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Integrative metabonomics as potential method for diagnosis of thyroid malignancy
Yuan Tian1, Xiu Nie2, Shan Xu1
1CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Centre for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Wuhan, 430071, P.R. China.
Scientific Reports
|October 22, 2015
Summary
Metabonomics successfully distinguished malignant thyroid nodules from benign ones using (1)H NMR spectroscopy. This technique offers a promising tool for accurate preoperative diagnosis of thyroid cancer.
Area of Science:
- Biochemistry
- Oncology
- Analytical Chemistry
Background:
- Distinguishing benign from malignant thyroid nodules is clinically challenging.
- Accurate diagnosis is crucial as malignant nodules require surgery, while benign ones may not.
- Novel diagnostic techniques are needed for reliable preoperative assessment.
Purpose of the Study:
- To investigate the potential of metabonomics for differentiating thyroid lesions.
- To distinguish between benign and malignant thyroid tumors using metabolic profiling.
- To identify metabolic pathway disturbances associated with thyroid lesions.
Main Methods:
- Utilized (1)H NMR spectroscopy to analyze thyroid tissue metabonomes.
- Employed pattern recognition techniques for data analysis.
- Complemented NMR data with Gas Chromatography-Flame Ionization Detection/Mass Spectrometry (GC-FID/MS) for fatty acid analysis.
Main Results:
- Thyroid lesion tissues were clearly distinguishable from healthy tissues.
- Malignant tumors were accurately differentiated from benign tumors based on metabolic profiles with high sensitivity and specificity.
- Thyroid lesions showed significant alterations in energy metabolism, protein turnover, and biosynthesis pathways.
Conclusions:
- Metabonomics, coupled with pattern recognition, can effectively differentiate thyroid lesions.
- The metabolic profiles of malignant nodules differ significantly from benign ones.
- This technology holds potential for rapid and accurate preoperative diagnosis of malignant thyroid nodules.
