O Andrén; K Fall; S-O Andersson; M A Rubin; T A Bismar; M Karlsson; J-E Johansson; L A Mucci
Journal:  Br J Cancer       Date:  2007-08-28       Impact factor: 7.640

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1.  Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer.

Authors:  Bide Liu; Xun Li; Jiuzhi Li; Hongyong Jin; Hongliang Jia; Xiaohu Ge
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2.  Prognostic Significance of Serum PSA Level and Telomerase, VEGF and GLUT-1 Protein Expression for the Biochemical Recurrence in Prostate Cancer Patients after Radical Prostatectomy.

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4.  Identification of seven long noncoding RNAs signature for prediction of biochemical recurrence in prostate cancer.

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Journal:  Asian J Androl       Date:  2019 Nov-Dec       Impact factor: 3.285

5.  Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer.

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Journal:  Front Genet       Date:  2020-11-25       Impact factor: 4.599

6.  Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis.

Authors:  Peng Zhang; Xiaodong Tan; Daoqiang Zhang; Qi Gong; Xuefeng Zhang
Journal:  PLoS One       Date:  2021-05-04       Impact factor: 3.240

7.  Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy.

Authors:  Qiang Su; Zhenyu Liu; Chi Chen; Han Gao; Yongbei Zhu; Liusu Wang; Meiqing Pan; Jiangang Liu; Xin Yang; Jie Tian
Journal:  Cancer Med       Date:  2021-08-28       Impact factor: 4.452

8.  A Novel Angiogenesis-Related Gene Signature to Predict Biochemical Recurrence of Patients with Prostate Cancer following Radical Therapy.

Authors:  Bohan Fan; Yicun Wang; Xin Zheng; Xin Zhang; Zijian Zhang; Xiaopeng Hu
Journal:  J Oncol       Date:  2022-06-28       Impact factor: 4.501

9.  A novel gene signature to predict immune infiltration and outcome in patients with prostate cancer.

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Journal:  Oncoimmunology       Date:  2020-06-01       Impact factor: 8.110

10.  A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.

Authors:  Bo Cheng; Qidan He; Yong Cheng; Haifan Yang; Lijun Pei; Qingfu Deng; Hao Long; Likun Zhu; Rui Jiang
Journal:  Front Genet       Date:  2020-02-04       Impact factor: 4.599

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