A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Simões, M (Author) Other; Pereira, T (Author) Other; Silva, F (Author) Other; Machado, JMF (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Pages: 2875-2882

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Istanbul, 5 December 2023 through 8 December 2023

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Other information

Authenticus ID: P-00Z-T2M

DOI: 10.1109/bibm58861.2023.10385571

Abstract (EN): Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7