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*

*Corresponding author for this work

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 - Dec 2021

Keywords

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

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Fingerprint

Dive into the research topics of 'LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data'. Together they form a unique fingerprint.

Cite this