Identification of a 5-Gene Signature Predicting Progression and Prognosis of Clear Cell Renal Cell Carcinoma

Abstract

BACKGROUND Although the mortality rates of clear cell renal cell carcinoma (ccRCC) have decreased in recent years, the clinical outcome remains highly dependent on the individual patient. Therefore, identifying novel biomarkers for ccRCC patients is crucial. MATERIAL AND METHODS In this study, we obtained RNA sequencing data and clinical information from the TCGA database. Subsequently, we performed integrated bioinformatic analysis that includes differently expressed genes analysis, gene ontology and KEGG pathway analysis, protein-protein interaction analysis, and survival analysis. Moreover, univariate and multivariate Cox proportional hazards regression models were constructed. RESULTS As a result, we identified a total of 263 dysregulated genes that may participate in the metastasis of ccRCC, and established a predictive signature relying on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4, which could serve as significant progressive and prognostic biomarkers for ccRCC. CONCLUSIONS We identified differentially expressed genes that may be involved in the metastasis of ccRCC. Moreover, a predictive signature based on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4 could be an independent prognostic factor for ccRCC.

Figure 1

The volcano plot for DEGs related to metastasis. The x-axis is -log10(FDR) and the y-axis is logFC. The red dots represent upregulated genes and green dots represent downregulated genes.

Figure 2

Go term and KEGG pathway analysis for DEGs. (A) Top 10 molecular function (MF) processes. (B) Cellular component (CC). (C) Top 10 biological processes (BP). (D) KEGG pathway analysis.

Figure 3

The most significant module. The color and the size of a node indicates the number of proteins interacting with the designated protein.

Figure 4

(A–T) Survival-related upregulated genes. Kaplan-Meier survival curves were generated for genes with P<0.05 in multivariate Cox regression analysis.

Figure 5

The 5-gene predictive signature in ccRCC. (A) Kaplan-Meier curve of OS in the low- and high-risk groups. (B) Kaplan-Meier curve of DFS in the low- and high-risk groups. (C) ROC curve for the 3-year survival prediction by the 5-gene signature. (D) ROC curve for the 3-year disease-free survival prediction by the 5-gene signature. (E) ROC curve for the 5-year survival prediction. (F) ROC curve for the 5-year disease-free survival prediction. (G) Risk scores distribution among OS cohort. (H) Risk scores distribution among DFS cohort.

Figure 6

Expression pattern of the 5-gene signature in OS and DFS cohort. (A–C). In the OS cohort, the expression levels of OTX1, MATN4, and PI3 were significantly higher in the high-risk group. (D–F). In the DFS cohort, the expression levels of OTX1, MATN4, and PI3 were significantly higher in the high-risk group.

Figure 7

Heatmap of the 5 genes. (A) Heatmap of the OS cohort. (B) Heatmap of the DFS cohort. Red indicates the high-risk group, while blue indicates the low-risk group.