In this study, we constructed a model based on multiple prognostic-related genes and clinical parameters to predict OS of ccRCC patients. Therefore, more novel prognosis-related genes could be uncovered by different bioinformatics analysis process and used to establish a more accurate prognostic models than conventional clinical parameters.
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Nowadays, gene-based prognostic models containing other clinical parameters in predicting OS of cancer patients including ccRCC have been investigated numerously but they have not been widely accepted and exerted on the clinical practice. Thus, it is the best way to develop a comprehensive prognostic evaluation system including multiple biomarkers which can improve the predictive accuracy. However, 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. Besides, more and more single signature have been explored to predict the OS of ccRCC patients, such as CX3CR1, miR-497 and LncRNA CADM1-AS1. For example, the tumor node metastasis (TNM) classification system is most widely used to estimate prognosis and guide treatment in patients with cancer. Therefore, 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.īiomarkers used to predict overall survival (OS) can range from clinical parameters, endogenous substances and pathohistological characteristics of tumor to specific mutated gene. Among the RCC subtypes, clear-cell renal cell carcinoma (ccRCC) is the most common one and comprises the majority of kidney cancer deaths.
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According to the latest data from the World Health Organization, there are more than 140,000 RCC–related deaths per year. Renal cell carcinoma (RCC) ranks among the top ten cancer diagnoses worldwide, which account for 5% and 3% of all new cancer cases in males and females, respectively. In 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. Functional enrichment analysis suggested several enriched biological pathways related to cancer. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. 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. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram.
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GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Time-dependent receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Tumor samples were divided into two sets. Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial. Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising.