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      A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

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          Abstract

          Over the past decades, the incidence and mortality rates of breast cancer (BC) have increased rapidly; however, molecular biomarkers that can reliably detect BC are yet to be discovered. Our study aimed to identify a novel signature that can predict the prognosis of patients with BC. Data from the TCGA-BRCA cohort were analyzed using univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis was performed to build a stable prognostic model. Subsequently, Kaplan–Meier (K–M) and receiver operating characteristic (ROC) analyses were performed to demonstrate the predictive power of our gene signature. Each patient was assigned to either a low- or high-risk group. Patients with high-risk BC had poorer survival than those with low-risk BC. Cox regression analysis suggested that our signature was an independent prognostic factor. Additionally, decision curve analysis and calibration accurately predicted the capacity of our nomogram. Thus, based on the differentially expressed genes (DEGs) of mitophagy-related tumor classification, we established a 13-gene signature and robust nomogram for predicting BC prognosis, which can be beneficial for the diagnosis and treatment of BC.

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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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            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

                Journal
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                15 August 2022
                05 August 2022
                : 14
                : 15
                : 6169-6186
                Affiliations
                [1 ]Department of Hepatobiliary Surgery, Ningbo First Hospital, Ningbo 315010, Zhejiang, China
                [2 ]Department of Molecular Pathology, Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
                [3 ]Medicine School, Ningbo University, Ningbo 315211, Zhejiang, China
                Author notes
                [*]

                Equal contribution

                Correspondence to: Yangyang Guo; email: 984054863@qq.com, https://orcid.org/0000-0002-4070-8703
                Correspondence to: Kai Hong; email: hongkai0629@163.com, https://orcid.org/0000-0001-8631-8236
                Article
                204209 204209
                10.18632/aging.204209
                9417220
                35939339
                bdf28a73-783d-4d05-b8f2-235b8e5f53c3
                Copyright: © 2022 Zhao et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 26 April 2022
                : 12 July 2022
                Categories
                Research Paper

                Cell biology
                breast cancer,biomarker,prognosis,treatment
                Cell biology
                breast cancer, biomarker, prognosis, treatment

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