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Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis
BACKGROUND: This study intended to determine important genes related to the prognosis and recurrence of breast cancer. METHODS: Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free surviva...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519567/ https://www.ncbi.nlm.nih.gov/pubmed/32977823 http://dx.doi.org/10.1186/s12957-020-02026-z |
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author | Zhou, Xiaoying Xiao, Chuanguang Han, Tong Qiu, Shusheng Wang, Meng Chu, Jun Sun, Weike Li, Liang Lin, Lili |
author_facet | Zhou, Xiaoying Xiao, Chuanguang Han, Tong Qiu, Shusheng Wang, Meng Chu, Jun Sun, Weike Li, Liang Lin, Lili |
author_sort | Zhou, Xiaoying |
collection | PubMed |
description | BACKGROUND: This study intended to determine important genes related to the prognosis and recurrence of breast cancer. METHODS: Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed. RESULTS: Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors. CONCLUSION: ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer. |
format | Online Article Text |
id | pubmed-7519567 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75195672020-09-29 Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis Zhou, Xiaoying Xiao, Chuanguang Han, Tong Qiu, Shusheng Wang, Meng Chu, Jun Sun, Weike Li, Liang Lin, Lili World J Surg Oncol Research BACKGROUND: This study intended to determine important genes related to the prognosis and recurrence of breast cancer. METHODS: Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed. RESULTS: Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors. CONCLUSION: ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer. BioMed Central 2020-09-25 /pmc/articles/PMC7519567/ /pubmed/32977823 http://dx.doi.org/10.1186/s12957-020-02026-z Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhou, Xiaoying Xiao, Chuanguang Han, Tong Qiu, Shusheng Wang, Meng Chu, Jun Sun, Weike Li, Liang Lin, Lili Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis |
title | Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis |
title_full | Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis |
title_fullStr | Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis |
title_full_unstemmed | Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis |
title_short | Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis |
title_sort | prognostic biomarkers related to breast cancer recurrence identified based on logit model analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519567/ https://www.ncbi.nlm.nih.gov/pubmed/32977823 http://dx.doi.org/10.1186/s12957-020-02026-z |
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