Identification and validation of an eight‐gene expression signature for predicting high Fuhrman grade renal cell carcinoma

Abstract

Clear cell renal cell carcinoma (ccRCC) is a malignancy with heterogeneous outcomes. Currently, renal mass biopsies are commonly employed to extract disease characteristics and aid prognosis. Although the pathological diagnosis of malignant disease is accurate in contemporary reports, the classification of Fuhrman grade using biopsy specimens remains far from promising. To generate a gene signature to distinguish high-grade ccRCC, we used the cancer genome atlas (TCGA) database to develop a gene expression signature for distinguishing high-grade (G3/4) from low-grade (G1/2) disease. The expression profile was further validated for performance and clinical use in 283 frozen renal cancer samples and 127 ex vivo renal mass biopsy samples, respectively. The area under curve (AUC) was used to quantify discriminative ability and was compared using the De-long test. Using the discovery dataset, we identified a 24-gene signature for high-grade disease with an AUC of 0.884. After applied to the development dataset, an eight-gene profile was defined and achieved an AUC of 0.823. Accuracy of eight-gene panel was maintained in the renal mass biopsies (RMB) samples (AUC = 0.821). In summary, using three-stage design, we validated an eight-gene expression signature for predicting high Fuhrman grade of ccRCC. This tool may help to reveal the characteristics of ccRCC biopsy specimens.

Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Co-expression Network Analysis

“…Wan et. al identified 8 genes that could be used to distinguish different grades of ccRCC by comparing different histological grades of ccRCC (grade I/II vs. grade III/IV) (Wan et al, 2017 ). However, this analysis method did not utilize a global level system biological analysis method, which might cause large false-positive results.…”

An Integrated Score and Nomogram Combining Clinical and Immunohistochemistry Factors to Predict High ISUP Grade Clear Cell Renal Cell Carcinoma

“…Our study represents the first attempt to construct an integrated model from combined clinical and IHC factors to predict high ISUP grade risk and one of its main strengths lies in the use of specimens routinely obtained during the assessment of ccRCC patients. An additional strength lies in the fact that the seven parameters used to construct our model are relatively easy to obtain in the clinical setting, when compared with the parameters required for more traditional multi-gene prediction models (47).…”

bHLH Transcription Factor Math6 Antagonizes TGF-β Signalling in Reprogramming, Pluripotency and Early Cell Fate Decisions

“…Contradictory literature exists with regard to Atoh8, as it was first described as a possible oncogene based on its copy number in a study performed on glioblastoma stem cells [12]. Subsequently, Atoh8 was found to be differentially regulated in renal carcinoma cells [13] and glioblastoma stem cells treated with retinoic acid [14]. A study performed on hepatocellular carcinomas (HCC), however, emphasized Atoh8 as a potential tumour suppressor gene, the absence of which imparts stem cell properties to cancer cells.…”