A Five-Gene Signature Predicts Prognosis in Patients with Kidney Renal Clear Cell Carcinoma

Abstract

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.

Genome-wide Analysis of the Alternative Splicing Profiles Revealed Novel Prognostic Index for Kidney Renal Cell Clear Cell Carcinoma

“…Further evaluation from Kaplan-Meier survival analysis and tROC curves reflected the preferable prognosis-predicting ability of seven PI models (PI-AA, PI-AD, PI-AT, PI-ES, PI-ME, PI-RI and PI-ALL). Although some prediction models composed of miRNAs or mRNAs have been devised by other researchers [28,29], we are confident of the strong risk-stratification capacity of our PIs. The highest AUC value from the seven PI models was close to 0.9, obviously higher than that of the five-gene signature in the study of Zhan Y et al [29].…”

“…Although some prediction models composed of miRNAs or mRNAs have been devised by other researchers [28,29], we are confident of the strong risk-stratification capacity of our PIs. The highest AUC value from the seven PI models was close to 0.9, obviously higher than that of the five-gene signature in the study of Zhan Y et al [29]. Unlike the study of Liang B et al, where the three miRNA constituents for prediction models came from the overlapping parts of OS markers, disease-free survival markers and diagnostic markers [28], AS events included for PI in the present study were selected from two steps: univariate Cox regression analysis and multivariate Cox regression analysis.…”

Biomarkers and Next Generation Sequencing in Chronic Kidney Disease

“…19,20 In addition, detecting genetic mutation in renal cell carcinoma could be used to predict the prognosis and monitor therapeutic response. 15,21 Moreover, studying pathogenesis by NGS revealed and identified several key molecules and pathways in initiation of renal fibrosis or kidney disease progression and renal cell function. [22][23][24] Therefore, NGS will be useful to identify novel therapeutic targets for kidney disease and consequently catalyze the precision medicine on renal diseases as an innovational tool.…”

A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

“…MicroRNA miR-26a and lncRNA MEG3 (maternally expressed 3) was reported to have an antitumor effect, and reduced miR-26a and MEG3 was also associated with poor prognostic outcomes ( Jia et al, 2014 ). Meanwhile, gene signatures have been widely used for prognosis of cancers ( Shi & He, 2016 ; Wang et al, 2016 ; Zhan et al, 2015 ). When it comes to OTSCC, Krishnan identified a 38-gene minimal signature by machine-learning method, which could predict tumor recurrence ( Krishnan et al, 2015 ).…”