| Peng Hu,Guoda Song,Bingliang Chen,Jianping Miao. Development of a redox-related prognostic signature for predicting biochemical-recurrence-free survival of prostate cancer. Oncol Transl Med, 2023, 9: 82-92. |
| Development of a redox-related prognostic signature for predicting biochemical-recurrence-free survival of prostate cancer |
| Received:August 28, 2022 Revised:April 20, 2023 |
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| KeyWord:prostate cancer (PCa); redox; prognostic signature; prognosis; bioinformatic |
| Author Name | Affiliation | Postcode | | Peng Hu | Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | 430030 | | Guoda Song | Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | 430030 | | Bingliang Chen | Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | 430030 | | Jianping Miao | Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | 430030 |
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| Abstract: |
| Objective: Prostate cancer (PCa) is one of the most common malignancies among elderly males.
However, effective prognostic biomarkers are currently lacking. Bioinformatic analysis was used to identify
patients at high risk of biochemical recurrence (BCR).
Methods: In our study, RNA sequencing and clinical data were downloaded from The Cancer Genome
Atlas (TCGA) dataset to serve as the training and internal validation sets. The GSE84042 dataset was used
as the external validation set. Batch effects were removed and normalized for the two datasets using “sva”
package. Univariate Cox, least absolute shrinkage and selection operator (LASSO) Cox, and multivariate
Cox regression analyses were successively performed to identify the redox-related gene (RRG) signature.
After performing univariate Cox, LASSO Cox, and multivariate Cox regression analyses, a signature
consisting of seven RRGs was established to predict BCR of patients with PCa, which included TP53, ADH5,
SRRT, SLC24A2, COL1A1, CSF3R, and TEX19. Kaplan-Meier and receiver operating characteristic curve
analyses showed good performance for the prognostic signature in the training and validation datasets.
Results: Univariate and multivariate Cox analyses showed that the RRG signature was an independent
prognostic factor for BCR of patients with PCa. Thereafter, the nomogram results revealed that it was able
to predict BCR of patients with PCa with high efficiency.
Conclusion: This study identified an independent prognostic signature and established a nomogram to
predict BCR in PCa. This signature can be used to identify patients with PCa with a high risk of BCR, and
personalized treatment can be applied. |
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