Yanan Ren1, Ting-You Wang1, Leah C Anderton2, Qi Cao3,4, Rendong Yang5. 1. The Hormel Institute, University of Minnesota, Austin, MN, 55912, USA. 2. Department of Biology, Cedarville University, Cedarville, OH, 45314, USA. 3. Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. 4. Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. 5. The Hormel Institute, University of Minnesota, Austin, MN, 55912, USA. yang4414@umn.edu.
Abstract
BACKGROUND: Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. RESULTS: As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. CONCLUSIONS: LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA .
BACKGROUND: Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. RESULTS: As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancerpatient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. CONCLUSIONS: LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA .
Authors: John N Weinstein; Eric A Collisson; Gordon B Mills; Kenna R Mills Shaw; Brad A Ozenberger; Kyle Ellrott; Ilya Shmulevich; Chris Sander; Joshua M Stuart Journal: Nat Genet Date: 2013-10 Impact factor: 38.330
Authors: Miriam Recalde; María Gárate-Rascón; José María Herranz; María Elizalde; María Azkona; Juan P Unfried; Loreto Boix; María Reig; Bruno Sangro; Maite G Fernández-Barrena; Puri Fortes; Matías A Ávila; Carmen Berasain; María Arechederra Journal: Cancers (Basel) Date: 2022-04-19 Impact factor: 6.575