Research Paper Volume 13, Issue 24 pp 26046—26062

An immune-related lncRNA risk coefficient model to predict the outcomes in clear cell renal cell carcinoma

Cheng Tang1, *, , GenYi Qu1, *, , Yong Xu1, , Guang Yang1, , Jiawei Wang1, , Maolin Xiang1, ,

  • 1 Department of Urology, The Affiliated Zhuzhou Hospital XiangYa Medical College CSU, Zhuzhou 412007, China
* Equal contribution

Received: September 17, 2021       Accepted: December 8, 2021       Published: December 26, 2021
How to Cite

Copyright: © 2021 Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Objective: Using model algorithms, we constructed an immune-related long non-coding RNAs (lncRNAs) risk coefficient model to predict outcomes for patients with clear cell renal cell carcinoma (ccRCC) to understand the infiltration of tumor immune cells and the sensitivity to immune-targeted drugs.

Methods: Open genes data were downloaded from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, and immune-related lncRNAs were obtained through Pearson correlation analysis. R language software was used to obtain differentially expressed immune-related lncRNAs and immune-related lncRNA pairs. The model was constructed using least absolute shrinkage and selector operation regression analysis, and receiver operator characteristic curves were drawn. The Akaike information criterion was used to distinguish the high-risk from the low-risk group. We also conducted correlation analysis for the high- and low-risk subgroups.

Results: We identified 27 immune-related lncRNAs pairs, 16 of which were included in the model construction. After merging clinical data, the areas under the curve of 1 -year, 3-year, and 5-year survival times of ccRCC patients were 0.867, 0.832, and 0.838, respectively. Subgroup analyses were conducted according to the cut-off value. We found that the high-risk group was associated with poor outcomes. The risk score and tumor stage were independent predictors of the outcome of ccRCC. The risk model predicted specific immune cell infiltration, immune checkpoint gene expression levels, and high-risk groups more sensitive to sunitinib targeted therapy.

Conclusion: We obtained prognostic-related novel ccRCC markers and risk model that predicts the outcome of patients with ccRCC and helps identify those who can benefit from sunitinib.


ccRCC: clear cell renal cell carcinoma; TCGA: The Cancer Genome Atlas; ImmPort: The Immunology Database and Analysis Portal; lncRNAs: long noncoding RNAs; GTF: Gene Transfer Format; DEirlncRNAs: Differential Expression immune-related long noncoding RNAs; FDR: false discovery rate; LASSO: Least Absolute Shrinkage and Selector Operation; RS: risk score; ROC: Receiver Operator Characteristic; OS: overall survival; AUC: area under the curve; AIC: Akaike information criterion; MCPcounter: Microenvironment Cell Populations-counter; IC50: half inhibitory concentration; HR: hazard ratio; HR.95L: 95% CI lower limit; HR.95H: 95% CI upper limit; logFC: log fold change.