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

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Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, 18057 Rostock, Germany
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Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt
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Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, 18057 Rostock, Germany
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Department of Nuclear Medicine, Rostock University Medical Center, 18057 Rostock, Germany
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Department of Urology, Rostock University Medical Center, 18057 Rostock, Germany
*
Author to whom correspondence should be addressed.
Cancers 2019, 11(9), 1293; https://doi.org/10.3390/cancers11091293
Received: 31 July 2019 / Revised: 20 August 2019 / Accepted: 28 August 2019 / Published: 2 September 2019
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. View Full-Text
Keywords: prostate cancer; radiogenomics; gene expression; miRNA expression; data integration prostate cancer; radiogenomics; gene expression; miRNA expression; data integration
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MDPI and ACS Style

Fischer, S.; Tahoun, M.; Klaan, B.; Thierfelder, K.M.; Weber, M.-A.; Krause, B.J.; Hakenberg, O.; Fuellen, G.; Hamed, M. A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer. Cancers 2019, 11, 1293. https://doi.org/10.3390/cancers11091293

AMA Style

Fischer S, Tahoun M, Klaan B, Thierfelder KM, Weber M-A, Krause BJ, Hakenberg O, Fuellen G, Hamed M. A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer. Cancers. 2019; 11(9):1293. https://doi.org/10.3390/cancers11091293

Chicago/Turabian Style

Fischer, Sarah, Mohamed Tahoun, Bastian Klaan, Kolja M. Thierfelder, Marc-André Weber, Bernd J. Krause, Oliver Hakenberg, Georg Fuellen, and Mohamed Hamed. 2019. "A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer" Cancers 11, no. 9: 1293. https://doi.org/10.3390/cancers11091293

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