The Comprehensive Analysis of m6A-Associated Anoikis Genes in Low-Grade Gliomas
<p>Analysis of six differentially expressed m6A-RGs (DE-m6A-RGs) and 214 differentially expressed anoikis-related genes (DE-ANRGs) in the TCGA-LGG cohort (<b>a</b>) Volcano plot and (<b>b</b>) heatmap for differentially expressed genes (DEGs) between LGG and normal samples (<b>c</b>) Venn plot to identify six DE-m6A-RGs (<b>d</b>) Venn plot to identify 214 DE-ANRGs (<b>e</b>) Bubble chart for the Gene Ontology (GO) analysis of six DE-m6A-RGs (<b>f</b>) Bubble chart for the enriched GO terms of 214 DE-ANRGs (<b>g</b>) Bar chart for the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of 214 DE-ANRGs.</p> "> Figure 2
<p>Spearman correlation analysis to identify 149 DE-m6A-ANRGs.Green nodes indicate M6A genes, red nodes indicate M6A-related DE-ANRG. Red edges indicate positive correlation and blue edges indicate negative correlation.</p> "> Figure 3
<p>A prognostic model was established based on four risk model genes. (<b>a</b>) Univariate Cox regression analysis to screen 10 survival-related genes. Green (HR < 1) indicates the protective factor, red (HR > 1) indicates the risk factor. (<b>b</b>) A least absolute shrinkage and selection operator (LASSO) regression model was built based on four risk model genes, including Cross-validation diagram (left) and LASSO coefficients profiles (right). The two vertical dashed lines in the chart are the logλ values corresponding to λmin (the logarithm of the minimum mean square error lambda, the left dashed line) and λ1se (the logarithm of the standard error of the minimum distance lambda, the right dashed line). From left to right along the x-axis, with the increases of lambda, the compression parameter increases and the absolute value of the coefficient decreases. The number on top are the number of variables with non-zero regression coefficients in the LASSO model. Variables with non-zero coefficients are important features for our screening. A line indicates a gene. (<b>c</b>) Kaplan–Meier survival curves of the risk model in LGG patients (<span class="html-italic">p</span> < 0.0001). Green indicates the low risk groups, red indicates the high risk groups. (<b>d</b>) Receiver operating characteristic (ROC) curves for the predictive accuracy of the risk model in LGG patients Different colors indicate the different followed-up years.</p> "> Figure 4
<p>Gene set enrichment analysis (GSEA) of four risk model genes (<b>a</b>) Results of GSEA for ANXA5. (<b>b</b>) Results of the GSEA of KIF18A (<b>c</b>) Results of GSEA for BRCA1. (<b>d</b>) Results of GSEA for HOXA10.</p> "> Figure 5
<p>Clinical correlation analysis of four risk model genes and the risk model (<b>a</b>) Correlation scatter plots for the relationship between four risk model genes and the risk score (<b>b</b>) Boxplot of risk scores in different clinical subtypes (<b>c</b>) Clinical stratification analysis for the risk model (<span class="html-italic">p</span> < 0.05).</p> "> Figure 6
<p>Construction of the nomogram and the effect of high/low-risk groups on LGG progression (<b>a</b>) Univariate and (<b>b</b>) Multivariate Cox regression analysis to screen independent prognostic factors, including age and risk score. (<b>c</b>) The nomogram was built based on the independent prognostic factors. (<b>d</b>) Calibration curves of the nomogram to predict survival at 1, 3, and 5 years (<b>e</b>) ROC curves to evaluate the predictive accuracy of the nomogram at 1, 3, and 5 years. (<b>f</b>) Gene set variation analysis (GSVA) of all genes in the high/low-risk groups.</p> "> Figure 7
<p>Immune-related analysis and drug susceptibility analysis of the risk model (<b>a</b>) Boxplot of the stromal score, immune score, and estimated score in high/low-risk groups (<b>b</b>) Histogram for the infiltration score of 28 immune cells in TCGA-LGG cohorts (<b>c</b>) Violin plot and (<b>d</b>) heatmap for the infiltration levels of 28 immune cells in high/low-risk groups (<b>e</b>) Correlation heatmap of four risk model genes and 25 significantly differentially expressed immune cells. ns indicates not significance, * <span class="html-italic">p</span> < 0.05, ** <span class="html-italic">p</span> < 0.01, *** <span class="html-italic">p</span> < 0.001, **** <span class="html-italic">p</span> < 0.0001.</p> "> Figure 8
<p>Construction of the mRNA-miRNA-lncRNA regulatory network based on four risk model genes (<b>a</b>) Volcano plot for differentially expressed miRNAs (DE-miRNAs) in the TCGA-LGG cohort. (<b>b</b>) Volcano plot for differentially expressed lncRNAs (DE-lncRNAs) in the TCGA-LGG cohort. (<b>c</b>) Venn plot to identify 16 intersected miRNAs (<b>d</b>) Venn plot to identify 21 intersected lncRNAs (<b>e</b>) The mRNA-miRNA-lncRNA regulatory network, red represents up-regulated genes, blue represents down-regulated genes.</p> "> Figure 9
<p>Expression variation of four prognostic genes (<b>a</b>) Boxplot of ANXA5, KIF18A, BRCA1, and HOXA10 in the TCGA-LGG cohort (wilcox.test). (<b>b</b>) Boxplot of ANXA5, KIF18A, BRCA1, and HOXA10 in the GSE16011 set (wilcox.test), **** <span class="html-italic">p</span> < 0.0001, ** <span class="html-italic">p</span> < 0.01, * <span class="html-italic">p</span> < 0.05. (<b>c</b>) The results of immunohistochemistry (IHC) methods for the protein expression levels of risk model genes between glioma and normal tissues through the human protein atlas (HPA) database The deeper the yellow in the diagram, the higher the protein expression of the target gene.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Acquisition of Data
2.2. Differential Expression Analysis and Functional Enrichment Analysis
2.3. Analysis of Protein-Protein Interaction (PPI)
2.4. Construction of the Risk Model
2.5. Analyses of Risk Model Genes
2.6. Clinical Correlation Analysis
2.7. Construction of the Independent Prognostic Model
2.8. Immune Infiltration Analysis and Drug Susceptibility Analysis
2.9. Construction of Competing Endogenous RNA (ceRNA) Network
2.10. External Validation of Risk Model Genes
3. Results
3.1. Identification and Functional Annotation Analysis of DE-m6A-RGs
3.2. Characterization of DE-m6A-ANRGs and Construction of the PPI Network
3.3. Acquisition and Assessment of the Risk Model
3.4. Risk Model Genes
3.5. Correlation Analysis of Clinical Characteristics
3.6. Acquisition of the Independent Prognostic Model and GSVA
3.7. Immune Microenvironment Analysis and Prediction of Potential Therapeutic Agents
3.8. mRNA-miRNA-lncRNA Regulatory Network/
3.9. Expression of Risk Model Genes
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
m6A | N6-methyladenosine |
LGG | low-grade glioma |
m6A-RGs | m6A-related genes |
ANRGs | anoikis-related genes |
DEGs | differentially expressed genes |
LASSO | least absolute shrinkage and selection operator |
GSEA | gene set enrichment analysis |
GO | Gene Ontology |
KEGG | Kyoto Encyclopedia of Genes and Genomes |
PPI | protein interaction |
ROC | receiver operating characteristic |
K-M | Kaplan–Meier |
GSVA | gene set variation analysis |
IC50 | the 50% inhibitory concentration |
ceRNA | competing endogenous RNA |
HPA | human protein atlas |
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Zheng, H.; Zhao, Y.; Zhou, H.; Tang, Y.; Xie, Z. The Comprehensive Analysis of m6A-Associated Anoikis Genes in Low-Grade Gliomas. Brain Sci. 2023, 13, 1311. https://doi.org/10.3390/brainsci13091311
Zheng H, Zhao Y, Zhou H, Tang Y, Xie Z. The Comprehensive Analysis of m6A-Associated Anoikis Genes in Low-Grade Gliomas. Brain Sciences. 2023; 13(9):1311. https://doi.org/10.3390/brainsci13091311
Chicago/Turabian StyleZheng, Hui, Yutong Zhao, Hai Zhou, Yuguang Tang, and Zongyi Xie. 2023. "The Comprehensive Analysis of m6A-Associated Anoikis Genes in Low-Grade Gliomas" Brain Sciences 13, no. 9: 1311. https://doi.org/10.3390/brainsci13091311