GC-MS-based metabolomic analysis of human papillary thyroid carcinoma tissue

Chen,Minjian; Shen,Meiping; Li,Yanyun; Liu,Cuiping; Zhou,Kun; Hu,Weiyue; Xu,Bo; Xia,Yankai; Tang,Wei

doi:10.3892/ijmm.2015.2368

GC-MS-based metabolomic analysis of human papillary thyroid carcinoma tissue

Authors:
- Minjian Chen
- Meiping Shen
- Yanyun Li
- Cuiping Liu
- Kun Zhou
- Weiyue Hu
- Bo Xu
- Yankai Xia
- Wei Tang
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Affiliations: Department of Endocrinology, Jiangyin People's Hospital, School of Medicine, Southeast University, Jiangyin, Jiangsu 214400, P.R. China, Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210036, P.R. China, State Key Laboratory of Reproductive Medicine, Institute of Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, P.R. China
Published online on: October 12, 2015 https://doi.org/10.3892/ijmm.2015.2368
Pages: 1607-1614

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Abstract

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Elucidating the molecular network that is altered in PTC may lead to the identification of the critical insight into the pathogenesis of PTC. Thus far, little is known regarding the global metabolomic alterations of PTC. Gas chromatography coupled with mass spectrometry‑based metabolomics was used to analyze metabolomic alterations in matched PTC and normal thyroid tissues obtained from the patients. Multivariate statistical analyses were employed to determine the significant metabolomic differences. The mRNA levels of the associated metabolic enzyme genes were further assayed with reverse transcription‑quantitative polymerase chain reaction analysis. Principal component analysis, partial least‑squares discriminant analysis (PLS‑DA) and orthogonal PLS‑DA models were established, which could clearly separate human normal thyroid and PTC samples, and identified that metabolites in carbohydrate metabolism, including glucose, fructose, galactose, mannose, 2‑keto‑D‑gluconic acid and rhamnose, consistently decreased, while metabolites in nucleotide metabolism, including malonic acid and inosine, and lipid metabolism, including cholesterol and arachidonic acid, significantly altered in PTC. Furthermore, the mRNA levels of metabolic enzyme genes, including glucose‑6‑phosphate dehydrogenase, phosphoglycerate kinase 1, lactate dehydrogenase A, phosphoglycerate dehydrogenase and prostaglandin‑endoperoxide synthase 2, significantly increased in PTC. Based on the metabolomic and mRNA data, various metabolites may be used for increased synthesis of nucleotides and oncogenic lipids in PTC, which may contribute to the pathogenesis of PTC. The present study provides a new understanding of the dysregulated metabolism in PTC and identifies potential avenues for the therapeutic intervention for this disease.

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