Systematic investigation of immune-related lncRNA landscape reveals a potential long non-coding RNA signature for predicting prognosis in kidney renal clear cell carcinoma.

Abstract

e16510 Background: Increasing evidence has revealed that long non-coding RNAs (lncRNAs) play a crucial role in cancer immunity. However, the comprehensive landscape of immune infiltration-associated lncRNAs and their potential roles in the prognosis and diagnosis of kidney renal clear cell carcinoma (KIRC) remains largely unexplored. Methods: The transcriptomics data, expression profiles, and clinical information of KIRC patients were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. Novel lncRNAs were identified by a custom pipeline. The infiltration of immune cells was calculated by ssGSEA. K-means consensus method was used to cluster KIRC samples in different groups. Immune-related lncRNAs were obtained by differential expression analysis based on these groups. Univariate Cox regression, LASSO regression, and multivariate Cox regression analysis were performed on GEO datasets to construct an immune-related lncRNAs signature for predicting the prognosis of KIRC. And then, validated the prognostic signature in TCGA data. Results: Based on 261 samples from KIRC patients, tens of thousands of novel lncRNA genes were identified. KIRC samples were clustered into three immune clusters based on the infiltration of immune cells. Based on these groups, 241 immune-related lncRNAs were obtained. Moreover, a large part of immune-related lncRNAs could be detected in urinary samples of KIRC. Through a series of analyses, three immune-related lncRNAs were identified as a prognostic signature for KIRC. KIRC patients in the high-risk group had a shorter OS than those in the low-risk group both in the training set (P < 0.001) and TCGA data (P < 0.001), respectively. The AUC was 0.9 in the training set. Univariate and multivariate Cox regression analysis confirmed that the immune-related lncRNAs signature could be an independent prognostic factor both in the training set and TCGA data. Conclusions: All these findings provide a more comprehensive lncRNAs catalog for investigating the lncRNAs and provide a novel approach for the effective prediction of clinical outcomes in KIRC.