Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma.

Journal: iScience
Published Date: Feb 13, 2025

Abstract

The growth of glioma is frequently accompanied by a hypoxic microenvironment. Nevertheless, the clinical implications of hypoxia have not been extensively investigated. Single-cell RNA sequencing analysis indicated a heterogeneous communication between different types of cells in the hypoxic microenvironment. Two hypoxia-related glioma subtypes, C1 and C2, show distinct prognostic and molecular differences. Subtype C2 gliomas have more immune and stromal cells, higher immune checkpoint gene expression, and worse prognosis than those in C1. Using machine learning, we developed an 11-gene signature predicting clinical outcomes in six cohorts, validated by RT-qPCR, effectively distinguishing high-risk and low-risk patients and reliably predicting overall and relapse-free survival. Moreover, the risk score is more accurate than conventional clinical variables, molecular characteristics, and 100 previously published signatures. High-risk gliomas show increased CD163, PD1, HIF1A, and PD-L1 expression. We developed a hypoxia-related classification to guide treatment decisions and a reliable prognostic tool.

Authors

  • Quanwei Zhou
    The National Key Clinical Specialty, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Zhaokai Zhou
    Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Youwei Guo
    Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621000, China.
  • Xuejun Yan
    NHC Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan, China.
  • Xingjun Jiang
    Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
  • Can Du
    Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
  • Yiquan Ke
    The National Key Clinical Specialty, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

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