Construction of an Epithelial-Mesenchymal Transition-Related Model for Clear Cell Renal Cell Carcinoma Prognosis Prediction

Abstract

Background: A rising amount of data demonstrates that the epithelial-mesenchymal transition (EMT) in clear cell renal cell carcinomas (ccRCC) is connected with the advancement of the cancer. In order to understand the role of EMT in ccRCC, it is critical to integrate molecules involved in EMT into prognosis prediction. The objective of this project was to establish a prognosis prediction model using genes associated with EMT in ccRCC.

Methods: We acquired the mRNA expression profiles and clinical information about ccRCC from TCGA database. In this study, we measured differentially expressed EMT-related genes (DEEGs) by two comparison groups (tumor versus normal tissues; "stages I-II" versus "stages III-IV" tumor tissues). Based on classification and regression random forest models, we identified the most important DEEGs in predicting prognosis. Afterwards, a risk-score model was created using the identified important DEEGs. The prediction ability of the risk-score model was calculated by the area under the curve (AUC). A nomogram for prognosis prediction was built using the risk-score in combination with clinical factors.

Results: Among the 72 DEEGs, the classification and regression random forest models identified six hub genes (DKK1, DLX4, IL6, KCNN4, RPL22L1, and SPDEF), which exhibited the highest importance values in both models. Through the expression of these six hub genes, a novel risk-score was developed for the prognosis prediction of ccRCC. ROC curves showed the risk-score performed well in both the training (0.749) and testing (0.777) datasets. According to the survival analysis, individuals who were separated into high/low-risk groups had statistically different outcomes in terms of prognosis. Besides, the risk-score model also showed outstanding ability in assessing the progression of ccRCC after treatment. In terms of nomogram, the concordance index (C-index) was 0.79. Additionally, we predicted the differences in response to chemotherapy drugs among patients from low- and high-risk groups.

Conclusion: Gene signatures related to EMT could be useful in predicting ccRCC prognosis.

Figure 1

Identification of DEGs in TCGA-KIRC cohort. (a) The volcano of DEGs between KIRC and normal kidney samples. (b) The heatmap of DEGs between KIRC and normal kidney samples. (c) The volcano of DEGs between “stages I-II” and “stages III-IV” tumor tissues. (d) The heatmap of DEGs between “stages I-II” and “stages III-IV” tumor tissues. In volcano plots, red dots indicate downregulation genes in KIRC or “stages III-IV,” whereas blue dots indicate upregulation genes. In heatmap plots, red indicates high-expression values, whereas blue indicates low-expression values.

Figure 2

Assessment and DEEGs signature in the training dataset. (a) Intersection of DEGs and EMT-related genes by the Venn plot. (b) Risk-score distributions, (c) survival time/statuses, and (d) heatmap of the hub DEEGs expression in the training dataset. (e) The AUC value of the risk-score in the training dataset. (f) Survival curves (OS) of risk-score groups in the training dataset.

Figure 3

Relationship between risk-score and clinical factors, including (a) stage IV, (b) T stage, (c) N stage, (d) M stage, (e) Age, and (f) laterality.

Figure 4

(a) The prognostic nomogram was constructed by age, stage, and risk-score. The calibration curve diagrams for (b) 1-year, (c) 3-year, and (d) 5-year had good agreement between the predicted probability and the actual probability.

Figure 5

Box plot of estimated IC50 values for (a) axitinib, (b) bortezomib, (c) cisplatin, (d) gefitinib, (e) sorafenib, (f) sunitinib, (g) temsirolimus, and (h) Vinblastine in low and high risk-score groups.

Figure 6

(a) Differential analysis of 14 immune fractions (CIBERSORT algorithm) between risk-score groups. (b) Differential analysis of stromal, immune, and tumor purity (ESTIMATE algorithm) between risk-score groups.

Figure 7

Overall survival analyses of the identified genes, including (a) DKK1, (b) DLX4, (c) IL6, (d) KCNN4, (e) RPL22L1, and (f) SPDEF in TCGA dataset. Red lines indicate patients with the high expression, whereas blue lines indicate patients with the low expression.