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      An autophagy-related diagnostic biomarker for uterine fibroids: FOS

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          Abstract

          Uterine fibroids (UFs) are the most common benign gynecologic tumors in reproductive-aged women. The typical diagnostic strategies of UFs are transvaginal ultrasonography and pathological feature, while molecular biomarkers are considered conventional options in the assessment of the origin and development of UFs in recent years. Here, we extracted the differential expression genes (DEGs) and differential DNA methylation genes (DMGs) of UFs from the Gene Expression Omnibus (GEO) database, GSE64763, GSE120854, GSE45188, and GSE45187. 167 DEGs with aberrant DNA methylation were identified, and further Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed by the relevant R package. We next discerned 2 hub genes (FOS, and TNFSF10) with autophagy involvement by overlapping 167 DEGs and 232 autophagic regulators from Human Autophagy Database. FOS was identified as the most crucial gene through the Protein–Protein Interactions (PPI) network with the correlation of the immune scores. Moreover, the down-regulated expression of FOS in UFs tissue at both mRNA and protein levels was validated by RT-qPCR and immunohistochemistry respectively. The area under the ROC curve (AUC) of FOS was 0.856, with a sensitivity of 86.2% and a specificity of 73.9%. Overall, we explored the possible biomarker of UFs undergoing DNA—methylated autophagy and provided clinicians with a comprehensive assessment of UFs.

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Biological Functions of Autophagy Genes: A Disease Perspective

              The lysosomal degradation pathway of autophagy plays a fundamental role in cellular, tissue, and organismal homeostasis and is mediated by evolutionarily conserved autophagy-related (ATG) genes. Definitive etiological links exist between mutations in genes that control autophagy and human disease, especially neurodegenerative, inflammatory disorders and cancer. Autophagy selectively targets dysfunctional organelles, intracellular microbes, and pathogenic proteins, and deficiencies in these processes may lead to disease. Moreover, ATG genes have diverse physiologically important roles in other membrane-trafficking and signaling pathways. This Review discusses the biological functions of autophagy genes from the perspective of understanding-and potentially reversing-the pathophysiology of human disease and aging.
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                Author and article information

                Contributors
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                17 April 2023
                2023
                : 10
                : 1153537
                Affiliations
                [1] 1Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [2] 2Department of Oncology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [3] 3Medical Record Department, Women and Children’s Hospital of Chongqing Medical University , Chongqing, China
                Author notes

                Edited by: Yujiao Deng, The Second Affiliated Hospital of Xi’an Jiaotong University, China

                Reviewed by: Hoda Elkafas, University of Illinois Chicago, United States; Junying Tang, First Affiliated Hospital of Chongqing Medical University, China; Qiwei Yang, Second Military Medical University, China

                *Correspondence: Hanwang Zhang, hwzhang605@ 123456126.com

                These authors have contributed equally to this work

                This article was submitted to Precision Medicine, a section of the journal Frontiers in Medicine

                Article
                10.3389/fmed.2023.1153537
                10150886
                14309a4e-5dad-4edb-b11c-23c078f83006
                Copyright © 2023 Cai, Li, Long, Liao, Gong, Zheng and Zhang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 February 2023
                : 29 March 2023
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 47, Pages: 10, Words: 5593
                Funding
                This work was supported by the Foundation of Tongji Hospital (No. 2020JZKT469).
                Categories
                Medicine
                Original Research

                uterine fibroids,autophagy,fos,bioinformatics analysis,biomarker

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