Abstract
Simple SummaryIn this report, we identified biomarkers for tumor progression from tissue samples of intermediate/high-risk ccRCC. Using the molecular findings and the clinical data, we developed an improved prognostic model which could help to provide better individualized management recommendations.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.
Highlights
Clear cell renal cell carcinoma is the most frequent renal tumor, accounting for 80–90% of cases, and has the greatest malignant potential of all renal cell carcinoma subtypes
Gene set enrichment analysis (GSEA) based on Hallmark, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome databases identified that differentially expressed genes (DEG) were positively enriched in pathways, such as the epithelialmesenchymal transition (EMT), ECM proteoglycans, non-integrin membrane ECM interactions, extracellular matrix organization, MYC targets V1, and PID syndecan 1 pathway, among others (Figure 1B)
We evaluated the predictive capability for disease progression of our combined model in 202 intermediate/high-risk Clear cell renal cell carcinoma (ccRCC) samples obtained from the The Cancer Genome Atlas (TCGA) cohort
Summary
Clear cell renal cell carcinoma (ccRCC) is the most frequent renal tumor, accounting for 80–90% of cases, and has the greatest malignant potential of all renal cell carcinoma subtypes. As far as we know, none of the proposed classifiers for ccRCC are currently used in clinical practice, nor validated in intermediate/high-risk patients. These patients exhibit a metastatic potential of over 30% [4] and a high mortality rate of between 20–50% at 5 years [12], and yet no adjuvant strategies are recommended by the European Guidelines [13]. We examined gene expression profiles in intermediate/high-risk ccRCC to identify prognostic biomarkers and develop a combined prognostic algorithm, including clinicopathological features and molecular biomarkers, to better predict the potential risk of recurrence after surgery
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