Research Paper Volume 15, Issue 24 pp 15578—15598
Development of a prognostic model for glioblastoma multiforme based on the expression levels of efferocytosis-related genes
- 1 Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science, Shandong University, Shandong, Jinan 250012, China
- 2 Department of Radiation Oncology, Qilu Hospital of Shandong University, Shandong, Jinan 250012, China
Received: September 14, 2023 Accepted: December 4, 2023 Published: December 29, 2023
https://doi.org/10.18632/aging.205422How to Cite
Copyright: © 2023 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Glioblastoma multiforme (GBM) is one of the most common and aggressive brain tumors. The microenvironment of GBM is characterized by its highly immunosuppressive nature with infiltration of immunosuppressive cells and the expression levels of cytokines. Efferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. Efferocytosis plays a noticeable function in the formation of immunosuppressive environment. This study aimed to develop an efferocytosis-related prognostic model for GBM. The bioinformatic methods were utilized to analyze the transcriptomic data of GBM and normal samples. Clinical and RNA-seq data were sourced from TCGA database comprising 167 tumor samples and 5 normal samples, and 167 tumor samples for which survival information was available. Transcriptomic data of 1034 normal samples were collected from the Genotype-Tissue Expression (GTEx) database as a control sample supplement to the TCGA database. In the end, 167 tumor samples and 1039 normal samples were obtained for transcriptome analysis. Efferocytosis-related differentially expressed genes (ERDEGs) were obtained by intersecting 7487 differentially expressed genes (DEGs) between GBM and normal samples along with 1189 hub genes. Functional enrichment analyses revealed that ERDEGs were mainly involved in cytokine-mediated immune responses. Moreover, 9 prognosis-related genes (PRGs) were identified by the least absolute shrinkage and selection operator (LASSO) regression analysis, and a prognostic model was therefore developed. The nomogram combining age and risk score could effectively predict GBM patients’ prognosis. GBM patients in the high-risk group had higher immune infiltration, invasion, epithelial-mesenchymal transition, angiogenesis scores and poorer tumor purity. In addition, the high-risk group exhibited higher half maximal inhibitory concentration (IC50) values for temozolomide, carmustine, and vincristine. Expression analysis indicated that PRGs were overexpressed in GBM cells. PDIA4 knockdown reduced efferocytosis in vitro. In summary, the proposed prognostic model for GBM based on efferocytosis-related genes exhibited a robust performance.
Abbreviations
GBM: Glioblastoma multiforme; TMZ: Temozolomide; WGCNA: Weighted gene co-expression network analysis; ERDEGs: Efferocytosis-related differentially expressed genes; DEGs: Differentially expressed genes; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; TCGA: The Cancer Genome Atlas; LASSO: Least absolute shrinkage and selection operator; PRGs: Prognosis-related genes; K-M: Kaplan-Meier; GSEA: Gene Set Enrichment Analysis; EMT: Epithelial-mesenchymal transition; IC50: Half maximal inhibitory concentration.