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.

A large body of research has recently focused on minimizing task latency in mobile edge computing (MEC). Researchers in the field have typically posed the minimization exercise as a non-convex optimization problem and solved it either mathematically or metaheuristically, overlooking the fact that neither provides the optimal solution. Specifically, the mathematical paradigm provides a lower bound on the optimal solution, and the metaheuristic paradigm provides an upper bound. The present work is the first, to the best of our knowledge, to explore this fact and quantify the gap between the two bounds, where the optimal solution actually lies. To this end, we craft a baseline MEC model and formulate a non-convex optimization problem with the objective to minimize task latency. We then define three partitioning vectors, one for the workloads and two for the MEC resources. By searching for the optimal values of these three partitioners, the solution lower bound is obtained mathematically using Lagrange multipliers, and the solution upper bound is obtained metaheuristically twice using two algorithms, genetic and bee colony. The experimental results show that the gap between the two bounds can be as small as $$2\%$$ and as large as $$14\%$$ , depending on the model parameters, reasserting the fact that settling with only one solution paradigm can be dreadfully misleading.