Stratification of glioma based on stemness scores in bulk and single-cell transcriptomes

Zeinab Abdelrahman*, Alaa Abdelatty, Jiangti Luo, Amy Jayne McKnight, Xiaosheng Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Background
Brain tumours are known to have a high mortality and morbidity rate due to their localised and frequent invasive growth. The concept that glioma resistance could originate from the dissimilarity in the vulnerability of clonogenic glial stem cells to chemotherapeutic drugs and radiation has driven the scientific community to reexamine the comprehension of glioma growth and strategies that target these cells or modify their stemness.

Methods
Based on the enrichment scores of 12 stemness signatures, we identified glioma subtypes in both tumour bulks and single cells by clustering analysis. Furthermore, we comprehensively compared molecular and clinical features among the glioma subtypes.

Results
Consistently, in seven different datasets, hierarchical clustering uncovered three subtypes of glioma, termed Stem-H, Stem-M, and Stem-L, with high, medium, and low stemness signatures, respectively. Stem-H and Stem-L exhibited the most unfavorable and favourable overall and disease-free survival, respectively. Stem-H showed the highest enrichment scores of the EMT, invasion, proliferation, differentiation, and metastasis processes signatures, while Stem-L displayed the lowest. Stem-H harboured a greater proportion of late-stage tumours compared to Stem-L. Moreover, Stem-H manifested higher tumour mutation burden, DNA damage repair and cell cycle activity, intratumour heterogeneity, and a more frequent incidence of TP53 and EGFR mutations than Stem-L. In contrast, Stem-L had higher O6-Methylguanine-DNA Methyltransferase (MGMT) methylation levels.

Conclusion
The classification of glioma based on stemness may offer new insights into the biology of the tumour, as well as more accurate clinical management of the disease.

Original languageEnglish
Article number108304
Number of pages15
JournalComputers in Biology and Medicine
Volume175
Early online date24 Apr 2024
DOIs
Publication statusPublished - Jun 2024
EventFestival of Genomics and Biodata - EXCEL, London, United Kingdom
Duration: 24 Jan 202425 Jan 2024

Keywords

  • Glioma
  • Stemness
  • Clustering Analysis
  • Stemness subtypes
  • Molecular features

ASJC Scopus subject areas

  • Health Informatics
  • Genetics

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