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      Role of androgen receptor signaling pathway-related lncRNAs in the prognosis and immune infiltration of breast cancer

      research-article
      1 , 2 , 3 , 3 , 4 , , 5 ,
      Scientific Reports
      Nature Publishing Group UK
      Oncology, Risk factors

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          Abstract

          Androgen receptor (AR) is strong association with breast cancer (BRCA). We aimed to investigate the effect of the androgen receptor signaling pathway-related long non-coding RNAs (ARSP-related lncRNAs) on the process of subtype classification and the tumor microenvironment (TME) of breast cancer (BRCA). Our study screen ARSP-related lncRNAs for the construction of a risk model. The single-sample gene set enrichment analysis (ssGSEA) method was used to detect the differences between the immune responses generated by the patients belonging to the low- and high-risk groups. The relationship between the ARSP-related lncRNAs and TME was explored following the process of cluster analysis. The univariate Cox analysis and the Lasso regression analysis method was used to screen nine of these lncRNAs to develop a risk model. It was observed that risk score could function as an independent prognostic factor, affecting the prognoses of patients suffering from BRCA. The validity of the model was assessed by analyzing the generated calibration curves and a nomogram. Additionally, the effect of the risk score on the extent of immune cell infiltration realized in TME was explored. M2 macrophages correlated positively, whereas NK cells, CD4+ T cells, and naive B cells correlated negatively with the risk score. Results obtained using the cluster analysis indicated that immune scores correlated with clustered subtypes. Finally, the risk score and cluster subtypes were analyzed to study the sensitivity of the patients toward different drugs to identify the appropriate therapeutic agents. The prognoses of patients suffering from BRCA can be accurately predicted by ARSP-related lncRNAs.

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          Most cited references39

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Robust enumeration of cell subsets from tissue expression profiles

            We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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              KEGG: integrating viruses and cellular organisms

              Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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                Author and article information

                Contributors
                liugw8318@163.com
                chenjuan@usc.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 November 2022
                30 November 2022
                2022
                : 12
                : 20631
                Affiliations
                [1 ]GRID grid.412017.1, ISNI 0000 0001 0266 8918, Hengyang Medical School, , University of South China, ; Hengyang, 421001 Hunan People’s Republic of China
                [2 ]GRID grid.412017.1, ISNI 0000 0001 0266 8918, Key Laboratory of Tumor Cellular and Molecular Pathology, College of Hunan Province, Cancer Research Institute, , University of South China, ; Hengyang, 421001 Hunan People’s Republic of China
                [3 ]GRID grid.413432.3, ISNI 0000 0004 1798 5993, Department of Breast and Thyroid Surgery, Hengyang Medical School, , The Second Affiliated Hospital, University of South China, ; Hengyang, 421001 Hunan China
                [4 ]GRID grid.452847.8, ISNI 0000 0004 6068 028X, Department of Thyroid and Breast Surgery, , The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, ; Shenzhen, 518035 Guangdong China
                [5 ]GRID grid.412017.1, ISNI 0000 0001 0266 8918, Department of Radiotherapy, , The Second Affiliated Hospital, Hengyang Medical School, University of South China, ; Hengyang, 421001 Hunan China
                Article
                25231
                10.1038/s41598-022-25231-0
                9712677
                36450882
                178750e5-1ffb-49c3-acaf-81c24a1d7d22
                © The Author(s) 2022

                Open Access This 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/.

                History
                : 28 July 2022
                : 28 November 2022
                Funding
                Funded by: The Scientific Research Program of Hunan Provincial Health Commission
                Award ID: 20201969
                Award Recipient :
                Funded by: The Second People's Hospital of Shenzhen High-level Capital Project
                Award ID: 4004009
                Award Recipient :
                Funded by: The Clinical Research Center for Breast & Thyroid Disease Prevention in Hunan Province
                Award ID: 2018SK4001
                Award Recipient :
                Funded by: The Guiding Project of Clinical Medical Technology Innovation in Hunan Province
                Award ID: 2021SK51706
                Award Recipient :
                Categories
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                © The Author(s) 2022

                Uncategorized
                oncology,risk factors
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                oncology, risk factors

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