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Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning

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Area of Science:

  • Oncology
  • Immunology
  • Genomics

Background:

  • Glioblastoma (GBM) is a highly aggressive brain tumor with poor outcomes.
  • GBM stem-like cells are key drivers of tumor growth and progression.
  • A comprehensive analysis of stemness indices and their link to GBM immunology is needed.

Purpose of the Study:

  • To systematically assess stemness indices in GBM.
  • To investigate the association between stemness and immunological properties.
  • To develop a prognostic classifier for GBM.

Main Methods:

  • Calculated mRNA expression-based stemness indices (mRNAsi) and corrected mRNAsi (c-mRNAsi) in 874 GBM samples from four cohorts.
  • Utilized OCLR algorithm for index calculation and CIBERSORTx/ssGSEA for functional and immune analysis.
  • Validated prognostic value and model performance using statistical methods.

Main Results:

  • Higher mRNAsi/c-mRNAsi correlated with better prognosis across GBM cohorts.
  • The c-mRNAsi retained prognostic significance after accounting for confounding variables.
  • High c-mRNAsi was associated with immune suppression and reduced immune cell infiltration, despite activated immune-related pathways.

Conclusions:

  • Stemness indices, particularly c-mRNAsi, are valuable prognostic markers in GBM.
  • GBM stemness is linked to a distinct immune microenvironment characterized by suppression.
  • A c-mRNAsi-based classifier can aid in predicting GBM patient prognosis.