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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer
BACKGROUND: Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. METHODS: First, the linear fitting method was used to identify differen...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786248/ https://www.ncbi.nlm.nih.gov/pubmed/31608179 http://dx.doi.org/10.7717/peerj.7821 |
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author | Zhang, Xiaoming Zhuang, Jing Liu, Lijuan He, Zhengguo Liu, Cun Ma, Xiaoran Li, Jie Ding, Xia Sun, Changgang |
author_facet | Zhang, Xiaoming Zhuang, Jing Liu, Lijuan He, Zhengguo Liu, Cun Ma, Xiaoran Li, Jie Ding, Xia Sun, Changgang |
author_sort | Zhang, Xiaoming |
collection | PubMed |
description | BACKGROUND: Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. METHODS: First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). RESULTS: Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. CONCLUSION: Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer. |
format | Online Article Text |
id | pubmed-6786248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67862482019-10-11 Integrative transcriptome data mining for identification of core lncRNAs in breast cancer Zhang, Xiaoming Zhuang, Jing Liu, Lijuan He, Zhengguo Liu, Cun Ma, Xiaoran Li, Jie Ding, Xia Sun, Changgang PeerJ Bioinformatics BACKGROUND: Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. METHODS: First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). RESULTS: Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. CONCLUSION: Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer. PeerJ Inc. 2019-10-07 /pmc/articles/PMC6786248/ /pubmed/31608179 http://dx.doi.org/10.7717/peerj.7821 Text en ©2019 Zhang et al. https://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0) , which permits using, remixing, and building upon the work non-commercially, as long as it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Zhang, Xiaoming Zhuang, Jing Liu, Lijuan He, Zhengguo Liu, Cun Ma, Xiaoran Li, Jie Ding, Xia Sun, Changgang Integrative transcriptome data mining for identification of core lncRNAs in breast cancer |
title | Integrative transcriptome data mining for identification of core lncRNAs in breast cancer |
title_full | Integrative transcriptome data mining for identification of core lncRNAs in breast cancer |
title_fullStr | Integrative transcriptome data mining for identification of core lncRNAs in breast cancer |
title_full_unstemmed | Integrative transcriptome data mining for identification of core lncRNAs in breast cancer |
title_short | Integrative transcriptome data mining for identification of core lncRNAs in breast cancer |
title_sort | integrative transcriptome data mining for identification of core lncrnas in breast cancer |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786248/ https://www.ncbi.nlm.nih.gov/pubmed/31608179 http://dx.doi.org/10.7717/peerj.7821 |
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