Characterization of RNA‐binding motif 3 (RBM3) protein levels and nuclear architecture changes in aggressive and recurrent prostate cancer
| Autor: | Neil M. Carleton, Robert W. Veltri, Christine Davis, Prakash Kulkarni, M. Craig Miller, Guangjing Zhu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
Oncology Biochemical recurrence Cancer Research medicine.medical_specialty Biology Logistic regression Cross-validation Cohort Studies Prostate cancer Internal medicine Biomarkers Tumor medicine Humans Cell Nucleus Prostatectomy Tissue microarray Receiver operating characteristic Area under the curve breakpoint cluster region Prostatic Neoplasms RNA-Binding Proteins Original Articles Prognosis medicine.disease ROC Curve Neoplasm Grading Neoplasm Recurrence Local Algorithms |
| Zdroj: | Cancer Rep (Hoboken) |
| ISSN: | 2573-8348 |
| DOI: | 10.1002/cnr2.1237 |
| Popis: | Background The RNA-binding motif protein 3 (RBM3) has been shown to be up-regulated in several types of cancer, including prostate cancer (PCa), compared to normal tissues. Increased RBM3 nuclear expression has been linked to improved clinical outcomes. Aims Given that RBM3 has been hypothesized to play a role in critical nuclear functions such as chromatin remodeling, DNA damage response, and other post-transcriptional processes, we sought to: (1) quantify RBM3 protein levels in archival PCa samples; (2) 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 & Results This 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 twenty-two 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 to other Gleason scores (P < 0.0001) and significantly down-regulated in recurrent PCa samples compared to non-recurrent samples (P = 0.0377). An eleven-feature nuclear morphometric MLR model accurately identified aggressive PCa, yielding a receiver operating characteristic area under the curve (ROC-AUC) of 0.90 (P < 0.0001) in the raw data set and 0.77 (95% CI: 0.83-0.97) for LOOCV testing. The same eleven-feature model was then used to predict recurrence, yielding a ROC-AUC of 0.92 (P = 0.0004) in the raw data set and 0.76 (95% CI: 0.64-0.87) for LOOCV testing. Conclusions The RBM3 biomarker alone is a strong prognostic marker for the prediction of aggressive PCa and biochemical recurrence. Further, RBM3 appears to be down-regulated in aggressive and recurrent tumors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|