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

Zaoqu Liu, Long Liu, Siyuan Weng, Chunguang Guo, Qin Dang, Hui Xu, Libo Wang, Taoyuan Lu, Yuyuan Zhang, Zhenqiang Sun () and Xinwei Han ()
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Zaoqu Liu: The First Affiliated Hospital of Zhengzhou University
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

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-28421-6

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