Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.
Zaoqu LiuLong LiuSiyuan WengChunguang GuoQin DangHui XuLibo WangTaoyuan LuYuyuan ZhangZhenqiang SunXin-Wei HanPublished in: Nature communications (2022)
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.
Keyphrases
- free survival
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- long non coding rna
- poor prognosis
- artificial intelligence
- network analysis
- peritoneal dialysis
- type diabetes
- big data
- minimally invasive
- prognostic factors
- randomized controlled trial
- long noncoding rna
- systematic review
- binding protein
- deep learning
- gene expression
- dna methylation
- peripheral blood
- patient reported outcomes
- genome wide analysis
- weight loss
- genome wide identification