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      Significance of chitinase-3-like protein 1 in the pathogenesis of inflammatory diseases and cancer

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          Abstract

          Chitinase-3-like protein 1 (CHI3L1) is a secreted glycoprotein that mediates inflammation, macrophage polarization, apoptosis, and carcinogenesis. The expression of CHI3L1 is strongly upregulated by various inflammatory and immunological diseases, including several cancers, Alzheimer’s disease, and atherosclerosis. Several studies have shown that CHI3L1 can be considered as a marker of disease diagnosis, prognosis, disease activity, and severity. In addition, the proinflammatory action of CHI3L1 may be mediated via responses to various proinflammatory cytokines, including tumor necrosis factor-α, interleukin-1β, interleukin-6, and interferon-γ. Therefore, CHI3L1 may contribute to a vast array of inflammatory diseases. However, its pathophysiological and pharmacological roles in the development of inflammatory diseases remain unclear. In this article, we review recent findings regarding the roles of CHI3L1 in the development of inflammatory diseases and suggest therapeutic approaches that target CHI3L1.

          Protein CHI3L1: a potential game-changer in inflammatory disease treatment

          Chitinase-3-like protein 1 (CHI3L1) is a secreted glycoprotein with diverse roles in inflammation, macrophage polarization, apoptosis, and carcinogenesis. Pro-inflammatory effects of CHI3L1 are attributed to its response to various pro-inflammatory cytokines, including tumor necrosis factor-α, interleukin-1β, interleukin-6, and interferon-γ. Consequently, CHI3L1 is implicated in a wide range of inflammatory diseases. This review summarizes the significance of CHI3L1 as a potential target in multiple inflammatory diseases and cancer by analyzing data from platforms like Open Targets and other data-analysis tools. Furthermore, we have utilized platforms like STRING to identify potential target proteins for CHI3L1 in various inflammatory diseases and cancer. In this article, we provide a comprehensive review of recent findings regarding the involvement of CHI3L1 in the development of inflammatory diseases and cancer. Finally, we propose therapeutic approaches targeting CHI3L1 for treatment of these diseases. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

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          The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

          Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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            The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

            A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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              DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

              The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype–phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.
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                Author and article information

                Contributors
                sondj@chungbuk.ac.kr
                jinthong@chungbuk.ac.kr
                Journal
                Exp Mol Med
                Exp Mol Med
                Experimental & Molecular Medicine
                Nature Publishing Group UK (London )
                1226-3613
                2092-6413
                4 January 2024
                4 January 2024
                February 2024
                : 56
                : 1
                : 1-18
                Affiliations
                [1 ]College of Pharmacy and Medical Research Center, Chungbuk National University, ( https://ror.org/02wnxgj78) 194-31, Osongsaengmyeong 1-ro, Osong-eup, Cheongju-si, Chungbuk 28160 Republic of Korea
                [2 ]College of Pharmacy, Kyungpook National University, ( https://ror.org/040c17130) 80 Daehakro, Bukgu, Daegu, 41566 Republic of Korea
                [3 ]Senelix Co. Ltd., 25, Beobwon-ro 11-gil, Songpa-gu, Seoul, 05836 Republic of Korea
                [4 ]PRESTI GEBIOLOGICS Co. Ltd., Osongsaengmyeong 1-ro, Osong-eup, Cheongju-si, Chungbuk 28161 Republic of Korea
                [5 ]Autotelic Bio Inc., Osongsaengmyeong 1-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungbuk 28160 Republic of Korea
                Author information
                http://orcid.org/0000-0002-9382-6914
                http://orcid.org/0000-0001-6656-6523
                http://orcid.org/0000-0002-6534-9575
                Article
                1131
                10.1038/s12276-023-01131-9
                10834487
                38177294
                046c8c17-a31b-4f70-9469-20df9d2f3018
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 March 2023
                : 6 August 2023
                : 28 August 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003725, National Research Foundation of Korea (NRF);
                Award ID: MRC2017R1A5A2015541
                Award ID: 2021RIS-001
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award ID: MRC2017R1A5A2015541
                Award Recipient :
                Categories
                Review Article
                Custom metadata
                © Korean Society for Biochemical and Molecular Biology 2024

                Molecular medicine
                predictive markers,tumour biomarkers,atherosclerosis,alzheimer's disease
                Molecular medicine
                predictive markers, tumour biomarkers, atherosclerosis, alzheimer's disease

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