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Updated: Jan 30, 2026
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Identification a novel set of 6 differential expressed genes in prostate cancer that can potentially predict
1Department of Urology, National Key Clinical Specialty of Urology, The Second Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, 300211, China.
Purpose:
Approximately, 30% patients after radical prostatectomy (RP) will undergo post-operative biochemical recurrence (BCR). Present stratification method by TNM staging and Gleason score was not adequate to screen high-risk patients. In this study, we intended to identify a novel set of differentially expressed gene (DEG) signature that can predict BCR after RP.
Materials/Patients:
358 patients after RP with follow-up data were extracted from The Cancer Genome Atlas (TCGA), among which 61 patients had undergone BCR. Key DEGs were confirmed by the intersection of GSE35988 and TCGA_PCa dataset, and their gene expression data were also extracted from TCGA_PCa dataset. Kaplan-Meier plot and Cox proportion hazard regression model were applied to assess the relationship between risk score and survival outcome (BCR).
Results:
310 DEGs were confirmed in two prostate cancer dataset. 6 DEGs (SMIM22, NINL, NRG2, TOP2A, REPS2, and TPCN2) were selected to construct a risk score formula. The risk score was a powerful predictive factor independent of TNM stage (HR 3.045, 95% CI 1.655-5.602, p < 0.001).
Conclusion:
In this study, a novel 6-gene signature with robust predictive ability on post-operative BCR was constructed and 4 genes (SMIM22, NRG2, NINL and TPCN2) in the 6-gene signature were not reported to be associated with prostate cancer.
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