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Research Paper|Volume 15, Issue 13|pp 6264—6291

Identification of natural killer cell-related characteristics to predict the clinical prognosis and immune microenvironment of patients with low-grade glioma

Fei Sun1,2, Hongtao Lv1, Baozhi Feng1, Jiaao Sun3, Linyun Zhang1, Bin Dong1
  • 1Department of Neurosurgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
  • 2Department of Neurosurgery, Xinhua Hospital Affiliated to Dalian University, Dalian, Liaoning, China
  • 3Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
* Equal contribution
Received: March 8, 2023Accepted: June 15, 2023Published: July 5, 2023
https://doi.org/10.18632/aging.204850

Copyright: © 2023 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Individuals with low-grade glioma (LGG) have a dismal prognosis, and most patients will eventually progress to high-grade disease. Therefore, it is crucial to accurately determine their prognoses.

Methods: Seventy-nine NK cell genes were downloaded from the LM22 database and univariate Cox regression analysis was utilized to detect NK cell-related genes affecting prognosis. Molecular types were established for LGG using the “ConsensusClusterPlus” R package. The results from a functional enrichment analysis and the immune microenvironment were intensively explored to determine molecular heterogeneity and immune characteristics across distinct subtypes. Furthermore, a RiskScore model was developed and verified using expression profiles of NK cells, and a nomogram consisting of the RiskScore model and clinical traits was constructed. Moreover, pan-cancer traits of NK cells were also investigated.

Results: The C1 subtype included the greatest amount of immune infiltration and the poorest prognosis among well-established subtypes. The majority of enriched pathways were those involved in tumor progression, including epithelial-mesenchymal transition and cell cycle pathways. Differentially expressed genes among distinct subtypes were determined and used to develop a novel RiskScore model. This model was able to distinguish low-risk patients with LGG from those with high-risk disease. An accurate nomogram including the RiskScore, disease grade and patient’s age was constructed to predict clinical outcomes of LGG patients. Finally, a pan-cancer analysis further highlighted the crucial roles of NK cell-related genes in the tumor microenvironment.

Conclusions: An NK cell-related RiskScore model can accurately predict the prognoses of patients with LGG and provide valuable insights into personalized medicine.