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...

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Detalles Bibliográficos
Autores principales: Zhang, Xiaoming, Zhuang, Jing, Liu, Lijuan, He, Zhengguo, Liu, Cun, Ma, Xiaoran, Li, Jie, Ding, Xia, Sun, Changgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Bioinformatics
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
Descripción
Sumario: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.

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