Abstract
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.
Highlights
Renal cell carcinoma (RCC) accounts for 2–3% of all adult malignancies and has a mortality rate greater than 40% [1]
We aimed to develop a predictive diagnostic method for early-stage renal cell carcinoma (RCC) based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs)
We aimed to develop a new tool for the prediction and diagnosis of early-stage RCC using NMR-based metabolomics and self-organizing maps (SOMs)
Summary
Renal cell carcinoma (RCC) accounts for 2–3% of all adult malignancies and has a mortality rate greater than 40% [1]. The incidence of RCC (all stages) is increasing yearly [2]. More than 30% of RCC patients have metastatic disease at the time of diagnosis. This can be attributed to the lack of symptoms typically associated with early-stage RCC [3]. Clinical symptoms such as pain, the presence of a mass, or hematuria are generally not sufficient for early diagnosis [4]. Radiological methods for RCC diagnosis such as ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography are not always accurate [5, 6]. Renal biopsy and histological diagnosis are invasive and time-consuming. The development of new diagnostic strategies is critical for the prevention and management of RCC
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