Prognostic Gene Expression-Based Signature in Clear-Cell Renal Cell Carcinoma

The inaccuracy of the current prognostic algorithms and the potential changes in the therapeutic management of localized ccRCC demands the development of an improved prognostic model for these patients. To this end, we analyzed whole-transcriptome profiling of 26 tissue samples from progressive and...

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Bibliographic Details
Published in:Cancers 2022-08, Vol.14 (15), p.3754
Main Authors: Roldán, Fiorella L, Izquierdo, Laura, Ingelmo-Torres, Mercedes, Lozano, Juan José, Carrasco, Raquel, Cuñado, Alexandra, Reig, Oscar, Mengual, Lourdes, Alcaraz, Antonio
Format: Article
Language:English
Subjects:
Biomarkers
Disease
Disease management
Gene expression
Genomes
Kidney cancer
Medical prognosis
Medical research
Metastasis
Patients
Renal cell carcinoma
Risk assessment
Risk groups
Software
Surgery
Surveillance
Transcriptomes
Tumors
Variables
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Summary:The inaccuracy of the current prognostic algorithms and the potential changes in the therapeutic management of localized ccRCC demands the development of an improved prognostic model for these patients. To this end, we analyzed whole-transcriptome profiling of 26 tissue samples from progressive and non-progressive ccRCCs using Illumina Hi-seq 4000. Differentially expressed genes (DEG) were intersected with the RNA-sequencing data from the TCGA. The overlapping genes were used for further analysis. A total of 132 genes were found to be prognosis-related genes. LASSO regression enabled the development of the best prognostic six-gene panel. Cox regression analyses were performed to identify independent clinical prognostic parameters to construct a combined nomogram which includes the expression of CERCAM, MIA2, HS6ST2, ONECUT2, SOX12, TMEM132A, pT stage, tumor size and ISUP grade. A risk score generated using this model effectively stratified patients at higher risk of disease progression (HR 10.79; p < 0.001) and cancer-specific death (HR 19.27; p < 0.001). It correlated with the clinicopathological variables, enabling us to discriminate a subset of patients at higher risk of progression within the Stage, Size, Grade and Necrosis score (SSIGN) risk groups, pT and ISUP grade. In summary, a gene expression-based prognostic signature was successfully developed providing a more precise assessment of the individual risk of progression.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers14153754