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Predicting Cancer Stage from Circulating microRNA: A Comparative Analysis of Machine Learning Algorithms
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Translational Bioinformatics)ORCID iD: 0000-0003-4191-8435
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Translational Bioinformatics)ORCID iD: 0000-0001-9242-4852
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Sweden. (Translational Bioinformatics)ORCID iD: 0000-0003-4697-0590
2023 (English)In: Bioinformatics and Biomedical Engineering: 10th International Work-Conference, IWBBIO 2023, Meloneras, Gran Canaria, Spain, July 12–14, 2023, Proceedings, Part I / [ed] Ignacio Rojas; Olga Valenzuela; Fernando Rojas Ruiz; Luis Javier Herrera; Francisco Ortuño, Cham: Springer, 2023, p. 103-115Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, serum-based tests for early detection and detection of tissue of origin are being developed. Circulating microRNA has been shown to be a potential source of diagnostic information that can be collected non-invasively. In this study, we investigate circulating microRNAs as predictors of cancer stage. Specifically, we predict whether a sample stems from a patient with early stage (0-II) or late stage cancer (III-IV). We trained five machine learning algorithms on a data set of cancers from twelve different primary sites. The results showed that cancer stage can be predicted from circulating microRNA with a sensitivity of 71.73%, specificity of 79.97%, as well as positive and negative predictive value of 54.81% and 89.29%, respectively. Furthermore, we compared the best pan-cancer model with models specialized on individual cancers and found no statistically significant difference. Finally, in the best performing pan-cancer model 185 microRNAs were significant. Comparing the five most relevant circulating microRNAs in the best performing model with the current literature showed some known associations to various cancers. In conclusion, the study showed the potential of circulating microRNA and machine learning algorithms to predict cancer stage and thus suggests that further research into its potential as a non-invasive clinical test is warranted. 

Place, publisher, year, edition, pages
Cham: Springer, 2023. p. 103-115
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13919
Keywords [en]
cancer stage, circulating microRNA, liquid biopsy, machine learning, Clinical research, Diseases, Forecasting, Learning algorithms, RNA, Cancer models, Comparative analyzes, Diagnostics informations, Late stage, Machine learning algorithms, Machine-learning, Potential sources
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:his:diva-23058DOI: 10.1007/978-3-031-34953-9_8Scopus ID: 2-s2.0-85164958861ISBN: 978-3-031-34952-2 (print)ISBN: 978-3-031-34953-9 (electronic)OAI: oai:DiVA.org:his-23058DiVA, id: diva2:1784745
Conference
10th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2023 Meloneras 12 July 2023 through 14 July 2023 Code 297199
Funder
Knowledge Foundation, 20170302Knowledge Foundation, 20200014Swedish Research Council, 2022–06725
Note

Part of the book sub series: Lecture Notes in Bioinformatics (LNBI) Electronic ISSN 2366-6331 Print ISSN 2366-6323

© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

This work was supported by the University of Skövde, Swede nunder grants from the Knowledge Foundation (20170302, 20200014). The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at Chalmers University of Technology partially funded by the Swedish Research Council through grant agreement no. 2022–06725.

Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2023-12-19Bibliographically approved

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Stahlschmidt, Sören RichardUlfenborg, BenjaminSynnergren, Jane

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