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Transcriptional Expression of Zics As an Independent Indicator of Survival in Gliomas Zhaocheng Han, Jingnan Jia, Yangting Lv, Rongyanqi Wang & Kegang Cao*

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Transcriptional Expression of Zics As an Independent Indicator of Survival in Gliomas Zhaocheng Han, Jingnan Jia, Yangting Lv, Rongyanqi Wang & Kegang Cao* www.nature.com/scientificreports OPEN Transcriptional expression of ZICs as an independent indicator of survival in gliomas Zhaocheng Han, Jingnan Jia, Yangting Lv, Rongyanqi Wang & Kegang Cao* The functional signifcance of the zinc-fnger of the cerebellum (ZIC) gene family in gliomas remains to be elucidated. Clinical data from patients with gliomas, containing expression levels of ZIC genes, were extracted from CCLE, GEPIA2 and The Human Protein Atlas (HPA). Univariate survival analysis adjusted by Cox regression via OncoLnc was used to determine the prognostic signifcance of ZIC expression. We used cBioPortal to explore the correlation between gene mutations and overall survival (OS). ZIC expression was found to be related to immune cell infltration in gliomas via TIMER analysis. GO term and KEGG pathway enrichment analyzes were performed with Metascape. PPI networks were constructed using STRING. The expression levels of ZIC1/3/4/5 in gliomas were signifcantly diferent from those in normal samples. High expression levels of ZIC1/5 were associated with poor OS in brain low-grade glioma (LGG) patients, while low ZIC3 expression combined was related to favorable OS in glioblastoma multiforme (GBM). ZIC alterations were associated with poor prognosis in LGG patients and related to favorable prognosis in GBM patients. We observed that the expression of ZICs was related to immune cell infltration in glioma patients. ZICs were enriched in several pathways and biological processes involving Neuroactive ligand-receptor interaction (hsa04080). The PPI network revealed that some proteins coexpressed with ZICs played a role in the pathogenesis of gliomas. Diferences in the expression levels of ZIC genes could provide a signifcant marker for predicting prognosis in gliomas. Abbreviations GBM Glioblastoma multiforme LGG Brain low-grade glioma CNS Central nervous system OS Overall survival PPI Protein–protein network GO Gene ontology KEGG Kyoto encyclopedia of genes and genomes ZIC Zinc-fnger of the cerebellum Gliomas are the most common type of primary tumour arising from glial cells, representing 75% of malignant brain tumours1. Te World Health Organization (WHO) divides gliomas, including low-grade gliomas (LGG) and high-grade gliomas (HGG), into 4 stages 2. WHO stage IV glioma, also known as glioblastoma multiforme (GBM), has a remarkably poor prognosis and accounts for 2.9% of cancer-related deaths but only 1.4% of cancers1,3. Over the years, although great progress has been made in surgery and chemoradiation, the overall median survival of patients is approximately 12.6 months and has not improved signifcantly 4,5. Existing studies have suggested that glioma progression is associated with the regulatory networks of genes6,7. Terefore, inves- tigating biomarkers underlying glioma tumorigenesis might be helpful for improving therapeutic strategies. Some studies have confrmed that aberrant expression of mRNAs is involved in the carcinogenesis process 8. Moreover, glioblastoma cell migration and invasion were suppressed by the expression of some mRNAs9. Human zinc-fnger of the cerebellum (ZIC) family genes are indispensable during development. A previous study showed that ZIC5, a member of the ZIC family, drives melanoma aggressiveness by activating FAK and STAT3 10. In addi- tion, ZIC1 could reprogramme glioma cells into neuron-like cells, which suggested that ZIC family members were associated with glioma 11. Whether ZICs have prognostic value in glioma patients remains unclear. Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang Rd., Dongcheng District, Beijing 100700, China. *email: [email protected] Scientifc Reports | (2021) 11:17532 | https://doi.org/10.1038/s41598-021-93877-3 1 Vol.:(0123456789) www.nature.com/scientificreports/ To determine the role of ZICs as prognostic biomarkers in LGG and GBM, we performed a comprehensive bioinformatics analysis using multiple datasets and further explored these data to identify the role of ZICs in glioma. Methods Broad Institute Cancer Cell Line Encyclopedia. Broad Institute Cancer Cell Line Encyclopedia (CCLE) (https:// porta ls. broad insti tue. org/ ccle), an open-access database containing data on 1457 cell lines and 84,434 genes, is a resource for that uses model cancer cell lines to accelerate cancer research12. In this study, scatterplots for ZIC expression were obtained from CCLE. GEPIA2. GEPIA2 (http:// www. gepia2. cancer- pku. cn/# index) is a comprehensive online platform that pro- vides fast and customized delivery of functionalities from the genotype-tissue expression (GTEx) and TCGA (Te Cancer Genome Atlas) databases13. In our study, we used GEPIA2 to analyze the transcriptional expression of ZIC family members among diferent cancer tissues and their corresponding contiguous normal control sam- ples. Te prognostic values of genes were validated using a Kaplan–Meier curve. The human protein atlas. Te Human Protein Atlas (http:// www. prote inatl as. org) includes immunohis- tochemistry-based expression data for almost 20 extremely common cancers, and every tumour type includes 12 individual tumour samples14. In our study, the gene expression of ZICs was explored using immunofuorescence data from brain-related cell lines. Next, the protein expression of ZIC1/3 in human normal tissues was deter- mined by immunohistochemistry and compared with that in glioma tissues. OncoLnc. OncoLnc (https:// www. oncol nc. org) contains survival data from 8647 patients from Te Cancer Genome Atlas (TCGA) and includes data from 21 cancer studies. Users can download the survival data or cre- ate Kaplan–Meier plots15. In this study, the prognostic value of ZIC expression in LGG and GBM patients was analyzed using this tool. We downloaded the survival data of cancer patients who had been divided into low and high expression groups by median to further analyze the utility of ZICs in predicting survival. cBioPortal. cBioPortal (http:// www. cbiop ortal. org) is a comprehensive web resource for the exploration and analysis of multidimensional cancer genomics data 16. In our study, we used Glioblastoma Multiforme(TCGA, PanCancer Atlas) dataset to analyze the genomic data of ZICs, which included mRNA expression z-scores relative to diploid samples (RNASeq V2RSEM) with a z-score threshold of ± 1.8, mutations and putative copy number alterations with the GISTIC tool. Teir relationship of ZIC genetic mutations with overall survival was analyzed with Kaplan–Meier plots, and the log-rank test was used to determine the signifcance of diferences between the survival curves. A P value < 0.05 was considered statistically signifcant. TIMER. TIMER (http:// cistr ome. shiny apps. io/ timer/), a user-friendly interactive web resource, identifed the proportions of diferent immune cells based on expression data and the corresponding clinical impact of these cells for systematic evaluation17,18. In this study, we used the “gene module” to analyze the relationship between the infltration of immune cells and ZIC expression levels. Te “survival module” was used to further explore the association between the infltration of immune cells, ZIC expression and clinical outcome. Metascape. Metascape (http:// metas cape. org) is a publicly available tool for gene set enrichment analysis and gene annotation19. In this study, we found more than 100 genes similar to ZICs via the “similar genes detec- tion module” of GEPIA213. Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway20–22 enrichment analysis and Gene Ontology enrichment analysis of ZICs were performed, providing enriched cellular compo- nent (CC), biological process (BP) and molecular function (MF) terms23–26. Te minimum enrichment was set as 3, and a P value cutof of 0.05 was considered statistically signifcant. STRING. STRING (http:// string- db. org/) is a powerful web resource covering 5090 organisms that supports functional exploration of genome-wide experimental datasets. Users are allowed to perform gene set enrich- ment analysis and visualize subsets of enriched proteins as interaction networks 27. To construct a protein–pro- tein interaction (PPI) network of diferentially expressed ZIC family members, we explored additional proteins related to ZICs with STRING. We used a minimum required interaction score cutof of ≥ 0.7 (high confdence) to obtain the signifcant PPIs. Statistical methods. Te association of ZIC mRNA expression with patient survival was analyzed by Cox regression using SPSS sofware version 20.0. We used this sofware to evaluate the efect of expression of ZIC family members on prognosis. Results Transcriptional levels of ZICs in gliomas tissues. As shown in Fig. 1, the mRNA expression levels of ZIC family members were measured and compared to those in corresponding normal tissues via the GEPIA2 database. Signifcant upregulation of ZIC1/3/4 was found in brain low-grade glioma patients, while ZIC5 was downregulated. Te mRNA expression of ZIC1 were highly expressed in glioblastoma patients, and ZIC3 and ZIC4 were downregulated expression. Te results regarding ZICs gene expression between the CCLE database and GEPIA2 were discrepant partly, which could have been caused by the diferent classifcations of brain and Scientifc Reports | (2021) 11:17532 | https://doi.org/10.1038/s41598-021-93877-3 2 Vol:.(1234567890) www.nature.com/scientificreports/ Figure 1. Transcriptional expression of ZICs in various types of cancer. (A–E) ZIC expression patterns in 1457 cell lines representing 40 distinct tumour types (CCLE). (F–J)
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