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      Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer

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          Abstract

          Introduction

          Extensive research is directed to uncover new biomarkers capable to stratify breast cancer patients into clinically relevant cohorts. However, the overall performance ranking of such marker candidates compared to other genes is virtually absent. Here, we present the ranking of all survival related genes in chemotherapy treated basal and estrogen positive/HER2 negative breast cancer.

          Methods

          We searched the GEO repository to uncover transcriptomic datasets with available follow-up and clinical data. After quality control and normalization, samples entered an integrated database. Molecular subtypes were designated using gene expression data. Relapse-free survival analysis was performed using Cox proportional hazards regression. False discovery rate was computed to combat multiple hypothesis testing. Kaplan-Meier plots were drawn to visualize the best performing genes.

          Results

          The entire database includes 7,830 unique samples from 55 independent datasets. Of those with available relapse-free survival time, 3,382 samples were estrogen receptor-positive and 696 were basal. In chemotherapy treated ER positive/ERBB2 negative patients the significant prognostic biomarker genes achieved hazard rates between 1.76 and 3.33 with a p value below 5.8E−04. The significant prognostic genes in adjuvant chemotherapy treated basal breast cancer samples reached hazard rates between 1.88 and 3.61 with a p value below 7.2E−04. Our integrated platform was extended enabling the validation of future biomarker candidates.

          Conclusions

          A reference ranking for all genes in two chemotherapy treated breast cancer cohorts is presented. The results help to neglect those with unlikely clinical significance and to focus future research on the most promising candidates.

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          Most cited references33

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Breast Cancer Treatment

            Breast cancer will be diagnosed in 12% of women in the United States over the course of their lifetimes and more than 250 000 new cases of breast cancer were diagnosed in the United States in 2017. This review focuses on current approaches and evolving strategies for local and systemic therapy of breast cancer.
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              Triple-negative breast cancer.

              Triple-negative breast cancer, so called because it lacks expression of the estrogen receptor, progesterone receptor, and HER2, is often, but not always, a basal-like breast cancer. This review focuses on its origin, molecular and clinical characteristics, and treatment.
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                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                18 July 2021
                2021
                18 July 2021
                : 19
                : 4101-4109
                Affiliations
                [a ]Semmelweis University Dept. of Bioinformatics, Tűzoltó utca 7-9., 1094 Budapest, Hungary
                [b ]TTK Momentum Cancer Biomarker Research Group, Institute of Enzymology, Magyar Tudósok körútja 2., 1117 Budapest, Hungary
                [c ]Semmelweis University 2nd Dept. of Pediatrics, Tűzoltó utca 7-9., 1094 Budapest, Hungary
                Author notes
                [* ]Address: Department of Bioinformatics, Semmelweis University, Tüzoltó u. 7-9, 1094 Budapest, Hungary. gyorffy.balazs@ 123456med.semmelweis-univ.hu
                Article
                S2001-0370(21)00304-4
                10.1016/j.csbj.2021.07.014
                8339292
                34527184
                b9329be1-811e-480b-b1b7-ebd3f93e1907
                © 2021 The Author

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 February 2021
                : 15 July 2021
                : 16 July 2021
                Categories
                Research Article

                survival,breast cancer,chemotherapy,biomarkers,prognosis,kaplan-meier plot,molecular subtype

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