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

Bioengineered

Si Shi¹  Yalan Lei¹  Jie Hua¹  Jiang Liu¹  Xianjun Yu¹  Wei Wang¹  Rong Tang¹  Bo Zhang¹  Qingcai Meng¹  Jin Xu¹  Chen Liang¹ 

[1] Fudan University Shanghai Cancer Center;

关键词: immune-related lncrna;    pancreatic cancer;    random survival forest;    immune infiltration;    risk model;   

DOI  :  10.1080/21655979.2021.1951527

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown