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      • Record: found
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      High RRM2 expression has poor prognosis in specific types of breast cancer

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      Author(s): 1 , 2 , * , , 3 , * ,
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      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          Background

          RRM2 plays an important role in different malignant tumors, but there are few studies in breast cancer. Public databases were used to analyze the expression of RRM2 in breast cancer and its prognostic value.

          Materials and methods

          A total of 2,509 breast cancer samples were downloaded from the METABRIC database. The relationship between RRM2 expression and clinical pathology was evaluated. Using the BCIP database and real-time-PCR, and western blotting, RRM2 mRNA and protein expression of RRM2 in breast cancer tissues and cell lines were evaluated. Univariate and multivariate analysis defined independent prognostic factors that affected the overall survival of patients with breast cancer. The Kaplan-Meier method was used to study the relationship between the high expression of RRM2 and overall survival and distant metastasis-free survival (DMFS) of breast cancer patients. Finally, We performed Gene Set Enrichment Analysis (GSEA) and obtained the relevant pathways associated with high expression of RRM2 potentially influencing breast cancer progression.

          Results

          RRM2 expression was significantly correlated with age, tumor size, grade, menopausal status, molecular typing, ER, PR, and Her-2 of patients with breast cancer(P<0.05). Univariate and multivariate regression analysis showed that RRM2, the number of positive lymph nodes, ER, Her-2, tumor size, and tumor stage can be used as independent prognostic factors for overall survival of patients with breast cancer. Kaplan-Meier analysis showed that in patients with Luminal A and Normal like breast cancers and Stage1 and stage2 breast cancers, patients with high expression of RRM2 had worse overall survival and DMFS. The analysis of the GSEA pathway showed that RRM2 is mainly enriched in the ERBB signaling pathway and other pathways.

          Conclusion

          The high expression of RRM2 has a worse prognosis in patients with breast cancer with specific features. It can be used as a biomarker for the prognosis of breast cancer.

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          Most cited references47

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          Kenneth Livak, Thomas D. Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
          Article 0 comments Cited 23875 times – based on 0 reviews     Review now
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
            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.
            Article 0 comments Cited 12867 times – based on 0 reviews     Review now
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              Cancer Statistics, 2021

              Rebecca Siegel, Kimberly D Miller, Hannah Fuchs … (2021)
              Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
              Article 0 comments Cited 6982 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Shen-chao Shi: Role: ConceptualizationRole: Data curationRole: SoftwareRole: VisualizationRole: Writing – original draft
                Yi Zhang: Role: ConceptualizationRole: Data curationRole: Writing – original draftRole: Writing – review & editing
                Tao Wang:
                ORCID: https://orcid.org/0000-0001-9415-7524
                Role: ConceptualizationRole: Formal analysisRole: Writing – review & editing
                Surinder K. Batra: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 15 March 2022
                Publication date Collection: 2022
                Volume: 17
                Issue: 3
                Electronic Location Identifier: e0265195
                Affiliations
                [1 ] Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
                [2 ] Department of Endocrinology, Hubei No. 3 People’s Hospital of Jianghan University, Wuhan, China
                [3 ] Department of Thyroid and Breast Surgery, Hubei No. 3 People’s Hospital of Jianghan University, Wuhan, China
                University of Nebraska Medical Center, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-9415-7524
                Article
                Publisher ID: PONE-D-21-23611
                DOI: 10.1371/journal.pone.0265195
                PMC ID: 8923511
                PubMed ID: 35290409
                SO-VID: 65f8a3e3-d669-4bc0-b360-b5a4abc50043
                Copyright © © 2022 Shi et al
                License:

                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
                Date received : 29 July 2021
                Date accepted : 24 February 2022
                Page count
                Figures: 9, Tables: 1, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100010906, NSAF Joint Fund;
                Award ID: 81771718
                Award Recipient :
                This work was supported by National Natural Science Foundation of China (81771718).
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Cancers and Neoplasms
                Subject: Breast Tumors
                Subject: Breast Cancer
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Prognosis
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Gene Expression
                Subject: Biology and life sciences
                Subject: Cell biology
                Subject: Signal transduction
                Subject: Cell signaling
                Subject: VEGF signaling
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Lymphatic System
                Subject: Lymph Nodes
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Lymphatic System
                Subject: Lymph Nodes
                Subject: Biology and Life Sciences
                Subject: Molecular Biology
                Subject: Molecular Biology Techniques
                Subject: Molecular Biology Assays and Analysis Techniques
                Subject: Gene Expression and Vector Techniques
                Subject: Protein Expression
                Subject: Research and Analysis Methods
                Subject: Molecular Biology Techniques
                Subject: Molecular Biology Assays and Analysis Techniques
                Subject: Gene Expression and Vector Techniques
                Subject: Protein Expression
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Cancers and Neoplasms
                Subject: Lung and Intrathoracic Tumors
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Cancers and Neoplasms
                Subject: Gastrointestinal Tumors
                Subject: Gastric Cancer
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files. The Distant metastasis-free survival (DMFS) online analysis data were obtained through the website https://kmplot.com/analysis/. Breasts Cancer Integrative Platform (BCIP) data obtained through http://www.omicsnet.org/bcancer/database.

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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