Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma

Interferon plays a crucial role in the pathogenesis and progression of tumors. Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant urinary system tumor. An effective predictive model is required to evaluate the prognosis of patients to optimize treatment. RNA-sequencing data and...

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Bibliographic Details
Published in:Journal of inflammation research 2021-01, Vol.14, p.4969-4985
Main Authors: Liu, Lixiao, Du, Xuedan, Fang, Jintao, Zhao, Jinduo, Guo, Yong, Zhao, Ye, Zou, Chengyang, Yan, Xiaojian, Li, Wenfeng
Format: Article
Language:English
Subjects:
Analysis
Apoptosis
Biological response modifiers
Cancer therapies
Carcinoma, Renal cell
Cell cycle
Cell survival
Clear cell-type renal cell carcinoma
Cytokines
Cytotoxicity
Development and progression
drug sensitivity
Gene expression
Genes
Immune checkpoint inhibitors
Immunosuppressive agents
Interferon gamma
Kidney cancer
Medical prognosis
Metastases
Microenvironments
nomogram
Original Research
Patients
Prediction models
Prognosis
prognostic signature
qpcr
Risk groups
Survival analysis
Tumors
γ-Interferon
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Summary:Interferon plays a crucial role in the pathogenesis and progression of tumors. Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant urinary system tumor. An effective predictive model is required to evaluate the prognosis of patients to optimize treatment. RNA-sequencing data and clinicopathological data from TCGA were involved in this retrospective study. The IFN-γ response genes with significantly different gene expression were screened out. Univariate Cox regression, LASSO regression and multivariate Cox regression were used to establish a new prognostic scoring model for the training group. Survival curves and ROC curves were drawn, and nomogram was constructed. At the same time, we conducted subgroup analysis and experimental verification using our own samples. Finally, we evaluated the relatedness between the prognostic signature and immune infiltration landscapes. In addition, the sensitivity of different risk groups to six drugs and immune checkpoint inhibitors was calculated. The IFN-γ response-related signature included 7 genes: C1S, IFI44, ST3GAL5, NUP93, TDRD7, DDX60, and ST8SIA4. The survival curves of the training and testing groups showed the model's effectiveness (P = 4.372e-11 and P = 1.08e-08, respectively), the ROC curves showed that the signature was stable, and subgroup analyses showed the wide applicability of the model (P
ISSN:1178-7031
1178-7031
DOI:10.2147/JIR.S334041