小年
(2023-12-31 22:02):
#paper doi: 10.1038/s41467-022-28421-6. Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nat Commun. 2022 Feb 10;13(1):816. 在该项研究中，作者开发了一种基于机器学习的整合程序，用于构建共识免疫相关lncRNA特征(称为IRLS)。整体思路为：免疫浸润共识簇的开发和验证-鉴定源自免疫浸润模式的lncRNA模块-101种机器学习算法筛选最佳预测模型。通过对模型的评估发现，IRLS和AJCC分期的结合可以进一步提高模型的预测能力。作者还通过免疫相关lnRNA预后评分模型与已知发表了的基因signature多套数据集中进行比较分析，计算当前癌症类型中已发表的signature的C-index，重点体现本研究中lncRNAs的优势。
这篇免疫相关lncRNA的文章值得我们学习的最大亮点是，不像常规预后模型文章那样只用2-4种机器学习算法然后取交集（cox、lasso、RF等等），而是通过整合了10种不同的机器学习算法（包括Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (Lasso), Gradient Boosting Machine (GBM), Random Forest, Elastic Net, Stepwise Cox, Ridge, CoxBoost, Super Partial Correlation (SuperPC), and Partial Least Squares with Cox regression (plsRcox)），并评估了101种算法组合这些机器学习算法，直至筛选出最优的预后模型。
Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer
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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.
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