A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

Abstract

Background: Kidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients.

Results: We identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set.

Conclusion: Our findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients.

Keywords: Bioinformatics; Kidney renal clear cell carcinoma; LASSO penalty; Prognostic model.

Fig. 1

DEGs in KIRC vs adjacent normal tissues. a. Volcano plot visualizing the DEGs screened using limma. The red and green points represent the significantly upregulated and downregulated DEGs, respectively (logFC> 2 or logFC<(− 2) with adjusted P < 0.05). Features selected by the LASSO penalty are also marked. b. Heatmap showing that the 333 DEGs are involved in renal system development, kidney epithelium development, renal tubule development, and kidney development

Fig. 2

Construction of the KIRC-specific gene risk score system A. LASSO coefficient of the 7 survival-related genes. B-C. Prognostic classifier analysis of the patients in the internal testing set. The distribution of risk score and patients survival time and status, and the lower one is heat map of the genes in prognostic classifier. D. ROC curve for the survival of high- and low-risk groups

Fig. 3

The distribution of RS, ROC curves and Kaplan-Meier survival in the testing and ICGC sets. a-c. Internal testing cohort. d-f. ICGC validation cohort

Fig. 4

Nomogram for predicting 3- and 5-year OS. a. We added up the points identified on the points scale for each variable that can be projected onto the scales to indicate the probability of 3- and 5-year OS. b. Calibration plot showing the prediction of OS. The nomogram-predicted probability of OS is plotted on the x-axis; actual OS is plotted on the y-axis