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      Transcriptome in paraffin samples for the diagnosis and prognosis of adrenocortical carcinoma

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          Abstract

          Design

          Molecular classification is important for the diagnosis and prognosis of adrenocortical tumors (ACT). Transcriptome profiles separate adrenocortical adenomas ‘C2’ from carcinomas, and identify two groups of carcinomas ‘C1A’ and ‘C1B’, of poor and better prognosis respectively. However, many ACT cannot be profiled because of improper or absent freezing procedures, a mandatory requirement so far. The main aim was to determine transcriptome profiles on formalin-fixed paraffin-embedded (FFPE) samples, using the new 3’-end RNA-sequencing technology. A secondary aim was to demonstrate the ability of this technique to explore large FFPE archives, by focusing on the rare oncocytic ACT variants.

          Methods

          We included 131 ACT: a training cohort from Cochin hospital and an independent validation cohort from Wuerzburg hospital. The 3’ transcriptome was generated from FFPE samples using QuantSeq (Lexogen, Vienna, Austria) and NextSeq500 (Illumina, San Diego, CA, USA).

          Results

          In the training cohort, unsupervised clustering identified three groups: ‘C1A’ aggressive carcinomas ( n = 28, 29%), ‘C1B’ more indolent carcinomas ( n = 28, 29%), and ‘C2’ adenomas ( n = 39, 41%). The prognostic value of FFPE transcriptome was confirmed in the validation cohort (5-year OS: 26% in ‘C1A’ ( n = 26) and 100% in ‘C1B’ ( n = 10), P  = 0.003). FFPE transcriptome was an independent prognostic factor in a multivariable model including tumor stage and Ki-67 (OS HR: 7.5, P  = 0.01). Oncocytic ACT ( n = 19) did not form any specific cluster. Oncocytic carcinomas ( n = 6) and oncocytic ACT of uncertain malignant potential ( n = 4) were all in ‘C1B’.

          Conclusions

          The 3’ RNA-sequencing represents a convenient solution for determining ACT molecular class from FFPE samples. This technique should facilitate routine use and large retrospective studies.

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          Most cited references39

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

                Author and article information

                Journal
                Eur J Endocrinol
                Eur J Endocrinol
                EJE
                European Journal of Endocrinology
                Bioscientifica Ltd (Bristol )
                0804-4643
                1479-683X
                10 March 2022
                01 June 2022
                : 186
                : 6
                : 607-617
                Affiliations
                [1 ]Université de Paris , Institut Cochin, INSERM U-1016, CNRS UMR-8104, Paris, France
                [2 ]Endocrinology , AP-HP Hôpital Cochin, Paris, France
                [3 ]Institut Curie , INSERM U900, MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Paris, France
                [4 ]Division of Endocrinology and Diabetes , Department of Internal Medicine I, University Hospital, University of Wuerzburg, Wuerzburg, Germany
                [5 ]Pathology , AP-HP Hôpital Cochin, Paris, France
                [6 ]Genetics and Molecular Biology , AP-HP Hôpital Cochin, Paris, France
                [7 ]Digestive and Endocrine Surgery , AP-HP Hôpital Cochin, Paris, France
                [8 ]Institute of Metabolism and System Research , University of Birmingham, Birmingham, UK
                [9 ]Centre for Endocrinology , Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK
                Author notes
                Correspondence should be addressed to C L Ronchi or G Assié; Email: C.L.Ronchi@ 123456bham.ac.uk or guillaume.assie@ 123456aphp.fr
                Author information
                http://orcid.org/0000-0002-7922-2065
                http://orcid.org/0000-0002-1881-8762
                http://orcid.org/0000-0001-6170-6398
                http://orcid.org/0000-0001-5020-2071
                Article
                EJE-21-1228
                10.1530/EJE-21-1228
                9066577
                35266879
                be7cf157-109b-48ad-a136-ee1cd3500e10
                © The authors

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 09 December 2021
                : 10 March 2022
                Categories
                Clinical Study

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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