Abstract
BackgroundClear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.MethodsRaw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways.ResultsPADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer.ConclusionsIn our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.
Highlights
Clear cell renal cell carcinoma comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising
More and more single signature have been explored to predict the overall survival (OS) of Clear cell renal cell carcinoma (ccRCC) patients, such as CX3CR1 [5], miR-497 [6] and LncRNA CADM1-AS1 [7]
Datasets sources and processing Raw counts of RNA-sequencing data and corresponding clinical information (Additional file 1: Table S1) from 533 KIRC and 78 paracancerous samples were obtained from The Cancer Genome Atlas (TCGA) dataset in April 2018, in which the method of acquisition and application complied with the guidelines and policies
Summary
Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial. Among the RCC subtypes, clear-cell renal cell carcinoma (ccRCC) is the most common one and comprises the majority of kidney cancer deaths [3]. Identifying reliable prognostic tools for predicting the clinical outcomes and helping make decisions regarding observation, surgery, drug therapy and conservative options is obviously crucial for now. Biomarkers used to predict overall survival (OS) can range from clinical parameters, endogenous substances and pathohistological characteristics of tumor to specific mutated gene. It is a challenge to predict survival of patients with ccRCC using single parameter by reason of the impact of wide variability of outcomes and genetic heterogeneity [8]. It is the best way to develop a comprehensive prognostic evaluation system including multiple biomarkers which can improve the predictive accuracy
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