Abstract
Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.
Highlights
Glioma is a common primary malignant brain tumor [1], with the average incidence of approximately six cases per 100,000 people in the world [2]
We selected the topranking 5000 gene expression values of the GSE4290 dataset based on the median absolute derivation (MAD) algorithm for performing Weighted gene coexpression network analysis (WGCNA)
Integrated transcriptome analysis was conducted in a total of 251 samples, including 179 high-grade gliomas (HGG) and 72 lowgrade glioma (LGG) samples, derived from four GEO datasets, among which 7 HGG and 15 LGG in the GSE45921 dataset, 19 HGG and 7 LGG in the GSE15824 dataset, 45 HGG and 5 LGG in the GSE43378 dataset, and 108 HGG and 45 LGG in the GSE4290 dataset
Summary
Glioma is a common primary malignant brain tumor [1], with the average incidence of approximately six cases per 100,000 people in the world [2]. As time goes by, LGG will progress to invasive HGG [4,5,6]. On the basis of the statistical data of the Chinese Glioma Genome Atlas (CGGA), the overall survival (OS) of LGG is 78.1 months, with a 5-year survival rate of 67% [7]. Once patients progress to HGG, their OS will shorten to 14.4 months and the 5-year survival rate is roughly 9%. Even though they adopt positive treatments, such as surgery and chemotherapy, as well as radiotherapy, their 2-year survival rate is merely 43% [8]
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