Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer
- PMID: 35145098
- PMCID: PMC8831564
- DOI: 10.1038/s41467-022-28421-6
Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer
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.
© 2022. The Author(s).
Conflict of interest statement
The authors declare no competing interests.
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References
-
- Sung H, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J. Clin. 2021;68:394–424. - PubMed
-
- Weiser MR. AJCC 8th edition: colorectal cancer. Ann. Surg. Oncol. 2018;25:1454–1455. - PubMed
-
- Mahoney KM, Rennert PD, Freeman GJ. Combination cancer immunotherapy and new immunomodulatory targets. Nat. Rev. Drug Discov. 2015;14:561–584. - PubMed
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