Differential expression analysis of clear cell renal cell carcinomas in The Cancer Genome Atlas distinguishes an aggressive subset enriched with chromosomes 7 and 12 gains. Issue 7 (4th May 2020)

• Record Type: Journal Article
• Authors: Pan, Chin‐Chen; Yeh, Yi‐Chen; Wang, Yu‐Chao; Chang, Yen‐Hwa
• Is Part Of: Histopathology. Volume 76:Issue 7(2020)
• Page Start: 950
• Page End: 958
• Publication Date: 2020-05-04
• Subjects: clear cell renal cell carcinoma -- The Cancer Genome Atlas -- fluorescence in‐situ hybridisation -- genetic variation; Histology, Pathological -- Periodicals; 611.018
• DOI: 10.1111/his.14104
• Languages: English
• ISSNs: 0309-0167

Abstract

Abstract : Aims: The Cancer Genome Atlas (TCGA) provides an integrated resource for investigating the genetic, phenotypical and clinical characteristics of cancer. In this study, we aimed to define distinct subsets of clear cell renal cell carcinoma (ccRCC) through differential expression and principal component analyses. Methods and results: We used DESeq2 to examine the expression profiles of 472 cases in TCGA. After a process of segregation and regrouping, we compared the mutation and copy number variation landscapes to discern two major clusters: cluster 1, composed mainly of classic ccRCC, and cluster 2, which was associated with gains at chromosomes 7 and 12. Gene set enrichment analysis disclosed that cluster 2 tumours were enriched in genes involving epithelial–mesenchymal transition. Histologically, cluster 2 tumours frequently exhibited cell elongation or spindling. Patients with cluster 2 tumours or tumours harbouring chromosomes 7 or 12 gains had a significantly greater cumulative incidence of mortality. We then employed fluorescence in‐situ hybridisation with probes against chromosomes 7 and 12 in a cohort of 119 cases of ccRCC from our institute for validation. Chromosomes 7 and 12 gains were associated with lower survival rates in both univariate and multivariate analyses. Conclusions: Our study demonstrates that genetic data obtained through appropriate molecular methodologies can be a useful adjunct to help predict prognosis. It also provides an example of exploring TCGA to extract meaningful information that can eventually contribute to precision medicine.