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Updated: Mar 13, 2026
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
Development and confirmation of potential gene classifiers of human clear cell renal cell carcinoma using
Oystein S Eikrem1,2, Philipp Strauss1,2, Christian Beisland1,3
1a Department of Clinical Medicine , University of Bergen , Bergen , Norway.
Objective:
A previous study by this group demonstrated the feasibility of RNA sequencing (RNAseq) technology for capturing disease biology of clear cell renal cell carcinoma (ccRCC), and presented initial results for carbonic anhydrase-9 (CA9) and tumor necrosis factor-α-induced protein-6 (TNFAIP6) as possible biomarkers of ccRCC (discovery set) [Eikrem et al. PLoS One 2016;11:e0149743]. To confirm these results, the previous study is expanded, and RNAseq data from additional matched ccRCC and normal renal biopsies are analyzed (confirmation set).
Materials And Methods:
Two core biopsies from patients (n = 12) undergoing partial or full nephrectomy were obtained with a 16 g needle. RNA sequencing libraries were generated with the Illumina TruSeq® Access library preparation protocol. Comparative analysis was done using linear modeling (voom/Limma; R Bioconductor).
Results:
The formalin-fixed and paraffin-embedded discovery and confirmation data yielded 8957 and 11,047 detected transcripts, respectively. The two data sets shared 1193 of differentially expressed genes with each other. The average expression and the log2-fold changes of differentially expressed transcripts in both data sets correlated, with R² = .95 and R² = .94, respectively. Among transcripts with the highest fold changes were CA9, neuronal pentraxin-2 and uromodulin. Epithelial-mesenchymal transition was highlighted by differential expression of, for example, transforming growth factor-β1 and delta-like ligand-4. The diagnostic accuracy of CA9 was 100% and 93.9% when using the discovery set as the training set and the confirmation data as the test set, and vice versa, respectively. These data further support TNFAIP6 as a novel biomarker of ccRCC. TNFAIP6 had combined accuracy of 98.5% in the two data sets.
Conclusions:
This study provides confirmatory data on the potential use of CA9 and TNFAIP6 as biomarkers of ccRCC. Thus, next-generation sequencing expands the clinical application of tissue analyses.
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