Characterization of long non-coding RNA transcriptome in clear-cell renal cell carcinoma by next-generation deep sequencing

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Abstract

Introduction:

Long non-coding RNA (lncRNA) have proven to play key roles in cell physiology from nuclear organization and epigenetic remodeling to post-transcriptional regulation. Last decade, gene expression based-classifications have been developed in clear-cell renal cell carcinoma (ccRCC) to identify distinct subtypes of disease and predict patient's outcome. However, there are no current lncRNA comprehensive characterizations in ccRCC.

Patients and methods

RNA-sequencing profiles of 475 primary ccRCC samples from the Cancer Genome Atlas (TCGA) were used to assess expressed lncRNA and identify lncRNA-based classification. In addition, integrative analysis was performed to correlate tumor subtypes with copy-number alterations and somatic mutations.

Results

Using stringent criteria, we identified 1934 expressed lncRNA and assessed their chromatin marks. Unsupervised clustering unravels four lncRNA subclasses in ccRCC associated with distinct clinicopathological and genomic features of this disease. Cluster C2 (23.4%) defines the most aggressive tumours, with the highest Fuhrman grade and stage and the worst overall survival time. Furthermore, cluster C2 is enriched for 9p deletion and chromatin remodeler BAP1 somatic mutations. Interestingly, cluster C4 (7.8%) is related to a tumor subtype arising from the distal tubules of the nephron. Consistent with its distinct ontogeny, cluster C4 is devoid of classical alterations seen in ccRCC, bears frequent 1p deletion and 17q gain, and is enriched for MiTF/TFE translocations. In addition, reexaminations of copy-number data from one side and tumor histology by pathologists from the other side reveal misclassified tumors within C4 cluster including chromophobe RCC and clear cell papillary RCC.

Conclusion

This study establishes a foundation for categorizing lncRNA subclasses, which may contribute to understand tumor ontogeny and help predicting patients' outcome in ccRCC.

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