Molecular pathway activation features linked with transition from normal skin to primary and metastatic melanomas in human
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Denis Shepelin1,2, Mikhail Korzinkin1,3, Anna Vanyushina4, Alexander Aliper4, Nicolas Borisov3,5, Raif Vasilov5, Nikolay Zhukov3,6, Dmitry Sokov7, Vladimir Prassolov8, Nurshat Gaifullin9, Alex Zhavoronkov10, Bhupinder Bhullar11, Anton Buzdin1,4,5
1Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR
2Group for Genomic Analysis of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
3First Oncology Research and Advisory Center, Moscow, Russia
4Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
5National Research Centre “Kurchatov Institute”, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
6Pirogov Russian National Research Medical University, Department of Oncology, Hematology and Radiotherapy, Moscow, Russia
7Moscow 1st Oncological Hospital, Moscow Russia
8Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Mosow, Russia
9Moscow State University, Faculty of Fundamental Medicine, Moscow, Russia
10Insilico Medicine, Inc, ETC, Johns Hopkins University, Baltimore, MD, USA
11Novartis Institute for Biomedical Research, Basel, Switzerland
Anton Buzdin, e-mail: [email protected]
Keywords: transition from nevus to primary and metastatic melanoma, OncoFinder, intracellular molecular networks, metabolic and signaling pathways, machine learning algorithms
Received: January 17, 2015 Accepted: November 11, 2015 Published: November 26, 2015
Melanoma is the most aggressive and dangerous type of skin cancer, but its molecular mechanisms remain largely unclear. For transcriptomic data of 478 primary and metastatic melanoma, nevi and normal skin samples, we performed high-throughput analysis of intracellular molecular networks including 592 signaling and metabolic pathways. We showed that at the molecular pathway level, the formation of nevi largely resembles transition from normal skin to primary melanoma. Using a combination of bioinformatic machine learning algorithms, we identified 44 characteristic signaling and metabolic pathways connected with the formation of nevi, development of primary melanoma, and its metastases. We created a model describing formation and progression of melanoma at the level of molecular pathway activation. We discovered six novel associations between activation of metabolic molecular pathways and progression of melanoma: for allopregnanolone biosynthesis, L-carnitine biosynthesis, zymosterol biosynthesis (inhibited in melanoma), fructose 2, 6-bisphosphate synthesis and dephosphorylation, resolvin D biosynthesis (activated in melanoma), D-myo-inositol hexakisphosphate biosynthesis (activated in primary, inhibited in metastatic melanoma). Finally, we discovered fourteen tightly coordinated functional clusters of molecular pathways. This study helps to decode molecular mechanisms underlying the development of melanoma.
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