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09 December 2020 : Database Analysis

Identification of a 6-Gene Signature Associated with Resistance to Tyrosine Kinase Inhibitors: Prognosis for Clear Cell Renal Cell Carcinoma

Qinke Li1ABCE, Wenbo Yang1CD, Maoqing Lu1DF, Ronggui Zhang1AG*

DOI: 10.12659/MSM.927078

Med Sci Monit 2020; 26:e927078

Abstract

BACKGROUND: Tyrosine kinase inhibitors (TKIs) are used to treat metastatic disease associated with clear cell renal cell carcinoma (ccRCC); however, most patients develop resistance after 6 to 15 months. As such, identifying biomarkers of TKI resistance may be useful for prognosis.

MATERIAL AND METHODS: We analyzed ChIP-seq data related to TKI resistance from the Gene Expression Omnibus and RNA-Seq and clinical data from The Cancer Genome Atlas database. We used univariate Cox analysis and Cox regression/Lasso analysis to determine a risk score. The Kaplan-Meier estimate and receiver operating characteristic curve verified the risk score’s sensitivity and specificity. The stratified analysis and the univariate and multivariate analyses revealed its predictive power. We predicted survival time by constructing a nomogram.

RESULTS: Of the 32 differentially expressed genes (DEGs) related to TKI resistance, 6 (ACE2, MMP24, SLC44A4, C1R, C1ORF194, ADAMTS15) were used to establish a risk score. Kaplan-Meier analysis showed that high-risk patients had shorter median survival times than low-risk patients, notably among those with metastatic disease (1.51 vs. 4.55 years). The stratified analysis revealed that patients with advanced disease had relatively higher risk scores than patients at early stages (P<0.001). Univariate analysis independently associated the 6-DEGs signature with the prognosis of metastatic ccRCC (hazard ratio, 1.217; 95% confidence interval, 1.090–1.358). The nomogram we constructed based on 6-DEGs signature and clinical parameters predicted survival time accurately.

CONCLUSIONS: We identified a 6-DEGs signature that permitted us to establish a risk score related to TKI resistance that can serve as a reliable biomarker for predicting the survival of patients with ccRCC.

Keywords: Biological Markers, Carcinoma, Renal Cell, Drug Resistance, Prognosis

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750