Original Article
Genome-wide mutation profiling and related risk signature for prognosis of papillary renal cell carcinoma
Abstract
Background: The papillary renal cell carcinoma (pRCC) is a rare subtype of renal cell carcinoma with limited investigation. Our study aimed to explore a robust signature to predict the prognosis of pRCC from the perspective of mutation profiles.
Methods: In this study, we downloaded the simple nucleotide variation data of 288 pRCC samples from The Cancer Genome Atlas (TCGA) database. “GenVisR” package was utilized to visualize gene mutation profiles in pRCC. The PPI network was conducted based on the STRING database and the modification was performed via Cytoscape software (Version 3.7.1). Top 50 mutant genes were selected and Cox regression method was conducted to identify the hub prognostic mutant signature in pRCC using “survival” package. Mutation Related Signature (MRS) risk score was established by multivariate Cox regression method. Receiver Operating Characteristic (ROC) curve drawn by “timeROC” was conducted to assess the predictive accuracy of overall survival (OS) and Kaplan-Meier analysis was then performed. Relationships between mutants and expression levels were compared by Wilcox rank-sum test. Function enrichment pathway analysis for mutated genes was performed by “org.Hs.eg.db”, “clusterProfiler”, “ggplot2” and “enrichplot” packages. Gene Set Enrichment Analysis was exploited using the MRS as the phenotypes, which worked based on the JAVA platform. All statistical analyses were achieved by R software (version 3.5.2). P value <0.05 was considered to be significant.
Results: The mutation landscape in waterfall plot revealed that a list of 49 genes that were mutated in more than 10 samples, of which 6 genes (TTN, MUC16, KMT2C, MET, OBSCN, LRP2) were mutated in more than 20 samples. Besides, non-synonymous was the most frequent mutation effect, and missense mutation was one of the most common mutation types in mutated genes across 248 samples. The AUC of MRS model consisted of 17 prognostic mutant signatures was 0.907 in 3-year OS prediction. Moreover, pRCC patients with high level of MRS showed the worse survival outcomes compared with that in low-level MRS group (P=0). In addition, correlation analysis indicated that 6 mutated genes (BAP1, OBSCN, NF2, SETD2, PBRM1, DNAH1) were significantly associated with corresponding expression levels. Last, functional enriched pathway analysis showed that these mutant genes were involved in multiple cancer-related crosstalk, including PI3K-AKT signaling pathway, JAK-STAT signaling pathway, extracellular matrix (ECM)-receptor interaction or cell cycle.
Conclusions: In summary, our study was the first attempt to explore the mutation-related signature for predicting survival outcomes of pRCC based on the high-throughput data, which might provide valuable information for further uncovering the molecular pathogenesis in pRCC.
Methods: In this study, we downloaded the simple nucleotide variation data of 288 pRCC samples from The Cancer Genome Atlas (TCGA) database. “GenVisR” package was utilized to visualize gene mutation profiles in pRCC. The PPI network was conducted based on the STRING database and the modification was performed via Cytoscape software (Version 3.7.1). Top 50 mutant genes were selected and Cox regression method was conducted to identify the hub prognostic mutant signature in pRCC using “survival” package. Mutation Related Signature (MRS) risk score was established by multivariate Cox regression method. Receiver Operating Characteristic (ROC) curve drawn by “timeROC” was conducted to assess the predictive accuracy of overall survival (OS) and Kaplan-Meier analysis was then performed. Relationships between mutants and expression levels were compared by Wilcox rank-sum test. Function enrichment pathway analysis for mutated genes was performed by “org.Hs.eg.db”, “clusterProfiler”, “ggplot2” and “enrichplot” packages. Gene Set Enrichment Analysis was exploited using the MRS as the phenotypes, which worked based on the JAVA platform. All statistical analyses were achieved by R software (version 3.5.2). P value <0.05 was considered to be significant.
Results: The mutation landscape in waterfall plot revealed that a list of 49 genes that were mutated in more than 10 samples, of which 6 genes (TTN, MUC16, KMT2C, MET, OBSCN, LRP2) were mutated in more than 20 samples. Besides, non-synonymous was the most frequent mutation effect, and missense mutation was one of the most common mutation types in mutated genes across 248 samples. The AUC of MRS model consisted of 17 prognostic mutant signatures was 0.907 in 3-year OS prediction. Moreover, pRCC patients with high level of MRS showed the worse survival outcomes compared with that in low-level MRS group (P=0). In addition, correlation analysis indicated that 6 mutated genes (BAP1, OBSCN, NF2, SETD2, PBRM1, DNAH1) were significantly associated with corresponding expression levels. Last, functional enriched pathway analysis showed that these mutant genes were involved in multiple cancer-related crosstalk, including PI3K-AKT signaling pathway, JAK-STAT signaling pathway, extracellular matrix (ECM)-receptor interaction or cell cycle.
Conclusions: In summary, our study was the first attempt to explore the mutation-related signature for predicting survival outcomes of pRCC based on the high-throughput data, which might provide valuable information for further uncovering the molecular pathogenesis in pRCC.
