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Erschienen in:
2023 | OriginalPaper | Buchkapitel
Novel Gene Signature for Bladder Cancer Stage Identification
verfasst von : Iñaki Hulsman, Luis Javier Herrera, Daniel Castillo, Francisco Ortuño
Erschienen in: Bioinformatics and Biomedical Engineering
Verlag: Springer Nature Switzerland
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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.