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. 2022 Dec 10;11(24):3997.
doi: 10.3390/cells11243997.

SARS-CoV-2 Pattern Provides a New Scoring System and Predicts the Prognosis and Immune Therapeutic Response in Glioma

Fan Jiang  1 , Deng-Feng Lu  1 , Zheng Zhan  1 , Gui-Qiang Yuan  1 , Guang-Jie Liu  1 , Jing-Yu Gu  1 , Xiao-Ou Sun  1 , Zhong Wang  1
Affiliations

SARS-CoV-2 Pattern Provides a New Scoring System and Predicts the Prognosis and Immune Therapeutic Response in Glioma

Fan Jiang et al. Cells. .

Abstract

Objective: Glioma is the most common primary malignancy of the adult central nervous system (CNS), with a poor prognosis and no effective prognostic signature. Since late 2019, the world has been affected by the rapid spread of SARS-CoV-2 infection. Research on SARS-CoV-2 is flourishing; however, its potential mechanistic association with glioma has rarely been reported. The aim of this study was to investigate the potential correlation of SARS-CoV-2-related genes with the occurrence, progression, prognosis, and immunotherapy of gliomas.

Methods: SARS-CoV-2-related genes were obtained from the human protein atlas (HPA), while transcriptional data and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Glioma samples were collected from surgeries with the knowledge of patients. Differentially expressed genes were then identified and screened, and seven SARS-CoV-2 related genes were generated by LASSO regression analysis and uni/multi-variate COX analysis. A prognostic SARS-CoV-2-related gene signature (SCRGS) was then constructed based on these seven genes and validated in the TCGA validation cohort and CGGA cohort. Next, a nomogram was established by combining critical clinicopathological data. The correlation between SCRGS and glioma related biological processes was clarified by Gene set enrichment analysis (GSEA). In addition, immune infiltration and immune score, as well as immune checkpoint expression and immune escape, were further analyzed to assess the role of SCRGS in glioma-associated immune landscape and the responsiveness of immunotherapy. Finally, the reliability of SCRGS was verified by quantitative real-time polymerase chain reaction (qRT-PCR) on glioma samples.

Results: The prognostic SCRGS contained seven genes, REEP6, CEP112, LARP4B, CWC27, GOLGA2, ATP6AP1, and ERO1B. Patients were divided into high- and low-risk groups according to the median SARS-CoV-2 Index. Overall survival was significantly worse in the high-risk group than in the low-risk group. COX analysis and receiver operating characteristic (ROC) curves demonstrated excellent predictive power for SCRGS for glioma prognosis. In addition, GSEA, immune infiltration, and immune scores indicated that SCRGS could potentially predict the tumor microenvironment, immune infiltration, and immune response in glioma patients.

Conclusions: The SCRGS established here can effectively predict the prognosis of glioma patients and provide a potential direction for immunotherapy.

Keywords: COVID-19; glioma; immunotherapy; molecular mechanism; prognosis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Identification of DEGs and biological characteristics of these genes by GO enrichment analysis and KEGG pathway analysis. (A,B) Heatmap (A) and a volcano plot (B) of 74 DEGs in normal and glioma groups. The logFC is presented. (C,E) Results of GO enrichment analysis. (D,F) Results of KEGG pathway analysis. The larger the circle, the redder the color and the stronger the correlation. DEGs, differential expressed genes; GO, gene ontology; KEGG, Kyoto encyclopedia of genes and genomes.
Figure 2
Figure 2
Final acquisition of prognostic DEGs for developing a risk model in TCGA cohort. (A) LASSO coefficient profiles of SARS-Cov-2 related genes. (B) Partial likelihood deviance of different numbers of variables revealed by the LASSO regression model (The log(λ) sequence plot of SARS-Cov-2 related genes using LASSO regression). (C) Forest plot of SARS-Cov-2-related genes. DEGs, differentially expressed genes; LASSO, least absolute shrinkage, and selection operator. ** p < 0.01, *** p < 0.001.
Figure 3
Figure 3
Correlation between SARS-Cov-2-related genes and validation of SCRGS in TCGA cohort. (A) Interactions between SARS-Cov-2 related genes. (B) Overall survival of different SARS-CoV-2 related genes in glioma. (C) Heatmap of SCRGS expression in high- and low-risk groups (upper), distribution and median value of SC2I (middle), and the distributions of survival status, survival time, and SC2I (below). (D) PCA analysis of SARS-CoV-2-related gene signature. (E) Time-dependent ROC analysis of SARS-CoV-2-related gene signature. SCRGS, SARS-CoV-2-related gene signature; SC2I, SARS-CoV-2 index; PCA, principal component analysis; ROC, receiver operating characteristic.
Figure 4
Figure 4
Correlation between SC2I and clinicopathological characteristics in TCGA dataset. (A) Heatmap of correlation between risk groups and subtype, WHO grade, IDH mutation status, MGMT promoter status, gender, age, survival status, and mRNA expression of SARS-CoV-2-related gene. (B) Different distribution of SC2Is among glioma subgroups (C,D) Univariate and multivariate COX regression analysis of the combination of SC2I and clinicopathological characteristics. (E) ROC curves of SC2I and clinicopathological characteristics. SC2I, SARS-CoV-2 index; TCGA, The Cancer Genome Atlas; IDH, isocitrate dehydrogenase; MGMT, O6-methylguanyl DNA methyltransferase; ROC, receiver operating characteristic. * p < 0.05, *** p < 0.001, **** p < 0.0001.
Figure 5
Figure 5
Prediction of the survival of glioma patients by nomogram, and GSEA analysis between different risk groups in TCGA cohort. (A) Nomogram used for predicting glioma patients was constructed. (B–D) Calibration plots for predicting 1-, 2- and 3-year overall survival. (E,F) GO and KEGG functional enrichment analysis. GSEA, gene set enrichment analysis; TCGA, The Cancer Genome Atlas; GO, gene ontology; KEGG, Kyoto encyclopedia of genes and genomes.
Figure 6
Figure 6
Correlation between the modeling genes and infiltrating immune cells based on ssGSEA. (A) Comparison of immune cell infiltration level (B) and immune score (C) between high- and low-isk groups. ssGSEA, single sample gene set enrichment analysis. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 7
Figure 7
Comparison of immune checkpoint expression levels (A), scores for glioma microenvironment (B–D), and correlation analysis between TIDE evaluation (E–G) in TCGA dataset between high- and low-risk groups. TIDE, tumor immune dysfunction and exclusion; TCGA, The Cancer Genome Atlas. * p < 0.05, ** p < 0.01, *** p < 0.001, ns, no significance.
Figure 8
Figure 8
Validation of SCRGS in CGGA dataset. (A) Survival curve of SCRGS risk model in high- and low-risk groups. (B) Patients in the CGGA dataset were divided into high- and low-risk groups according to the increasing SC2I (upper). The distributions of survival status, survival time, and SC2I (middle). Heatmap of the modeling genes expression in high- and low-risk groups (below). (C) Time-dependent ROC analysis of SCRGS. (D) QRT-PCR validation of SC2I between WHO II-III grade and WHO IV grade groups (left), and IDH wildtype and IDH mutant groups. SCRGS, SARS-CoV-2-related gene signature; CGGA, Chinese Glioma Genome Atlas; SC2I, SARS-CoV-2 index; ROC, receiver operating characteristic. qRT-PCR, quantitative real-time polymerase chain reaction; SC2I, SARS-CoV-2 index; IDH, isocitrate dehydrogenase. * p < 0.05; ns, no significance.
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