The talk will be given by Sarah Fischer from the Research Group Medical Bioinformatics of the Universitätsmedizin Rostock.

Background

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 are not sufficiently accurate 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 stages.

Results

We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for PCa. 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 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.

Conclusion

The integrated regulatory analysis of coding and non-coding RNAs helps to unravel the molecular mechanisms behind PCa upstaging and to expand our knowledgebase of potential stage-specific diagnostic factors, that are correlated with PCa aggressiveness- related imaging features. Our prediction model based on the combined set of clinical parameters, and of molecular features of genes and miRNAs has the potential to yield clinically relevant results for characterizing PCa aggressiveness.