Table11_Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer.XLSX

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.胃癌（Gastric cancer, GC）是最常见的恶性肿瘤之一，预后不良。免疫治疗作为涵盖胃癌在内的多种恶性肿瘤的治疗手段，受到了广泛关注。然而，并非所有患者均可对免疫治疗产生响应。目前亟需新型模型来精准预测胃癌患者的预后及免疫治疗疗效。长链非编码RNA（Long noncoding RNAs, lncRNAs）在恶性肿瘤的发生与进展中发挥关键作用。近期多项研究已在多种癌症中筛选出多种与预后相关的长链非编码RNA特征标记物，但此类研究仍存在一定局限性。本研究通过整合分析，采用多种特征筛选方法构建风险预测模型，包括单变量与多变量Cox回归、最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）、逐步筛选法、子集筛选法，以及上述方法的组合策略。本研究借助机器学习算法，在癌症基因组图谱（The Cancer Genome Atlas, TCGA）队列的胃癌患者数据中构建了一条包含9个长链非编码RNA的特征标记物，用于预测患者预后。在训练队列中得到风险模型后，我们进一步在测试队列、全数据集以及两套外部基因表达汇编（Gene Expression Omnibus, GEO）数据集中验证了该模型的预后预测效能。此外，本研究还探讨了该风险模型在预测免疫细胞浸润、免疫治疗响应以及基因组突变方面的作用。研究结果表明，该风险模型具备精准预测胃癌患者预后结局与免疫治疗响应的潜力。本研究成果为通过机器学习算法整合多种筛选方法构建风险模型提供了新思路。