Research Papers:

Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps

Hong Zheng, Jiansong Ji, Liangcai Zhao, Minjiang Chen, An Shi, Linlin Pan, Yiran Huang, Huajie Zhang, Baijun Dong and Hongchang Gao _

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Oncotarget. 2016; 7:59189-59198. https://doi.org/10.18632/oncotarget.10830

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Hong Zheng1, Jiansong Ji2, Liangcai Zhao1, Minjiang Chen1,2, An Shi3, Linlin Pan1, Yiran Huang3, Huajie Zhang1, Baijun Dong3, Hongchang Gao1

1School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China

2Lishui Central Hospital, The Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, 323000, China

3Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China

Correspondence to:

Hongchang Gao, email: [email protected]

Baijun Dong, email: [email protected]

Keywords: artificial intelligence, early diagnosis, metabolome, metabolic recovery, precision medicine

Received: March 22, 2016     Accepted: July 09, 2016     Published: July 24, 2016


Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.

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