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Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma

Authors Zheng Y, Wen Y, Cao H, Gu Y, Yan L, Wang Y, Wang L, Zhang L, Shao F

Received 21 September 2020

Accepted for publication 3 February 2021

Published 22 March 2021 Volume 2021:14 Pages 2085—2100


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr XuYu Yang

Yan Zheng,1,* Yibo Wen,2,* Huixia Cao,1 Yue Gu,1 Lei Yan,1 Yanliang Wang,1 Limeng Wang,1 Lina Zhang,1 Fengmin Shao1

1Henan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial People’s Hospital, Zhengzhou, 450052, Henan, People’s Republic of China; 2Clinical Systems Biology Laboratories, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Fengmin Shao
Henan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial People’s Hospital, Henan, People’s Republic of China
Email [email protected]

Background: Immunotherapy has revolutionized the treatment of clear cell renal cell carcinoma (ccRCC). However, the therapy is constrained by drug resistance. Therefore, further characterization of immune infiltration in ccRCC is needed to improve its efficacy.
Methods: Here, we adopted the CIBERSORT method to analyze the level of 22 immune cells, and analyzed the correlation of immune cells and clinical parameters in ccRCC in The Cancer Genome Atlas. We used consensus clustering to cluster ccRCC and identified differently expressed genes (DEGs) between hot and cold tumors using the “Limma” package, and then performed enrichment analysis of DEGs. Finally, we constructed and validated a Cox regression model using the “survival”, “glmnet”, and “survivalROC” packages, implemented in R.
Results: Regulatory T cells upregulated in tumor tissue increased during tumor progression, and correlated with poor overall survival in ccRCC. Consensus clustering identified four clusters of ccRCC. To elucidate the underlying mechanisms of immune cell infiltration, we subdivided these four clusters into two major types, immune hot and cold, and identified DEGs between them. The results revealed different transcription profiles in the two tumor types, with hot tumors being enriched in immune-related signaling, whereas cold tumors were enriched in extracellular matrix remodeling and the phosphatidylinositol 3-kinase–AKT (PI3K/AKT) pathway. We further identified hub genes and prognostic-related genes from the DEGs, and constructed a Cox regression model for predicting the overall survival of patients with ccRCC. The areas under the receiver operating characteristics curve for the risk model for the training, testing, and external Zhengzhou validation cohorts were 0.834, 0.733, and 0.812, respectively. Notably, gene sets in the prediction model could also predict the overall survival of patients receiving immunotherapy.
Conclusion: These findings provide a comprehensive characterization of immune infiltration in ccRCC, while the constructed model can be used effectively to predict the overall survival of ccRCC patients.

Keywords: clear renal cell carcinoma, transcriptome profiling, immune infiltration, Cox risk model

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