Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Abstract

Background: 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.Methods: Raw 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.Results: PADI1, 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. Conclusions: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. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'

Gene Expression and Co-expression Networks Are Strongly Altered Through Stages in Clear Cell Renal Carcinoma

“…PLG gene also presents a remarkable decrease throughout stages advance ( Figure 3 ). Previously, PLG has been reported has decreased and a possible biomarker for renal carcinoma (Luo et al, 2018 ; Zhang et al, 2020 ).…”

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

“…For the survival period, a critical threshold can be found to divide the survival period into two risk levels. The Youden index is calculated by using (10), survival value with the largest Youden Index is the critical threshold for survival time. Here, the threshold for survival is 67.39 months.…”

Identification of a Novel Signature and Construction of a Nomogram Predicting Overall Survival in Clear Cell Renal Cell Carcinoma

“…The Robust Rank Aggregation (RRA) method can solve this problem, which directly integrates the lists of differentially expressed genes (DEGs) analyzed by different datasets ( Kolde et al, 2012 ) and identifies more robust cancer-related gene sets ( Griffith et al, 2006 ). Besides, the combination of novel signatures with clinicopathological information may improve the prediction of prognosis in ccRCC patients, but this has not been widely applied in clinical practice ( Chen et al, 2019a ; Zhang et al, 2020 ). Thus, it is necessary to find more novel signatures through comprehensive bioinformatics to establish a more accurate nomogram than just the clinicopathological information.…”