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Target RNA Modification for Epigenetic Drug Repositioning in Neuro- Blastoma: Computational Omics Proximity Between Repurposing Drug and Disease

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Target RNA Modification for Epigenetic Drug Repositioning in Neuro- Blastoma: Computational Omics Proximity Between Repurposing Drug and Disease www.aging-us.com AGING 2020, Vol. 12, No. 19 Research Paper Target RNA modification for epigenetic drug repositioning in neuro- blastoma: computational omics proximity between repurposing drug and disease Pei Ma1,2, Lifeng Yue3, Sen Zhang2, Dacheng Hao4, Zhihong Wu5, Lijia Xu1, Guanhua Du2, Peigen Xiao1 1Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China 2State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China 3Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China 4Biotechnology Institute, School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116021, China 5Department of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China Correspondence to: Lijia Xu, Peigen Xiao, Guanhua Du; email: [email protected], [email protected], [email protected] Keywords: neuroblastoma, drug repurposing, gene expression, the connectivity map, L1000 FWD Received: December 4, 2019 Accepted: June 29, 2020 Published: October 12, 2020 Copyright: © 2020 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT RNA modifications modulate most steps of gene expression. However, little is known about its role in neuroblastoma (NBL) and the inhibitors targeting it. We analyzed the RNA-seq (n=122) and CNV data (n=78) from NBL patients in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The NBL sub-clusters (cluster1/2) were identified via consensus clustering for expression of RNA modification regulators (RNA-MRs). Cox regression, principle component analysis and chi-square analysis were used to compare differences of survival, transcriptome, and clinicopathology between clusters. Cluster1 showed significantly poor prognosis, of which RNA-MRs’ expression and CNV alteration were closely related to pathologic stage. RNA-MRs and functional related prognostic genes were obtained using spearman correlation analysis, and queried in CMap and L1000 FWD database to obtain 88 inhibitors. The effects of 5 inhibitors on RNA-MRs were confirmed in SH-SY5Y cells. The RNA-MRs exhibited two complementary regulation functions: one conducted by TET2 and related to translation and glycolysis; another conducted by ALYREF, NSUN2 and ADARB1 and related to cell cycle and DNA repair. The perturbed proteomic profile of HDAC inhibitors was different from that of others, thus drug combination overcame drug resistance and was potential for NBL therapy with RNA-MRs as therapeutic targets. INTRODUCTION The deregulation development of neural crest, a stem/progenitor cell population, is considered critical in Neuroblastoma (NBL) is a clinical heterogeneous NBL initiation [2, 3]. Nowadays patients with severe malignancy of the peripheral nervous system, which NBL still have poor survival rates (<50%) despite accounts for ~15% of all childhood cancer death [1]. intensive multimodality therapy [4]. Therefore, there is www.aging-us.com 19022 AGING an urgent need to identify NBL vulnerabilities for scale. Data for these databases come from the ‘one potential pharmacological intervention with more molecule-one target-one disease’ paradigm, which efficiency and less toxicity. identify effective compounds that affect specific proteins [14]. However, the major limiting factor is that Epigenetic modification of RNA modulates most steps of pharmaceuticals are designed to target individual factors gene expression, including DNA transcription, post- in a disease system, but complex diseases are multi- transcriptional RNA modifications, and mRNA factorial in nature and vulnerable at multiple attacks. translation [5, 6]. Four RNA modifications regarding Thus, in this study, we use a state-of-the-art methylation or isomerization, have been reported computational omics proximity strategy [15], which is abundant and important for differentiation of neural recently developed for drug repositioning. We integrate progenitor cells and neurological diseases: 5-methyl- the “omics” data at transcriptional and proteomic levels, cytosines (m5C), 5-hydroxymethylcytosine (hm5C), which are obtained by L1000 platform and liquid adenosine to inosine editing (A-I editing), and N6- chromatography-mass spectrometry, respectively. Due methyladenosine (m6A) [7], whereas limited study reports to a high degree of statistical dependencies between their functions in NBL. RNA modification regulators gene expression, the L1000 technology uses a (RNA-MRs) are three kinds of proteins involved in computational model for 978 landmark genes to capture modification: methyltransferase (writer, W), demethylase most of the information contained within the entire (eraser, E), and RNA-binding protein (reader, R) [8]. The transcriptome [16]. Many databases and web servers are m5C is methylated by NSUN2 or TRDMT1 (formerly developed based on data obtained by L1000 technology, DNMT2), read by ALYREF, and oxidized to hm5C by such as Connectivity Map (CMap) and L1000 fireworks TETs [9]. The A-I editing is catalyzed by ADAR, display (L1000FWD) databases. L1000FWD provides ADARB1, and ADATs [10]. The m6A is catalyzed by a interactive visualization of L1000 profiles of 12716 multiprotein complex containing enzymes (METTL3 and genes from 20449 compounds in 68 cell lines [17]. It METTL14) and accessory proteins (VIRMA, ZC3H13, supplied transcriptome changes for single compound WTAP, and RBM15), and demethylases by FTO and only. Meanwhile, CMap provides L1000 profiles of ALKBH5 [11]. The RNA-MRs regulate expressions of 12328 genes from 42,080 perturbagens (19,811 small tumor driver/suppressor genes, and are functionally linked molecule compounds, 18,493 shRNAs, 3,462 cDNAs, to survival, proliferation, differentiation, invasion and and 314 biologics) in 77 cell lines [16]. Besides drug resistance of tumor cells. Recently the crosstalk transcriptome changes, CMap integrates perturbagen- between RNA modifications is also reported to provide a induced proteomics data regarding phosphorylation potential exquisite functional control [8, 12]. Therefore changes (P100) and histone modifications (GCP) [15]. RNA-MRs emerge as important regulators, prognostic Therefore, CMap facilitates the overall functional markers and therapeutic targets in cancer [8]. mapping, whereas limits query input for optimization. Here we query both CMap and L1000FWD to obtain an Currently no therapeutic target on RNA modification is optimized global profile of the drug perturbation. clinically available, despite our ever-growing However, there is a main technical limitation for this understanding of its biology. Development strategies for study. Data in CMap and L1000FWD are generated in epigenetic drug targeting DNA modification support a cell lines, which leads a possible different from that in reference as following: 1) inhibitors for a targetable clinic. enzymatic activity and associated binding pockets, 2) antagonists for reader proteins, and 3) inhibitors of Here, by focusing directly on human data, we firstly used downstream or upstream effectors for undruggable or a series of computational models to confirm a tumor-suppressing regulators [9, 10]. However, our core prognostic gene set containing RNA-MRs and understanding of structures of RNA-MRs remains function-related genes. This included: 1) clustering incomplete, and developing a brand-new drug consumes in NBL patients according to RNA-MRs’ expression; 2) an enormous amount of time, money, and effort. difference evaluation between sub-clusters for trans- Therefore, it is a cost-effective way to develop a criptome profile, copy number variation (CNV) profile, computational drug repositioning approach, which gene function, survival and clinicopathology; 3) predicts potential known drugs targeting RNA-MRs in prognostic values of RNA-MRs and their downstream or NBL [13]. These drugs encompass potential enzyme upstream related genes. Then we calculated omics inhibitor, reader antagonists, and inhibitors of down- proximity between core prognostic gene set and data in stream or upstream effectors. L100FWD and CMap. This included: 1) reasonability evaluation of core prognostic gene set as a reduced Many commonly used traditional drug databases, such representation of differentially expressed genes (DEGs); as DrugBank, ChEMBL, and ZINC, are developed to 2) drug candidates in CMap and L1000 FWD via predict potential drug–target associations on a large similarity query for core prognostic gene set; 3) www.aging-us.com 19023 AGING proteomic profiles and chemical structure which had functional relationship (Figure 1C). The characteristics of drug candidates; 4) drug response contributions of 20 RNA-MRs on NBL severity signature biomarker network for confirmation signal or was studied as Figure 1D. combination repurposing of drug candidates. Finally, we performed biological experiments to confirm 7 predicted The age, International Neuroblastoma Staging System drug candidates. Further, omics proximity strategy could (INSS) stage, MYCN gene amplification, DNA ploidy, also be used for predicting drug candidates
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