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      Integrated analyses of Mendelian randomization, eQTL, and single-cell transcriptome identify CCN3 as a potential biomarker in aortic dissection

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          Abstract

          Plasma secretory proteins are associated with various diseases, including aortic dissection (AD). However, current research on the correlation between AD and plasma protein levels is scarce or lacks specificity. This study aimed to explore plasma secretory proteins as potential biomarkers for AD. Through genome-wide association studies, expression quantitative trait locus (eQTL) analysis, and human plasma protein profiling, we identified DBNL, NPC2, SUMF2, and TFPI as high-risk genes and CCN3, PRKCSH, TEX264, and TGFBR3 as low-risk genes for AD. Further cell localization and differential expression analysis of these eight genes were conducted using single-cell data. We also examined their expression in three Gene Expression Omnibus datasets, measured their mRNA levels in AD versus normal tissues using qPCR, and assessed their protein levels in patients’ blood versus healthy individuals using enzyme-linked immunosorbent assay. Our findings suggest that CCN3, consistently downregulated in both mRNA and plasma levels during AD, may have a protective role. Initial enrichment analyses of differentially expressed CCN3 cells suggested their involvement in focal adhesion, actin cytoskeleton regulation, and the PI3K-Akt signaling pathway.

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          Most cited references60

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                wuqingchencqmu@126.com
                zhangchengcqmu@126.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 December 2024
                30 December 2024
                2024
                : 14
                : 32062
                Affiliations
                [1 ]Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, ( https://ror.org/033vnzz93) Chongqing, China
                [2 ]Department of Cell Biology and Genetics, Center for Molecular Medicine and Oncology Research, Chongqing Medical University, ( https://ror.org/017z00e58) Chongqing, China
                [3 ]Department of Biochemistry, Chongqing Medical and Pharmaceutical College, ( https://ror.org/05gvw2741) Chongqing, China
                [4 ]Department of Cardiothoracic Surgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, ( https://ror.org/023rhb549) Chongqing, China
                Article
                83611
                10.1038/s41598-024-83611-0
                11685893
                39738466
                a4b79210-0690-47df-afe1-6254a1ebf33c
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 22 May 2024
                : 16 December 2024
                Funding
                Funded by: Innovation Fund for Graduate Students of Chongqing Medical Universities
                Award ID: CYYY-BSYJSCXXXM-202337
                Award Recipient :
                Funded by: Fund of the First Affiliated Hospital of Chongqing Medical University
                Award ID: NO. PYJJ2022-07
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82270506
                Award Recipient :
                Funded by: Natural Science Foundation of Chongqing, China
                Award ID: CSTB2022NSCQ-MSX0817
                Award Recipient :
                Funded by: Project of innovation team for Graduate Teaching
                Award ID: CYYY-YJSJXCX-202318
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                aortic dissection,plasma secretory proteins,ccn3,gwas,eqtl,diagnostic markers,aortic diseases

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