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Background: Vitamins not only play a pivotal role in maintaining homeostasis of the body, but also have complex impacts on the occurrence and progression of tumors. However, the effects of vitamins on glioma and the underlying mechanism have not been fully elucidated.
Methods: Vitamin -related genes were extracted from the Molecular Signature Database v7.1 (MSigDB). The overlapping overall survival (OS)-related genes in The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and GSE16011 cohorts screened out by univariate COX regression analysis were utilized to construct a risk model based on the TCGA cohort via random survival forest analysis and multivariate COX regression analysis. The powerful prognostic predictive potential of the vitamin-related risk signature was verified by Kaplan–Meier survival analysis and receiver operating characteristic (ROC) analysis in the three datasets. The ssGSEA method of the GSVA package was used for functional enrichment and immune cell component analyses. ESTIMATE score analysis was used for auxiliary analysis of glioma immune characteristics. A nomogram was constructed and assessed based on the TCGA dataset.
Results: The vitamin-related six-gene (POSTN, IRX5, EEF2, RAB27A, MDM2, and ENO1) risk signature constructed based on the TCGA dataset accurately predicted the outcomes of glioma patients and credibly distinguished between different levels and molecular subtypes of glioma in the TCGA, CGGA, and GSE16011 cohorts. Gliomas with high risk scores exhibited high immune scores, low tumor purity, and immunosuppressive features. The nomogram constructed by combining the vitamin-related risk signature and clinicopathological factors precisely predicted the 1-, 3-, and 5-year OS of glioma patients.
Conclusions: Our study revealed that the vitamin-related six-gene risk signature, as an independent prognostic factor, could accurately distinguish the grade, molecular subtype, and immune characteristics of glioma.
Keywords
Vitamins, Risk signature, Prognosis, Immunosuppressive, Glioma
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This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.pdf
Additional file 1(.pdf): Table S1. The vitamin-related gene sets acquired from MSigDB. Table S2. 622 vitamin‐related genes. Table S3. Clinical and molecular characteristics of patients included in this study. Table S4. 482 OS-related genes screened out by univariate COX regression analysis in TCGA cohort. Table S5. 354 OS-related genes screened out by univariate COX regression analysis in CGGA cohort. Table S6. 290 OS-related genes screened out by univariate COX regression analysis in GSE16011 cohort. Table S7. 193 overlapped OS-related genes in the TCGA, CGGA, GSE16011 datasets.
- Additionalfile2.pdf
Additional file 2(.pdf): Figure S1. Risk score and survival status distribution of glioma patients in the GSE16011 cohort. Figure S2. (A, B) Genetic alteration of the six genes in the TCGA glioma dataset. Figure S3. Functional enrichment analysis of the vitamin-related risk signature in the CGGA cohort. (A, B) Top 30 GO processes (A) and top 30 KEGG pathways (B) enriched in the high-risk group in the CGGA dataset. (C, D) GO (C) and KEGG (D) analyses of genes overexpressed in the high-risk group. Figure S4. Functional enrichment analysis of the vitamin-related risk signature in the GSE16011 cohort. (A, B) Top 30 GO processes (A) and top 30 KEGG pathways (B) enriched in the high-risk group in the GSE16011dataset. (C, D) GO (C) and KEGG (D) analyses of genes overexpressed in the high-risk group. Figure S5. ESTIMATE score analysis of the risk signature in the GSE16011 dataset. (A−D) ESTIMATE, immune, and stromal scores, and tumor purity in the low- and high-risk groups in the GSE16011 dataset. (E−H) Correlation of ESTIMATE, immune, and stromal scores, tumor purity, and risk scores in the GSE16011 dataset. Figure S6. Innate (A) and adaptive (B) immune cells distribution in the high- and low-risk groups in the GSE16011 cohort. Figure S7. (A, B) Heatmap of cancer-immunity cycle inhibitors (A) and immune checkpoints (B) in the GSE16011 cohort. Figure S8. (A−D) Univariate (left) and multivariate (right) COX regression analyses of the effect of the risk signature and clinicopathological factors on the OS of glioma patients in the CGGA (A, B) and GSE16011 (C, D) datasets. (E−J) Net benefit (E−G) and net reduction (H−J) curves evaluating the nomogram in predicting 1-, 3-, and 5-year OS rates of glioma patients in the TCGA dataset.
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
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Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 22 Sep, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Translational Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Vitamins not only play a pivotal role in maintaining homeostasis of the body, but also have complex impacts on the occurrence and progression of tumors. However, the effects of vitamins on glioma and the underlying mechanism have not been fully elucidated.
Methods: Vitamin -related genes were extracted from the Molecular Signature Database v7.1 (MSigDB). The overlapping overall survival (OS)-related genes in The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and GSE16011 cohorts screened out by univariate COX regression analysis were utilized to construct a risk model based on the TCGA cohort via random survival forest analysis and multivariate COX regression analysis. The powerful prognostic predictive potential of the vitamin-related risk signature was verified by Kaplan–Meier survival analysis and receiver operating characteristic (ROC) analysis in the three datasets. The ssGSEA method of the GSVA package was used for functional enrichment and immune cell component analyses. ESTIMATE score analysis was used for auxiliary analysis of glioma immune characteristics. A nomogram was constructed and assessed based on the TCGA dataset.
Results: The vitamin-related six-gene (POSTN, IRX5, EEF2, RAB27A, MDM2, and ENO1) risk signature constructed based on the TCGA dataset accurately predicted the outcomes of glioma patients and credibly distinguished between different levels and molecular subtypes of glioma in the TCGA, CGGA, and GSE16011 cohorts. Gliomas with high risk scores exhibited high immune scores, low tumor purity, and immunosuppressive features. The nomogram constructed by combining the vitamin-related risk signature and clinicopathological factors precisely predicted the 1-, 3-, and 5-year OS of glioma patients.
Conclusions: Our study revealed that the vitamin-related six-gene risk signature, as an independent prognostic factor, could accurately distinguish the grade, molecular subtype, and immune characteristics of glioma.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.pdf
Additional file 1(.pdf): Table S1. The vitamin-related gene sets acquired from MSigDB. Table S2. 622 vitamin‐related genes. Table S3. Clinical and molecular characteristics of patients included in this study. Table S4. 482 OS-related genes screened out by univariate COX regression analysis in TCGA cohort. Table S5. 354 OS-related genes screened out by univariate COX regression analysis in CGGA cohort. Table S6. 290 OS-related genes screened out by univariate COX regression analysis in GSE16011 cohort. Table S7. 193 overlapped OS-related genes in the TCGA, CGGA, GSE16011 datasets.
- Additionalfile2.pdf
Additional file 2(.pdf): Figure S1. Risk score and survival status distribution of glioma patients in the GSE16011 cohort. Figure S2. (A, B) Genetic alteration of the six genes in the TCGA glioma dataset. Figure S3. Functional enrichment analysis of the vitamin-related risk signature in the CGGA cohort. (A, B) Top 30 GO processes (A) and top 30 KEGG pathways (B) enriched in the high-risk group in the CGGA dataset. (C, D) GO (C) and KEGG (D) analyses of genes overexpressed in the high-risk group. Figure S4. Functional enrichment analysis of the vitamin-related risk signature in the GSE16011 cohort. (A, B) Top 30 GO processes (A) and top 30 KEGG pathways (B) enriched in the high-risk group in the GSE16011dataset. (C, D) GO (C) and KEGG (D) analyses of genes overexpressed in the high-risk group. Figure S5. ESTIMATE score analysis of the risk signature in the GSE16011 dataset. (A−D) ESTIMATE, immune, and stromal scores, and tumor purity in the low- and high-risk groups in the GSE16011 dataset. (E−H) Correlation of ESTIMATE, immune, and stromal scores, tumor purity, and risk scores in the GSE16011 dataset. Figure S6. Innate (A) and adaptive (B) immune cells distribution in the high- and low-risk groups in the GSE16011 cohort. Figure S7. (A, B) Heatmap of cancer-immunity cycle inhibitors (A) and immune checkpoints (B) in the GSE16011 cohort. Figure S8. (A−D) Univariate (left) and multivariate (right) COX regression analyses of the effect of the risk signature and clinicopathological factors on the OS of glioma patients in the CGGA (A, B) and GSE16011 (C, D) datasets. (E−J) Net benefit (E−G) and net reduction (H−J) curves evaluating the nomogram in predicting 1-, 3-, and 5-year OS rates of glioma patients in the TCGA dataset.
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