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. 2022 May 11;14(9):4050-4068.
doi: 10.18632/aging.204071. Epub 2022 May 11.

Comprehensive analysis of histone deacetylases genes in the prognosis and immune infiltration of glioma patients

Lin Shen  1 , Yanyan Li  2 , Na Li  1 , Liangfang Shen  1 , Zhanzhan Li  1   3
Affiliations

Comprehensive analysis of histone deacetylases genes in the prognosis and immune infiltration of glioma patients

Lin Shen et al. Aging (Albany NY). .

Abstract

The occurrence and development of tumors are closely related to histone deacetylases (HDACs). However, their relationship with the overall biology and prognosis of glioma is still unknown. In the present study, we developed and validated a prognostic model for glioma based on HDAC genes. Glioma patients can be divided into two subclasses based on eleven HDAC genes, and patients from the two subclasses had markedly different survival outcomes. Then, using six HDAC genes (HDAC1, HDAC3, HDAC4, HDAC5, HDAC7, and HDAC9), we established a prognostic model for glioma patients, and this prognostic model was validated in an independent cohort. Furthermore, the calculated risk score from six HDACA genes expression was found to be an independent prognostic factor that could predict the five-year overall survival of glioma patients well. High-risk patients have changes in multiple complex functions and molecular signaling pathways, and the gene alterations of high- and low-risk patients were significantly different. We also found that the different survival outcomes of high- and low-risk patients could be related to the differences in immune filtration levels and the tumor microenvironment. Subsequently, we identified several small molecular compounds that could be favorable for glioma patient treatment. Finally, the expression levels of HDAC genes from the prognostic model were validated in glioma and nontumor tissue samples. Our results revealed the clinical utility and potential molecular mechanisms of HDAC genes in glioma. A model based on six HDAC genes can predict the overall survival of glioma patients well, and these genes are potential therapeutic targets.

Keywords: bioinformatics; glioma; histone deacetylases; immune infiltration; prognostic factor.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no conflicts of interest related to this study.

Figures

Figure 1
Figure 1
Glioma patients can be separated into two subclasses using HDAC genes. (A) The flow chart of data analysis. (B) The correlation circle plot among eleven HDAC genes. (C) The consensus matrix plot identified the best grouping (k = 2). (D) Principal component analysis of glioma subclasses in the TCGA dataset. (E) The corrected t-SNE2 analysis for two subclasses. (F) The Kaplan-Meier survival curve for two subclasses in TCGA dataset. (G) The correlation of different clinical parameters and HDAC gens expressions with subclasses.
Figure 2
Figure 2
Development and validation of prognostic model based on HDAC genes. (A) Forest plot of univariate cox regression for HDAC genes in glioma patients. (B) LASSO regression of the 11 OS-related HDAC genes. (C) Cross-validation for turning parameters selection in the LASSO regression. (D) Kaplan-Meier survival curve of high- and low-risk groups from developed prognostic model based on 6 HDAC genes in TCGA. (E) Distributions of risk scores and survival time of glioma patients in TCGA. (F) PCA plot for high- and low-risk group in TCGA. (G) Kaplan-Meier survival curve of high- and low-risk groups from validated prognostic model based on 6 HDAC genes in CGGA. (H) Distributions of risk scores and survival time of glioma patients in CGGA. (I) PCA plot for high- and low-risk group in CGGA. (J and K) The receiver operating characteristic curve for predicting 1-year, 2-year, and 3-year survival rate of glioma patients in TCGA and CGGA.
Figure 3
Figure 3
Stratified analyses of established HDAC-related genes prognostic model in TCGA. (A and B) Age (>41 vs. ≤41). (C and D) Gender (male vs. female). (E and F) Histology (LGG vs. GBM). (G–I) WHO stage (II, III and IV). (J–L) Type of tumors (Primary, secondary vs. recurrent). (M and N) 1p19q (Non-codel and codel). (O and P) mutant and wildtype. (Q and R) Radiotherapy (Yes vs. No). (S and T) Chemotherapy (Yes vs. No).
Figure 4
Figure 4
Association between HDAC genes and clinical characteristics in glioma patients. (A) Heatmap indicated the expression of HDAC genes between two subclasses. (B) Heatmap of associations among risk stratifications and clinical parameters and six HDAC genes expression. Comparisons of risk score among different clinical parameters: (C) age (>41 vs. ≤41), (D) gender (male vs. female), (E) WHO stage (II, III, IV). (F) histology (LGG vs. GBM). (G) PRS type (primary, recurrent, and secondary). (H) IDH mutation status (mutant vs. wild type). (I) 1p19q codeletion status (codel vs. non-codel). (J) radiotherapy status (No vs. Yes). (K) chemotherapy (No vs. Yes).
Figure 5
Figure 5
Independent prognosis analyses of HDAC-related genes model. (A and B) univariate and multivariate cox regression of risk score based on HDAC genes in TCGA. (C) The receiver operating characteristic curve of risk score for predicting 5-year survival rate in TCGA. (D and E) univariate and multivariate cox regression of risk score based on HDAC genes in CGGA. (F) The receiver operating characteristic curve of risk score for predicting 5-year survival rate in CGGA. (G–I) Calibration curves of 1-eyar, 3-year, and 5-year OS in TCGA. (J) Nomograph model established in CGGA cohort.
Figure 6
Figure 6
GO enrichment (A) and KEGG pathways analysis (B) based on differently expressed genes between high- and low risk groups.
Figure 7
Figure 7
Landscape of mutation profiles between high- and low-risk groups. (A and B) Waterfall plots of mutation information in each sample. (C and D) Bar graph of variant classification. (E and F) somatic interactions plot (co-occurrence and exclusive).
Figure 8
Figure 8
Immune status analysis between high- and low-risk group. (A) The ssGSEA scores of immune cells. (B) The ssGSEA scores of immune-related functions. (C–E) Comparisons of Estimated, immune and stromal score between high-and low-risk group. (F–K) Correlation between risk score and immune markers (Macrophages M0, Monocytes, NK cells activated, Macrophages M1, M2, and Mast cells activated) in glioma patients.
Figure 9
Figure 9
Top 16 kinds of drug associated with HDAC member.
Figure 10
Figure 10
Expression of HDAC genes in glioma and non-tumor tissue. (A) HDAC1, (B) HDAC3, (C) HDAC4, (D) HDAC5, (E) HDAC7, (F) HDAC9.
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References

    1. Weller M, Wick W, Aldape K, Brada M, Berger M, Pfister SM, Nishikawa R, Rosenthal M, Wen PY, Stupp R, Reifenberger G. Glioma. Nat Rev Dis Primers. 2015; 1:15017. 10.1038/nrdp.2015.17 - DOI - PubMed
    1. Chen R, Smith-Cohn M, Cohen AL, Colman H. Glioma Subclassifications and Their Clinical Significance. Neurotherapeutics. 2017; 14:284–97. 10.1007/s13311-017-0519-x - DOI - PMC - PubMed
    1. Kan LK, Drummond K, Hunn M, Williams D, O'Brien TJ, Monif M. Potential biomarkers and challenges in glioma diagnosis, therapy and prognosis. BMJ Neurol Open. 2020; 2:e000069. 10.1136/bmjno-2020-000069 - DOI - PMC - PubMed
    1. Bai J, Varghese J, Jain R. Adult Glioma WHO Classification Update, Genomics, and Imaging: What the Radiologists Need to Know. Top Magn Reson Imaging. 2020; 29:71–82. 10.1097/RMR.0000000000000234 - DOI - PubMed
    1. Snape TJ, Warr T. Approaches toward improving the prognosis of pediatric patients with glioma: pursuing mutant drug targets with emerging small molecules. Semin Pediatr Neurol. 2015; 22:28–34. 10.1016/j.spen.2014.12.003 - DOI - PubMed

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