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.
Highlights
Long noncoding RNAs are recently implicated in modifying immunology in colorectal cancer (CRC)
The candidate biomarkers that facilitate the clinical selection of patients for immune checkpoint inhibitors (ICIs) treatment include programmed death-ligand 1 (PD-L1) expression, tumour mutation burden (TMB), neoantigen load (NAL), and mismatch repair deficiency/microsatellite instability-high (MSIH), but these approaches are limited by spatiotemporal heterogeneity, moderate accuracy, or small percentage populations[5,6,7]
According to 28 immune cells infiltration assessed by single-sample gene set enrichment analysis[21], we performed a consensus cluster analysis[22], in which all CRC samples were initially divided into k (k = 2–9) clusters
Summary
Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Colorectal cancer (CRC) is characterised by strong heterogeneity and aggressiveness, with high prevalence and mortality[1] This mortality can be largely attributed to disease progression and inadequate treatment[2]. The candidate biomarkers that facilitate the clinical selection of patients for ICI treatment include programmed death-ligand 1 (PD-L1) expression, tumour mutation burden (TMB), neoantigen load (NAL), and mismatch repair deficiency (dMMR)/microsatellite instability-high (MSIH), but these approaches are limited by spatiotemporal heterogeneity, moderate accuracy, or small percentage populations[5,6,7]. We attempted to apply immune-related lncRNAs to develop and validate a risk stratification signature in 2509 CRC patients from 17 independent public datasets and a clinical inhouse cohort to assess the prognosis, recurrence, and benefits of fluorouracil-based ACT, bevacizumab, and ICI treatment in CRC. This work may help optimise precision treatment and further improve the clinical outcomes of CRC patients
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