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

Autor(es)

Machicao, Jeaneth ^{[1, 2]} ; Craighero, Francesco ^[3] ; Maspero, Davide ^{[3, 4]} ; Angaroni, Fabrizio ^[3] ; Damiani, Chiara ^{[5, 6]} ; Graudenzi, Alex ^{[4, 7]} ; Antoniotti, Marco ^{[3, 7]} ; Bruno, Odemir M. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Carlos - Brazil

^[2] Univ Sao Paulo, Sch Engn, Sao Carlos - Brazil

^[3] Univ Milano Bicocca, Dept Informat Syst & Commun, Milan - Italy

^[4] Consiglio Nazl Ric IBFM CNR, Inst Mol Bioimaging & Physiol, Milan - Italy

^[5] Univ Milano Bicocca, Dept Biotechnol & Biosci, Milan - Italy

^[6] Sysbio Ctr Syst Biol, Milan - Italy

^[7] Univ Milano Bicocca, Bicocca Bioinformat Biostat & Bioimaging Ctr B4, Milan - Italy

• Tipo de documento: Artigo Científico
• Fonte: CURRENT GENOMICS; v. 22, n. 2, p. 88-97, 2021.
• Citações Web of Science: 0

Resumo

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 diagnostic and prognostic strategies. Methods: We explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting 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 database, 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 effective choice to recover useful information while filtering out noise from data. Overall, the best accuracy is achieved with SVMs, which exhibit performances similar to those obtained when gene expression 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. (AU)

• Processo FAPESP: 20/03514-9 - Avaliação dos efeitos das áreas protegidas brasileiras nas comunidades locais com base no uso e reutilização de dados biológicos, ambientais e socioeconômicos
• Beneficiário: Marina Jeaneth Machicao Justo
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado