A five-gene signature predicts overall survival of patients with papillary renal cell carcinoma

Abstract

Background: The present study aims to investigate the gene expression changes in papillary renal cell carcinoma(pRCC) and screen several genes and associated pathways of papillary renal cell carcinoma progression.

Methods: The papillary renal cell carcinoma RNA sequencing (RNA-seq) data set was downloaded from TCGA (The Cancer Genome Atlas). We identified the differentially expressed mRNAs between cancer and normal tissues and performed annotation of differentially expressed mRNAs to figure out the functions and pathways they were enriched in. Then, we constructed a risk score that relied on the 5-mRNA. The optimal value for the patients'classification risk level was identified by ROC analysis. The relationship between mRNA expression and prognosis of papillary renal cell carcinoma was evaluated by univariate Cox regression model. The 5-mRNA based risk score was validated in both complete set and testing set.

Result: In general, the 5-mRNA (CCNB2, IGF2BP3, KIF18A, PTTG1, and BUB1) were identified and validated, which can predict papillary renal cell carcinoma patient survival. This study revealed the 5-mRNA expression profile and the potential function of a single mRNA as a prognostic target for papillary renal cell carcinoma.

Conclusion: In addition, these findings may have significant implications for potential treatments options and prognosis for patients with papillary renal cell carcinoma.

Fig 1

(a)The Venn polt of up-regulated DEG. (b)The Venn polt of down-regulated DEG.

Fig 2. Volcano map with edgeR and DESeq2.

Fig 3. Statistic frequency analysis of the significantly altered mRNAs.

Fig 4. mRNA risk score analysis of the training set.

The mRNA signature risk score distribution heat-map of the mRNA expression profiles.Rows represent mRNAs, and columns represent patients.

Fig 5

(a) ROC for 5 mRNA,Method = KM. Receiver operating characteristic (ROC) analyzes the sensitivity and specificity of the survival time by risk score based on the 5-mRNA signature. The red dot indicates the optimal cut-off point. (b) Method Kaplan Meier. Kaplan-Meier estimated the survival time of the training set patients using risk score based on the 5 mRNA signature. The plot was used to visualize the survival probability for the low-risk versus high-risk group of patients based on the optimal cut-off point.

Fig 6

(a)Method Kaplan Meier. Kaplan-Meier estimated the survival time of the testing set patients using a risk score based on 5 mRNA signature. (b) Method Kaplan Meier. Kaplan-Meier estimated the survival time of the complete set patients from using a risk score based on 5 mRNA signature. The plot was used to visualize the survival probability for the low-risk versus high-risk group of patients based on the optimal cut-off point.

Fig 7. Stratified Cox hazard analysis of five gene signature on clinicopathological characteristics in pRCC cohort.

Fig 8. The comparative gene expression level of CCNB2, IGF2BP3, KIF18A, PTTG1, and BUB1 in normal tissue and pRCC tissue from TCGA database, respectively.

Fig 9

(a) biological process group of GO analysis. (b) cellular component group of GO analysis. (c) molecular function group of GO analysis.(d) The KEGG analysis. Biological function and KEGG pathway analysis of target genes. The overlapping target genes were predicted using the R language. (a-c) The enriched GO biological processes of target genes. (d) The enriched KEGG pathways of target genes.