Abstract
This article presents a study that aimed to identify the stages of bladder cancer based on gene expression data. The dataset used in the study was obtained from the GDC repository and included 406 cases of bladder cancer and 431 files from the TCGA-BLCA project. The study categorized the cases into three classes based on disease stages: Stage 2, Stage 3, and Stage 4. The methodology employed R programming language and the KnowSeq library for the study development. The authors identified genes that showed significant differences in expression among the classes and created a matrix of differentially expressed genes (DEG). Machine learning models, including feature selection algorithms and classification models such as KNN and SVM, were constructed to predict the bladder cancer stages. The results revealed that the mRMR feature selection algorithm performed the best, and the 8 most relevant genes were used to build the classification models.
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Acknowledgements
This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project PID2021-128317OB-I00 and the projects from Junta de Andalucia P20-00163.
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Hulsman, I., Herrera, L.J., Castillo, D., Ortuño, F. (2023). Novel Gene Signature for Bladder Cancer Stage Identification. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_7
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