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Integrated omics and gene expression analysis identifies the loss of metabolite–metabolite correlations in small cell lung cancer

Authors Yu L, Li K, Xu Z, Cui G, Zhang X

Received 21 February 2018

Accepted for publication 10 May 2018

Published 6 July 2018 Volume 2018:11 Pages 3919—3929

DOI https://doi.org/10.2147/OTT.S166149

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Narasimha Reddy Parine

Peer reviewer comments 2

Editor who approved publication: Dr XuYu Yang


Li Yu,1 Kefeng Li,2 Zhaoguo Xu,1 Guoyuan Cui,1 Xiaoye Zhang1

1Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China; 2School of Medicine, University of California–San Diego, San Diego, CA, USA

Background and objective: Small cell lung cancer (SCLC) is the most aggressive type of lung carcinoma with high metastatic potential and chemoresistance upon relapse. Cancer cells remodel the existing metabolic pathways for their benefits and the perturbations in cellular metabolism are the hallmark of cancer. However, the extent of these changes remains largely unknown for SCLC.
Materials and methods: We characterized the metabolic perturbations in SCLC cells (SCLCC) by metabolomics. Large-scale correlation analysis was performed between metabolites. Targeted proteomics and gene expression analysis were employed to investigate the changes of key enzymes and genes in the disturbed pathways.
Results: We found dramatic decrease of metabolite–metabolite correlations in SCLCC compared with normal control cells and non-small cell lung cancer cells. Pathway analysis revealed that the loss of correlations was associated with the alternations of fatty acid oxidation, urea cycle, and purine salvage pathway in SCLCC. Targeted proteomics and gene expression analysis confirmed significant changes of the expression for the key enzymes and genes in the pathways in SCLCC including the upregulation of carbamoyl phosphate synthase 1 (urea cycle) and carnitine palmitoyltransferase 1A (fatty acid oxidation), and the downregulation of hypoxanthine–guanine phosphoribosyltransferase and adenine phosphoribosyltransferase in purine salvage pathway.
Conclusion: We demonstrated the loss of metabolite–metabolite correlations in SCLCC associated with the upregulation of fatty acid oxidation and urea cycle and the downregulation of purine salvage pathways. Our findings provide insights into the metabolic reprogramming in SCLCC and highlight the potential therapeutic targets for the treatment of SCLC.

Keywords: small cell lung cancer, metabolomics, metabolite–metabolite correlations, purine salvage pathway, urea cycle, fatty acid oxidation

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