A Novel Necroptosis-Related Prognostic Signature of Glioblastoma Based on Transcriptomics Analysis and Single Cell Sequencing Analysis

Abstract

Background: Glioblastoma (GBM) is the most common and deadly brain tumor. The clinical significance of necroptosis (NCPS) genes in GBM is unclear. The goal of this study is to reveal the potential prognostic NCPS genes associated with GBM, elucidate their functions, and establish an effective prognostic model for GBM patients. Methods: Firstly, the NCPS genes in GBM were identified by single-cell analysis of the GSE182109 dataset in the GEO database and weighted co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Three machine learning algorithms (Lasso, SVM-RFE, Boruta) combined with COX regression were used to build prognostic models. The subsequent analysis included survival, immune microenvironments, and mutations. Finally, the clinical significance of NCPS in GBM was explored by constructing nomograms. Results: We constructed a GBM prognostic model composed of NCPS-related genes, including CTSD, AP1S1, YWHAG, and IER3, which were validated to have good performance. According to the above prognostic model, GBM patients in the TCGA and CGGA groups could be divided into two groups according to NCPS, with significant differences in survival analysis between the two groups and a markedly worse prognostic status in the high NCPS group (p < 0.001). In addition, the high NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they may be more likely to benefit from immunotherapy. Conclusions: Four genes (CTSD, AP1S1, YWHAG, and IER3) were screened through three machine learning algorithms to construct a prognostic model for GBM. These key and novel diagnostic markers may become new targets for diagnosing and treating patients with GBM.

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

“…Subsequently, 255 studies were enrolled, the characteristics of each study were shown in Supplementary Table 2 . Among these, 161 studies were conducted in China [ 16 , 18 , 20 – 21 , 23 – 29 , 31 , 34 , 36 – 37 , 40 – 44 , 47 – 48 , 50 , 53 , 55 , 64 , 66 – 69 , 71 – 74 , 80 , 82 – 87 , 89 , 92 – 95 , 97 – 98 , 102 – 105 , 107 , 109 – 113 , 115 – 118 , 120 – 124 , 126 – 127 , 135 – 137 , 139 – 144 , 146 – 152 , 154 – 155 , 157 , 160 – 164 , 166 , 168 – 170 , 172 – 173 , 177 – 179 , 184 – 190 , 193 – 195 , 197 – 197 , 206 – ...…”

“…To improve prognostic and predictive survival power in GBM patients, researchers [ 69 , 91 – 92 , 94 – 95 , 97 , 109 , 131 , 137 – 138 , 144 , 147 , 172 , 186 , 189 , 195 , 197 , 200 , 222 , 242 , 251 , 253 ], identified a panel of 2, 3, 4, 6,7, 8, 13, or 14 genes using mRNA expression datasets. They established a risk score model that performed well in survival prediction.…”

Prognosis and immunoinfiltration analysis of angiogene-related genes in grade 4 diffuse gliomas

“…Studies investigating the mechanisms underlying the pathogenesis of advance-grade gliomas have pointed out the contribution of ferroptosis [ 10 ], necroptosis [ 11 ], pyroptosis [ 12 ], autophagy [ 13 ], and metabolic reprogramming [ 14 ], among other processes. However, little is currently known about the impact of angiogenesis-related genes on the onset and progression of grade 4 diffuse gliomas.…”