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Gene expression analysis plays a crucial role in understanding the progression of liver cancer, as genetic alterations drive tumorigenesis. This study aims to obtain gene signatures for liver cancer using RNA-Seq data from three tissue types: healthy liver, cholangiocarcinoma (CHOL), and hepatocellular carcinoma (LIHC). Additionally, we explore liver cancer staging to assess disease progression through gene expression analysis. Our approach integrates differential expression analysis, feature selection, and machine learning classification to identify key biomarkers and improve diagnostic accuracy.

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Machine Learning-Based Diagnosis and Staging of Liver Cancer Using RNA-Seq Data

  • 会议论文

  • 发表日期：

    2026年

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  • Martina Álvarez,
  • Antonio José Heredia,
  • Ignacio Garach,
  • Ignacio Rojas,
  • Luis Javier Herrera,
  • Francisco Manuel Ortuño

摘要

Gene expression analysis plays a crucial role in understanding the progression of liver cancer, as genetic alterations drive tumorigenesis. This study aims to obtain gene signatures for liver cancer using RNA-Seq data from three tissue types: healthy liver, cholangiocarcinoma (CHOL), and hepatocellular carcinoma (LIHC). Additionally, we explore liver cancer staging to assess disease progression through gene expression analysis. Our approach integrates differential expression analysis, feature selection, and machine learning classification to identify key biomarkers and improve diagnostic accuracy.