Análisis de supervivencia de los pacientes con cáncer de próstata con factores patológicos desfavorables tratados con prostatectomía radical y radioterapia de rescate tras la recidiva y persistencia bioquímica

Bernardos, G. Barbas; Amo, F. Herránz; Segundo, Carmen González San; Velasco, J. Caño; Ríos, D. Subirá; Gárate, M. Moralejo; Castro, J. Mayor de; Chamizo, J. Aragón; Fernández, C. Hernández

doi:10.1016/j.acuro.2020.02.001

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…It was estimated that there were 248,530 new cases and 34,130 deaths in the United States in 2021. 1 Radical prostatectomy is considered as the first-line treatment option for PCa patients, but recurrence (biochemical, BCR; or clinical) frequently occurs in 10-40% of patients after curative surgery, [2][3][4] which leads to the cumulative 5-year recurrence-free survival (RFS) rate of only about 65%. 3,5 Clinically, prostate-specific antigen (PSA) level, 6 Gleason score, 7 and tumor, node, metastasis (TNM) staging 8 are widely used to predict tumor recurrence.…”

Section: Introductionmentioning

confidence: 99%

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Cai

Yang

Chen

et al. 2021

PGPM

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Background: Radical prostatectomy is the main treatment for prostate cancer (PCa), a common cancer type among men. Recurrence frequently occurs in a proportion of patients. Therefore, there is a great need to early screen those patients to specifically schedule adjuvant therapy to improve the recurrence-free survival (RFS) rate. This study aims to develop a biomarker to predict RFS for patients with PCa based on the data of methylation, an important heritable contributor to carcinogenesis. Methods: Methylation expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus database (GSE26126), and the European Bioinformatics Institute (E-MTAB-6131). The stable co-methylation modules were identified by weighted gene co-expression network analysis. The genes in modules were overlapped with differentially methylated RNAs (DMRs) screened by MetaDE package in three datasets, which were used to screen the prognostic genes using least absolute shrinkage and selection operator analyses. The prognostic performance of the prognostic signature was assessed by survival curve analysis. Results: Five co-methylation modules were considered preserved in three datasets. A total of 192 genes in these 5 modules were overlapped with 985 DMRs, from which a signature panel of 11 methylated messenger RNAs and 3 methylated long non-coding RNAs was identified. This signature panel could independently predict the 5-year RFS of PCa patients, with an area under the receiver operating characteristic curve (AUC) of 0.969 for the training TCGA dataset and 0.811 for the testing E-MTAB-6131 dataset, both of which were higher than the predictive accuracy of Gleason score (AUC = 0.689). Also, the patients with the same Gleason score (6-7 or 8-10) could be further divided into the high-risk group and the low-risk group. Conclusion: These results suggest that our prognostic model may be a promising biomarker for clinical prediction of RFS in PCa patients.

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Section: Introductionmentioning

confidence: 99%

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Cai

Yang

Chen

et al. 2021

PGPM

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“…Previous studies have identified several clinicopathological factors including Gleason score, TNM stage, and margin status as predictive variables for BCR [ 5 , 6 ]. However, for the great heterogeneity of PCa, prognosis varies significantly in PCa patients with same tumor stage and similar clinicopathological features [ 7 , 8 ]. Therefore, more ideal biomarker and predictive model are warranted to be developed.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

et al. 2020

Disease Markers

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Background. Postoperative early biochemical recurrence (BCR) was an essential indicator for recurrence and distant metastasis of prostate cancer (PCa). The aim of this study was to construct a cancer stem cell- (CSC-) associated gene set-based signature to identify a subgroup of PCa patients who are at high risk of early BCR. Methods. The PCa dataset from The Cancer Genome Atlas (TCGA) was randomly separated into discovery and validation set. Patients in discovery set were divided into early BCR group and long-term survival group. Propensity score matching analysis and differentially expressed gene selection were used to identify candidate CSC-associated genes. The LASSO Cox regression model was finally performed to filter the most useful prognostic CSC-associated genes for predicting early BCR. Results. By applying the LASSO Cox regression model, we built a thirteen-CSC-associated gene-based early BCR-predicting signature. In the discovery set, patients in high-risk group showed significantly poorer BCR free survival than that patients in low-risk group (HR: 4.91, 95% CI: 2.75–8.76, P < 0.001 ). The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, P = 0.005 ). Time-dependent ROC at 1 year suggested that the CSC gene signature ( AUC = 0.800 ) possessed better predictive value than any other clinicopathological features in the entire TCGA cohort. Additionally, survival decision curve analysis revealed a considerable clinical usefulness of the CSC gene signature. Conclusions. We successfully developed a CSC-associated gene set-based signature that can accurately predict early BCR in PCa cancer.

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Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer

Lu,

Zhao,

Wang

et al. 2024

Clin Transl Oncol

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Análisis de supervivencia de los pacientes con cáncer de próstata con factores patológicos desfavorables tratados con prostatectomía radical y radioterapia de rescate tras la recidiva y persistencia bioquímica

Cited by 4 publications

References 24 publications

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

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

Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer

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