# A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC

## TL;DR

A new immune-related long non-coding RNA (lncRNA) model predicts survival and immune features in clear cell kidney cancer.

## Contribution

A novel lncRNA-based risk model is developed to predict prognosis and immune landscape in ccRCC patients.

## Key findings

- 38 immune-related lncRNA pairs were selected to build a risk model for ccRCC prognosis.
- High-risk patients correlate with tumor-infiltrating immune cells but not with M2 macrophages or neutrophils.
- The model is positively linked to genes like CTLA, LAG3, and SETD2, and predicts drug sensitivity.

## Abstract

Background: As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown.

Methods: The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features.

Results: In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs.

Conclusions: The new risk model may be a new method to predict the prognosis and immune status of ccRCC.