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      Identification of cuproptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

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          Abstract

          Breast cancer (BC) is now the most frequent and lethal cancer among women. Cuproptosis is a newly identified programmed cell death process that has been connected to tumor therapeutic sensitivity, patient outcomes, and the genesis of cancer. Cuproptosis-related genes (CRGs) are involved in breast cancer, although their roles and potential mechanisms are still unclear. First, we examined the effect of gene mutations and copy number changes on overall survival in 1168 breast cancer samples. Breast cancer patients were split into two molecular categories as determined by the variation in CRG based on clinicopathological traits, overall survival, and cell-infiltrating traits in tumor microenvironments. In addition, we created and validated a CRG score to calculate breast cancer patients' OS. Finally, we created a comprehensive nomogram for the clinical use of the CRG score. Patients whose CRG scores were low showed increased odds of developing OS, a larger mutation load, and immunological activation than those with high CRG scores. The CRG score, the cancer stem cell index, and the responsiveness to chemotherapy or targeted therapies were also shown to be statistically significantly correlated. Our thorough examination of CRGs in breast cancer patients demonstrated that they may be useful predictors of prognosis, clinical characteristics, and tumor microenvironment. These findings provide fresh insight into CRGs in breast cancer and might inspire brand-new approaches to both diagnosing and treating patients there.

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          Most cited references48

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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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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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                19 August 2022
                2022
                : 13
                : 996836
                Affiliations
                [1] 1 Department of General Surgery, Taihe Hospital, Hubei University of Medicine , Shiyan, China
                [2] 2 Hubei Key Laboratory of Embryonic Stem Cell Research, Taihe Hospital, Hubei University of Medicine , Shiyan, China
                [3] 3 Department of General Surgery, Affiliated Haikou Hospital of Xiangya Medical College, Central South Univesity , Haikou, China
                Author notes

                Edited by: Fu Wang, Xi'an Jiaotong University, China

                Reviewed by: Yajun Liu, Xiangya Hospital, Central South University, China; Wei Liu, Guangzhou Red Cross Hospital, China; Dong Tang, Northern Jiangsu People's Hospital (NJPH), China

                *Correspondence: Jianquan Zhang, zjq197015@ 123456163.com ; Zhi Li, lizhi2019@ 123456taihehospital.com

                †These authors have contributed equally to this work

                This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2022.996836
                9437337
                36059516
                60c16cce-ce5a-4237-b245-36ff72e6bc0d
                Copyright © 2022 Li, Zhang, Wang, Wang, Xue, Shi, Wang, Wang 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
                : 18 July 2022
                : 02 August 2022
                Page count
                Figures: 9, Tables: 0, Equations: 0, References: 48, Pages: 14, Words: 5165
                Categories
                Immunology
                Original Research

                Immunology
                breast cancer,cuprotosis,tumor microenvironments,prognosis,crg 
                Immunology
                breast cancer, cuprotosis, tumor microenvironments, prognosis, crg 

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