Abstract
Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation, such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels, and Gleason score still lack accuracy for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa pathological stages. We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for 298 PCa patients. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ± 0.75) with corresponding aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage. Our prediction model exhibits high potential to yield clinically relevant results for characterizing PCa aggressiveness.
Highlights
Prostate cancer (PCa) is the second most common cancer and affects millions of men every year [1,2]
We evaluated the biological evidence for the PCa-GRN network in more depth to better assess the functional integrity of the biological processes underlying the etiology of PCa progression and upstaging
It might be worthwhile to investigate the predictive power of other molecular features (e.g., differentially methylated regions (DMRs)) identified from other OMICs datasets, which were associated with the biomarkers we found, since these biomarkers yielded prediction accuracies close to the ones based on clinical features, in predicting pathological stage
Summary
Prostate cancer (PCa) is the second most common cancer and affects millions of men every year [1,2]. The prognosis and determination of best treatment strategies for PCa patients depend on the correct estimation of PCa TNM (Tumor-Node-Metastasis)-stages, based on the universal TNM tumor stage classification, which refer to the degree by which cancer has spread inside the prostate, to the nearby tissues such as seminal vesicles and bladder, and beyond [4]. Cancers 2019, 11, 1293 are based on histological examination of transrectal biopsy samples and clinical parameters exhibit important shortcomings related to tumor heterogeneity, the invasive collection of tumor tissue, and the failure to distinguish between clinically relevant grades/stages of cancer [5].
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