Discovery of lipid biomarkers correlated with disease progression in clear cell renal cell carcinoma using desorption electrospray ionization imaging mass spectrometry
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Keita Tamura1,2, Makoto Horikawa1,3, Shumpei Sato1, Hideaki Miyake2 and Mitsutoshi Setou1,3,4,5
1Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
2Department of Urology, Hamamatsu University School of Medicine, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
3International Mass Imaging Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
4Preeminent Medical Photonics Education and Research Center, Hamamatsu, Shizuoka, Japan
5Department of Anatomy, The University of Hong Kong, Hong Kong, China
Mitsutoshi Setou, email: [email protected]
Keywords: clear cell renal cell carcinoma; desorption electrospray ionization imaging mass spectrometry; lipid; biomarker; disease progression
Abbreviations: ccRCC: clear cell renal cell carcinoma; DESI-IMS: desorption electrospray ionization imaging mass spectrometry; MIR: maximum intensity ratio
Received: October 27, 2018 Accepted: February 09, 2019 Published: March 01, 2019
Clear cell renal cell carcinoma (ccRCC) often results in recurrence or metastasis, and there are only a few clinically effective biomarkers for early diagnosis and personalized therapy. Metabolic changes have been widely studied using mass spectrometry (MS) of tissue lysates to identify novel biomarkers. Our objective was to identify lipid biomarkers that can predict disease progression in ccRCC by a tissue-based approach. We retrospectively investigated lipid molecules in cancerous tissues and normal renal cortex tissues obtained from patients with ccRCC (n = 47) using desorption electrospray ionization imaging mass spectrometry (DESI-IMS). We selected eight candidate lipid biomarkers showing higher signal intensity in cancerous than in normal tissues, with a clear distinction of the tissue type based on the images. Of these candidates, low maximum intensity ratio (cancerous/normal) values of ions of oleic acid, m/z 389.2, and 391.3 significantly correlated with shorter progression-free survival compared with high maximum intensity ratio values (P = 0.011, P = 0.022, and P < 0.001, respectively). This study identified novel lipid molecules contributing to the prediction of disease progression in ccRCC using DESI-IMS. Our findings on lipid storage may provide a new diagnostic or therapeutic strategy for targeting cancer cell metabolism.
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