Dysregulated Expression and Methylation Analysis Identified <i>TLX1NB</i> as a Novel Recurrence Marker in Low-Grade Gliomas

Duan, Hongzhou; Yang, Zuozhen; Li, Chen; Zhang, Jiayong; Shen, Shengli; Yuan, Changwei; Wang, Yingjin

doi:https://doi.org/10.1155/2020/5069204

International Journal of Genomics

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Abstract Introduction Materials and Methods Results Discussion Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

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Epigenomic and Epitranscriptomic Mechanisms of Human Diseases

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Volume 2020 | Article ID 5069204 | https://doi.org/10.1155/2020/5069204

Dysregulated Expression and Methylation Analysis Identified TLX1NB as a Novel Recurrence Marker in Low-Grade Gliomas

Hongzhou Duan,¹Zuozhen Yang,²Chen Li,³Jiayong Zhang,¹Shengli Shen,¹Changwei Yuan,¹and Yingjin Wang¹

Academic Editor: Xingshun Xu

Received17 Jul 2020

Accepted22 Sept 2020

Published12 Oct 2020

Abstract

Low-grade gliomas (LGGs) are the most common CNS tumors, and the main therapy for LGGs is complete surgical resection, due to its curative effect. However, LGG recurrence occurs frequently. Biomarkers play a crucial role in evaluating the recurrence and prognosis of LGGs. Numerous studies have focused on LGG prognosis. However, the multiomics research investigating the roles played by gene methylation and expression in LGG recurrence remains limited. In this study, we integrated the TCGA and GEO datasets, analyzing RNA and methylation data for recurrence (R) and nonrecurrence (NR) groups. We found a low expression of TLX1NB and high methylation in recurrence patients. Low expression of TLX1NB is associated with poor survival (OS: ). The expression of TLX1NB is likely to play a role in the prognosis of LGG. Therefore, TLX1NB may represent an alternative early biomarker for the recurrence of low-grade gliomas.

1. Introduction

Low-grade glioma (LGG) is an uncommon type of the primary central nervous system tumor classified by the WHO as Class I and II [1, 2]. Usually, LGG is inactive, and the main therapy strategy is complete surgical resection because this treatment can be curative. However, even if the tumor is resected, the tumor cells resistant to irradiation and chemotherapy may grow gradually; thus, the neoplasm can still relapse at some time point during the clinical course [3–6]. Due to recurrence and metastasis, the prognosis of LGG remains controversial [7, 8]. Because these tumors have a long asymptomatic natural history, whether patients with limited lesions and few symptoms are given active or delayed treatment and the timing of postoperative radiotherapy and chemotherapy have not been determined [9]. Thus, it is of great importance to understand the underlying molecular mechanisms governing LGG recurrence and to identify novel recurrence-associated biomarkers [5, 10].

Comprehensive multiomics provides a deeper and more comprehensive understanding of specific genes and enables us to identify functional genes for biomarker mining [11]. The Cancer Genome Atlas (TCGA) offers multidimensional maps of the key genomic changes in cancer, including alterations in DNA, RNA, copy number, and methylation [12].

Previous studies have revealed several biomarkers for LGG prognosis. For example, high methylation level of HIST1H2BK [13], the MGMT promoter [14], 1p/19q [15], the TERT promoter [16], IDH [16], and mTORC1/2 [17] are associated with prognosis in LGG. However, the dysregulation of the expression of genes associated with LGG recurrence has not been fully characterized.

The objective of the present study was to explore possible clinical biomarkers of recurrence in LGG through multifaceted analysis of recurrence patterns in intracranial LGG.

2. Materials and Methods

2.1. Data Sources

RNA read count data, expression matrix data, copy number variation data, and DNA methylation data (Illumina Human Methylation 450 k Array) of LGG were obtained from The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov). Clinical information on LGG, including survival and recurrence data, was downloaded from the GDC Data Portal (https://portal.gdc.cancer.gov/) [12]. The gene expression matrix of GSE35158 was downloaded from the Gene Expression Omnibus (GEO) database.

2.2. Preprocessing and Analysis of RNA Data

We defined patients with recurrence as the case group and patients without recurrence as the control group. The RNA read count data were preprocessed via DESeq2 1.26.0 (R package) [18]; and adjusted value < 0.05 were selected as the threshold. All differentially expressed genes were obtained. Bar plots of different genes were generated by ggplot2 3.3.0 (R package) [19]. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and data visualization were performed by clusterProfiler 3.14.3 (R package) [20].

2.3. Preprocessing and Analysis of DNA Methylation Data

The DNA methylation data were preprocessed and normalized using minfi 1.32.0 (R package) [21]. Differentially methylated regions with a change value >0.1 or <-0.1 and a value < 0.05 were selected as the methylation change threshold. All different methylated regions were obtained. Annotation was performed via the online tool wANNOVAR [22] (http://wannovar.wglab.org).

2.4. Overlap Statistics between RNA and Methylation and Copy Number Variation (CNV) Analysis

Upregulated RNA and hypomethylated genes or downregulated RNA and hypermethylated genes were extracted. Copy number variation analysis of target genes was performed by ggpubr 0.2.5 (R package) (https://github.com/kassambara/ggpubr).

2.5. Survival Analysis and Statistics

Survival plots of selected genes were generated by survival 3.1-12 (R package) and survminer 0.4.6 (R package) [23], and a value < 0.05 was selected as the significance level.

2.6. GSEA Analysis

We calculated the correlation score between the target gene and all other genes and then ranked the correlation score from high to low. Finally, we obtained the gene list. We selected oncogenic gene sets (C6) as the input dataset, performed GSEA analysis by clusterProfiler 3.14.3 (R package) [20], and visualized pathways of interest by enrichplot 1.6.1 (R package) [20].

3. Results

3.1. Differentially Expressed Genes between Recurrence and Nonrecurrence Samples

We selected the samples with recurrence information and RNA sequencing data and eventually obtained 65 recurrence and 188 nonrecurrence data for further RNA expression analysis. We set and adjusted value < 0.05 as a significant change threshold, and finally, we obtained 329 upregulated and 125 downregulated genes at the RNA level (Figure 1(a) and Table 1).

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(b)

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Figure 1

Differentially expressed genes between recurrence and nonrecurrence samples, number statics, and functional enrichment. (a) Upregulated (red point ()) and downregulated (blue point ()) genes between recurrence and nonrecurrence samples, adjusted value < 0.05, were selected as thresholds. (b) Differentially expressed gene-enriched GO terms; a value < 0.05 was selected as the threshold. (c) Differentially expressed gene-enriched pathways; a value < 0.05 was selected as the threshold.

Through GO and KEGG functional analyses, the differentially expressed genes were determined to be primarily involved in neuroactive ligand−receptor interaction pathways (Figure 1(b)) and DNA-binding transcription activator activity (Figure 1(c)).

3.2. Identification of Methylation Regions between Recurrence and Nonrecurrence Samples

A total of 485,577 loci of DNA methylation were obtained from the TGCA database. After NA values were removed, 467,971 loci were retained for further analysis. Fifty-nine candidate DMRs were obtained from the analysis results. After annotation, removing unannotated DMRs, four hypermethylated regions were obtained for further cross-overlap analysis (Table 2).

3.3. Integrated Analysis for RNA Expression, DNA Methylation, and Copy Number Variation

Genes were selected following the criterions: high methylation at DNA level and low expression at RNA level or low methylation at DNA level and high expression at RNA level. TLX1NB was identified by this approach (Figure 2(a)). The copy number variation of TLX1NB was investigated, and no difference between nonrecurrence and recurrence was found (Figure 2(b), ). These results indicated that the downregulation of TLX1NB may not be associated with copy number alterations. To evaluate the methylation effect on TLX1NB, we compared methylation and RNA expression levels between recurrence and nonrecurrence groups. We found methylation of TLX1NB upregulated at 24% in the recurrence group compared with the control (Figure 2(c)), meanwhile at RNA expression level, the expression of TLX1NB downregulated at 80% (Figure 2(d)).

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Figure 2

Gene overlap for RNA and methylation and copy number variation for TLX1NB. (a) Overlap for low expression at the RNA level (threshold: adjusted value < 0.05, ) and high methylation at the DNA level (threshold: ). (b) Copy number variation analysis for TLX1NB between recurrence and nonrecurrence. A -value < 0.05 was selected as the threshold. (c) Methylation level of TLX1NB between recurrence and nonrecurrence. A value < 0.05 was selected as the significance threshold. (d) RNA expression level of TLX1NB between recurrence and nonrecurrence. A value < 0.05 was selected as the significance threshold.

3.4. Gene Set Enrichment Analysis of TLX1NB

To further investigate the potential functions of TLX1NB, GSEA was performed based on the LGG expression data. TLX1NB was negatively correlated with typical tumor driver genes, such as P53, EGFR, TBK1, and STK33 (Figure 3). These results implied that TLX1NB is involved in typical cancer pathways and tumor proliferation processes.

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To understand the expression of TLX1NB in LGG patients in different subtypes, we employed dataset GSE35158 to analyze 80 patients’ expression profiles. We observed that there was no difference in the TLX1NB expression between grade II and grade III patients (Figure 4(a), , Wilcoxon’s test); however, there was a significantly increased TLX1NB expression level in the neuroblastic subtype, which was associated with Hu immunopositivity and a mature neuronal gene set in GSE35158 (Figure 4(b), , Kruskal–Wallis test). We also found decreased expression of TLX1NB in the PTEN deletion group (Figure 4(c), , Kruskal–Wallis test). To explore the relationship between TLX1NB, PTEN promoter methylation, and IDH deletion, we analyzed TLX1NB expression and PTEN promoter in GSE35158 and TLX1NB expression and IDH mutation in TCGA and GSE35158. No significant change of TLX1NB expression was observed between PTEN methylation group and nonmethylation group in GSE35158 (Figure 4(d)). TLX1NB was significantly upregulated in the IDH mutation group in the TCGA dataset (Figure 4(e)); however, no difference was observed in the GSE35158 dataset (Figure 4(f)). The result for IDH mutation was controversial. So, we resume that TLX1NB has the potential to be a new biomarker in LGG associated with recurrence and prognosis.

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Figure 4

TLX1NB distribution in different subsets. (a) TLX1NB distribution between grade II and III LGG. A value < 0.05 was selected as the significance threshold. (b) High expression of TLX1NB in the NB group compared with PG and EPL. A value < 0.05 was selected as the significance threshold. NB: neuroblastic; PG: preglioblastoma; EPL: early progenitor-like. (c) Low expression of TLX1NB in the PTEN deletion group. A value < 0.05 was selected as the significance threshold. (d) No difference for TLX1NB between PTEN methylation and unmethylation group. A value < 0.05 was selected as the significance threshold. (e) High expression of TLX1NB in the IDH1 mutation group in the TCGA dataset. A value < 0.05 was selected as the significance threshold. (f) No difference for TLX1NB between IDH1 mutation and wildtype group in GSE35158. A value < 0.05 was selected as the significance threshold.

3.5. TLX1NB Predicts Survival Level in LGG

From the previous results, we could assume that low expression of TLX1NB is associated with adverse consequences of LGG. Thus, we analyzed the prognosis of LGG patients in TCGA. Five hundred twenty-eight patients were divided into two groups: the high TLX1NB expression group and the low TLX1NB expression group. We observed that patients in the low TLX1NB expression group had shorter overall survival (OS) (Figure 5, , log-rank test).

4. Discussion

According to CBTRUS (Central Brain Tumor Registry of the United States), glioma is the most common CNS tumor, accounting for ~27% of CNS tumors [1] and leading to severe disability and mortality. According to the WHO central nervous tumor histological grading standards, such tumors such as grade I to II astrocytomas, papillary glioneuronal tumors, and vascular central gliomas are collectively referred to as low-grade gliomas [2]. LGGs account for 15% to 30% of all gliomas. LGGs are more common in children and young people with an average age of onset of 30 to 45 [4]. Although surgical resection is the main method of treatment, the tumor cannot be completely removed in the true sense due to the invasive growth of gliomas, the principle of tumor location, and the maximum protection of nerve function, which affects the survival of the patient [5]. Therefore, research that affects the risk of glioma recurrence may provide evidence to guide clinical treatment and facilitate the development of personalized strategies.

Regarding recurrence, previous studies revealed that for instance, high-level methylation of the MGMT promoter leads to hypermutation at recurrence, 1p/19q codeletion or IDH mutation is associated with longer overall survival and better treatment response, and TP53 mutation is associated with a worse prognosis [5, 10, 14, 16, 24, 25]. However, the number of recurrence studies focusing on gene expression remains notably limited. In this study, we comprehensively analyzed TCGA and GEO data and observed that TLX1NB may be a potential recurrence biomarker. Previous studies have demonstrated that TLX1NB can be a prognostic lncRNA biomarker in lung adenocarcinoma [26]; however, there are no other studies revealing the role of TLX1NB in CNS cancer.

DNA copy number variations (CNVs) are an important component of genetic variation, affecting a greater fraction of the genome than single-nucleotide polymorphisms (SNPs) [27]. Therefore, we investigated the CNVs of TLX1NB and found no difference between the recurrence and nonrecurrence groups.

TLX1NB is adjacent to the TLX1 gene, and we hypothesize that its role could be associated with TLX1 function as a cis-regulatory factor [28]. Therefore, we correlated TLX1NB and TLX1 and found that TLX1NB was positively coexpressed with TLX1NB (Supplementary Figure 1). TLX1, a member of the NK-linked or NK-like (NKL) subfamily, is involved in the specification of neuronal cell fates. Thus, we hypothesize that TLX1NB and TLX1 together drive function. Our gene set enrichment analysis showed that TLX1NB was associated with typical cancer driver genes, such as P53, EGFR, STK33, and TBK1. These results demonstrate the role played by TLX1NB in cancer pathways. We also found that TLX1NB was negatively correlated with the transcription activating gene set (Supplementary Figure 2a) and the IL2 gene set (Supplementary Figure 2b).

5. Conclusion

We investigated the relationship between TLX1NB in LGG. TLX1NB is a predictor of LGG survival; reduced expression of TLX1NB worsens the prognosis and survival of LGG patients. TLX1NB probably affects LGG through the tumor activating pathway and could be a meaningful biomarker for LGG.

Data Availability

The raw data can download following the link in our manuscript, and analyzed data has been showed in table 1 and table 2. Regarding the intermediate process data we are willing to provide anytime when any researcher raise their request.

Conflicts of Interest

All authors declare no conflicts of interest. All authors have no conflicts of interest, financial or otherwise.

Authors’ Contributions

Hongzhou Duan contributed to the concept and analysis design. Zuozhen Yang and Jiayong Zhang contributed to the bioinformatic data analysis and statistical analysis. Shengli Shen, Changwei Yuan, and Yingjin Wang contributed to the literature search, data acquisition, and manuscript preparation. Chen Li contributed to the manuscript revision and finalization. Hongzhou Duan and Zuozhen Yang contributed equally to this work.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (81541119), the Peking University First Hospital Cross Clinical Study Foundation, and the Youth Clinical Research Project of Peking University First Hospital (2019CR02).

Supplementary Materials

Supplementary Figure 1: correlation analysis between TLX1NB and TLX1 (), and a value < 0.05 was selected as the significance threshold. Supplementary Figure 2: GSEA analysis of TLX1NB in the TCGA LGG dataset. Oncogenic gene sets (C6) were used as the input dataset, and a value < 0.05 was selected as the significance threshold. ATF2 (Figures S2a) and IL2 (Figures S2b) gene sets were enriched. (Supplementary Materials)

References

Q. T. Ostrom, G. Cioffi, H. Gittleman et al., “CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012-2016,” Neuro-Oncology, vol. 21, Supplement_5, pp. v1–v100, 2019.
View at: Publisher Site | Google Scholar
A. Gupta and T. Dwivedi, “A simplified overview of World Health Organization classification update of central nervous system tumors 2016,” Journal of Neurosciences in Rural Practice, vol. 8, no. 4, pp. 629–641, 2017.
View at: Publisher Site | Google Scholar
E. Le Rhun, S. Taillibert, and M. C. Chamberlain, “Current management of adult diffuse infiltrative low grade gliomas,” Current Neurology and Neuroscience Reports, vol. 16, no. 2, p. 15, 2016.
View at: Publisher Site | Google Scholar
A. F. Hottinger, M. E. Hegi, and B. G. Baumert, “Current management of low-grade gliomas,” Current Opinion in Neurology, vol. 29, no. 6, pp. 782–788, 2016.
View at: Publisher Site | Google Scholar
F. X. Ferracci, K. Michaud, and H. Duffau, “The landscape of postsurgical recurrence patterns in diffuse low-grade gliomas,” Critical Reviews in Oncology/Hematology, vol. 138, pp. 148–155, 2019.
View at: Publisher Site | Google Scholar
K. A. Jaeckle, P. A. Decker, K. V. Ballman et al., “Transformation of low grade glioma and correlation with outcome: an NCCTG database analysis,” Journal of Neuro-Oncology, vol. 104, no. 1, pp. 253–259, 2011.
View at: Publisher Site | Google Scholar
Y. Fukuya, S. Ikuta, T. Maruyama et al., “Tumor recurrence patterns after surgical resection of intracranial low-grade gliomas,” Journal of Neuro-Oncology, vol. 144, no. 3, pp. 519–528, 2019.
View at: Publisher Site | Google Scholar
A. Paľa, J. Coburger, M. Scherer et al., “To treat or not to treat? A retrospective multicenter assessment of survival in patients with IDH-mutant low-grade glioma based on adjuvant treatment,” Journal of Neurosurgery, vol. 1, pp. 1–8, 2019.
View at: Google Scholar
P. de Blank, P. Bandopadhayay, D. Haas-Kogan, M. Fouladi, and J. Fangusaro, “Management of pediatric low-grade glioma,” Current Opinion in Pediatrics, vol. 31, no. 1, pp. 21–27, 2019.
View at: Publisher Site | Google Scholar
P. de Blank, M. Fouladi, and J. T. Huse, “Molecular markers and targeted therapy in pediatric low-grade glioma,” Journal of Neuro-Oncology, 2020.
View at: Google Scholar
T. Rodosthenous, V. Shahrezaei, and M. Evangelou, “Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study,” Bioinformatics, article btaa530, 2020.
View at: Publisher Site | Google Scholar
R. L. Grossman, A. P. Heath, V. Ferretti et al., “Toward a shared vision for cancer genomic data,” The New England Journal of Medicine, vol. 375, no. 12, pp. 1109–1112, 2016.
View at: Publisher Site | Google Scholar
W. Liu, Z. Xu, J. Zhou et al., “High levels of HIST1H2BK in low-grade glioma predicts poor prognosis: a study using CGGA and TCGA data,” Frontiers in Oncology, vol. 10, p. 627, 2020.
View at: Publisher Site | Google Scholar
R. Mathur, Y. Zhang, M. R. Grimmer et al., “MGMT promoter methylation level in newly diagnosed low-grade glioma is a predictor of hypermutation at recurrence,” Neuro-Oncology, noaa059, 2020.
View at: Publisher Site | Google Scholar
S. R. van der Voort, F. Incekara, M. M. J. Wijnenga et al., “Predicting the 1p/19q codeletion status of presumed low-grade glioma with an externally validated machine learning algorithm,” Clinical Cancer Research, vol. 25, no. 24, pp. 7455–7462, 2019.
View at: Publisher Site | Google Scholar
R. Fukuma, T. Yanagisawa, M. Kinoshita et al., “Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network,” Scientific Reports, vol. 9, no. 1, p. 20311, 2019.
View at: Publisher Site | Google Scholar
A. Arnold, M. Yuan, A. Price, L. Harris, C. G. Eberhart, and E. H. Raabe, “Synergistic activity of mTORC1/2 kinase and MEK inhibitors suppresses pediatric low-grade glioma tumorigenicity and vascularity,” Neuro-Oncology, vol. 22, no. 4, pp. 563–574, 2020.
View at: Publisher Site | Google Scholar
M. I. Love, W. Huber, and S. Anders, “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2,” Genome Biology, vol. 15, no. 12, p. 550, 2014.
View at: Publisher Site | Google Scholar
K. Ito and D. Murphy, “Application of ggplot 2 to pharmacometric graphics,” CPT: Pharmacometrics & Systems Pharmacology, vol. 2, pp. 1–16, 2013.
View at: Google Scholar
G. Yu, L. G. Wang, Y. Han, and Q. Y. He, “clusterProfiler: an R package for comparing biological themes among gene clusters,” OMICS, vol. 16, no. 5, pp. 284–287, 2012.
View at: Publisher Site | Google Scholar
M. J. Aryee, A. E. Jaffe, H. Corrada-Bravo et al., “Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays,” Bioinformatics, vol. 30, no. 10, pp. 1363–1369, 2014.
View at: Publisher Site | Google Scholar
X. Chang and K. Wang, “wANNOVAR: annotating genetic variants for personal genomes via the web,” Journal of Medical Genetics, vol. 49, no. 7, pp. 433–436, 2012.
View at: Publisher Site | Google Scholar
A. Kassambara, M. Kosinski, and P. Biecek, survminer: drawing survival curves using'ggplot 2'. R package version 0.3, vol. 1, 2017.
S. Y. Cho, S. Kim, G. Kim, P. Singh, and D. W. Kim, “Integrative analysis of KIF4A, 9, 18A, and 23 and their clinical significance in low-grade glioma and glioblastoma,” Scientific Reports, vol. 9, no. 1, p. 4599, 2019.
View at: Publisher Site | Google Scholar
Y. Y. Wang, T. Zhang, S. W. Li et al., “Mapping p 53 mutations in low-grade glioma: a voxel-based neuroimaging analysis,” AJNR. American Journal of Neuroradiology, vol. 36, no. 1, pp. 70–76, 2015.
View at: Publisher Site | Google Scholar
X. Li, B. Li, P. Ran, and L. Wang, “Identification of ceRNA network based on a RNA-seq shows prognostic lncRNA biomarkers in human lung adenocarcinoma,” Oncology Letters, vol. 16, no. 5, pp. 5697–5708, 2018.
View at: Publisher Site | Google Scholar
A. Shlien and D. Malkin, “Copy number variations and cancer,” Genome Medicine, vol. 1, no. 6, p. 62, 2009.
View at: Publisher Site | Google Scholar
F. Kopp and J. T. Mendell, “Functional classification and experimental dissection of long noncoding RNAs,” Cell, vol. 172, no. 3, pp. 393–407, 2018.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2020 Hongzhou Duan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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