AUTHOR=Ma Chao , Zhang Xin , Zhao Xudong , Zhang Nan , Zhou Sixin , Zhang Yonghui , Li Peiyu 

TITLE=Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.690530

DOI=10.3389/fgene.2021.690530

ISSN=1664-8021

ABSTRACT=Background: Accumulating evidence has demonstrated that immune-related long noncoding ribonucleic acids (irlncRNAs) can serve as prognostic markers of overall survival (OS) in patients with colorectal cancer (CRC). In the present study, we aimed to construct a prognostic model to predict the survival and immune landscape of CRC patients using irlncRNA pairs with no requirement for a specific expression level.
Methods: Clinical and transcriptome profiling data of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) database to identify differentially expressed (DE) irlncRNAs. We obtained irlncRNA pairs that were significantly correlated with the prognosis of patients by univariable Cox regression analysis and constructed a prognostic model by Lasso and multivariate Cox regression analyses. Then, a receiver operating characteristic (ROC) curve was drawn, and the area under the curve was calculated to confirm the reliability of the model. After distinguishing CRC patients in the high- or low-risk groups based on the optimal cutoff value, we then evaluated the risk model from the perspectives of survival, clinicopathological characteristics, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy and molecules related to immune checkpoints.
Results: A prognostic model consisting of fifteen irlncRNA pairs was established and was significantly associated with patient survival in a cohort from the TCGA (p < 0.001, HR = 1.089, 95% CI [1.067–1.112]). Compared with other clinicopathological characteristics, by both univariate and multivariate Cox analyses, we found that the model could serve as an independent prognostic factor in the TCGA cohort (p<0.001). With the prognostic model, we could effectively differentiate between high- and low-risk patients based on aggressive clinicopathological characteristics, a specific tumor immune infiltration status, sensitivity to antitumor drugs and kinase inhibitors, and the expression levels of specific molecules related to immune checkpoints.
Conclusion: The results of the present study support the prognostic model established with irlncRNA pairs as a promising marker for prognosis prediction in CRC patients.