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. 2021 Jun 29;21(1):746.
doi: 10.1186/s12885-021-08367-6.

Identification of an independent immune-genes prognostic index for renal cell carcinoma

Guangyao Li #  1 , Xiyi Wei #  1 , Shifeng Su #  1 , Shangqian Wang  1 , Wei Wang  1 , Yichun Wang  1 , Xianghu Meng  1 , Jiadong Xia  1 , Ninghong Song  2   3 , Chao Qin  4
Affiliations

Identification of an independent immune-genes prognostic index for renal cell carcinoma

Guangyao Li et al. BMC Cancer. .

Abstract

Background: Considerable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC.

Methods: Files containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis.

Results: Via differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients' survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways.

Conclusion: In general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.

Keywords: Clear cell renal cell carcinoma; Immune risk score model; Immune-related genes; Nomogram; Prognosis.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
Differential gene expression profiles. A The volcano plot showed 540 differential expressions of genes in ccRCC and normal tissues based on TCGA data-base. B Heat map of the differentially expressed genes (topmost 10 upregulated and downregulated genes). The colors from green to red in the heat map represent a low-to-high level of expression. Red and green dots mean up-and down-regulated genes, respectively, and the black ones represent genes that are not differentially expressed. All the data and pictures were analyzed and then generated by R statistical language version 3.6.1 (https://www.R-project.org)
Fig. 2
Fig. 2
Gene Ontology (A) and Kyoto Encyclopedia of Genes and Genomes
Fig. 3
Fig. 3
A–B The construction of the risk score model using the LASSO Cox regression model and 18 prognostic immune-related genes. C Distribution of immune-related risk scores and survival status in the training group. D Heatmap of model immune genes between the high-risk and low-risk sets (separated by median value) in the training group
Fig. 4
Fig. 4
A K-M curve for analyzing high-and low-risk cases in the training group. B K-M curve for analyzing high- and low-risk cases in the test group. C K-M curve for analyzing of high- and low-risk cases in the whole TCGA group. D ROC curve, depending on time, for analyzing the training group. E ROC curve, depending on time, for analyzing the testing group. F ROC curve, depending on time, for analyzing the whole TCGA group
Fig. 5
Fig. 5
Cox proportional risk model for overall survival of related elements in ccRCC patients. A-B Univariate- and multivariate-Cox regressions analyses for 7 clinical prognostic factors influencing OS, respectively. C Nomogram for forecasting 3-year and 5-year prognosis of ccRCC. D–E Plots present the calibration curves used to compare the predicted and actual 3-and 5-year OS
Fig. 6
Fig. 6
Correlation analysis of 18 immune genes with pathological grade, tumor stage and TNM in ccRCC patients. A Correlation between 18 immune genes and pathological grade of ccRCC patients. B Correlation between 18 immune genes and tumor staging of ccRCC patients. C-E Correlation of 18 immune genes with tumor, node, and metastasis classification in ccRCC patients
Fig. 7
Fig. 7
Correlation analysis of immune-related gene risk score and clinicopathological elements. A Age. B Pathological Grade. C Tumor Stage. D T. E N. F M
Fig. 8
Fig. 8
GSEA of the risk scores of immune genes
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