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

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

doi:10.1186/s12864-021-07900-y

Abstract

BackgroundLong 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.ResultsAs 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.ConclusionsLncGSEA 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.

Highlights

Long non-coding RNAs are a growing focus in cancer research
The widely-used approach to identify downstream target genes and pathways regulated by a Long non-coding RNAs (lncRNAs) is leveraging RNA inference to inhibit the lncRNA expression followed by microarray or RNA-seq gene expression profiling and differential gene expression (DGE) analysis in cancer cell line models [3]
The performance of lncGSEA predictions were assessed with four evaluation criteria as described below: First, we compared the consistency of predictions with different gene ranking approaches

Summary

Introduction

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. The widely-used approach to identify downstream target genes and pathways regulated by a lncRNA is leveraging RNA inference to inhibit the lncRNA expression followed by microarray or RNA-seq gene expression profiling and differential gene expression (DGE) analysis in cancer cell line models [3]. This strategy can directly nominate lncRNA associated pathways, laboratory techniques and expenses are required for these in vitro experiments and most of the detected lncRNAs have no such data available. The clinical relevance of those models has been continuously questioned

Results

Discussion

Conclusion