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      Different Molecular Phenotypes of Progression in BRAF- and RAS-Like Papillary Thyroid Carcinoma

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          Abstract

          Background

          Papillary thyroid carcinoma (PTC) can be classified into two distinct molecular subtypes, BRAF-like (BL) and RAS-like (RL). However, the molecular characteristics of each subtype according to clinicopathological factors have not yet been determined. We aimed to investigate the gene signatures and tumor microenvironment according to clinicopathological factors, and to identify the mechanism of progression in BL-PTCs and RL-PTCs.

          Methods

          We analyzed RNA sequencing data and corresponding clinicopathological information of 503 patients with PTC from The Cancer Genome Atlas database. We performed differentially expressed gene (DEG), Gene Ontology, and molecular pathway enrichment analyses according to clinicopathological factors in each molecular subtype. EcoTyper and CIBERSORTx were used to deconvolve the tumor cell types and their surrounding microenvironment.

          Results

          Even for the same clinicopathological factors, overlapping DEGs between the two molecular subtypes were uncommon, indicating that BL-PTCs and RL-PTCs have different progression mechanisms. Genes related to the extracellular matrix were commonly upregulated in BL-PTCs with aggressive clinicopathological factors, such as old age (≥55 years), presence of extrathyroidal extension, lymph node metastasis, advanced tumor-node-metastasis (TNM) stage, and high metastasis-age-completeness of resection-invasion-size (MACIS) scores (≥6). Furthermore, in the deconvolution analysis of tumor microenvironment, cancer-associated fibroblasts were significantly enriched. In contrast, in RL-PTCs, downregulation of immune response and immunoglobulin-related genes was significantly associated with aggressive characteristics, even after adjusting for thyroiditis status.

          Conclusion

          The molecular phenotypes of cancer progression differed between BL-PTC and RL-PTC. In particular, extracellular matrix and cancer-associated fibroblasts, which constitute the tumor microenvironment, would play an important role in the progression of BL-PTC that accounts for the majority of advanced PTCs.

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          Most cited references34

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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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              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.

                Author and article information

                Journal
                Endocrinol Metab (Seoul)
                Endocrinol Metab (Seoul)
                ENM
                Endocrinology and Metabolism
                Korean Endocrine Society
                2093-596X
                2093-5978
                August 2023
                18 July 2023
                : 38
                : 4
                : 445-454
                Affiliations
                [1 ]Department of Medicine, CHA University School of Medicine, Seongnam, Korea
                [2 ]Department of Biomedical Science, Graduate School, CHA University, Seongnam, Korea
                [3 ]Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
                [4 ]Department of Internal Medicine, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Korea
                Author notes
                Corresponding author: Young Shin Song. Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Korea Tel: +82-2-870-2210, Fax: +82-2-831-2826, E-mail: yssongmd@ 123456gmail.com
                Author information
                http://orcid.org/0009-0002-7561-9169
                http://orcid.org/0000-0003-4603-1999
                Article
                enm-2023-1702
                10.3803/EnM.2023.1702
                10475970
                37461149
                d943143e-af18-4ca8-8254-069180ad3dea
                Copyright © 2023 Korean Endocrine Society

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 March 2023
                : 16 May 2023
                : 7 June 2023
                Categories
                Original Article
                Thyroid

                thyroid cancer, papillary,transcriptome,gene expression profiling,tumor microenvironment

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