Unveiling a Novel Glioblastoma Deep Molecular Profiling: Insight into the Cancer Cell Differentiation-Related Mechanisms.

in ACS omega by Weili Zhou, Hongtao Ruan, Lihua Zhu, Shunqiang Chen, Muyi Yang

TLDR

  • The study developed a novel GBM risk classification using ensemble consensus clustering and multiomics data, which provides insights for personalized therapy

Abstract

the sophisticated cellular heterogeneity of cell populations in glioblastoma (GBM) has been a key factor influencing tumor progression and response to therapy. The lack of more precise stratification based on cellular differentiation status poses a great challenge to therapeutic strategies. harnessing the bulk multiomics and single-nucleus RNA sequencing data available from the National Center for Biotechnology Information (NCBI) and The Cancer Genome Atlas (TCGA) Program repositories, we developed a novel and accurate GBM risk classification using an ensemble consensus clustering approach based on the junction of prognosis and trajectory analysis. Comprehensive cluster labeling and multiomics data characterization were also performed. a novel GBM stratification model was constructed using 45 malignant cell fate genes: (a) energy metabolism-enhanced-type GBM; (b) invasion-enhanced-type GBM; (c) invasion-attenuated-type GBM; and (d) glycolysis-dominant energy metabolism-enhanced-type GBM. The biological plausibility of the model was verified through a range of comprehensive analyses of multiomics data, showing that cases with invasion-attenuated-type were the best prognosis and energy metabolism-enhanced-type the poorest. the study has uncovered GBM complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying tumorigenesis. This precise stratification system provided implications for further studies of individual therapies.

Overview

  • The study aims to develop a novel GBM risk classification using ensemble consensus clustering based on prognosis and trajectory analysis
  • The study utilizes bulk multiomics and single-nucleus RNA sequencing data from the NCBI and TCGA repositories to identify 45 malignant cell fate genes
  • The primary objective is to uncover the complex cellular heterogeneity and differentiated hierarchy of cell populations underlying GBM tumorigenesis

Comparative Analysis & Findings

  • Four novel GBM stratification models were constructed: (a) energy metabolism-enhanced-type; (b) invasion-enhanced-type; (c) invasion-attenuated-type; and (d) glycolysis-dominant energy metabolism-enhanced-type
  • Comprehensive cluster labeling and multiomics data characterization were performed to verify the biological plausibility of the model
  • The study found that cases with invasion-attenuated-type had the best prognosis, while energy metabolism-enhanced-type had the poorest

Implications and Future Directions

  • The study has implications for developing personalized therapies for individual patients based on their unique cellular heterogeneity
  • Further studies are needed to validate the findings and explore novel therapies targeting energy metabolism and invasion in GBM
  • The study provides a framework for integrating multiomics data and trajectory analysis to understand complex cellular heterogeneity in cancer
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