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      Pancreatic RECK inactivation promotes cancer formation, epithelial-mesenchymal transition, and metastasis

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          Abstract

          RECK is downregulated in various human cancers; however, how RECK inactivation affects carcinogenesis remains unclear. We addressed this issue in a pancreatic ductal adenocarcinoma (PDAC) mouse model and found that pancreatic Reck deletion dramatically augmented the spontaneous development of PDAC with a mesenchymal phenotype, which was accompanied by increased liver metastases and decreased survival. Lineage tracing revealed that pancreatic Reck deletion induced epithelial-mesenchymal transition (EMT) in PDAC cells, giving rise to inflammatory cancer-associated fibroblast–like cells in mice. Splenic transplantation of Reck-null PDAC cells resulted in numerous liver metastases with a mesenchymal phenotype, whereas reexpression of RECK markedly reduced metastases and changed the PDAC tumor phenotype into an epithelial one. Consistently, low RECK expression correlated with low E-cadherin expression, poor differentiation, metastasis, and poor prognosis in human PDAC. RECK reexpression in the PDAC cells was found to downregulate MMP2 and MMP3, with a concomitant increase in E-cadherin and decrease in EMT-promoting transcription factors. An MMP inhibitor recapitulated the effects of RECK on the expression of E-cadherin and EMT-promoting transcription factors and invasive activity. These results establish the authenticity of RECK as a pancreatic tumor suppressor, provide insights into its underlying mechanisms, and support the idea that RECK could be an important therapeutic effector against human PDAC.

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          Most cited references47

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Journal
                J Clin Invest
                J Clin Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                15 September 2023
                15 September 2023
                15 September 2023
                : 133
                : 18
                : e161847
                Affiliations
                [1 ]Department of Gastroenterology and Hepatology,
                [2 ]Department of Drug Discovery Medicine, Medical Innovation Center,
                [3 ]Division of Hepato-Biliary-Pancreatic Surgery and Transplantation, Department of Surgery, and
                [4 ]Department of Molecular Oncology, Kyoto University Graduate School of Medicine, Kyoto, Japan.
                Author notes
                Address correspondence to: Akihisa Fukuda, Department of Gastroenterology and Hepatology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan. Phone: 81.75.751.4319; Email: fukuda26@ 123456kuhp.kyoto-u.ac.jp .
                Author information
                http://orcid.org/0000-0002-1967-5644
                http://orcid.org/0000-0002-1940-596X
                http://orcid.org/0000-0003-3836-1648
                http://orcid.org/0000-0003-0789-6623
                http://orcid.org/0000-0002-3173-4360
                http://orcid.org/0000-0003-4970-4212
                http://orcid.org/0000-0001-5903-8677
                http://orcid.org/0000-0001-5311-363X
                http://orcid.org/0000-0002-7060-4104
                http://orcid.org/0000-0001-5493-6312
                http://orcid.org/0000-0002-4001-4824
                http://orcid.org/0000-0003-3407-1918
                http://orcid.org/0000-0002-7340-6066
                Article
                161847
                10.1172/JCI161847
                10503799
                37712427
                cc924c1f-4b0f-4aa3-a8ed-764593761dcb
                © 2023 Masuda et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 May 2022
                : 27 July 2023
                Funding
                Funded by: Grants-in-Aid from KAKENHI
                Award ID: 19H03639,19K16712,19K22619,20H03659,21K9480
                Funded by: AMED-PRIME
                Award ID: 21gm6010022h0004
                Funded by: Takeda Foundation
                Award ID: 2019056665
                Funded by: Princess Takamatsu Cancer Research Fund
                Award ID: 17-24924
                Funded by: Mitsubishi Foundation
                Award ID: 201910037,281119
                Funded by: Uehara Foundation
                Award ID: 201720143
                Funded by: Naito Foundation
                Award ID: 20829-1
                Funded by: Ichiro Kanehara Foundation
                Award ID: 20KI037
                Funded by: the Project for Cancer Research and Therapeutic Evolution
                Award ID: 21 cm0106177h0002
                Categories
                Research Article

                gastroenterology,oncology,cancer,mouse models,tumor suppressors

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