Construction of a novel risk model based on the random forest algorithm to distinguish pancreatic cancers with different prognoses and immune microenvironment features

Immune-related long noncoding RNAs (irlncRNAs) are actively involved in regulating the immune status. This study aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in predicting prognosis and the immune landscape in pancreatic cancer. The transcriptome profiles and clinical information of 176 pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen 1903 immune-related lncRNAs (irlncRNAs) using Pearson’s correlation analysis (R > 0.5; p < 0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and minimal depth. Next, Cox regression analysis was used to establish a risk model with 3 irlncRNAs (LINC00462, LINC01887, RP11-706C16.8) that was evaluated by Kaplan-Meier analysis, the areas under the curve (AUCs) of the receiver operating characteristics and the C-index. Additionally, we performed Cox regression analysis to establish the clinical prognostic model, which showed that the risk score was an independent prognostic factor (p < 0.001). A nomogram and calibration plots were drawn to visualize the clinical features. The Wilcoxon signed-rank test and Pearson’s correlation analysis further explored the irlncRNA signatures and immune cell infiltration, as well as the immunotherapy response.

免疫相关长链非编码RNA（immune-related long noncoding RNAs, irlncRNAs）可积极参与免疫状态的调控。本研究旨在构建irlncRNAs的风险预测模型，并进一步探究其在胰腺癌预后预测及免疫景观中的作用。
本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中获取了176例胰腺癌患者的转录组图谱及临床信息。
从ImmPort数据库下载的免疫相关基因（immune-related genes, irgenes）经Pearson相关分析（R>0.5；P<0.001）筛选，共得到1903个免疫相关长链非编码RNA。
通过随机生存森林（random survival forest, RSF）与生存树分析，基于变量重要性（variable importance, VIMP）及最小深度指标，筛选出9个与总生存期（overall survival, OS）显著相关的irlncRNAs。
随后采用Cox回归分析构建了包含3个irlncRNAs（LINC00462、LINC01887、RP11-706C16.8）的风险预测模型，并通过Kaplan-Meier生存分析、受试者工作特征曲线下面积（AUCs）及C指数对该模型进行验证。
此外，本研究通过Cox回归分析构建了临床预后模型，结果显示风险评分是独立的预后因素（P<0.001）。
本研究绘制了列线图（nomogram）与校准曲线（calibration plots）以可视化展示临床特征。
最后，通过Wilcoxon符号秩检验与Pearson相关分析，本研究进一步探究了irlncRNA特征与免疫细胞浸润及免疫治疗响应的关联。