Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. C...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of molecular sciences Vol. 22; no. 5; p. 2420
Main Authors:	Mathias, Carolina, Groeneveld, Clarice S, Trefflich, Sheyla, Zambalde, Erika P, Lima, Rubens S, Urban, Cícero A, Prado, Karin B, Ribeiro, Enilze M S F, Castro, Mauro A A, Gradia, Daniela F, de Oliveira, Jaqueline C
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 28-02-2021 MDPI
Subjects:	Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - genetics Breast Neoplasms - metabolism Cell Line, Tumor Cell viability co-expression Computational Biology Computer applications Estrogens Female Gene expression Gene Expression Regulation, Neoplastic Gene regulation Gene Regulatory Networks Heterogeneity Humans LINC00504 lncRNA luminal A MCF-7 Cells Medical prognosis Metastasis Non-coding RNA Nucleotides Patients Prognosis RNA polymerase RNA, Long Noncoding - metabolism Tumors co-expression breast cancer lncRNA LINC00504 luminal A
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1422-0067 1661-6596 1422-0067
DOI:	10.3390/ijms22052420