Abstract
Background: Long noncoding (lnc)RNAs and glycolysis are both recognized as key regulators of cancers. Some lncRNAs are also reportedly involved in regulating glycolysis metabolism. However, glycolysis-associated lncRNA signatures and their clinical relevance in cancers remain unclear. We investigated the roles of glycolysis-associated lncRNAs in cancers. Methods: Glycolysis scores and glycolysis-associated lncRNA signatures were established using a single-sample gene set enrichment analysis (GSEA) of The Cancer Genome Atlas pan-cancer data. Consensus clustering assays and genomic classifiers were used to stratify patient subtypes and for validation. Fisher’s exact test was performed to investigate genomic mutations and molecular subtypes. A differentially expressed gene analysis, with GSEA, transcription factor (TF) activity scoring, cellular distributions, and immune cell infiltration, was conducted to explore the functions of glycolysis-associated lncRNAs. Results: Glycolysis-associated lncRNA signatures across 33 cancer types were generated and used to stratify patients into distinct clusters. Patients in cluster 3 had high glycolysis scores and poor survival, especially in bladder carcinoma, low-grade gliomas, mesotheliomas, pancreatic adenocarcinomas, and uveal melanomas. The clinical significance of lncRNA-defined groups was validated using external datasets and genomic classifiers. Gene mutations, molecular subtypes associated with poor prognoses, TFs, oncogenic signaling such as the epithelial-to-mesenchymal transition (EMT), and high immune cell infiltration demonstrated significant associations with cluster 3 patients. Furthermore, five lncRNAs, namely MIR4435-2HG, AC078846.1, AL157392.3, AP001273.1, and RAD51-AS1, exhibited significant correlations with glycolysis across the five cancers. Except MIR4435-2HG, the lncRNAs were distributed in nuclei. MIR4435-2HG was connected to glycolysis, EMT, and immune infiltrations in cancers. Conclusions: We identified a subgroup of cancer patients stratified by glycolysis-associated lncRNAs with poor prognoses, high immune infiltration, and EMT activation, thus providing new directions for cancer therapy.
Original language | English |
---|---|
Article number | 59 |
Pages (from-to) | 59 |
Journal | BMC Medicine |
Volume | 19 |
Issue number | 1 |
DOIs | |
Publication status | Published - Feb 25 2021 |
Keywords
- Epithelial-to-mesenchymal transition (EMT)
- Glycolysis
- Immune infiltrations
- Long noncoding RNAs (lncRNAs)
- MIR4435-2HG
ASJC Scopus subject areas
- General Medicine
Fingerprint
Dive into the research topics of 'Glycolysis-associated lncRNAs identify a subgroup of cancer patients with poor prognoses and a high-infiltration immune microenvironment'. Together they form a unique fingerprint.Datasets
-
Additional file 2 of Glycolysis-associated lncRNAs identify a subgroup of cancer patients with poor prognoses and a high-infiltration immune microenvironment
Liu, A.-J. (Contributor), Lee, Y.-T. (Creator), Chen, K.-C. (Creator), Huang, T.-W. (Contributor), Shih, C.-M. (Contributor), Chen, P.-H. (Creator) & Ho, K.-H. (Contributor), Figshare, 2021
DOI: 10.6084/m9.figshare.14111152.v1, https://doi.org/10.6084%2Fm9.figshare.14111152.v1
Dataset
-
Additional file 9 of Glycolysis-associated lncRNAs identify a subgroup of cancer patients with poor prognoses and a high-infiltration immune microenvironment
Liu, A.-J. (Contributor), Huang, T.-W. (Contributor), Chen, P.-H. (Creator), Ho, K.-H. (Contributor), Lee, Y.-T. (Creator), Chen, K.-C. (Creator) & Shih, C.-M. (Contributor), Figshare, 2021
DOI: 10.6084/m9.figshare.14111173.v1, https://doi.org/10.6084%2Fm9.figshare.14111173.v1
Dataset
-
Additional file 6 of Glycolysis-associated lncRNAs identify a subgroup of cancer patients with poor prognoses and a high-infiltration immune microenvironment
Ho, K.-H. (Contributor), Chen, P.-H. (Creator), Chen, K.-C. (Creator), Shih, C.-M. (Contributor), Lee, Y.-T. (Creator), Huang, T.-W. (Contributor) & Liu, A.-J. (Contributor), Figshare, 2021
DOI: 10.6084/m9.figshare.14111164.v1, https://doi.org/10.6084%2Fm9.figshare.14111164.v1
Dataset