TIMELESS is a Potential Molecular Marker in Glioma: A Study Based on Multiple Databases and Methods

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However, its relationship with the prognosis of glioma patients is rarely reported. Our purpose is to explore the role of TIMELESS in glioma. Main body: Based on 1814 glioma samples from multiple databases such as TCGA, CGGA, GEO, we use a variety of bioinformatics methods to verify the mechanism of action of TIMELESS in glioma from mRNA to protein, from appearance to mechanism analysis, from clinical features to prognosis. Then, use the CMap tool to predict potential drugs that inhibit the expression of TIMELESS . First, we found TIMELESS is highly expressed in glioma from the mRNA level to the protein level. Second, TIMELESS is an independent risk factor for prognosis and has a suitable clinical diagnosis value in glioma. It was also positively correlated with WHO grade, age, histology, and negatively correlated with IDH1 mutation and 1p19q codeletion. Third, Base excision, cell cycle, and mismatch repair pathway were activated by TIMELESS in glioma. At last, we predict small molecules that potentially inhibit TIMELESS such as 8-azaguanine, gw8510, 6-thioguanosine, and ursodeoxycholic acid. Conclusion: This study is the first comprehensive analysis of TIMELESS , revealing the relationship between the novel oncogene and the clinical characteristics of patients with glioma, and the mechanism leading to poor prognosis. It also provides a potential biomarker for the diagnosis and treatment of glioma. Our research hopes to reveal the pathological progress of glioma at the genetic level. Surgery Oncology glioma TIMELESS biomarker oncology small molecule compound. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Glioma is a kind of primary malignant tumor derived from neuroepithelial cells[ 1 ]. Its annual incidence rate accounts for about 1.6% of systemic tumors, but its mortality rate accounts for 2.5% of systemic tumors[ 2 ]. CT and MRI is currently the main assistant examination for the diagnosis of glioma[ 3 , 4 ]. Patients with glioma in the middle and late stages often have typical imaging changes and a series of obvious dysfunctions[ 4 ]. Surgical treatment, adjuvant radiochemotherapy are the main means of clinical treatment. However, most patients after treatment, neurological dysfunction, and poor prognosis often bring huge economic and psychological burdens to society and families[ 5 ]. With the remarkable effect of CAR-T in treating leukemia, molecular targeted therapy has rekindled hope[ 6 ]. Therefore, in order to improve the treatment effect of glioma patients and restore the treatment confidence of the majority of patients, it is urgent to find new effective molecular markers. At present, those special molecular markers discovered by scientists have been widely used in the early diagnosis of glioma and the personalized treatment of patients. For instance, Isocitrate dehydrogenase (IDH1), in low-grade glioma, mutations often show that the prognosis of IDH mutant is better than that of wild type[ 7 ]. Next, MGMT can promote the successful escape of tumor cells from alkylated chemotherapeutic drugs, resulting in patients being insensitive to drugs and having poor therapeutic effects[ 8 ]. In addition, Epidermal growth factor receptor (EGFR) participates in the formation of tumors through various channels, often related to the prognosis of glioblastoma[ 9 , 10 ]. However, due to the diversity of glioma formation and the complexity of the tumor microenvironment, existing theories are difficult to fully reveal the specific pathogenesis of glioma[ 11 ]. Therefore, the discovery of new molecular markers will help to further reveal the new pathology of the glioma mechanism process and provide new molecular targets for evaluating patient prognosis and discovering new potential therapeutic drugs. Cytogenetic and molecular genetic studies have shown that tumor development is a complex process with multiple factors and stages[ 12 , 13 ]. Genomic instability is considered to be a driving force in all stages of tumorigenesis[ 14 ]. TIMELESS participates in the maintenance of genome stability through multiple channels. For example, Anthony et al. found that TIMELESS can compile a conserved protein and gather it in the replication site of the nucleus, thereby contributing to DNA replication efficiency by combining Tipin[ 15 ]. Lauren and Si et al. found that TIMELESS migrated to DNA loss sites in a PARP1-dependent manner, participated in the DNA repair process, and homologous recombination[ 16 , 17 ]. Further research found that TIMELESS can also promote the malignant behavior of tumors and poor prognosis of patients. TIMELESS promotes the insensitivity of nasopharyngeal carcinoma to cisplatin through Wnt/b-catenin signaling pathway, thereby promoting tumor epithelial cell metastasis[ 18 ]. TIMELESS overexpression promotes the proliferation of colorectal cancer cells[ 19 ]. Qiu et al. found that overexpression of TIMELESS promotes poor prognosis in patients with kidney and liver cancer[ 20 ]. However, there is still little research about the role and mechanism of TIMELESS in glioma. Our purpose is to use large samples in multiple databases to explain the correlation between TIMELESS expression and clinical features, and further explore the potential molecular mechanism of TIMELESS involvement in tumor formation and development. Therefore, we strongly believe that TIMELESS can be used as a potential molecular marker for clinical diagnosis and treatment and provides a new direction for revealing the pathological mechanism of glioma. MATERIALS & METHODS Data Extraction GEPIA ( http://gepia.cancer-pku.cn/ ) is a well-known online tool for visual analysis of TCGA and GTEx data. Because of its simple interface, simple operation, and effective analysis, it is popular with researchers. Our study investigated the expression level of TIMELESS in various human tumor tissues and corresponding normal samples from this database. Among them, they include 163 GBM samples, 518 LGG samples, and 207 normal brain tissues. The Chinese Glioma Genome Atlas (CGGA) ( http://www.cgga.org.cn/ ) is an open database that has been published for several years, especially used for glioma research and can provide a platform for researchers to analyze clinical information, data expression, gene copy, methylation[ 21 ]. Apart from missing data such as survival time, gender, and so on, out study obtained the RNA-seq of 748 glioma tissue samples and mRNA chip data of 268 glioma tissue samples, and for further analysis and processing. The filtered information is shown in Table S1 and Table S2. The Tumor Genome Atlas (TCGA) ( http://cancergenome.nih.gov/ ) is a landmark public free database of cancer genomics, which contains many types of data such as genome, epigenome, transcriptome, and proteome. They have improved our ability to diagnose, treat, and prevent cancer and will continue to make it available to anyone in the research community[ 22 ]. After the deletion of missing clinical information data, we finally collected information on 653 tumor samples, seeing Table S3. GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) is an open database serving the public, which is characterized by containing gene sequencing and chip data of multiple organisms based on multiple platforms[ 23 ]. It can provide a continuous data set so that researchers can systematically study the direction of interest. GSE4290 included 77 glioma and 23 normal brain tissue samples, GSE116520 included 34 glioma and 8 normal brain tissue samples and GSE50161 included 34 glioma and 13 normal brain tissue samples. We use the limma package in the R (version 3.6.1) language to analyze TIMELESS expression in glioma tissue and normal brain tissue[ 24 ](Fold Change value of (|log2FC|) > 1 and p -value < 0.05). The Human Protein Atlas (HPA) ( http://www.proteinatlas.org/ ) is an open and free web tool that contains high-resolution images of millions of normal human tissues, cancer tissues, and human cell line proteins[ 25 ]. In order to obtain the difference in TIMELESS protein levels, we downloaded the TIMELESS immunohistochemistry results from the database and divided them into the normal group, high-grade glioma group, and low-grade glioma group. Gene Set Enrichment Analysis Gene Set Enrichment Analysis (GSEA) is used to evaluate the distribution trend of genes in a pre-defined gene set in the gene table sorted by phenotype correlation, so as to judge their contribution to phenotype[ 26 ]. We first standardize the processing of the original data from multiple databases (CGGA RNA-seq, CGGA microarray, and TCGA RNA-seq), and then divide the genes into two groups (high expression group, low expression group) according to the level of TIMELESS . GSEA 4.0.2 jar software was used in this study. The number of permutations was adjusted to 1000, and the genome database was changed to the KEGG cell signaling pathway. Cmap The connectivity map (CMap) ( https://cmap.ihmc.us/ ) helps us understand human diseases and accelerate the discovery of new therapies by creating and analyzing large perturbation data sets. We first use correlation analysis to obtain 10 genes positively related to TIMELESS and 10 genes negatively related. Then, we use the GPL570 platform to convert related genes into probe information, and then upload it to the CMap tool to analyze potential inhibitors of TIMELESS ( p < 0.05 and enrichment <-0.88). Statistical analysis The original data in this study were analyzed by Perl and R software. Wilcox test was analysed the expression of TIMELESS between tumor tissue and non-tumor tissue. We use the R language to calculate the relationship between TIMELESS and the survival time of glioma patients through the Cox regression and Kaplan-Meier method and draw a survival curve. Then, univariate and multivariate analysis reveals whether high expression of TIMELESS was an independent factor for patients' poor prognosis. Cox regression evaluates the value of TIMELESS as an independent factor. We discuss the relationship between clinical data and TIMELESS expression in glioma by using Wilcox or Kruskal test. Results Abnormally high expression of TIMELESS in glioma. First, we analyzed the expression of TIMELESS in a variety of tumors and corresponding normal tissues through the GEPIA tool, and found that TIMELESS was widely high expression in BLCA, BRCA, CESC, COAD, DLBC, GBM, LGG, LUSC, OV, READ, SKCM, STAD, TGCT, THYM, UCEC, UCS, but was the low expression in LAML(Fig. 1 A). To further study the mechanism of TIMELESS in the tumor, we chose glioma as the research object. Through GSE4290 (77 glioma tissue samples and 23 normal brain tissue samples), GSE50161 (34 glioma tissue samples and 13 normal brain tissue samples), and GSE11652 (34 glioma tissue samples and 8 normal brain tissue samples) database analysis, we again verified that TIMELESS was indeed highly expressed in glioma (Fig. 1 B-D, p < 0.001). Through the mutual verification of multiple data sets, we can effectively improve the credibility of our data results. This study will lay a foundation for further study of the mechanism of TIMELESS in glioma. High expression of TIMELESS was an unfavorable factor for the prognosis of glioma patients. In order to evaluate the relationship between the high expression of TIMELESS and the prognosis of glioma patients, the original data from the CGGA RNA-seq, CGGA microarray, and TCGA RNA-seq databases were divided into a high expression group and a low expression group according to the expression of TIMELESS . Firstly, Compared with the low expression group of TIMELESS in 376 glioma patients through the Kaplan-Meier method, the high expression of TIMELESS in 372 glioma patients showed that the survival time of patients in the high expression group was significantly lower than that in the low expression group based on the CGGA RNA-seq (Fig. 2 A). Secondly, Compared with the low expression group in 134 glioma patients through the Kaplan-Meier method, the high expression of TIMELESS in 134 glioma patients showed that TIMELESS can also reduce the survival time of glioma patients based on the CGGA microarray (Fig. 2 B). Finally, in order to test the effect of TIMELESS expression in different ethnic groups on the prognosis of patients, we further verified the impact of high expression of TIMELESS on the prognosis of patients in TCGA RNA-seq, because the dataset mainly contains American race, while CGGA database contains Chinese race. It was found that the increased expression level of TIMELESS could significantly reduce the overall survival of glioma patients including 653 glioma patients (Fig. 2 C). The results were surprisingly consistent with the CGGA database. However, whether high expression of TIMELESS was an independent factor for poor prognosis requires further study. High expression of TIMELESS was an independent factor for poor prognosis of glioma patients We used univariate and multivariate analysis to analyze the relationship between clinical sample information and the survival status of glioma patients. From the analysis of the common results in the above three databases, we found that TIMELESS expression and glioma grade is closely related to the prognosis of glioma patients (Fig. 3 A- 3 F). The detailed results are as follows: in univariate analysis, TIMELESS expression and patient survival time have significant statistical significance in CGGA RNA-seq (HR: 1.733, 95% CI: 1.587–1.892, p < 0.001, Fig. 3 A), CGGA microarray (HR: 1.902; 95% CI: 1.636–2.212, p < 0.001, Fig. 3 C) and TCGA RNA-seq (HR: 1.087, 95% CI: 1.064–1.109, p < 0.001, Fig. 3 E). In multivariate analysis, it also has statistical significance in CGGA RNA-seq (HR: 1.252, 95% CI: 1.137–1.379, p < 0.001, Fig. 3 B), CGGA microarray (HR: 1.483, 95% CI: 1.234–1.783, p < 0.001, Fig. 3 D) and TCGA RNA-seq (HR: 1.034, 95% CI: 1.004–1.065, p < 0.05, Fig. 3 F). At the same time, in univariate analysis, glioma grade and patient survival time have significant statistical significance in CGGA RNA-seq (HR: 2.883, 95% CI: 2.526–3.291, p < 0.001, Fig. 3 A), CGGA microarray (HR: 2.567; 95% CI: 2.125–3.100, p < 0.001, Fig. 3 C) and TCGA RNA-seq (HR: 4.634, 95% CI:3.727–5.760, p < 0.001, Fig. 3 E). In multivariate analysis, it also has statistical significance in CGGA RNA-seq (HR: 2.506, 95% CI: 1.825–3.441, p < 0.001, Fig. 3 B), CGGA microarray (HR: 2.402, 95% CI: 1.389–4.155, p < 0.005, Fig. 3 D) and TCGA RNA-seq (HR: 3.044, 95% CI: 2.401–3.860, p < 0.001, Fig. 3 F). But, due to incomplete clinical data in the TCGA RNA-seq database and the HR value of the age crosses the invalid line in the CGGA microarray multivariate analysis, whether PRS type and age can be used as an independent factor for glioma patients requires further analysis. In conclusion, TIMELESS High expression is an independent factor for poor prognosis of glioma patients. TIMELESS high expression has diagnostic value for patient prognosis To explore whether the high expression of TIMELESS has diagnostic value for the prognosis of glioma patients, we analyzed the above three sets of data and by cox regression. the high expression of TIMELESS as an independent factor for glioma patients with a poor prognosis has clinical diagnostic value in 1 year, 3 years, and 5 years from CGGA RNA-seq (Fig. 4 A), CGGA microarray (Fig. 4 B), and TCGA RNA-seq (Fig. 4 C). The results suggest that TIMELESS can be used as a biomarker for the diagnosis and individualized treatment of glioma. The relationship between TIMELESS and clinical features of poor prognosis in patients with glioma. The results of Wilcox or Kruskal test correlation analysis showed that the abnormally high expression of TIMELESS was positively correlated with WHO grade, PRS type, chemo status, and histology (Fig. 5 A, C, D, G, p < 0.001), but IDH1 mutation and 1p19q codeletion states were negatively correlated (Fig. 5 E, F, p < 0.001) in the data of CGGA RNA-sEq. Meanwhile, it was positively correlated with WHO grade, histology, and negatively correlated with IDH1 mutation (Fig. 5 A, E, G, p < 0.001) in CGGA microarray. Finally, it also was positively correlated with WHO grade, age in the data of TCGA RNA-seq (Fig. 5 A, B, p < 0.001). By analyzing the above three databases, we found that the low expression of TIMELESS is closely related to low tumor grade, 1p19q joint deletion, and IDH1 mutation. As our clinical knowledge knows, 1p19q joint deletion, IDH1 mutations often occur in low-grade glioma, and patients often have a relatively good prognosis. Therefore, it further confirms indirectly supports that TIMELESS was a carcinogenic gene in glioma. However, the mechanism by which TIMELESS participates in the malignant behavior of glioma requires my next to reveal. Cell signaling pathways of TIMELESS in glioma We divided the tissue samples into the high expression group and low expression group based on the expression level of TIMELESS . Then, GSEA analysis was used to reveal the activation of cancer-related cell signaling pathways caused by high expression of TIMELESS . Potential cellular molecular pathways were selected out by the consistent results of three databases together enriched (Table 1 ). The TIMELESS high expression may participate in tumor development by activating cell cycle, mismatch repair, and DNA replication cell signaling pathways (Fig. 6 A-C). Table 1 The gene set enriches the high TIMELESS in three databases. GENE SET NAME CGGA RNA-seq CGGA microarray TCGA RNA-seq NOM p-value FDR q-value NOM p-value FDR q-value NOM p-value FDR q-value CELL CYCLE 0.000 0.061 0.000 0.000 0.000 7.52E-04 DNA REPLICATION 0.000 0.108 0.000 0.018 0.000 0.005 MISMATCH REPAIR 0.002 0.113 0.008 0.043 0.000 0.005 BASE EXCISION 0.002 0.203 0.039 0.125 0.000 0.005 HOMOLOGOUS RECOMBINATION 0.006 0.180 0.000 4.00E-04 0.000 0.005 Gene sets with NOM P-value < 0.05 and FDR q-value < 0.25 were considered as significantly enriched, NOM: nominal; FDR: false discovery rate. Abnormally high expression of TIMELESS protein levels in glioma We have verified the abnormally high expression of TIMELESS in glioma from the level of mRNA. However, the level of TIMELESS protein is unclear. So, we downloaded the immunohistochemical film of TIMELESS protein expression in glioma tissue and normal brain tissue from The Human Protein Atlas website. Then divided into three groups according to tumor grade, including the normal group, low-grade group, high-grade group. We found that the TIMELESS protein expression level was markedly higher in both the low-level group and the high-level group than the normal group (Fig. 7 ). Drugs of potential inhibit TIMELESS In order to find drugs that potentially inhibit expression of TIMELESS , we first performed a correlation analysis between TIMELESS and other differential genes. Select 10 positively related genes and 10 negatively related genes and draw a circle diagram (Fig. 8 ). Next, we uploaded these 20 related genes to the CMap website and screened out 4 small molecule compounds that potentially inhibit TIMELESS , such as 8-azaguanine, gw8510, 6-Thioguanosine, and ursodeoxycholic acid (Fig. 9 , Table 2 ). Some of these drugs have been found to have clear anti-tumor effects in other studies. This reflects the reliability of our predictions. Table 2 Small molecule compounds predicted by CMap NO. CMap name enrichment p 1 GW-8510 -0.929 0.00002 2 roxithromycin -0.904 0.00016 3 ursodeoxycholic acid -0.885 0.003 4 thioguanosine -0.882 0.00048 5 8-azaguanine -0.786 0.00426 Enrichment<-0.7, p < 0.05. connectivity map: CMap Discussion In recent years, a large number of studies have shown that TIMELESS can affect the process of tumor formation through proliferation, migration, and inhibition of apoptosis[ 19 , 27 , 28 ]. However, the effect of TIMELESS on the prognosis of glioma patients and its molecular mechanism has not been elucidated. We use the advantages of large samples to reveal the role of TIMELESS in glioma. From mRNA to protein levels, we found that TIMELESS was highly expressed in glioma relative to normal brain tissues (Fig. 1 , Fig. 7 ). At the same time, our conclusions are mutually verified with previous studies. For example, Bianco found that TIMELESS was abnormally highly expressed in non–small-cell lung cancers (NSCLC) and breast cancers[ 29 ]. Zhou confirmed TIMELESS expression was higher in cervical cancer from both public databases and clinical samples[ 30 ]. Wang also verified that TIMELESS was highly expressed in glioma[ 31 ]. However, the study did not elaborate on the relationship between TIMELESS and the clinical characteristics of glioma patients, and the underlying molecular mechanism. We firstly found TIMELESS was associated with the prognosis of glioma patients. As described in Fig. 2 – 4 , high expression of TIMELESS was an independent risk factor for the prognosis of glioma patients and has clinical diagnostic value. In other tumors, we also found that TIMELESS is closely related to clinical prognosis. For instance, TIMELESS in lung cancer promotes the malignant characteristics of tumors and leads to a poor prognosis for patients[ 32 ]. Zhang et al. found that TIMELESS promotes the malignant progression of tumors and poor prognosis in cervical carcinoma[ 33 ]. Just like the result of Fig. 5 , it indicates that the expression level of TIMELESS was positively correlated with the age of patients and the grade of glioma. As we all know, grade Ⅲ-Ⅳ glioma show diffuse infiltrating growth with high heterogeneity. The postoperative radiotherapy and chemotherapy were often resistant, and the patient's prognosis was less than ideal[ 34 ]. It is completely consistent with our point of view in this experiment. Therefore, we speculate that the high expression of TIMELESS was mainly involved in the malignant behavior in the late stage of the tumor, which provides a direction for the next step to explore the specific molecular mechanism of TIMELESS in the tumor. We used GSEA to analyze the three databases and found that the high expression of TIMELESS may participate in the malignant progression of the tumor through the cell cycle, mismatch repair, and DNA replication cell signaling pathways. GSEA and enrichment analysis (GO analysis and Pathway analysis) are two commonly used to predict the molecular mechanism of participation. Enrichment analysis often focuses on a part of significantly different gene groups, which is easy to miss some genes that are not significantly differentially expressed but have important biological significance[ 35 ]. GSEA does not need to set the threshold of differential genes in advance and analyze several genes from the expression profile level according to the overall trend of actual data[ 36 ]. In addition, our predicted pathway has been reported to be associated with tumorigenicity. Emmanuel found that mismatch repair genes mutations in ovarian cancer can promote the growth of tumor cells to a distance and worsen the clinical physiology of patients[ 37 ]. Kristin discovered that the excision repair system was abnormally high in the early stage of many tumor cells and promotes tumor malignant progression[ 38 ]. Evan found that the process of tumor formation was diverse, but the cell cycle was a part that cannot be ignored[ 39 ]. Therefore, through the above description, we can draw a general conclusion that the abnormal increase of TIMELESS in glioma may lead to poor prognosis through a variety of cell signaling pathways. Using the advantages of public databases, we have demonstrated that the high expression of TIMELESS was closely related to the malignant behavior of high-grade glioma. However, there are some flaws in this experiment. First, the clinical information of some samples in the database was incomplete, the treatment plan was not clear and the database also has a regional. We use a combination of multiple databases to expand the sample size to make up for the problem and avoid sample bias. Second, the cell signaling pathway of GSEA analysis did not prove with traditional experiments. But, the GSEA method has been reported in many articles and has certain feasibility. Besides, TIMELESS may have other effects and this article cannot specifically reveal all the pathogenesis. Conclusion The high expression of TIMELESS was closely related to the prognosis of glioma patients and can be used as an independent prognostic factor in the clinic. It can also be used as a molecular marker for screening with glioma and provides a potential target for gene therapy of glioma. Declarations Ethics approval and consent to participate All of our research data are from public databases and do not involve ethical issues. Competing interests The authors declare that they have no competing interests. Funding The authors received no funding for this work Authors' contributions ZDL, ZSR and ZBH were major contributors in writing the manuscrip and designed the study, XBC and WZ performed the literature search, BFL and JLW revised the manuscript, YQG and YZG made a critical improvement on the experiment and manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Lapointe S, Perry A, Butowski N. Primary brain tumours in adults. Lancet. 2018;392(10145):432–46. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2018;68(6):394–424. Ellingson B, Wen P, Cloughesy T. Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma. Neurooncology. 2018;20(4):457–71. 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Proliferation, cell cycle and apoptosis in cancer. Nature. 2001;411(6835):342–8. Supplementary Files TableS1.docx TableS2.docx TableS3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-132735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":6806831,"identity":"c9b23950-bd9c-47f8-a980-b1fc2eadb5be","order_by":0,"name":"Zhendong Liu","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhendong","middleName":"","lastName":"Liu","suffix":""},{"id":6806832,"identity":"1e136211-51c2-4b29-9284-583c0bce2482","order_by":1,"name":"Zhishuai Ren","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhishuai","middleName":"","lastName":"Ren","suffix":""},{"id":6806833,"identity":"621fa88d-515f-4e36-8b83-f7003e3d5a4e","order_by":2,"name":"Zhibin Han","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Han","suffix":""},{"id":6806834,"identity":"26967192-b911-4079-ad22-5b067fd0d7cd","order_by":3,"name":"Xingbo Cheng","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xingbo","middleName":"","lastName":"Cheng","suffix":""},{"id":6806835,"identity":"c55bdd20-b3f5-47cb-a0a5-570d1134b0e4","order_by":4,"name":"Wang Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Zhang","suffix":""},{"id":6806836,"identity":"0cb16ae6-7b1b-4844-8fee-424528f676e6","order_by":5,"name":"Binfeng Liu","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Binfeng","middleName":"","lastName":"Liu","suffix":""},{"id":6806837,"identity":"1e9bb0a7-8400-4427-9230-8529aba65167","order_by":6,"name":"Jialin Wang","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jialin","middleName":"","lastName":"Wang","suffix":""},{"id":6806838,"identity":"b42739fa-161d-46fb-86ae-65f9065fb792","order_by":7,"name":"Yanzheng Gao","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-3690-1954","institution":"Henan Provincial People's Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yanzheng","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2020-12-20 16:40:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-132735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-132735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4487398,"identity":"28901456-2d95-41ab-9b52-a42c777bd583","added_by":"auto","created_at":"2020-12-23 21:17:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157999,"visible":true,"origin":"","legend":"The expression level changes of TIMELESS in glioma. (A): TIMELESS is a widely expressed in a variety of tumors: red indicates cancer with up-regulation of TIMELESS, while green indicates a decrease in the expression of TIMELESS in malignant tissues. (B-D): TIMELESS expression is higher in glioma than normal brain tissues from GEO (GSE4290, GSE50161, GSE116520, p\u003c0.001).","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/12516a9c60e523270a5fcc9d.png"},{"id":4487527,"identity":"ecc1a3b3-3902-4c1b-bca2-69d9b8fe2755","added_by":"auto","created_at":"2020-12-23 21:20:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90018,"visible":true,"origin":"","legend":"Correlation analysis of TIMELESS and survival time of patients based on multiple databases. (A): in CGGA RNA-seq; (B): in CGGA microarray; (C): in TCGA RNA-seq. p\u003c0.05. CGGA RNA-seq: the Chinese Glioma Genome Atlas RNA- Sequence, CGGA microarray: the Chinese Glioma Genome Atlas microarray, TCGA RNA-seq: The Cancer Genome Atlas RNA- Sequence.\n\n","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/cd6f42fe62b80c58acbaf7c8.png"},{"id":4487529,"identity":"292dc15f-fd74-441b-b891-17d4a7f65c51","added_by":"auto","created_at":"2020-12-23 21:20:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150048,"visible":true,"origin":"","legend":"TIMELESS is an independent factor of poor prognosis in glioma through univariate and multivariate analysis. (A, C, E) are univariate analysis from CGGA RNA-seq, CGGA microarray and TCGA RNA-seq. (B, D, F) are multivariate analysis from CGGA RNA-seq, CGGA microarray and TCGA RNA-seq. ","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/d8b72bff66b9a4df2ae69958.png"},{"id":4487396,"identity":"cfbadde9-f755-4c02-bd7b-b4898b024df0","added_by":"auto","created_at":"2020-12-23 21:17:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79857,"visible":true,"origin":"","legend":"TIMELESS has certain clinical diagnostic value through ROC curve analysis. (A): in CGGA RNA-seq; (B): in CGGA microarray; (C) in TCGA RNA-seq. \n\n","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/34e61308d9f2aefbe51a1c60.png"},{"id":4487703,"identity":"1c7c1a20-92d2-43e8-b683-8b98e7c77264","added_by":"auto","created_at":"2020-12-23 21:23:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":301575,"visible":true,"origin":"","legend":"Correlation analysis of TIMELESS and clinical features in CGGA RNA-seq, CGGA microarray and TCGA RNA-seq datasets. A: WHO Grade; B: age; C: PRS type; D: Chemo Status; E: IDH Mutation Status; F: 1p19q codeletion status; G: histology. PRS: post-resuscitation syndrome, IDH: Isocitrate dehydrogenase.\n\n","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/d9ca982d1e24a247ae64a9dc.png"},{"id":4487400,"identity":"e2a86c11-c13c-47bb-acf1-b649762a6cec","added_by":"auto","created_at":"2020-12-23 21:17:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":246534,"visible":true,"origin":"","legend":"Enrichment of TIMELESS potential cell signaling pathways through GSEA. (A): CELL CYCLE signaling pathway; (B): MISMATCH REPAIR signaling pathway; (C): BASE EXCISION signaling pathway. EC: Enrichment score.","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/8472b606d9e95c1e2922ca3f.png"},{"id":4487532,"identity":"89fd0301-f06f-4dad-9233-8e71e473fc13","added_by":"auto","created_at":"2020-12-23 21:20:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1405309,"visible":true,"origin":"","legend":"Expression of TIMELESS protein from glioma tissue and normal brain tissue. (A, B): in normal brain tissue; (C, D): in low grade glioma tissue; (E, F): in high grade glioma tissue. M: male, F: female. ID from ATLAS.\n\n","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/c4dca0358cb6d6295dd1e856.png"},{"id":4487530,"identity":"82b1a980-8ec6-4d91-bad0-fd680b793fb9","added_by":"auto","created_at":"2020-12-23 21:20:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":370107,"visible":true,"origin":"","legend":"Correlation analysis between TIMELESS and other differential genes. (A, B): Screen out the top 10 differential genes with positive and negative correlations. ","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/f46c3273d2068b844452fca3.png"},{"id":4487528,"identity":"a333bd43-9ce5-42ac-8dbf-2729c5307775","added_by":"auto","created_at":"2020-12-23 21:20:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":256256,"visible":true,"origin":"","legend":"Small molecule compounds potentially inhibit TIMELESS expression. (A): gw8510; (B): 8-azaguanine; (C) 6-thioguanosine; (D): ursodeoxycholic acid.","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/61062da208061254fc28558b.png"},{"id":13639233,"identity":"b3d87606-5575-4475-a37d-2c04cd4589cb","added_by":"auto","created_at":"2021-09-17 08:54:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4432500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/2adc9a80-acb7-435d-af96-b898a1f42ef8.pdf"},{"id":4487533,"identity":"cd9ee7a3-8d0b-45bc-a103-0d0b866477a6","added_by":"auto","created_at":"2020-12-23 21:20:06","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":19772,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/cb9c948320ed364a1d7b9cfa.docx"},{"id":4487406,"identity":"259186b6-5912-45e2-bc71-693f2738cffd","added_by":"auto","created_at":"2020-12-23 21:17:06","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":19373,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/c9b57c9e3cdf998486156fe0.docx"},{"id":4487403,"identity":"673457ff-677f-466f-880d-e31557cdc81e","added_by":"auto","created_at":"2020-12-23 21:17:06","extension":"docx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":17153,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-132735/v1/2ffddd77dd8bf215639dbe87.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eTIMELESS is a Potential Molecular Marker in Glioma: A Study Based on Multiple Databases and Methods\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eGlioma is a kind of primary malignant tumor derived from neuroepithelial cells[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its annual incidence rate accounts for about 1.6% of systemic tumors, but its mortality rate accounts for 2.5% of systemic tumors[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CT and MRI is currently the main assistant examination for the diagnosis of glioma[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Patients with glioma in the middle and late stages often have typical imaging changes and a series of obvious dysfunctions[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Surgical treatment, adjuvant radiochemotherapy are the main means of clinical treatment. However, most patients after treatment, neurological dysfunction, and poor prognosis often bring huge economic and psychological burdens to society and families[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. With the remarkable effect of CAR-T in treating leukemia, molecular targeted therapy has rekindled hope[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, in order to improve the treatment effect of glioma patients and restore the treatment confidence of the majority of patients, it is urgent to find new effective molecular markers.\u003c/p\u003e \u003cp\u003eAt present, those special molecular markers discovered by scientists have been widely used in the early diagnosis of glioma and the personalized treatment of patients. For instance, Isocitrate dehydrogenase (IDH1), in low-grade glioma, mutations often show that the prognosis of IDH mutant is better than that of wild type[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Next, MGMT can promote the successful escape of tumor cells from alkylated chemotherapeutic drugs, resulting in patients being insensitive to drugs and having poor therapeutic effects[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, Epidermal growth factor receptor (EGFR) participates in the formation of tumors through various channels, often related to the prognosis of glioblastoma[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, due to the diversity of glioma formation and the complexity of the tumor microenvironment, existing theories are difficult to fully reveal the specific pathogenesis of glioma[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the discovery of new molecular markers will help to further reveal the new pathology of the glioma mechanism process and provide new molecular targets for evaluating patient prognosis and discovering new potential therapeutic drugs.\u003c/p\u003e \u003cp\u003eCytogenetic and molecular genetic studies have shown that tumor development is a complex process with multiple factors and stages[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Genomic instability is considered to be a driving force in all stages of tumorigenesis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003cem\u003eTIMELESS\u003c/em\u003e participates in the maintenance of genome stability through multiple channels. For example, Anthony et al. found that \u003cem\u003eTIMELESS\u003c/em\u003e can compile a conserved protein and gather it in the replication site of the nucleus, thereby contributing to DNA replication efficiency by combining Tipin[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Lauren and Si et al. found that \u003cem\u003eTIMELESS\u003c/em\u003e migrated to DNA loss sites in a PARP1-dependent manner, participated in the DNA repair process, and homologous recombination[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Further research found that \u003cem\u003eTIMELESS\u003c/em\u003e can also promote the malignant behavior of tumors and poor prognosis of patients. \u003cem\u003eTIMELESS\u003c/em\u003e promotes the insensitivity of nasopharyngeal carcinoma to cisplatin through Wnt/b-catenin signaling pathway, thereby promoting tumor epithelial cell metastasis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. \u003cem\u003eTIMELESS\u003c/em\u003e overexpression promotes the proliferation of colorectal cancer cells[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Qiu et al. found that overexpression of \u003cem\u003eTIMELESS\u003c/em\u003e promotes poor prognosis in patients with kidney and liver cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, there is still little research about the role and mechanism of \u003cem\u003eTIMELESS\u003c/em\u003e in glioma.\u003c/p\u003e \u003cp\u003eOur purpose is to use large samples in multiple databases to explain the correlation between \u003cem\u003eTIMELESS\u003c/em\u003e expression and clinical features, and further explore the potential molecular mechanism of \u003cem\u003eTIMELESS\u003c/em\u003e involvement in tumor formation and development. Therefore, we strongly believe that \u003cem\u003eTIMELESS\u003c/em\u003e can be used as a potential molecular marker for clinical diagnosis and treatment and provides a new direction for revealing the pathological mechanism of glioma.\u003c/p\u003e "},{"header":"MATERIALS \u0026 METHODS ","content":"\u003ch2\u003eData Extraction\u003c/h2\u003e \u003cp\u003eGEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003c/span\u003e ) is a well-known online tool for visual analysis of TCGA and GTEx data. Because of its simple interface, simple operation, and effective analysis, it is popular with researchers. Our study investigated the expression level of \u003cem\u003eTIMELESS\u003c/em\u003e in various human tumor tissues and corresponding normal samples from this database. Among them, they include 163 GBM samples, 518 LGG samples, and 207 normal brain tissues.\u003c/p\u003e \u003cp\u003eThe Chinese Glioma Genome Atlas (CGGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cgga.org.cn/\u003c/span\u003e\u003c/span\u003e) is an open database that has been published for several years, especially used for glioma research and can provide a platform for researchers to analyze clinical information, data expression, gene copy, methylation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Apart from missing data such as survival time, gender, and so on, out study obtained the RNA-seq of 748 glioma tissue samples and mRNA chip data of 268 glioma tissue samples, and for further analysis and processing. The filtered information is shown in Table S1 and Table S2.\u003c/p\u003e \u003cp\u003eThe Tumor Genome Atlas (TCGA) ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cancergenome.nih.gov/\u003c/span\u003e\u003c/span\u003e) is a landmark public free database of cancer genomics, which contains many types of data such as genome, epigenome, transcriptome, and proteome. They have improved our ability to diagnose, treat, and prevent cancer and will continue to make it available to anyone in the research community[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. After the deletion of missing clinical information data, we finally collected information on 653 tumor samples, seeing Table S3.\u003c/p\u003e \u003cp\u003eGEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) is an open database serving the public, which is characterized by containing gene sequencing and chip data of multiple organisms based on multiple platforms[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It can provide a continuous data set so that researchers can systematically study the direction of interest. GSE4290 included 77 glioma and 23 normal brain tissue samples, GSE116520 included 34 glioma and 8 normal brain tissue samples and GSE50161 included 34 glioma and 13 normal brain tissue samples. We use the limma package in the R (version 3.6.1) language to analyze \u003cem\u003eTIMELESS\u003c/em\u003e expression in glioma tissue and normal brain tissue[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e](Fold Change value of (|log2FC|)\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe Human Protein Atlas (HPA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.proteinatlas.org/\u003c/span\u003e\u003c/span\u003e) is an open and free web tool that contains high-resolution images of millions of normal human tissues, cancer tissues, and human cell line proteins[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In order to obtain the difference in \u003cem\u003eTIMELESS\u003c/em\u003e protein levels, we downloaded the \u003cem\u003eTIMELESS\u003c/em\u003e immunohistochemistry results from the database and divided them into the normal group, high-grade glioma group, and low-grade glioma group.\u003c/p\u003e \n\u003ch2\u003eGene Set Enrichment Analysis\u003c/h2\u003e\n \u003cp\u003eGene Set Enrichment Analysis (GSEA) is used to evaluate the distribution trend of genes in a pre-defined gene set in the gene table sorted by phenotype correlation, so as to judge their contribution to phenotype[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We first standardize the processing of the original data from multiple databases (CGGA RNA-seq, CGGA microarray, and TCGA RNA-seq), and then divide the genes into two groups (high expression group, low expression group) according to the level of \u003cem\u003eTIMELESS\u003c/em\u003e. GSEA 4.0.2 jar software was used in this study. The number of permutations was adjusted to 1000, and the genome database was changed to the KEGG cell signaling pathway.\u003c/p\u003e \n\u003ch2\u003eCmap\u003c/h2\u003e\n \u003cp\u003eThe connectivity map (CMap) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cmap.ihmc.us/\u003c/span\u003e\u003c/span\u003e) helps us understand human diseases and accelerate the discovery of new therapies by creating and analyzing large perturbation data sets. We first use correlation analysis to obtain 10 genes positively related to \u003cem\u003eTIMELESS\u003c/em\u003e and 10 genes negatively related. Then, we use the GPL570 platform to convert related genes into probe information, and then upload it to the CMap tool to analyze potential inhibitors of \u003cem\u003eTIMELESS\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and enrichment \u0026lt;-0.88).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe original data in this study were analyzed by Perl and R software. Wilcox test was analysed the expression of \u003cem\u003eTIMELESS\u003c/em\u003e between tumor tissue and non-tumor tissue. We use the R language to calculate the relationship between \u003cem\u003eTIMELESS\u003c/em\u003e and the survival time of glioma patients through the Cox regression and Kaplan-Meier method and draw a survival curve. Then, univariate and multivariate analysis reveals whether high expression of \u003cem\u003eTIMELESS\u003c/em\u003e was an independent factor for patients' poor prognosis. Cox regression evaluates the value of \u003cem\u003eTIMELESS\u003c/em\u003e as an independent factor. We discuss the relationship between clinical data and \u003cem\u003eTIMELESS\u003c/em\u003e expression in glioma by using Wilcox or Kruskal test.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003e \u003cb\u003eAbnormally high expression of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e \u003cb\u003ein glioma.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFirst, we analyzed the expression of \u003cem\u003eTIMELESS\u003c/em\u003e in a variety of tumors and corresponding normal tissues through the GEPIA tool, and found that \u003cem\u003eTIMELESS\u003c/em\u003e was widely high expression in BLCA, BRCA, CESC, COAD, DLBC, GBM, LGG, LUSC, OV, READ, SKCM, STAD, TGCT, THYM, UCEC, UCS, but was the low expression in LAML(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further study the mechanism of \u003cem\u003eTIMELESS\u003c/em\u003e in the tumor, we chose glioma as the research object. Through GSE4290 (77 glioma tissue samples and 23 normal brain tissue samples), GSE50161 (34 glioma tissue samples and 13 normal brain tissue samples), and GSE11652 (34 glioma tissue samples and 8 normal brain tissue samples) database analysis, we again verified that \u003cem\u003eTIMELESS\u003c/em\u003e was indeed highly expressed in glioma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Through the mutual verification of multiple data sets, we can effectively improve the credibility of our data results. This study will lay a foundation for further study of the mechanism of \u003cem\u003eTIMELESS\u003c/em\u003e in glioma.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHigh expression of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e \u003cb\u003ewas an unfavorable factor for the prognosis of glioma patients.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn order to evaluate the relationship between the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e and the prognosis of glioma patients, the original data from the CGGA RNA-seq, CGGA microarray, and TCGA RNA-seq databases were divided into a high expression group and a low expression group according to the expression of \u003cem\u003eTIMELESS\u003c/em\u003e. Firstly, Compared with the low expression group of \u003cem\u003eTIMELESS\u003c/em\u003e in 376 glioma patients through the Kaplan-Meier method, the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e in 372 glioma patients showed that the survival time of patients in the high expression group was significantly lower than that in the low expression group based on the CGGA RNA-seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Secondly, Compared with the low expression group in 134 glioma patients through the Kaplan-Meier method, the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e in 134 glioma patients showed that \u003cem\u003eTIMELESS\u003c/em\u003e can also reduce the survival time of glioma patients based on the CGGA microarray (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Finally, in order to test the effect of \u003cem\u003eTIMELESS\u003c/em\u003e expression in different ethnic groups on the prognosis of patients, we further verified the impact of high expression of \u003cem\u003eTIMELESS\u003c/em\u003e on the prognosis of patients in TCGA RNA-seq, because the dataset mainly contains American race, while CGGA database contains Chinese race. It was found that the increased expression level of \u003cem\u003eTIMELESS\u003c/em\u003e could significantly reduce the overall survival of glioma patients including 653 glioma patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The results were surprisingly consistent with the CGGA database. However, whether high expression of \u003cem\u003eTIMELESS\u003c/em\u003e was an independent factor for poor prognosis requires further study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHigh expression of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e \u003cb\u003ewas an independent factor for poor prognosis of glioma patients\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe used univariate and multivariate analysis to analyze the relationship between clinical sample information and the survival status of glioma patients. From the analysis of the common results in the above three databases, we found that \u003cem\u003eTIMELESS\u003c/em\u003e expression and glioma grade is closely related to the prognosis of glioma patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The detailed results are as follows: in univariate analysis, \u003cem\u003eTIMELESS\u003c/em\u003e expression and patient survival time have significant statistical significance in CGGA RNA-seq (HR: 1.733, 95% CI: 1.587\u0026ndash;1.892, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), CGGA microarray (HR: 1.902; 95% CI: 1.636\u0026ndash;2.212, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and TCGA RNA-seq (HR: 1.087, 95% CI: 1.064\u0026ndash;1.109, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In multivariate analysis, it also has statistical significance in CGGA RNA-seq (HR: 1.252, 95% CI: 1.137\u0026ndash;1.379, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), CGGA microarray (HR: 1.483, 95% CI: 1.234\u0026ndash;1.783, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and TCGA RNA-seq (HR: 1.034, 95% CI: 1.004\u0026ndash;1.065, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). At the same time, in univariate analysis, glioma grade and patient survival time have significant statistical significance in CGGA RNA-seq (HR: 2.883, 95% CI: 2.526\u0026ndash;3.291, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), CGGA microarray (HR: 2.567; 95% CI: 2.125\u0026ndash;3.100, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and TCGA RNA-seq (HR: 4.634, 95% CI:3.727\u0026ndash;5.760, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In multivariate analysis, it also has statistical significance in CGGA RNA-seq (HR: 2.506, 95% CI: 1.825\u0026ndash;3.441, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), CGGA microarray (HR: 2.402, 95% CI: 1.389\u0026ndash;4.155, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and TCGA RNA-seq (HR: 3.044, 95% CI: 2.401\u0026ndash;3.860, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). But, due to incomplete clinical data in the TCGA RNA-seq database and the HR value of the age crosses the invalid line in the CGGA microarray multivariate analysis, whether PRS type and age can be used as an independent factor for glioma patients requires further analysis. In conclusion, \u003cem\u003eTIMELESS\u003c/em\u003e High expression is an independent factor for poor prognosis of glioma patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e \u003cb\u003ehigh expression has diagnostic value for patient prognosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore whether the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e has diagnostic value for the prognosis of glioma patients, we analyzed the above three sets of data and by cox regression. the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e as an independent factor for glioma patients with a poor prognosis has clinical diagnostic value in 1 year, 3 years, and 5\u0026nbsp;years from CGGA RNA-seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), CGGA microarray (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and TCGA RNA-seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The results suggest that \u003cem\u003eTIMELESS\u003c/em\u003e can be used as a biomarker for the diagnosis and individualized treatment of glioma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relationship between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e \u003cb\u003eand clinical features of poor prognosis in patients with glioma.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe results of Wilcox or Kruskal test correlation analysis showed that the abnormally high expression of \u003cem\u003eTIMELESS\u003c/em\u003e was positively correlated with WHO grade, PRS type, chemo status, and histology (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, C, D, G, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but \u003cem\u003eIDH1\u003c/em\u003e mutation and \u003cem\u003e1p19q\u003c/em\u003e codeletion states were negatively correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the data of CGGA RNA-sEq.\u0026nbsp;Meanwhile, it was positively correlated with WHO grade, histology, and negatively correlated with IDH1 mutation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, E, G, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in CGGA microarray. Finally, it also was positively correlated with WHO grade, age in the data of TCGA RNA-seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). By analyzing the above three databases, we found that the low expression of \u003cem\u003eTIMELESS\u003c/em\u003e is closely related to low tumor grade, \u003cem\u003e1p19q\u003c/em\u003e joint deletion, and \u003cem\u003eIDH1\u003c/em\u003e mutation. As our clinical knowledge knows, \u003cem\u003e1p19q\u003c/em\u003e joint deletion, \u003cem\u003eIDH1\u003c/em\u003e mutations often occur in low-grade glioma, and patients often have a relatively good prognosis. Therefore, it further confirms indirectly supports that \u003cem\u003eTIMELESS\u003c/em\u003e was a carcinogenic gene in glioma. However, the mechanism by which \u003cem\u003eTIMELESS\u003c/em\u003e participates in the malignant behavior of glioma requires my next to reveal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCell signaling pathways of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e \u003cb\u003ein glioma\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe divided the tissue samples into the high expression group and low expression group based on the expression level of \u003cem\u003eTIMELESS\u003c/em\u003e. Then, GSEA analysis was used to reveal the activation of cancer-related cell signaling pathways caused by high expression of \u003cem\u003eTIMELESS\u003c/em\u003e. Potential cellular molecular pathways were selected out by the consistent results of three databases together enriched (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003eTIMELESS\u003c/em\u003e high expression may participate in tumor development by activating cell cycle, mismatch repair, and DNA replication cell signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe gene set enriches the high TIMELESS in three databases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGENE SET NAME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCGGA RNA-seq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCGGA microarray\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTCGA RNA-seq\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOM p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFDR q-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOM p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFDR q-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNOM p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR q-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCELL CYCLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.52E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA REPLICATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMISMATCH REPAIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASE EXCISION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMOLOGOUS RECOMBINATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eGene sets with NOM P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered as significantly enriched, NOM: nominal; FDR: false discovery rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAbnormally high expression of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e \u003cb\u003eprotein levels in glioma\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe have verified the abnormally high expression of \u003cem\u003eTIMELESS\u003c/em\u003e in glioma from the level of mRNA. However, the level of \u003cem\u003eTIMELESS\u003c/em\u003e protein is unclear. So, we downloaded the immunohistochemical film of \u003cem\u003eTIMELESS\u003c/em\u003e protein expression in glioma tissue and normal brain tissue from The Human Protein Atlas website. Then divided into three groups according to tumor grade, including the normal group, low-grade group, high-grade group. We found that the \u003cem\u003eTIMELESS\u003c/em\u003e protein expression level was markedly higher in both the low-level group and the high-level group than the normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDrugs of potential inhibit\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTIMELESS\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn order to find drugs that potentially inhibit expression of \u003cem\u003eTIMELESS\u003c/em\u003e, we first performed a correlation analysis between \u003cem\u003eTIMELESS\u003c/em\u003e and other differential genes. Select 10 positively related genes and 10 negatively related genes and draw a circle diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Next, we uploaded these 20 related genes to the CMap website and screened out 4 small molecule compounds that potentially inhibit \u003cem\u003eTIMELESS\u003c/em\u003e, such as 8-azaguanine, gw8510, 6-Thioguanosine, and ursodeoxycholic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Some of these drugs have been found to have clear anti-tumor effects in other studies. This reflects the reliability of our predictions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSmall molecule compounds predicted by CMap\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMap name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eenrichment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGW-8510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eroxithromycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eursodeoxycholic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethioguanosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8-azaguanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eEnrichment\u0026lt;-0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. connectivity map: CMap\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn recent years, a large number of studies have shown that \u003cem\u003eTIMELESS\u003c/em\u003e can affect the process of tumor formation through proliferation, migration, and inhibition of apoptosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the effect of \u003cem\u003eTIMELESS\u003c/em\u003e on the prognosis of glioma patients and its molecular mechanism has not been elucidated. We use the advantages of large samples to reveal the role of \u003cem\u003eTIMELESS\u003c/em\u003e in glioma. From mRNA to protein levels, we found that \u003cem\u003eTIMELESS\u003c/em\u003e was highly expressed in glioma relative to normal brain tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). At the same time, our conclusions are mutually verified with previous studies. For example, Bianco found that \u003cem\u003eTIMELESS\u003c/em\u003e was abnormally highly expressed in non\u0026ndash;small-cell lung cancers (NSCLC) and breast cancers[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Zhou confirmed \u003cem\u003eTIMELESS\u003c/em\u003e expression was higher in cervical cancer from both public databases and clinical samples[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Wang also verified that \u003cem\u003eTIMELESS\u003c/em\u003e was highly expressed in glioma[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the study did not elaborate on the relationship between \u003cem\u003eTIMELESS\u003c/em\u003e and the clinical characteristics of glioma patients, and the underlying molecular mechanism.\u003c/p\u003e \u003cp\u003eWe firstly found \u003cem\u003eTIMELESS\u003c/em\u003e was associated with the prognosis of glioma patients. As described in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, high expression of \u003cem\u003eTIMELESS\u003c/em\u003e was an independent risk factor for the prognosis of glioma patients and has clinical diagnostic value. In other tumors, we also found that \u003cem\u003eTIMELESS\u003c/em\u003e is closely related to clinical prognosis. For instance, \u003cem\u003eTIMELESS\u003c/em\u003e in lung cancer promotes the malignant characteristics of tumors and leads to a poor prognosis for patients[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Zhang et al. found that \u003cem\u003eTIMELESS\u003c/em\u003e promotes the malignant progression of tumors and poor prognosis in cervical carcinoma[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Just like the result of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it indicates that the expression level of \u003cem\u003eTIMELESS\u003c/em\u003e was positively correlated with the age of patients and the grade of glioma. As we all know, grade Ⅲ-Ⅳ glioma show diffuse infiltrating growth with high heterogeneity. The postoperative radiotherapy and chemotherapy were often resistant, and the patient's prognosis was less than ideal[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is completely consistent with our point of view in this experiment. Therefore, we speculate that the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e was mainly involved in the malignant behavior in the late stage of the tumor, which provides a direction for the next step to explore the specific molecular mechanism of \u003cem\u003eTIMELESS\u003c/em\u003e in the tumor.\u003c/p\u003e \u003cp\u003eWe used GSEA to analyze the three databases and found that the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e may participate in the malignant progression of the tumor through the cell cycle, mismatch repair, and DNA replication cell signaling pathways. GSEA and enrichment analysis (GO analysis and Pathway analysis) are two commonly used to predict the molecular mechanism of participation. Enrichment analysis often focuses on a part of significantly different gene groups, which is easy to miss some genes that are not significantly differentially expressed but have important biological significance[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. GSEA does not need to set the threshold of differential genes in advance and analyze several genes from the expression profile level according to the overall trend of actual data[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, our predicted pathway has been reported to be associated with tumorigenicity. Emmanuel found that mismatch repair genes mutations in ovarian cancer can promote the growth of tumor cells to a distance and worsen the clinical physiology of patients[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Kristin discovered that the excision repair system was abnormally high in the early stage of many tumor cells and promotes tumor malignant progression[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Evan found that the process of tumor formation was diverse, but the cell cycle was a part that cannot be ignored[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, through the above description, we can draw a general conclusion that the abnormal increase of \u003cem\u003eTIMELESS\u003c/em\u003e in glioma may lead to poor prognosis through a variety of cell signaling pathways.\u003c/p\u003e \u003cp\u003eUsing the advantages of public databases, we have demonstrated that the high expression of \u003cem\u003eTIMELESS\u003c/em\u003e was closely related to the malignant behavior of high-grade glioma. However, there are some flaws in this experiment. First, the clinical information of some samples in the database was incomplete, the treatment plan was not clear and the database also has a regional. We use a combination of multiple databases to expand the sample size to make up for the problem and avoid sample bias. Second, the cell signaling pathway of GSEA analysis did not prove with traditional experiments. But, the GSEA method has been reported in many articles and has certain feasibility. Besides, \u003cem\u003eTIMELESS\u003c/em\u003e may have other effects and this article cannot specifically reveal all the pathogenesis.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eThe high expression of \u003cem\u003eTIMELESS\u003c/em\u003e was closely related to the prognosis of glioma patients and can be used as an independent prognostic factor in the clinic. It can also be used as a molecular marker for screening with glioma and provides a potential target for gene therapy of glioma.\u003c/p\u003e "},{"header":"Declarations","content":" \u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eAll of our research data are from public databases and do not involve ethical issues.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no funding for this work\u003c/p\u003e \u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eZDL, ZSR and ZBH were major contributors in writing the manuscrip and designed the study, XBC and WZ performed the literature search, BFL and JLW revised the manuscript, YQG and YZG made a critical improvement on the experiment and manuscript. All authors read and approved the final manuscript.\u003c/p\u003e \u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLapointe S, Perry A, Butowski N. Primary brain tumours in adults. Lancet. 2018;392(10145):432\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2018;68(6):394\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllingson B, Wen P, Cloughesy T. Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma. Neurooncology. 2018;20(4):457\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmuro A, DeAngelis L. 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Int J Oncol. 2015;46(2):597\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianco J, Bergoglio V, Lin Y, et al. Overexpression of Claspin and Timeless protects cancer cells from replication stress in a checkpoint-independent manner. Nature communications. 2019;10(1):910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Zhang Y, Zou X, et al. Aberrantly Expressed Timeless Regulates Cell Proliferation and Cisplatin Efficacy in Cervical Cancer. Human gene therapy. 2020;31:385\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Chen Q. timelessThe analysis of deregulated expression of the genes in gliomas. J Cancer Res Ther. 2018;14:708-S12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshida K, Sato M, Hase T, et al. TIMELESS is overexpressed in lung cancer and its expression correlates with poor patient survival. 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Nature. 2001;411(6835):342\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"glioma, TIMELESS, biomarker, oncology, small molecule compound.","lastPublishedDoi":"10.21203/rs.3.rs-132735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-132735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e In recent years, some studies have shown that \u003cem\u003eTIMELESS\u003c/em\u003e, as an oncogene, is involved in the malignant progression of cancers. However, its relationship with the prognosis of glioma patients is rarely reported. Our purpose is to explore the role of \u003cem\u003eTIMELESS\u003c/em\u003e in glioma. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMain body:\u003c/strong\u003e Based on 1814 glioma samples from multiple databases such as TCGA, CGGA, GEO, we use a variety of bioinformatics methods to verify the mechanism of action of \u003cem\u003eTIMELESS\u003c/em\u003e in glioma from mRNA to protein, from appearance to mechanism analysis, from clinical features to prognosis. Then, use the CMap tool to predict potential drugs that inhibit the expression of \u003cem\u003eTIMELESS\u003c/em\u003e. First, we found \u003cem\u003eTIMELESS\u003c/em\u003e is highly expressed in glioma from the mRNA level to the protein level. Second, \u003cem\u003eTIMELESS\u003c/em\u003e is an independent risk factor for prognosis and has a suitable clinical diagnosis value in glioma. It was also positively correlated with WHO grade, age, histology, and negatively correlated with IDH1 mutation and \u003cem\u003e1p19q\u003c/em\u003e codeletion. Third, Base excision, cell cycle, and mismatch repair pathway were activated by \u003cem\u003eTIMELESS\u003c/em\u003e in glioma. At last, we predict small molecules that potentially inhibit \u003cem\u003eTIMELESS\u003c/em\u003e such as 8-azaguanine, gw8510, 6-thioguanosine, and ursodeoxycholic acid.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study is the first comprehensive analysis of \u003cem\u003eTIMELESS\u003c/em\u003e, revealing the relationship between the novel oncogene and the clinical characteristics of patients with glioma, and the mechanism leading to poor prognosis. It also provides a potential biomarker for the diagnosis and treatment of glioma. Our research hopes to reveal the pathological progress of glioma at the genetic level.\u003c/p\u003e","manuscriptTitle":"TIMELESS is a Potential Molecular Marker in Glioma: A Study Based on Multiple Databases and Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-23 21:17:03","doi":"10.21203/rs.3.rs-132735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"044be3b4-91b3-45f9-bce5-225aa569256c","owner":[],"postedDate":"December 23rd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1596837,"name":"Surgery"},{"id":1596838,"name":"Oncology"}],"tags":[],"updatedAt":"2021-03-02T15:25:22+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-23 21:17:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-132735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-132735","identity":"rs-132735","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}