Identification and Verification of Immune-Related Genes Prognostic Signature Based on ssGSEA for Adrenocortical Carcinoma (ACC)

Abstract

Purpose: Adrenocortical carcinoma (ACC) is an endocrine malignant tumor with poor prognosis. The study aimed to construct ACC immune-related gene prognostic signature and verify the efficacy of prognostic signature.

Methods: ACC RNA-seq data and clinical information are downloaded from TCGA databases and GEO databases. We used single sample gene set enrichment analysis (ssGSEA) to assess immune cell infiltration in ACC patients and ACC patients were divided into high- and low-immune cell infiltration clusters. The validity of ssGSEA grouping was verified using the ESTIMATE algorithm. A total of 275 differentially expressed immune-related genes (IRGs) were obtained from the intersection of IRGs and differentially expressed genes (DEGs) in high and low immune cell infiltration clusters. LASSO analysis was used to identify 13 IRGs that regulate the prognosis of ACC patients through immune infiltration. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that these 13 immune-related gene signatures were innovative and significant prognostic factors, which were independent of clinical features. Finally, ACC prognostic nomogram was constructed, ROC curve and calibration curve were drawn to evaluate the accuracy of the prognostic nomogram.

Results: LASSO regression analysis was used to screen out ACC survival-related genes. Univariate and multivariate Cox proportional risk regression models were used to analyze and construct the ACC prognosis nomogram. The AUC for predicting 1-, 3- and 5-year overall survival rate of ACC patients was 0.799, 0.966 and 0.969, suggesting good prediction accuracy. The calibration curve shows that the predicted results of the prognostic nomogram are in good agreement with the actual situation.

Conclusion: ssGSEA technique plays an important role in the construction of ACC prognostic model. Based on IRGs associated with survival independently predicted ACC prognosis, we identified thirteen immune-related genes as prognostic signature for ACC.

Keywords: ACC; adrenocortical carcinoma; immune; prognostic; risk score; single sample gene set enrichment analysis; ssGSEA.

Figure 1

Validation of the effectuality of immune clustering. (A) 79 ACC samples were divided into high immune cell infiltration group (Red) and low immune cell infiltration group (Blue). (B) The enrichment levels of 29 immune-related cells and types in the high immune cell infiltration group (Immunity_H) and the low immune cell infiltration group (Immunity_L). The tumor purity, ESTIMATE score, immune score and stromal score of every patient’s gene were showed combine with the clustering information. (C) The violin plot showed the difference in ESTIMATE score, immune score and stromal score between two clusters. (D) The expression of most HLAs was a significant difference in high- (red) and low- (blue) immune cell infiltration cluster. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 2

GSEA enrichment analysis. KEGG analysis of differentially expressed IRGs was performed and visualized via a dot plot (A) and bar plot (B).

Figure 3

Identification of differentially expressed IRGs between high and low immune cell infiltration clusters. (A) The volcano plot showed that 769 up-regulated genes (red) and 2111 down-regulated genes (green) between high and low immune cell infiltration cluster in TCGA dataset. Black dots mean meaningless (P>0.05). (B) Using venn diagram to pick up the intersection, 275 differentially expressed IRGs were obtained. (C) The heat map showed the expression levels of 2890 DEGs with high and low immune cell infiltration in 79 ACC samples from TCGA. (D) The heat map showed the expression levels of 275 differentially expressed IRGs.

Figure 4

Identification and verification of immune-related gene prognostic signature for ACC. The p-value and HR of selected genes in univariate Cox regression analysis (p < 0.05). Hazard radio>1 are the high-risk genes, shown in red. Hazard radio<1 are the low-risk genes, shown in green.

Figure 5

Establishment of Sankey diagram and PPI network. The protein interaction network analysis shows the relationship of protein interaction between nodes, and different color lines represent different meanings.

Figure 6

Construction of IRGs prognostic signature (A and B). The LASSO Cox analysis identified 13 genes associated with prognosis. (C) Train group, (D). Test group). The results of Kaplan–Meier survival analysis revealed that the survival rate of the low-risk group was significantly higher than that of the high-risk group (P<0.001). ((E) Train group, (F) Test group). The risk curve of every sample arranged by risk score. (G) Train group, (H) Test group). The scatter plot of ACC samples survival overview. The red and green dots stand for death and survival, respectively.

Figure 7

Verification of IRGs prognostic signature. ((A) Train group, (B) Test group). The ROC curve was used to evaluate the precision of risk score in predicting overall survival of ACC patients at 1-, 3-, and 5- year. ((C) Train group, (D) Test group). Calibration curve for 1-, 3-, and 5-year recurrence rate of ACC in TCGA datasets. (E) Univariate Cox regression demonstrated that clinical stage and the risk score could predict Overall Survival (OS). (F) Multivariate Cox regression showed that the risk score was independent prognostic factors. (G) Heatmap of correlation matrix between the immune cell and 13 IRGs signature. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 8

The establishment and evaluation of the nomogram (A). The nomogram used to predict the survival time (B). The ROC curve was used to evaluate the precision of nomogram in predicting overall survival of ACC patients at 1-, 3-, and 5- year. (C) Calibration maps used to predict the 1-, 3- and 5-year survival.