On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples

Background: The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis. Introduction: The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective di-agnostic and prognostic strategies. Methods: We explore the possibility of exploiting the topological properties of sample-specific met-abolic networks as features in a supervised classification task. Such networks are obtained by pro-jecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample. Results: We show the classification results on a labeled breast cancer dataset from the TCGA data-base, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effec-tive choice to recover useful information while filtering out noise from data. Overall, the best accu-racy is achieved with SVMs, which exhibit performances similar to those obtained when gene ex-pression profiles are used as features. Conclusion: These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.