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.
Bibliographical noteFunding 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.
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.
© 2021, The Author(s).
- Cancer transcriptome
- Long non-coding RNA
- Pathway analysis