Characterization Of Rna-Binding Motif 3 (Rbm3) Protein Levels And Nuclear Architecture Changes In Aggressive And Recurrent Prostate Cancer

BackgroundThe RNA-binding motif protein 3 (RBM3) has been shown to be upregulated in several types of cancer, including prostate cancer (PCa), compared with normal tissues. Increased RBM3 nuclear expression has been linked to improved clinical outcomes.AimsGiven that RBM3 has been hypothesized to play a role in critical nuclear functions such as chromatin remodeling, DNA damage response, and other posttranscriptional processes, we sought to (a) quantify RBM3 protein levels in archival PCa samples and (b) develop a nuclear morphometric model to determine if measures of RBM3 protein levels and nuclear features could be used to predict disease aggressiveness and biochemical recurrence.Methods and resultsThis study utilized two tissue microarrays (TMAs) stained for RBM3 that included 80 total cases of PCa stratified by Gleason score. A software-mediated image processing algorithm identified RBM3-positive cancerous nuclei in the TMA samples and calculated 22 features quantifying RBM3 expression and nuclear architecture. Multivariate logistic regression (MLR) modeling was performed to determine if RBM3 levels and nuclear structural changes could predict PCa aggressiveness and biochemical recurrence (BCR). Leave-one-out cross validation (LOOCV) was used to provide insight on how the predictive capabilities of the feature set might behave with respect to an independent patient cohort to address issues such as model overfitting. RBM3 expression was found to be significantly downregulated in highly aggressive GS >= 8 PCa samples compared with other Gleason scores (P<.0001) and significantly downregulated in recurrent PCa samples compared with nonrecurrent samples (P = .0377). An 11-feature nuclear morphometric MLR model accurately identified aggressive PCa, yielding a receiver operating characteristic area under the curve (ROC-AUC) of 0.90 (P<.0001) in the raw data set and 0.77 (95% CI, 0.83-0.97) for LOOCV testing. The same 11-feature model was then used to predict recurrence, yielding an ROC-AUC of 0.92 (P = .0004) in the raw data set and 0.76 (95% CI, 0.64-0.87) for LOOCV testing.ConclusionsThe RBM3 biomarker alone is a strong prognostic marker for the prediction of aggressive PCa and biochemical recurrence. Further, RBM3 appears to be downregulated in aggressive and recurrent tumors.