Clinicopathological and Molecular Prognostic Classifier for Intermediate/High-Risk Clear Cell Renal Cell Carcinoma

Cancers

卷:13

Juan J. Lozano¹  Raquel Carrasco²  Antonio Alcaraz²  Esther Díaz²  Mercedes Ingelmo-Torres²  Lourdes Mengual²  Laura Izquierdo²  Fiorella L. Roldán²  José Rubio³  Miguel Ramirez-Backhaus³  Oscar Reig⁴ 

[1] Bioinformatics Platform, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clinic, 08036 Barcelona, Spain;

[2] Department and Laboratory of Urology, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, 08036 Barcelona, Spain;

[3] Department of Urology, Oncologic Institute of Valencia, 46009 Valencia, Spain;

[4] Translational Genomics and Targeted Therapeutics in Solid Tumors, August Pi i Sunyer Biomedical Research Institute (IDIBAPS) and Medical Oncology Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain;

关键词: gene expression;    clear cell renal cell carcinoma;    disease progression;    prognostic factors;    biomarkers;    RNA sequencing;   

DOI  :  10.3390/cancers13246338

来源: DOAJ

【 摘 要 】

The probability of tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC) is highly variable, underlining the lack of predictive accuracy of the current clinicopathological factors. To develop an accurate prognostic classifier for these patients, we analyzed global gene expression patterns in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 4000. Expression levels of 22 selected differentially expressed genes (DEG) were assessed by nCounter analysis in an independent series of 71 ccRCCs. A clinicopathological-molecular model for predicting tumor progression was developed and in silico validated in a total of 202 ccRCC patients using the TCGA cohort. A total of 1202 DEGs were found between progressive and non-progressive intermediate/high-risk ccRCC in RNAseq analysis, and seven of the 22 DEGs selected were validated by nCounter. Expression of HS6ST2, pT stage, tumor size, and ISUP grade were found to be independent prognostic factors for tumor progression. A risk score generated using these variables was able to distinguish patients at higher risk of tumor progression (HR 7.27; p < 0.001), consistent with the results obtained from the TCGA cohort (HR 2.74; p < 0.002). In summary, a combined prognostic algorithm was successfully developed and validated. This model may aid physicians to select high-risk patients for adjuvant therapy.

【 授权许可】

Unknown