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      Identification of potential key ferroptosis- and autophagy-related genes in myelomeningocele through bioinformatics analysis

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          Abstract

          Myelomeningocele is a common congenital anomaly associated with polygenic disorders worldwide. However, the intricate molecular mechanisms underlying myelomeningocele remain elusive. To investigate whether ferroptosis and ferritinophagy contribute to the pathomechanism of myelomeningocele, differentially expressed genes (DEGs) were identified as novel biomarker and potential treatment agents. The GSE101141 dataset from Gene Expression Omnibus (GEO) was analyzed using GEO2R web tool to obtain DEGs based on |log2 fold change (FC)|≥1.5 and p < 0.05. Two datasets from the Ferroptosis Database (481 genes) and Autophagy Database (551 genes) were intersected with the DEGs from the GSE101141 dataset to identify ferroptosis- and autophagy-related DEGs using Venn diagrams. Functional and pathway enrichment, protein-protein interaction (PPI) network analyses were performed, and candidate genes were selected. Transcription factors (TFs), microRNAs (miRNAs), diseases and chemicals interacting with the candidate genes were identified. Receiver operating characteristic (ROC) curve analysis was performed to validate the diagnostic value of the candidate genes. Sixty ferroptosis-related and 74 autophagy-related DEGs were identified. These DEGs are involved in FoxO signaling pathway. Six candidate genes ( EGFR, KRAS, IL1B, SIRT1, ATM, and MAPK8) were selected. miRNAs such as hsa-miR-27a-3p, hsa-miR-877-5p, and hsa-miR-892b, and TFs including P53, POU3F2, TATA are involved in regulation of candidate genes. Diseases such as schizophrenia, fibrosis, and neoplasms are the most relevant to the candidate genes. Chemicals, such as resveratrol, curcumin, and quercetin may have significant implications in the treatment of myelomeningocele. The candidate genes, especially MAPK8, also showed a high diagnostic value for myelomeningocele. These results help to shed light on the molecular mechanism of myelomeningocele and may provide new insights into diagnostic biomarker in the amniotic fluid and potential therapeutic agents of myelomeningocele.

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

              A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                16 April 2024
                30 April 2024
                16 April 2024
                : 10
                : 8
                : e29654
                Affiliations
                [a ]Beijing Municipal Key Laboratory of Child Development and Nutriomics, Translational Medicine Laboratory, Capital Institute of Pediatrics, Beijing, 100020, China
                [b ]Department of Biochemistry and Molecular Biology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
                [c ]Department of Physiology, College of Medicine, Jiaxing University, Jiaxing, 314001, Zhejiang, China
                [d ]Department of Hepatobiliary and Pancreatic Surgery and Liver Transplant Center, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
                Author notes
                [* ]Corresponding author. Department of Hepatobiliary and Pancreatic Surgery and Liver Transplant Center, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China zhangli3788@ 123456163.com
                [** ]Corresponding author. Department of Physiology, College of Medicine, Jiaxing University, Jiaxing, Zhejiang, 314001, China. wangmin3826@ 123456zjxu.edu.cn
                Article
                S2405-8440(24)05685-8 e29654
                10.1016/j.heliyon.2024.e29654
                11040124
                38660270
                8867e717-0d68-44b0-922c-fe6d626fa7b9
                © 2024 The Author(s)

                This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

                History
                : 8 October 2023
                : 10 April 2024
                : 11 April 2024
                Categories
                Research Article

                myelomenigocele,ferroptosis,autophagy,gene expression omnibus,enrichment analysis,bioinformatics

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