Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer

Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to…

• NN
  National Natural Science Foundation of China

  Award: 81972663
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  Servier