LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

Yanan Ren, Ting You Wang, Leah C. Anderton, Qi Cao, Rendong Yang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number574
JournalBMC Genomics
Volume22
Issue number1
DOIs
StatePublished - Jul 27 2021

Bibliographical note

Funding Information:
We thank Dr. Jeffrey McDonald at the Hormel Institute for his technical support for computing facilities. Support from the Minnesota Supercomputer Institute (MSI) is also gratefully acknowledged.

Funding Information:
This work was supported by a pilot grant for prostate cancer research from the Hormel Institute and Young Investigator Award from the Prostate Cancer Foundation.

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • Cancer transcriptome
  • GSEA
  • Long non-coding RNA
  • Pathway analysis
  • RNA-seq
  • TCGA

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