Glycolysis-associated lncRNAs identify a subgroup of cancer patients with poor prognoses and a high-infiltration immune microenvironment

Our plain-English summary of Glycolysis-associated lncRNAs identify a subgroup of cancer patients with poor prognoses and a high-infiltration immune microenvironment, by K.H. Ho, T.W. Huang, C.M. Shih, et al (BMC Medicine, 2021).

The study

• Authors: K.H. Ho, T.W. Huang, C.M. Shih, Y.T. Lee, A.J. Liu, P.H. Chen, K.C. Chen
• Journal: BMC Medicine
• Published: 2021;19(1):59
• Cited by: 35 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

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.

In plain English

This computer analysis used existing Cancer Genome Atlas data on 10,121 tumor samples from 33 cancer types, including mesotheliomas. It did not study asbestos or any other exposure. The authors estimated each tumor's glycolysis (how cells turn sugar into energy) from gene activity. They then sought long noncoding RNAs (lncRNAs, genetic molecules that make no proteins) tracking that score. They found 1420 such lncRNAs and used them to group patients. In five cancer types, including mesotheliomas, 'cluster 3' patients were significantly associated with a poor prognosis. Most cluster 3 patients fell in a high immune infiltration group. Five lncRNAs were linked to glycolysis in all five cancers. In mesotheliomas, an analysis adjusted for copy-number changes and DNA methylation found no genes significantly associated with one of them, MIR4435-2HG. The mesothelioma group was small (84 patients), and outside mesothelioma data lacked survival information. Outside checks, mainly in bladder cancer and low-grade glioma, used protein-coding gene classifiers because those datasets lacked many lncRNAs. The authors concluded that these lncRNAs identified a subgroup with the poorest prognosis, high immune infiltration and gene-activity signs of epithelial-to-mesenchymal transition activation.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Computational (bioinformatics) analysis of existing tumor genetic data, with validation in outside datasets
• Who was studied: 10,121 tumor samples from 33 cancer types in The Cancer Genome Atlas. The analysis focused on five types: bladder carcinoma, low-grade gliomas, mesotheliomas (84 patients), pancreatic ductal adenocarcinomas and uveal melanomas.
• Exposure: No asbestos or other environmental exposure was studied. Glycolytic activity in each tumor was inferred from the expression of glycolysis-pathway genes and compared with lncRNA levels.
• Main results:
  • 1420 lncRNAs were associated with glycolytic activity in at least one cancer type.
  • In five cancer types, including mesotheliomas, patients in cluster 3 were significantly associated with a poor prognosis.
  • Across the five cancer types, most cluster 3 patients were in a high immune infiltration group.
  • The immune checkpoint genes PDCD1, IDO1 and CTLA4 were significantly upregulated in cluster 3 compared with cluster 1 in multiple cancer types.
  • Five lncRNAs were associated with glycolytic activity across all five cancers.
  • In mesothelioma and pancreatic cancer, MYC target pathways were significantly activated in cluster 3 compared with cluster 1.
  • In mesothelioma, a multivariate linear regression adjusting for copy number alterations and DNA methylation identified no genes significantly associated with MIR4435-2HG.
  • Because outside microarray datasets lacked many lncRNAs, validation used a classifier based on protein-coding genes, mainly in low-grade glioma and bladder carcinoma.
• The authors' conclusion: The authors concluded that glycolysis-correlated lncRNA signatures identified a subgroup of cancer patients with the poorest prognosis, a highly infiltrative immune microenvironment and EMT activation.
• Limitations the authors state:
  • The lack of publicly available RNA sequencing data limited validation of the genomic classifier.
  • More clinical data, from other cancer types or from large cohorts, are needed for validation.
  • Sample sizes for mesothelioma (84) and uveal melanoma (80) were small.
  • Outside datasets for mesothelioma, pancreatic cancer and uveal melanoma lacked survival information, which limited validation of the clusters' clinical importance.

Topics

Medical terms in this study, explained in our Lexicon: Carcinoma, Mesothelioma, Adenocarcinoma.

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