Oystein S Eikrem1,2, Philipp Strauss1,2, Christian Beisland1,3, Andreas Scherer4,5, Lea Landolt1, Arnar Flatberg6, Sabine Leh1,7, Vidar Beisvag6, Trude Skogstrand1, Karin Hjelle1,3, Anjana Shresta1, Hans-Peter Marti1,2. 1. a Department of Clinical Medicine , University of Bergen , Bergen , Norway. 2. b Department of Medicine , Haukeland University Hospital , Bergen , Norway. 3. c Department of Urology , Haukeland University Hospital , Bergen , Norway. 4. d Spheromics , Kontiolahti , Finland. 5. e Institute for Molecular Medicine Finland (FIMM), University of Helsinki , Helsinki , Finland. 6. f Department of Cancer Research and Molecular Medicine , Norwegian University of Science and Technology , Trondheim , Norway. 7. g Department of Pathology , Haukeland University Hospital , Bergen , Norway.
Abstract
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.
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.
Authors: Lea Landolt; Øystein Eikrem; Philipp Strauss; Andreas Scherer; David H Lovett; Christian Beisland; Kenneth Finne; Tarig Osman; Mohammad M Ibrahim; Gro Gausdal; Lavina Ahmed; James B Lorens; Jean Paul Thiery; Tuan Zea Tan; Miroslav Sekulic; Hans-Peter Marti Journal: Physiol Rep Date: 2017-06
Authors: Øystein Eikrem; Tedd C Walther; Arnar Flatberg; Vidar Beisvag; Philipp Strauss; Magnus Farstad; Christian Beisland; Even Koch; Thomas F Mueller; Hans-Peter Marti Journal: BMC Nephrol Date: 2018-09-05 Impact factor: 2.388
Authors: Richard C Zieren; Liang Dong; David J Clark; Morgan D Kuczler; Kengo Horie; Leandro Ferreira Moreno; Tung-Shing M Lih; Michael Schnaubelt; Louis Vermeulen; Hui Zhang; Theo M de Reijke; Kenneth J Pienta; Sarah R Amend Journal: Med Oncol Date: 2021-07-31 Impact factor: 3.064