Natural killer cell-related gene signature predicts malignancy of glioma and the survival of patients

BMC Cancer. 2022 Mar 2;22(1):230. doi: 10.1186/s12885-022-09230-y.

Abstract

Background: Natural killer (NK) cells-based therapies are one of the most promising strategies against cancer. The aim of this study is to investigate the natural killer cell related genes and its prognostic value in glioma.

Methods: The Chinese Glioma Genome Atlas (CGGA) was used to develop the natural killer cell-related signature. Risk score was built by multivariate Cox proportional hazards model. A cohort of 326 glioma samples with whole transcriptome expression data from the CGGA database was included for discovery. The Cancer Genome Atlas (TCGA) datasets was used for validation. GO and KEGG were used to reveal the biological process and function associated with the natural killer cell-related signature. We also collected the clinical pathological features of patients with gliomas to analyze the association with tumor malignancy and patients' survival.

Results: We screened for NK-related genes to build a prognostic signature, and identified the risk score based on the signature. We found that NK-related risk score was independent of various clinical factors. Nature-killer cell gene expression is correlated with clinicopathological features of gliomas. Innovatively, we demonstrated the tight relation between the risk score and immune checkpoints, and found NK-related risk score combined with PD1/PDL1 patients could predict the patient outcome.

Conclusion: Natural killer cell-related gene signature can predict malignancy of glioma and the survival of patients, these results might provide new view for the research of glioma malignancy and individual immunotherapy.

Keywords: Brain tumor; Glioma; Natural killer cell; Prognosis; Signature.

MeSH terms

  • Adult
  • Biomarkers, Tumor / genetics
  • Central Nervous System Neoplasms / genetics*
  • Databases, Genetic
  • Female
  • Glioma / genetics*
  • Humans
  • Killer Cells, Natural / metabolism*
  • Male
  • Prognosis
  • Risk Factors
  • Transcriptome / genetics

Substances

  • Biomarkers, Tumor