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Identification and Validation of Methylation-Driven Genes Prognostic Signature for Recurrence of Laryngeal Squamous Cell Carcino

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Identification and Validation of Methylation-Driven Genes Prognostic Signature for Recurrence of Laryngeal Squamous Cell Carcino Cui et al. Cancer Cell Int (2020) 20:472 https://doi.org/10.1186/s12935-020-01567-3 Cancer Cell International PRIMARY RESEARCH Open Access Identifcation and validation of methylation-driven genes prognostic signature for recurrence of laryngeal squamous cell carcinoma by integrated bioinformatics analysis Jie Cui3†, Liping Wang2†, Waisheng Zhong4†, Zhen Chen5, Jie Chen4*, Hong Yang3* and Genglong Liu1* Abstract Background: Recurrence remains a major obstacle to long-term survival of laryngeal squamous cell carcinoma (LSCC). We conducted a genome-wide integrated analysis of methylation and the transcriptome to establish methyla- tion-driven genes prognostic signature (MDGPS) to precisely predict recurrence probability and optimize therapeutic strategies for LSCC. Methods: LSCC DNA methylation datasets and RNA sequencing (RNA-seq) dataset were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs). By univariate and multivariate Cox regression analyses, fve genes of DNA MDGs was developed a recurrence-free survival (RFS)-related MDGPS. The predictive accuracy and clinical value of the MDGPS were evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA), and compared with TNM stage system. Additionally, prognostic value of MDGPS was validated by external Gene Expression Omnibus (GEO) database. According to 5 MDGs, the candidate small molecules for LSCC were screen out by the CMap database. To strengthen the bioinformatics analysis results, 30 pairs of clinical samples were evaluated by digoxigenin-labeled chromogenic in situ hybridization (CISH). Results: A total of 88 DNA MDGs were identifed, and fve RFS-related MDGs (LINC01354, CCDC8, PHYHD1, MAGEB2 and ZNF732) were chosen to construct a MDGPS. The MDGPS can efectively divide patients into high-risk and low- risk group, with the area under curve (AUC) of 0.738 (5-year RFS) and AUC of 0.74 (3-year RFS). Stratifcation analysis *Correspondence: [email protected]; [email protected]; lglong3@mail2. sysu.edu.cn †Jie Cui, Liping Wang and Waisheng Zhong contributed equally to this work 1 Department of Pathology, Afliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, Guangdong, P. R. China 3 Department of Head and Neck Surgery, Afliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, Guangdong, P. R. China 4 Department of Head Neck Surgery, Hunan Cancer Hospital and The Afliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410000, Hunan, P. R. China Full list of author information is available at the end of the article © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Cui et al. Cancer Cell Int (2020) 20:472 Page 2 of 19 afrmed that the MDGPS was still a signifcant statistical prognostic model in subsets of patients with diferent clinical variables. Multivariate Cox regression analysis indicated the efcacy of MDGPS appears independent of other clin- icopathological characteristics. In terms of predictive capacity and clinical usefulness, the MDGPS was superior to traditional TNM stage. Additionally, the MDGPS was confrmed in external LSCC cohorts from GEO. CMap matched the 9 most signifcant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. Finally, CISH analysis in 30 LSCC tissues and paired adjacent normal tissues revealed that MAGEB2 has signifcantly higher expres- sion of LSCC compared to adjacent non-neoplastic tissues; LINC01354, CCDC8, PHYHD1, and ZNF732 have signif- cantly lower expression of LSCC compared to adjacent non-neoplastic tissues, which were in line with bioinformatics analysis results. Conclusion: A MDGPS, with fve DNA MDGs, was identifed and validated in LSCC patients by combining transcrip- tome and methylation datasets analysis. Compared TNM stage alone, it generates more accurate estimations of the recurrence prediction and maybe ofer novel research directions and prospects for individualized treatment of patients with LSCC. Keywords: Laryngeal squamous cell carcinoma, DNA methylation-driven genes, Epigenetics, Prediction, Recurrence- free survival Background What’s more, changes in DNA methylation with a high As an aggressively malignant neoplasm, laryngeal squa- level of plasticity allows tumor cells to quickly adapt to mous cell carcinoma (LSCC) is one of the most preva- changes in metabolic restrictions or physiology during lence cancers in head and neck region, and represents the process of tumorigenesis [6, 7]. Hence, it is reason- 85–95% of all laryngeal cancer [1]. According to the able to analyses the DNA methylation pattern in the American Cancer Society, the estimated respective new tumor cells in order to fnd predictors for the recurrence cases and new death are 13 150 and 3 710 annually, with and novel therapeutic targets in LSCC patients. incidence and mortality rates of 4.0 and 1.1 per 100 000, Te availability of high throughput genomic assays respectively [2]. Current multimode treatments, includ- such as RNA-seq and DNA methylation-seq have opened ing surgery, radiotherapy, chemotherapy, targeted ther- the possibility for a comprehensive characterization of all apy and so on, are applied to cure LSCC patients [3]. molecular alterations of cancers, leading to the discovery Tough treatments have improved during the past dec- of new biomarkers of clinical and therapeutic value [8]. In ades, long-term survival is hampered as more than 40% present study, we performed a genome-wide integrated of LSCC patients experience disease recurrence approxi- analysis of methylation and the transcriptome to charac- mately at 5 years after radical treatment [4]. terize the crosstalk between DNA methylation and RNA Hence, identifying accurate predictive models and reli- regulation for patients with LSCC in Te Cancer Genome able biomarkers to screen out which subset of patients Atlas (TCGA) database. We identify methylation-driven with LSCC is apt to develop recurrence is urgently genes (MDGs), and then developed a methylation-driven needed, which help to optimize therapeutic strategies genes prognostic signature (MDGPS) capable of predict- and exploit valuable molecular targeted therapy in LSCC ing the recurrence-free survival (RFS), and further screen patients. correlated small molecule target drugs. Te proposed LSCC is a heterogeneous disease in terms of therapeu- MDGPS was validated in external datasets from the GEO tic response and clinical prognosis. To a certain extent, database. In additional, we assessed the predictive ability clinical heterogeneity can be related to distinct molecu- and clinical application of the MDGPS and compared it lar subtypes by gene expression pattern [5].RNA expres- to the TNM stage. sion profles usually exhibits relative stochastic and rapid variations, which can be directly related to important Materials and methods pathways in malignant cells. DNA methylation, serves Sample selection and data processing as a major epigenetic modifcation that is involved in the We downloaded DNA methylation (111 LSCC sam- transcriptional regulation of genes and maintains the sta- ples and 12 normal samples), RNA-sequencing profles bility of the genome, is less variable and semi-stable, but (117 LSCC samples and 16 normal samples) and clini- show large variations linked to the activity of cellular pro- cal information data (Additional fle 1: Material S1) of cesses. Terefore, the combination of transcriptome and LSCC patients from TCGA (https ://gdc.cance r.gov/), epigenetic status would be helpful to identify new mark- which recorded before February 14, 2020. Excluding the ers and improve the accuracy of recurrence prediction. unavailability of DNA methylation or gene transcriptome Cui et al. Cancer Cell Int (2020) 20:472 Page 3 of 19 datasets or RFS time < 1 month, as a result, a total of methylation (DM) value where gene with a positive DM 81 LSCC patients were enrolled in this analysis. Te value of signifes hypermethylation and gene with a nega- GSE27020 microarray dataset (https ://www.ncbi.nlm. tive DM value signifes hypomethylation can be applied nih.gov/geo/query /acc.cgi?acc=GSE27 020) comprises in subsequent analysis. 109 LSCC specimens with gene expression profles and the associated clinical characteristics (Additional fle 2: Functional enrichment and pathway analysis Material S2).GSE25727 microarray dataset (https :// of methylation‑driven genes www.ncbi.nlm.nih.gov/geo/query /acc.cgi?acc=GSE25 Gene
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