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      Gene Expression Profiling of Pancreas Neuroendocrine Tumors with Different Ki67-Based Grades

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          Abstract

          Simple Summary

          Ki67-based grading is a major prognostic parameter for pancreatic neuroendocrine tumors. Gene expression profiles of these tumors have been explored, yet their relationship with Ki67-based tumor grade has only been superficially investigated. To fill this gap, we analyzed differentially expressed genes across 29 cases of different grades. Our data provided the first proof that the switch from lower to higher grades is associated with a profound change in the transcriptome. The comparison of multiple samples from the same patients, including primaries and metastasis, showed that the major determinant of difference was tumor grade, irrespective of the anatomic location or patient of origin. These data call for further investigation of this association and of the role of Ki67 in affecting chromosomal stability in neuroendocrine tumors of different grades, which may clarify the basis of tumor progression and provide clues on how to interfere with it.

          Abstract

          Pancreatic neuroendocrine tumors (PanNETs) display variable aggressive behavior. A major predictor of survival is tumor grade based on the Ki67 proliferation index. As information on transcriptomic profiles of PanNETs with different tumor grades is limited, we investigated 29 PanNETs (17 G1, 7 G2, 5 G3) for their expression profiles, mutations in 16 PanNET relevant genes and LINE-1 DNA methylation profiles. A total of 3050 genes were differentially expressed between tumors with different grades ( p < 0.05): 1279 in G3 vs. G2; 2757 in G3 vs. G1; and 203 in G2 vs. G1. Mutational analysis showed 57 alterations in 11 genes, the most frequent being MEN1 (18/29), DAXX (7/29), ATRX (6/29) and MUTYH (5/29). The presence and type of mutations did not correlate with the specific expression profiles associated with different grades. LINE-1 showed significantly lower methylation in G2/G3 versus G1 tumors ( p = 0.007). The expression profiles of matched primaries and metastasis (nodal, hepatic and colorectal wall) of three cases confirmed the role of Ki67 in defining specific expression profiles, which clustered according to tumor grades, independently from anatomic location or patient of origin. Such data call for future exploration of the role of Ki67 in tumor progression, given its involvement in chromosomal stability.

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                23 April 2021
                May 2021
                : 13
                : 9
                : 2054
                Affiliations
                [1 ]Section of Pathology, Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy; michele.simbolo@ 123456univr.it (M.S.); andrea.mafficini@ 123456univr.it (A.M.); claudio.luchini@ 123456univr.it (C.L.)
                [2 ]ENETS Center of Excellence of Verona, 37134 Verona, Italy
                [3 ]Section of Anatomic Pathology, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00100 Roma, Italy; mirna.bilotta@ 123456gmail.com (M.B.); guido.rindi@ 123456unicatt.it (G.R.)
                [4 ]Anatomic Pathology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00100 Roma, Italy; frediano.inzani@ 123456policlinicogemelli.it (F.I.); gianluigi.petrone@ 123456policlinicogemelli.it (G.P.); antonio.bianchi@ 123456policlinicogemelli.it (A.B.); roberta.menghi@ 123456policlinicogemelli.it (R.M.); felice.giuliante@ 123456unicatt.it (F.G.)
                [5 ]ENETS Center of Excellence of Roma, 00100 Roma, Italy; giovanni.schinzari@ 123456policlinicogemelli.it
                [6 ]ARC-NET Applied Research on Cancer Centre, University of Verona, 37134 Verona, Italy
                [7 ]Pathology Unit, Department of Medicine and Surgery, University of Insubria, 21100 Varese, Italy; daniela.furlan@ 123456uninsubria.it (D.F.); stefano.larosa@ 123456chuv.ch (S.L.R.)
                [8 ]Section of Human Anatomy, Department of Neurosciences, Università Cattolica del Sacro Cuore, 00100 Roma, Italy; davide.bonvissuto@ 123456unicatt.it
                [9 ]Institute of Pathology, Lausanne University Hospital and University of Lausanne, 1001 Lausanne, Switzerland
                [10 ]Department of Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00100 Roma, Italy; ernesto.rossi@ 123456policlinicogemelli.it
                [11 ]Pituitary Unit, Department of Endocrinology and Diabetes, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00100 Roma, Italy
                [12 ]Digestive Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00100 Roma, Italy
                [13 ]Hepatobiliary Surgery Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00100 Roma, Italy
                [14 ]Section of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00100 Roma, Italy; stefania.boccia@ 123456unicatt.it
                Author notes
                [* ]Correspondence: aldo.scarpa@ 123456univr.it
                [†]

                These authors share first authorship.

                Author information
                https://orcid.org/0000-0002-0866-4499
                https://orcid.org/0000-0002-7471-3121
                https://orcid.org/0000-0003-4901-4908
                https://orcid.org/0000-0002-9061-6989
                https://orcid.org/0000-0002-1864-749X
                https://orcid.org/0000-0003-1678-739X
                https://orcid.org/0000-0003-2996-4404
                Article
                cancers-13-02054
                10.3390/cancers13092054
                8122987
                33922803
                16a4923e-2515-46ba-a4d9-c566525033c5
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 16 March 2021
                : 20 April 2021
                Categories
                Article

                pancreas,neuroendocrine tumor,net,ki67,grade,line-1,gene expression profiling

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