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

Author

Listed:
  • Zaoqu Liu

    (The First Affiliated Hospital of Zhengzhou University
    Interventional Institute of Zhengzhou University
    Interventional Treatment and Clinical Research Center of Henan Province)

  • Long Liu

    (The First Affiliated Hospital of Zhengzhou University)

  • Siyuan Weng

    (The First Affiliated Hospital of Zhengzhou University)

  • Chunguang Guo

    (The First Affiliated Hospital of Zhengzhou University)

  • Qin Dang

    (The First Affiliated Hospital of Zhengzhou University)

  • Hui Xu

    (The First Affiliated Hospital of Zhengzhou University)

  • Libo Wang

    (The First Affiliated Hospital of Zhengzhou University)

  • Taoyuan Lu

    (Zhengzhou University People’s Hospital)

  • Yuyuan Zhang

    (The First Affiliated Hospital of Zhengzhou University)

  • Zhenqiang Sun

    (The First Affiliated Hospital of Zhengzhou University)

  • Xinwei Han

    (The First Affiliated Hospital of Zhengzhou University
    Interventional Institute of Zhengzhou University
    Interventional Treatment and Clinical Research Center of Henan Province)

Abstract

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 fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.

Suggested Citation

  • Zaoqu Liu & Long Liu & Siyuan Weng & Chunguang Guo & Qin Dang & Hui Xu & Libo Wang & Taoyuan Lu & Yuyuan Zhang & Zhenqiang Sun & Xinwei Han, 2022. "Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28421-6
    DOI: 10.1038/s41467-022-28421-6
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    References listed on IDEAS

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    1. Yongsheng Li & Tiantongfei Jiang & Weiwei Zhou & Junyi Li & Xinhui Li & Qi Wang & Xiaoyan Jin & Jiaqi Yin & Liuxin Chen & Yunpeng Zhang & Juan Xu & Xia Li, 2020. "Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    2. Isidro Cortes-Ciriano & Sejoon Lee & Woong-Yang Park & Tae-Min Kim & Peter J. Park, 2017. "A molecular portrait of microsatellite instability across multiple cancers," Nature Communications, Nature, vol. 8(1), pages 1-12, August.
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