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

• Main Authors: Yanan Ren, Ting-You Wang, Leah C. Anderton, Qi Cao, Rendong Yang
• Format: Article
• Language: English
• Published: BMC 2021-07-01
• Series: BMC Genomics
• Subjects: Long non-coding RNA; GSEA; Pathway analysis; RNA-seq; TCGA; Cancer transcriptome
• Online Access: https://doi.org/10.1186/s12864-021-07900-y

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 .