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      Bioinformatics analyses of combined databases identify shared differentially expressed genes in cancer and autoimmune disease

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          Abstract

          Background

          Inadequate immunity caused by poor immune surveillance leads to tumorigenesis, while excessive immunity due to breakdown of immune tolerance causes autoimmune genesis. Although the function of immunity during the onset of these two processes appears to be distinct, the underlying mechanism is shared. To date, gene expression data for large bodies of clinical samples are available, but the resemblances of tumorigenesis and autoimmune genesis in terms of immune responses remains to be summed up.

          Methods

          Considering the high disease prevalence, we chose invasive ductal carcinoma (IDC) and systemic lupus erythematosus (SLE) to study the potential commonalities of immune responses. We obtained gene expression data of IDC/SLE patients and normal controls from five IDC databases (GSE29044, GSE21422, GSE22840, GSE15852, and GSE9309) and five SLE databases (GSE154851, GSE99967, GSE61635, GSE50635, and GSE17755). We intended to identify genes differentially expressed in both IDC and SLE by using three bioinformatics tools including GEO2R, the limma R package, and Weighted Gene Co-expression Network Analysis (WGCNA) to perform function enrichment, protein-protein network, and signaling pathway analyses.

          Results

          The mRNA levels of signal transducer and activator of transcription 1 (STAT1), 2'-5'-oligoadenylate synthetase 1 (OAS1), 2'-5'-oligoadenylate synthetase like (OASL), and PML nuclear body scaffold (PML) were found to be differentially expressed in both IDC and SLE by using three different bioinformatics tools of GEO2R, the limma R package and WGCNA. From the combined databases in this study, the mRNA levels of STAT1 and OAS1 were increased in IDC while reduced in SLE. And the mRNA levels of OASL and PML were elevated in both IDC and SLE. Based on Kyoto Encyclopedia of Genes and Genomes pathway analysis and QIAGEN Ingenuity Pathway Analysis, both IDC and SLE were correlated with the changes of multiple components involved in the Interferon (IFN)-Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway.

          Conclusion

          The expression levels of STAT1 and OAS1 manifest the opposite expression tendency across cancer and autoimmune disease. They are components in the IFN-JAK-STAT signaling pathway related to both tumorigenesis and autoimmune genesis. STAT1 and OAS1-associated IFN-JAK-STAT signaling could explain the commonalities during tumorigenesis and autoimmune genesis and render significant information for more precise treatment from the point of immune homeostasis.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12967-023-03943-9.

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          Most cited references89

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          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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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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              Is Open Access

              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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                Author and article information

                Contributors
                joe-zhao@ouhsc.edu
                xingshu@jlu.edu.cn
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                10 February 2023
                10 February 2023
                2023
                : 21
                : 109
                Affiliations
                [1 ]GRID grid.64924.3d, ISNI 0000 0004 1760 5735, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, , Jilin University, ; Changchun, 130012 China
                [2 ]GRID grid.266902.9, ISNI 0000 0001 2179 3618, Department of Physiology, , University of Oklahoma Health Sciences Center, ; Oklahoma City, OK 73104 USA
                [3 ]GRID grid.266902.9, ISNI 0000 0001 2179 3618, Department of Pathology, , University of Oklahoma Health Sciences Center, ; Oklahoma City, OK 73104 USA
                Article
                3943
                10.1186/s12967-023-03943-9
                9921081
                36765396
                8f2d39fa-7c2a-4966-9cb3-fb54d3522ec9
                © The Author(s) 2023

                Open AccessThis 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 licence, and indicate if changes were made. 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/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 1 December 2022
                : 30 January 2023
                Funding
                Funded by: National Key Research and Development Program of China
                Award ID: 2021YFA1500400
                Award Recipient :
                Funded by: Science & Technology Development Project in Jilin Province of China
                Award ID: 20210101004JC
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Medicine
                tumorigenesis,autoimmune genesis,invasive ductal carcinoma,systemic lupus erythematosus,stat1,oas1,oasl,pml,ifn-jak-stat signaling pathway

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