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      Comparative analysis of protein-protein interaction networks in metastatic breast cancer

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          Abstract

          Metastatic lesions leading causes of the majority of deaths in patients with the breast cancer. The present study aimed to provide a comprehensive analysis of the differentially expressed genes (DEGs) in the brain (MDA-MB-231 BrM2) and lung (MDA-MB-231 LM2) metastatic cell lines obtained from breast cancer patients compared with those who have primary breast cancer. We identified 981 and 662 DEGs for brain and lung metastasis, respectively. Protein-protein interaction (PPI) analysis revealed seven shared ( PLCB1, FPR1, FPR2, CX3CL1, GABBR2, GPR37, and CXCR4) hub genes between brain and lung metastasis in breast cancer. Moreover, GNG2 and CXCL8, C3, and PTPN6 in the brain and SAA1 and CCR5 in lung metastasis were found as unique hub genes. Besides, five co-regulation of clusters via seven important co-expression genes (COL1A2, LUM, SPARC, THBS2, IL1B, CXCL8, THY1) were identified in the brain PPI network. Clusters screening followed by biological process (BP) function and pathway enrichment analysis for both metastatic cell lines showed that complement receptor signalling, acetylcholine receptor signalling, and gastric acid secretion pathways were common between these metastases, whereas other pathways were site-specific. According to our findings, there are a set of genes and functional pathways that mark and mediate breast cancer metastasis to the brain and lungs, which may enable us understand the molecular basis of breast cancer development in a deeper levele to the brain and lungs, which may help us gain a more complete understanding of the molecular underpinnings of breast cancer development.

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          Most cited references73

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

            This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: SoftwareRole: Writing – original draft
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ValidationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Visualization
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                19 January 2022
                2022
                : 17
                : 1
                : e0260584
                Affiliations
                [1 ] Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
                [2 ] Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
                [3 ] Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
                [4 ] Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
                [5 ] Department of Cell and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
                [6 ] Department of Chemical, Biomolecular and Corrosion Engineering, The University of Akron, Akron, Ohio, United States of America
                [7 ] Department of Cellular Biotechnology, Cell Science Research Center, Royan Institute for Biotechnology, ACECR, Isfahan, Iran
                [8 ] Department of Toxicology and Pharmacology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
                [9 ] Faculty of Veterinary Medicine, Urmia University, Urmia, Iran
                [10 ] Department of Biology, Faculty of Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran
                [11 ] Department of Cellular Molecular Biology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
                [12 ] Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran
                [13 ] Department of Animal Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
                [14 ] Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
                Northumbria University, UNITED KINGDOM
                Author notes

                Competing Interests: This commercial funder provided support in the form of salaries for authors [SMR, FS, AS] but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                https://orcid.org/0000-0002-3803-9029
                https://orcid.org/0000-0002-9453-6951
                https://orcid.org/0000-0002-5983-3627
                Article
                PONE-D-20-38245
                10.1371/journal.pone.0260584
                8769308
                35045088
                4e35a808-bcd1-4854-a2c8-9b09d70bd04b
                © 2022 Hozhabri et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 December 2020
                : 12 November 2021
                Page count
                Figures: 8, Tables: 1, Pages: 23
                Funding
                Funded by: Systems Biology of Next Generation Company (SBNGC)
                Award ID: SBNGC13990313
                Award Recipient :
                Funded by: Systems Biology of Next Generation Company (SBNGC)
                Award ID: SBNGC13990313
                Award Recipient :
                Ali Salari grant no. SBNGC13990313 Systems Biology of Next Generation Company (SBNGC) Yes- Ali Salari and Seyed.M.R supervised the project. The funder provided support in the form of salaries for authors [SMR, FS, AS], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Neurological Tumors
                Brain Metastasis
                Medicine and Health Sciences
                Neurology
                Neurological Tumors
                Brain Metastasis
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Breast Tumors
                Breast Cancer
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Lung and Intrathoracic Tumors
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Metastatic Tumors
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Lung and Intrathoracic Tumors
                Secondary Lung Tumors
                Biology and Life Sciences
                Genetics
                Gene Expression
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Colorectal Cancer
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Gastrointestinal Tumors
                Gastric Cancer
                Custom metadata
                All relevant data are within the paper and its Supporting information files.

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