A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer

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.

Identification of MicroRNAs as Viable Aggressiveness Biomarkers for Prostate Cancer

“…When comparing the remaining potential miRNAs, according to the present bioinformatics analysis (miR-93-5p, miR-23c, miR-210-3p, miR-221-3p, and miR-592), all were suggested with important roles in PC screening [61], aggressiveness [8,62,63], and recurrence [7,18,64], or even new etiological values [65] were suggested. These miRNAs have also been highlighted with carcinogenic effects in other tumors, such as endometrial [66,67], osteosarcoma [68], hepatocellular [69], renal [17,70], or colorectal [71], among others [72][73][74].…”

“…An over-expression of miR-592 is suggested to promote proliferation, migration, and invasion of renal cell carcinoma cells by targeting SPRY2 (gene related to PI3K/AKT and MAPK/EPK signaling pathways) [17]. In PC, there are also data relating miR-592 with aggressiveness in combination with ANPEP, miR-217, and miR-6715b [18]. miR-375 has been included as one of the classical miRNAs' biomarkers in PC.…”

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

“…Unlike the other studies referenced in this selection, this was not an example of a radiomics model predicting genetic expression. They observed that by combining known genomic, radiomic, and clinical features, they improved the accuracy of their predictive model for the definitive tumor grade [ 47 ], also demonstrating possible evolution in the technical definition of the term radiogenomics.…”

“…A pertinent example that did not benefit from the implementation of AI was the earlier referenced STHLM3 study, which benefitted from incorporating multiple clinical, biochemical, and radiological biomarkers [ 183 ]. Similarly, the radiogenomic models were seemingly improved with additional clinical data sources, but utilized conventional radiomic features in their modeling [ 47 , 157 , 165 , 182 ]. Enriching such models with deep radiomic features is a promising setting for study.…”

“…More recently, radiomic models have been utilized to predict genetic characteristics, a field known as radiogenomics. These studies have explored the potential in characterizing a PCa’s underlying biological aggression [ 47 , 48 , 49 , 50 , 51 , 52 ].…”