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. 2022 Jun 23;22(1):691.
doi: 10.1186/s12885-022-09755-2.

A promising Prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes

Peng Cao  1 , Ji-Yue Wu  1 , Jian-Dong Zhang  1 , Ze-Jia Sun  1 , Xiang Zheng  1 , Bao-Zhong Yu  1 , Hao-Yuan Cao  1 , Fei-Long Zhang  1 , Zi-Hao Gao  1 , Wei Wang  2
Affiliations

A promising Prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes

Peng Cao et al. BMC Cancer. .

Abstract

Background: Renal cell carcinoma (RCC) is a third most common tumor of the urinary system. Nowadays, Immunotherapy is a hot topic in the treatment of solid tumors, especially for those tumors with pre-activated immune state.

Methods: In this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas (TCGA) database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. Then we established a prognostic risk model consisting of the genes most related to prognosis from four signatures to value prognosis of the RCC samples via Kaplan-Meier (KM) survival analysis. An independent data from International Cancer Genome Consortium (ICGC) database were used to test the predictive stability of the model. Furthermore, we performed landscape analysis to assess the difference of gene mutant in the RCC samples from TCGA. Finally, we explored the correlation between the selected genes and the level of tumor immune infiltration via Tumor Immune Estimation Resource (TIMER) platform.

Results: We used four genetic signatures to construct prognostic risk models respectively and found that each of the models could divide the RCC samples into high- and low-risk groups with significantly different prognosis, especially in advanced RCC. A comprehensive prognostic risk model was constructed by 8 candidate genes from four signatures (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) dividing the advanced RCC samples from TCGA database into high-risk and low-risk groups with a significant difference in cancer-specific survival (CSS). The stability of the model was verified by independent data from ICGC database. And the classification efficiency of the model was stable for the samples from different subgroups. Landscape analysis showed that mutation ratios of some genes were different between two risk groups. In addition, the expression levels of the selected genes were significantly correlated with the infiltration degree of immune cells in the advanced RCC.

Conclusions: Sum up, eight immune-related genes were screened in our study to construct prognostic risk model with great predictive value for the prognosis of advanced RCC, and the genes were associated with infiltrating immune cells in tumors which have potential to conduct personalized treatment for advanced RCC.

Keywords: Immunologic signature; Prognosis; RCC; Renal cell carcinoma; Tumor immunity.

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Conflict of interest statement

The authors declare no conflicts of interest in this work.

Figures

Fig. 1
Fig. 1
Prognostic risk models constructed by four immune-related signatures for overall survival (OS) in early and advanced RCC. A Classified efficiency of prognostic risk models constructed by four immune-related signatures (IFN-gamma signature, extended immune gene signature, cytotoxic T lymphocyte signature and HLA-A and HLA-B) in stage I + II RCC. B Classified efficiency of the four prognostic risk models for stage III + IV RCC. The p-value was shown in the survival plots
Fig. 2
Fig. 2
Prognostic risk models constructed by 8 genes combination for OS in advanced RCC. A Survival plots showed OS of high-risk group and low-risk group in advanced RCC. The risk score curve B and the scatter plot C were drawn according to risk score of every advanced RCC sample calculated by the model. D The heatmap indicated the expression levels of selected genes in the advanced RCC samples. High and low expressions were highlighted in red and blue, respectively. E The predicted value of the model was assessed by Time-dependent ROC curve. The p-value was shown in the survival plot
Fig. 3
Fig. 3
Validating the classified efficiency of the prognostic risk model constructed by 8 selected genes combination via data from ICGC. A Survival plots showed OS of high-risk group and low-risk group in advanced RCC from ICGC. The risk score curve B and the scatter plot C were drawn according to risk score of each RCC sample calculated by the model. (D) The heatmap indicated the expression levels of selected genes in the RCC samples. The p-value was shown in the survival plot
Fig. 4
Fig. 4
Gene mutation analysis. The landscape analysis showed the top 16 genes with mutation frequency in high-risk group A and low-risk group B of the advanced RCC. The histogram showed the number of mutations in the RCC samples. Annotation information of the samples included risk groups, clinical stages, living status and genders. Different colors represented different mutation types. C Genetic alterations of the 8 selected genes in RCC samples
Fig. 5
Fig. 5
Validating the stability of the prognostic risk model constructed by the 8 selected genes for different subtypes of advanced RCC. Survival plots all showed that high-risk RCC classified by the model resulted in unfavorable OS in different stages (A); genders (B); ages (C) and pathological patterns (C). The p values were shown in the survival plots
Fig. 6
Fig. 6
Association of the genes involved in the model with tumor immune infiltrates
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