Research Paper Volume 12, Issue 15 pp 15359—15373

Bioinformatics profiling integrating a four immune-related long non-coding RNAs signature as a prognostic model for papillary renal cell carcinoma

Yu Liu1,2,3, , Xin Gou1, , Zongjie Wei1, , Haitao Yu1,2, , Xiang Zhou1,2, , Xinyuan Li1,2, ,

  • 1 Department of Urology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
  • 2 Chongqing Key Laboratory of Molecular Oncology and Epigenetics, Chongqing, China
  • 3 Department of Urology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China

Received: April 23, 2020       Accepted: June 9, 2020       Published: July 27, 2020
How to Cite

Copyright © 2020 Liu 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.


Background: Papillary renal cell carcinoma (pRCC) was the 2nd most common subtype, accounting for approximately 15% incidence of renal cell carcinoma (RCC). Immune related long non-coding RNAs (IR-lncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A completed and meaningful IR-lncRs analysis based on abundant pRCC gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field.

Results: 17 IR-lncRs were selected by Pearson correlation analysis of immune score and the lncRNA expression level, and 5 sIRlncRs were significantly correlated with the OS of pRCC patients. 4 sIRlncRs (AP001267.3, AC026471.3, SNHG16 and ADAMTS9-AS1) with the most remarkable prognostic values were identified to establish the IRRS model and the OS of the low-risk group was longer than that in the high-risk group. The IRRS was certified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group showed significantly different distributions and immune status through PCA and GSEA. In addition, we further found the expression levels of SNHG16 was remarkably enhanced in female patients with more advanced T-stages, but ADAMTS9-AS1 showed the opposite results.

Conclusion: The IRRS model based on the identified 4 sIRlncRs showed the significant values on forecasting prognoses of pRCC patients, with the longer OS in the low-risk group.

Methods: We integrated the expression profiles of LncRNA and overall survival (OS) in the 322 pRCC patients based on the TCGA dataset. The immune scores calculated on account of the expression level of immune-related genes were used to verify the most relevant IR-lncRs. Survival-related IR-lncRs (sIRlncRs) were estimated by COX regression analysis in pRCC patients. The high-risk group and low-risk group were identified by the median immune-related risk score (IRRS) model established by the screened sIRlncRs. Functional annotation was displayed by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor were evaluated through microenvironment cell count records. The expression levels of sIRlncRs of pRCC samples were verified by real-time quantitative PCR.


GSEA: gene set enrichment analysis; IME: immune microenvironment; IRGs: immune-related genes; IR-lncRs: immune related long non-coding RNAs; IRRS: immune-related risk score; lncRNAs: long non-coding RNAs; OS: overall survival; pRCC: papillary renal cell carcinoma; PCA: principal component analysis; RCC: renal cell carcinoma; sIRlncRs: survival-related IR-lncRs; TCGA: The Cancer Genome Atlas.