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Recognition of early and late stages of bladder cancer using metabolites and machine learning

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Abstract

Introduction

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

Objectives

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

Methods

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity® Pathway Analysis (IPA®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

Results

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

Conclusion

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

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Author notes

  1. Valentina L. Kouznetsova, Elliot Kim and Eden L. Romm contributed equally to this work.

Authors and Affiliations

  1. Moores Cancer Center, UC San Diego, San Diego, USA

    Valentina L. Kouznetsova & Igor F. Tsigelny

  2. San Diego Supercomputer Center, UC San Diego, San Diego, USA

    Valentina L. Kouznetsova & Igor F. Tsigelny

  3. Department of Neurosciences, UC San Diego, San Diego, USA

    Igor F. Tsigelny

  4. REHS Program UC San Diego, San Diego, USA

    Elliot Kim & Alan Zhu

  5. CureMatch Inc., San Diego, USA

    Eden L. Romm & Igor F. Tsigelny

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Contributions

VK and IT designed the research. EK coordinated the data collection. VK, IT and EK analyzed the data and wrote the manuscript. AZ and ER created the data set for machine learning and executed it. All authors read and approved the manuscript.

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Correspondence to Igor F. Tsigelny.

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Kouznetsova, V.L., Kim, E., Romm, E.L. et al. Recognition of early and late stages of bladder cancer using metabolites and machine learning. Metabolomics 15, 94 (2019). https://doi.org/10.1007/s11306-019-1555-9

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