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Cancer Res. 2018 Nov 1;78(21):6059-6072. doi: 10.1158/0008-5472.CAN-17-2705. Epub 2018 Aug 22.

Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.

Author information

1
Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla, Spain.
2
Igenomix S.L., Valencia, Spain.
3
ProCURE, Catalan Institute of Oncology. Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, Barcelona, Spain.
4
Centre for Genomic Regulation, Barcelona, Spain.
5
Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla, Spain. joaquin.dopazo@juntadeandalucia.es.
6
Functional Genomics Node, INB-ELIXIR-es, FPS, Hospital Virgen del Rocío, Sevilla, Spain.
7
Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Sevilla, Spain.

Abstract

Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies.Significance: Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. Cancer Res; 78(21); 6059-72. ©2018 AACR.

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