Research Papers:

Molecular pathway activation features linked with transition from normal skin to primary and metastatic melanomas in human

Denis Shepelin, Mikhail Korzinkin, Anna Vanyushina, Alexander Aliper, Nicolas Borisov, Raif Vasilov, Nikolay Zhukov, Dmitry Sokov, Vladimir Prassolov, Nurshat Gaifullin, Alex Zhavoronkov, Bhupinder Bhullar and Anton Buzdin _

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Oncotarget. 2016; 7:656-670. https://doi.org/10.18632/oncotarget.6394

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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

Correspondence to:

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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