Three Novel Neuroblastoma Biomarkers Revealed by Integrative Analysis of GEO data

Three Novel Neuroblastoma Biomarkers Revealed by Integrative Analysis of GEO data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Three Novel Neuroblastoma Biomarkers Revealed by Integrative Analysis of GEO data Zijun Xiong, Mingjun Xu, Ping Yuan, Kefei Yu, Huanhuan Xing, Ruofan Yang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4173002/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Comprehensive bioinformatics analysis was used to identify the differentially expressed genes (DEGs) between neuroblastoma samples and normal samples in GSE54720 and GSE78061 datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on common DEGs. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. The top 15 hub genes were screened out. TAGLN3, KIF5C and SNAP91 were identified by alignment in the PubMed, OMIM, DisGeNET and GeneCards databases and validated by quantitative real-time polymerase chain reaction (qPCR). These three are have never been previously reported in the literature and experimentally validated. We identified a total of 37 commom DEGs from the two microarray databases. The KEGG pathway analysis showed that these DEGs were primarily involved in pathway related to dopaminergic synapses, motor proteins and phenylalanine metabolism related pathways. GO enrichment analysis showed that TAGLN3, KIF5C, and SNAP91 related pathway were mainly concentrated in axon guidance, axon genesis, axon development, distal axon, neuronal cell body, and synaptic vesicle transport, suggesting that they may be involved in biological functions such as protein binding, plasma membrane, membrane composition and nucleus. OMIM, DisGeNET, GeneCards databases, and PubMed have identified that TAGLN3, KIF5C, and SNAP91 were linked to proliferation, migration, and invasion of other tumors. Finally, the expression levels of TAGLN3, KIF5C and SNAP91 were significantly increased in SH-SY5Y cells compared with ARPE-19 cells as verified by qPCR, consistent with our bioinformatics analysis, suggesting that TAGLN3, KIF5C and SNAP91 may be involved in the occurrence and development of neuroblastoma. In this study, some key genes and molecules were identified by bioinformatics methods, revealing the potential pathogenic mechanism of neuroblastoma. These genes can serve as diagnostic indicators and therapeutic biomarkers for neuroblastoma, thereby enhancing our understanding of the molecular mechanisms underlying this disease. Biological sciences/Cancer Biological sciences/Neuroscience Neuroblastoma Bioinformatics analysis Biomarkers Differentially expressed genes qPCR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Neuroblastoma is a tumor that develops during childhood is the leading cause of pediatric cancer-related deaths in children aged 1–5 years. It is the second most common solid tumor in children under the age of 15 worldwide, after central nervous system tumors. Children diagnosed with neuroblastoma account for approximately 13% of all cancer deaths, with 7.5 deaths per 100,000 infants. In addition, 9.0% of all pediatric cancers are accounted by 1.3 new cases per 100,000 children under the age of 15 each year. Furthermore, 90% of children with the disease are diagnosed during the first five years of life (Manfred et al., 2003 ; Louis et al., 2015). Neuroblastoma derives from the neural crest and consists of undifferentiated neuroectodermal cells. Neuroblastoma is a type of cancer that typically originates in the adrenal medulla of the abdomen. Around 50% of cases begin in the adrenal gland, with the remainder occurring in the paraspinal sympathetic ganglia of the neck, chest, abdomen, or pelvis (Ayguny, 2018). One of the distinctive features of neuroblastoma is its clinical heterogeneity, which involves different sites of origin and often distant metastasis. Some tumors may regress spontaneously, while others may progress despite aggressive treatment (Mueller &Matthay, 2009 ; Castel et al., 2007). Early detection, diagnosis and treatment are crucial in improving the cure rate of neuroblastoma. Symptoms are usually absent in the early stages, and it may be difficult to detect a mass during a physical examination. Patients typically seek medical treatment for tumor growth, which compresses adjacent tissues and organs, causing symptoms such as occupation compulsion, eyeball protrusion, irritating cough, shortness of breath, hematuria, constipation, and abnormal urination. Unfortunately, most patients are diagnosed at a late stage (Chung et al., 2021 ; Dumba et al., 2015 ). Infants who are less than 18 months old at diagnosis with non-MYCN-amplified neuroblastoma usually have a better prognosis. However, for high-risk patients with metastasis, despite intensive multimodal therapy, such as standard chemotherapy, surgical resection, radiotherapy, and high-dose chemotherapy, the overall survival (OS) is less than 40% (Ayguny, 2018; Mueller &Matthay, 2009 ). Neuroblastoma usually occurs sporadically, but there are also familial cases, with account for approximately 1–2% of case (Ayguny, 2018). The molecular mechanism of neuroblastoma is currently unclear. However, research has shown that the abnormal genes expression is closely linked to the grading, treatment and prognosis of neuroblastoma. Additionally, approximately 2% of neuroblastoma patients have a positive family history (Shojaei-Brosseau et al., 2004 ). However, after the establishment of the International Neuroblastoma Risk Group (INRG) Task Force in 2004, the INRG Biology Committee reviewed data form 8800 patients in the INRG database and identified the most significant neuroblastoma biomarkers. The new INRG risk classification schema now includes MYCN status, 11q23 allele status, and ploidy, as agreed upon by consensus (Ambros et al., 2009 ). Furthermore, numerous molecules and pathways including ALK, the proteasome complex, and the PI3K/AKT/mTOR pathway have been targeted by drugs in preclinical or clinical trials (Pezeshki et al., 2021 ; Moreno et al., 2020 ). Moreover, several studies have shown promising results with immunotherapy drugs such as anti-GD2 antibodies, vaccines, and CAR-T-cell therapy in neuroblastoma patients (Richards et al., 2018; Chan & Chan, 2020). Therefore, it is necessary to screen, and identify biomarkers related to the genesis, development and prognosis of neuroblastoma. Additionally, new effective targets for the diagnosis and treatment of neuroblastoma must be found. This study, analyzed differentially expressed genes between tissues and adjacent tissues in two datasets (GSE54720 and GSE78061) obtained from the GEO database using R software. Further exploration of the potential biological functions of co-expressed differentially expressed genes in the two datasets was conducted using GO and KEGG pathway enrichment analysis. The protein interaction network was constructed using the STRING database and hub genes were identified using Cytoscape. Finally, we compared the top 15 hub genes that appeared in the OMIM, DisGeNET, GeneCards databases, and PubMed databases. Neuroblastoma-related genes present mentioned in articles and already validated were excluded. Verified by qPCR, TAGLN3, KIF5C, and SNAP91 were screened. These findings could provide insights into the genesis and progression of neuroblastoma, as well as potential therapeutic targets for future studies. Materials and methods Microarray data Two neuroblastoma-related gene expression datasets GSE54720 (Lavarino et al., 2015) and GSE78061 (Cole et al., 2016) were downloaded from the NCBI Gene Expression Synthesis (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), which showed in the Table 1 .The GSE54720 is based on the GPL13667 platform (Affymetrix Human Genome U219 Array, Agilent Technologies LTD, Santa Clara, CA, USA), and consisted of 20 neuroblastoma tumors and 4 non-pathological tissues (2 fetal brain and 2 adrenal gland) samples. The GSE78061 is based on the GPL6244 platform (Affymetrix Human Gene 1.0 ST Array, Thermo Fisher Scientific, Inc., Waltham, MA, USA), and consists of 25 human neuroblastoma cell lines and 4 retinal pigmented epithelium cell lines. Table 1 Neuroblastoma-related microarrays datasets in GEO databases. Dataset Platform Total Neuroblastoma Normal GSE54720 GPL13667 24 20 4 GSE78061 GPL6244 29 25 4 Data Preprocessing and Differential Expression Analysis The datasets were processed using R Studio as follows: The Limma package was used to standardize the matrix data and identify differentially expressed genes (DEGs)with log fold changes > 2 and AdjP-value < 0.05 between neuroblastoma and control cells for each dataset (Ritchie et al., 2015 ). DEGs were adjusted by the Benjamini-Hochberg method to handle p-values (Benjamini & Hochberg, 1995 ). The Venn diagram online web tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) was used to obtain co-DEGs between the two datasets. PPI network construction and Hub genes Identification STRING ( https://string-db.org ) is a database for analyzing known and predicted protein-protein interactions, which shows physical and functional interactions (von Mering et al., 2003 ). In this study, we constructed a protein-interaction (PPI) network of 37 DEGs using STRING, with the effective binding score set to > 0.4. Subsequently, the PPI network was imported into Cytoscape software (version 3.8.0), and the hub genes were screened using the 12 algorithms (EPC, BottleNeck, EcCentricity, Closeness, Radiality, Betweenness, Stress, Clustering, Coefficient, MCC, DMNC, MNC, and Degree) of Cytohubba in Cytoscape software (Chin et al., 2014 ; Doncheva et al., 2019 ; Szklarczyk et al., 2021 ;).Based on CytoHubba, Maximum Clique Centrality (MCC) were used to screen the top 15 genes (Chin et al., 2014 ), and the PPI network of hub genes was constructed. Enrichment analysis of DEGs with GO and KEGG The clusterProfiler package, org.Hs.eg.db package, ggplot2 package, and enrichplot package of R version 4.2.3 were used for hub gene enrichment analysis of gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) further explained the reliability of the results. The functional annotation of GO included biological process (BP), cell component (CC) and molecular function (MF) (Kanehisa & Goto, 2000 ). Comparison to Literature We collected the neuroblastoma genes by searching the OMIM ( https://omim.org/ ) database (Amberger et al., 2015 ), the DisGeNET ( https://www.disgenet.org/home/ ) database (Piñero et al., 2017 ), and the GeneCards( https://www.genecards.org/ ) database (Barshir et al., 2021 ; Safran et al., 2010 ). The keyword "neuroblastoma" was entered into OMIM, DisGeNET, and GeneCards databases, and the target genes in each database were obtained. These three databases’ targets were merged, duplicate targets were removed, and the remaining targets were the neuroblastoma targets we collected and used in the next study. Then, the Venn diagram online web tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) was used to obtain unknown genes associated with neuroblastoma between the top 15 hub genes and 3109 known neuroblastoma genes. Finally, we searched PubMed ( https://pubmed.ncbi.nlm.nih.gov/ ) (White, 2020 ), which has not yet seen articles reporting its expression in neuroblastoma, nor has it been experimentally validated to define it as a novel biomarker of neuroblastoma. Validation of gene expression by Quantitative real-time polymerase chain reaction (qPCR) ARPE-19 cells (cat. no. CL-0026, Pricella Life Science& Technology Co.,Ltd., Wuhan, China) were cultured in DMEM/F12 medium (Biosharp, Beijing, China) with 10% fetal bovine serum (HAKATA, Shanghai, China), and 1% (v/v) penicillin (100 U/ml)/streptomycin (100 µg /ml). SHSY5Y cells (cat. no. CL-0208, Pricella Life Science& Technology Co.,Ltd., Wuhan, China) were cultured in 1640 medium (Biosharp, Beijing, China) with 10% fetal bovine serum (HAKATA, Shanghai, China), and 1% (v/v) penicillin (100 U/ml)/streptomycin (100 µg /ml). Both cell lines were cultured at 37˚C under a humidified 5% CO 2 atmosphere. Total RNA was extracted (TRIzol reagent, Wuhan servicebio technologh CO.,LTD), and RNA was reverse-transcribed (RevertAid First Strand cDNA Synthesis Kit, Thermo Fisher Scientific, Inc.) for cDNA synthesis for 5 min at 25°C, followed by 30 min at 42°C, and terminating the reaction by heating at 85°C for 5 seconds. qPCR was conducted (HieffTM qPCR SYBR® Green Master Mix, Shanghai Yeasen BioTechnologies) as follows: 95˚C for 30s; 40 cycles of 95˚C for 15s, 60˚C for 30s, and 72˚C for 30s; followed by 10min at 72˚C. Primer sequences are listed in Table 2 . GAPDH was used as the reference gene. The 2 −ΔΔcq method was used to calculated relative expression of target genes (Livak and Schmittgen, 2001 ). All qPCR experiments were repeated three times and mean values were used. Table 2 Primer sequences for the validated genes. Primer name Primer sequence (5’-3’) H-TAGLN3-F GCAGAATCGGAGAGGCTTTTC H-TAGLN3-R GCATCCCGTACCCTGTCAT H-KIF5C-F ATCCCACGAATTGCCCATGAT H-KIF5C-R CCCTTTACATACGGGACTCTGT H-SNAP91-F CTGTCCCAGTCAGCACTTCT H-SNAP91-R ACAGAGGAAAGTGCAGCCAA F, forward; R, reverse. Statistical Methods R software was used for part of the data analysis and drawing, and the rest of the analysis came from the resource-sharing network data platform. Parameter setting: P <0.05 indicated that the difference was statistically significant. SPSS 23.0 software was used to analyze the experimental data, the measurement data with normal distribution was expressed as the mean ± standard error, and the differences between groups were evaluated by Student's t-test. P < 0.05 or P < 0.01 was considered statistically significant. Graphs were obtained using GraphPad Prism9.0. Results Microarray data information and identification of DEGs We performed background correction and normalization of the neuroblastoma expression microarray datasets GSE54720 and GSE78061. When filtering the GSE54720 dataset through the limma software package in R (AdjP-value 2), 547 DEGs were obtained, including 291 upregulated and 256 downregulated DEGs. Besides, 160 DEGs were screened from the GSE78061 dataset, including 100 upregulated and 60 downregulated DEGs. The R package was used to visualize DEGs. Red represents upregulated DGEs and green represents downregulated DEGs in the volcano plot, which is shown in Fig. 1 A and B. In addition, the cluster heatmap of the top 50 DEGs is shown in Fig. 1 C and D. From red to blue, the expression level of the gene in the sample gradually decreases. Then, the intersection of Venn diagram was used to obtain 37 common DEGs (Fig. 2 ). PPI network construction and hub gene determination We used STRING network-based protein interaction analysis to generate a PPI network from 37 DEGs overlapped in two datasets, confidence score > 0.4 (Fig. 3 ). Following further analysis in Cytoscape, the DEGs were selected by Maximum Clique Centrality (MCC) in CytoHubba and intersected. The top 15 DEGs were identified and visualized as hub genes, namely STMN2, GAP43, TAGLN3, ELAVL4, KIF5C, SNAP91, ISL1, GATA3, PHOX2B, CHGA, HAND2, INA, TUBB2B, CD44 and DDC (Fig. 4 ). GO and KEGG enrichment analysis of overlapped DEGs To understand the molecular functions and pathways involving DEGs, we conducted a functional enrichment analysis (Fig. 5 ). GO-based BP analysis showed that overlapped DEGs were significantly enriched in cardiac right ventricle morphogenesis, cardioblast proliferation, regulation of cardioblast proliferation, catecholamine metabolic process, catechol-containing compound metabolic process, regulation of cell proliferation involved in heart morphogenesis, catechol-containing compound biosynthetic process, catecholamine biosynthetic process, and so on. GO analysis of CC showed that overlapped DEGs were significantly enriched in the neuronal cell body, growth cone, site of polarized growth, neuronal dense core vesicle, dense core granule, and distal axon. Regarding MF, overlapped DEGs were significantly enriched in structural constituent of cytoskeleton, transcription coregulator binding, and DNA-binding transcription activator activity, RNA polymerase II-specific. In addition, KEGG analysis showed that DEGs were significantly enriched in the phenylalanine metabolism, dopaminergic synapse, tyrosine metabolism, and so on (Fig. 5 ). Comparison to Literature To identify the novel biomarkers, we downloaded 3109 known neuroblastoma genes from the OMIM database, the DisGeNET database, and the GeneCards database. The intersection of the Venn diagram was used to obtain 12 commonly known genes, we deleted duplicate neuroblastoma genes form three databases, leaving three unknown genes. A further search of PubMed, the 12 of top 15 hub genes has been reported in PubMed, and the expression in neuroblastoma tissues, cell lines or serum has been verified by different experiments, including STMN2, GAP43, ELAVL4, ISL1, GATA3, PHOX2B, CHGA, HAND2, INA, TUBB2B, CD44, and DDC, as shown in Table 3 . Finally, we obtained three neuroblastoma-related genes, TAGLN3, KIF5C, and SNAP91, which were defined as novel markers associated with neuroblastoma. None of these three genes, which we searched in PubMed, have yet been reported in articles for their expression in neuroblastoma, nor have they been experimentally verified (Fig. 6 ). Table 3 Neuroblastoma-related top 15 hub genes reported in PubMed. Genes Expression Detection method Sample Reference STMN2 ⬆ qRT-PCR & Immunohistochemistry Tissues Liu et al., 2022 GAP43 ⬆ WB & Northern blot analysis Cell lines Kim et al., 2000 ELAVL4 ⬆ QPCR Tissues & cells & cell lines Swerts et al., 2006 ISL1 ⬆ qRT-PCR Tissues & cells Li et al., 2021 GATA3 ⬆ Immunohistochemistry Tissues & cells Wiles et al., 2017 PHOX2B ⬆ Immunohistochemistry Tissues & cells Ma et al., 2021 CHGA ⬆ RT-qPCR & Immunohistochemistry Cell lines & tissues Braekeveldt et al., 2015 HAND2 ⬆ QPCR Cell lines & tissues Durbin et al., 2018 INA ⬆ Immunohistochemistry Tissues Willoughby et al., 2008 TUBB2B ⬆ RT-qPCR Cell lines Liu & Li, 2019 CD44 ⬆ QPCR Cell lines & tissues Vega et al., 2019 ⬇ Immunostaining Tissues Combaret et al., 1997 DDC ⬆ qRT-PCR Cell lines & tissues Cheung et al., 2008 Validation of gene expression qPCR was conducted to test the expressions of DEGs. TAGLN3, KIF5C and SNAP91 were validated, which have not previously been associated with neuroblastoma. Compared with ARPE-19 cells, TAGLN3, KIF5C and SNAP91 exhibited a significantly increased expression level in SH-SY5Y cells (Fig. 7 ). Of note, the qPCR results confirmed the bioinformatics analysis of DEGs in the GSE54720 and GSE78061 datasets. Discussion In this study, 37 common differentially expressed genes were screened using bioinformatics analysis form GSE54720 and GSE78061 datasets, and the 15 hub genes were finally identified. Then, TAGLN3, KIF5C, and SNAP91 were identified by alignment in PubMed, OMIM, DisGeNET, and GeneCards databases, which have never been reported in the literature or experimentally verified. Additionally, TAGLN3, KIF5C, and SNAP91 were all upregulated in 2 datasets (GSE54720 & GSE78061). Subsequently, TAGLN3, KIF5C and SNAP91 were high expression in the neuroblastoma SH-SY5Y cells by qPCR verified, consistent with our bioinformatics analysis. In our study, KEGG pathway analysis showed that 37 common differentially expressed genes were mainly related to dopaminergic synapses, phenylalanine metabolism and Tyrosine metabolism. Most human neuroblastoma cell lines are dopaminergic neuroblastoma cells and exhibit characteristics of dopaminergic neurons (Kovalevich & Langford, 2013 ). Researchers have been studying the relationship between neuroblastoma and dopamine for more than 20 years. Approximately 80% of patients with neuroblastoma exhibit increased expression levels of catecholamines and their metabolites, including dopamine, vanillylmandelic acid (VMA), and homovanillic acid (HVA), making these molecules promising tumor markers for the diagnosis of neuroblastoma (Candito et al., 1992 ; Nakagawara et al., 1988; LaBrosse et al., 1976 ). Therefore, the alteration of dopamine expression level mediated by its related signaling pathways may play an important role in the molecular mechanism of neuroblastoma. Additionally, both phenylalanine and tyrosine are essential amino acids in our diet. Many nutrients, such as amino acids, are required for the rapid proliferation of tumor cells and are also considered potential biomarkers for malignant diseases (Vettore et al., 2020 ). Tyrosine, phenylalanine, and tryptophan are reduced in the plasma of patients with esophageal cancer (Lai et al., 2005 ). In contrast, tyrosine, phenylalanine, and tryptophan are increased in the urine, gastric contents, and tissues of gastric cancer patients (Wiggins et al., 2015 ). Notably, phenylalanine is required for the production of the nonessential amino acid tyrosine (Womack & Rose, 1934 ). This conversion is catalyzed by phenylalanine hydroxylase, and it has been shown that the activity of phenylalanine hydroxylase can be altered in inflammation or malignancy (Deng et al., 2011 ; Tang et al., 2023 ). Consequently, Therefore, changes in phenylalanine and tyrosine metabolism mediated by phenylalanine-related and tyrosine-related signaling pathways may play a role in the initiation and development of neuroblastoma. GO term enrichment analysis showed that TAGLN3, KIF5C and SNAP91 expression in BP, CC, and MF are mainly associated with axon guidance, neuron projection guidance, axon genesis, axon development, distal axon, site of polarized growth, growth cone, neuronal cell body, and synaptic vesicle transport, indicating that may be involved in protein binding, plasma membrane, membrane composition, nucleus, and other biological functions. Followed qPCR, we found that TAGLN3, KIF5C, and SNAP91 were significantly high expression in human neuroblastoma SH-SY5Y cells. Current studies have reported that TAGLN3 belongs to the actin-binding protein family, also known as neuron protein 22, NP22, or NP25, and is only found in highly differentiated neural cells and involved in central nervous system development (Ren et al., 1994 ). The amino acid sequence of TAGLN3 shared homology (from 67–42%) with four other proteins, SM22alpha, calponin, myophilin, and mp20, suggesting a potential interaction of TAGLN3 with cytoskeletal elements and possible mediating regulatory signal transduction pathways in neurons (Ren et al., 1994 ; Fan et al., 2001 ). TAGLN3 is significantly downregulated in the brains of SAD patients and in glioma tissues (Arnaud et al., 2022 ; Su et al., 2022 ). In contrast, TAGLN3 is specifically expressed in brain tissue and upregulated in the frontal cortex and hippocampus of chronic alcoholics and rats (Kim et al., 2018 ; Mori et al., 2004 ; Ren et al., 1994 ). Upregulated TAGLN3 inhibits Notch signaling during hypothalamic development (Ratié et al., 2013 ). Studies by Zage et al. found that neuroblastoma tumor cell lines and patient tumors have essentially inactivated Notch signaling, and Notch pathway activation leads to decreased proliferation of neuroblastoma cells (Zage eta l., 2012). Taken together with our study that TAGLN3 expression level was significantly higher in the human neuroblastoma SH-SY5Y cell line than in the ARPE-19 cell line, we hypothesized that upregulated TAGLN3 affects the development and progression of neuroblastoma by blocking Notch activity. KIF5C, a member of the kinin-1 heavy chain family, which helps transport specific cargoes required for neurite maturation along microtubules, selectively transports molecules from the cell body, and is essential for neuronal development (Kanai et al., 2000 ; Poirier et al., 2013 ; Schäfer et al., 2008 ). Previous studies have shown that KIF5C is downregulated in colon and adenocarcinoma (Tong and Fan, 2023 ; Aquino et al., 2023 ), and highly expressed in mouse and human brain neurons during early developmental stage, and esophageal cancer (Kanai et al., 2000 ; Li et al., 2022 ; He et al., 2019 ). Mutations in the KIF5C gene can cause common neurodevelopmental disorders in children, including cortical dysplasia, microcephaly, epilepsy, developmental delay/mental retardation, and autism-like features (Schäfer et al., 2009; Willemsen et al., 2014 ). In addition, A miRNA has variety of target genes, and a gene can be regulated by multiple miRNAs. He et al. 's study found that KIF5C is a direct target of miR203, and KIF5C overexpression partially counteracted the tumor suppressive effect of miR-203 on esophageal carcinoma cells (He et al., 2019 ). Similarly, Zhao et al. showed that the expression of Sam68 expression was significantly upregulated in neuroblastoma tissues, and that miR-203 has an inhibitory effect on the malignant progression of neuroblastoma by targeting Sam68 (Zhao et al., 2015 ). Indeed, miR-203 deregulation has been shown to be associated with various cancer types, including glioma, hepatocellular carcinoma, esophageal carcinoma, and prostate carcinoma, among others (He et al., 2013 ; Furuta et al., 2010 ; He et al., 2019 ; Boll et al., 2013 ). Our study found that KIF5C is upregulated in neuroblastoma cell line SH-SY5Y, and we speculate that miR-203 target KIF5C is also involved in the development of neuroblastoma progression, but further studies are needed. SNAP91 (KIAA0656/AP180) encodes a synapse-associated protein with the highest expression in the brain (Ishikawa et al., 1998 ), which is mainly distributed in the polar part of the synapse and is involved in the vesicular transport of neurotransmitters (Schwartz et al., 2010 ). Previous studies have shown that SNAP91 is associated with neurological diseases, such as Parkinson's disease, schizophrenia, epilepsy, and Alzheimer's disease (Yemni et al., 2019 ; Kooet al., 2015 ; Takata et al., 2017 ; Cao et al., 2010 ). Previous studies have shown that SNAP91 is significantly reduced in the hippocampus of patients with Alzheimer's disease and low expression in glioma and acute lymphoblastic leukemia (Cao et al., 2010 ; Yu et al., 2022 ; Qi et al., 2021 ). In contrast, SNAP91 was overexpressed in prostate cancer (PCa), correlated with the metastatic phenotype of PCa, and promoted PCa tumor metastasis (Sun et al., 2021 ). In our study, the bioinformatics analysis and qPCR results are shown KIF5C is upregulated in neuroblastoma cell line SH-SY5Y. Thus, SNAP91 may be involved in the development of neuroblastoma. So far, no study has mentioned the role of TAGLN3, KIF5C, and SNAP91 in neuroblastoma. In this study, we analyzed data from two datasets from the GEO database and verified by qPCR, and found that Tagln3, KIF5C and Snap91 were upregulated in neuroblastoma. It also provides new ideas and theoretical basis for screening diagnostic, prognostic markers or therapeutic targets of neuroblastoma. However, the novel biomarkers of neuroblastoma screened by bioinformatics methods in this study still need further study. Declarations Ethical approval and consent to participate Not applicable to this study. Consent for publication Not applicable to this research. Availability of data and materials The data GSE54720 and GSE78061 used in this research were obtained from NCBI GEO Datasets (GEO, https://www.ncbi.nlm.nih.gov/gds). This study complies with its data use and publication rules. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Grants from the Natural Science Foundation project of Hubei Province (No. 2021CFB158), the Education Research Project of Hubei Province (No. D20222106) and the National Natural Science Foundation of China (No. 32060150). Author's contribution Z X, M X, P Y, J G and W L participated in the conception and design of the study. Z X collected data, did the statistical analysis and created the table and figures. Z X and M X wrote the first draft of the manuscript. W L, J G, P Y, K Y, H X, R Y, P Z, Q L, J Z, Z W and L Z participated in the revision of the manuscript. All authors read and agreed to the final manuscript and authorship arrangement. 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Notch pathway activation induces neuroblastoma tumor cell growth arrest. Pediatric Blood Cancer 58(5):682–689 Zhao D, Tian Y, Li P, Wang L, Xiao A, Zhang M, Shi T. 2015. MicroRNA-203 inhibits the malignant progression of neuroblastoma by targeting Sam68. Molecular Medicine Reports 12(4):5554–5560 Additional Declarations No competing interests reported. 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-4173002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288483407,"identity":"e57683d5-ffb4-46f4-aeaf-561d64fd8949","order_by":0,"name":"Zijun Xiong","email":"","orcid":"","institution":"HealthCare BigData Center, School of Public Health and Management, Hubei University of Medicine, Shiyan","correspondingAuthor":false,"prefix":"","firstName":"Zijun","middleName":"","lastName":"Xiong","suffix":""},{"id":288483408,"identity":"9fc50ef8-0202-469d-845b-e52bf682312c","order_by":1,"name":"Mingjun 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02:44:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4173002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4173002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54283187,"identity":"b9bcd545-5b84-4ecc-be63-ddd3e941f20e","added_by":"auto","created_at":"2024-04-08 09:38:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":807464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plot distribution of DEGs and heatmap of the top 50 DEGs between the two datasets.\u003c/strong\u003e The volcano plot of GSE54720 (A) and GSE78061(B). Heatmap for top 50 DEGs of GSE54720 (C) and GSE78061(D).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/43183225f5265e67af0f5371.jpeg"},{"id":54283183,"identity":"5b0019a4-a03a-4eba-a938-a00e8b81fd29","added_by":"auto","created_at":"2024-04-08 09:38:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of overlapped DEGs. \u003c/strong\u003eVenn diagram of 37 overlapped DEGs between GSE54720 and GSE78061.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/1f77eb579f3990c106bdee32.png"},{"id":54283675,"identity":"4f977515-99b1-4928-bb2f-2f415a667d56","added_by":"auto","created_at":"2024-04-08 09:46:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network of DGEs constructed in STRING.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/a5f507bc2c80e7f9d6dc7746.png"},{"id":54283186,"identity":"9c8cd8ab-f655-4139-84d4-a447d8fe30c7","added_by":"auto","created_at":"2024-04-08 09:38:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe top 15 hub genes\u003c/strong\u003e. Picking the top 15 rank genes (MCC Ranking method).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/8087d0bd0981369cce52fd00.png"},{"id":54283676,"identity":"b3873d13-9ace-40fe-a544-204a932bf0d2","added_by":"auto","created_at":"2024-04-08 09:46:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment and pathway enrichment analysis of overlapped DEGs. \u003c/strong\u003eAnalysis of component, and molecular function. KEGG pathway analysis of overlapped DEGs.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/50b2db30b7b293f9de4318a7.png"},{"id":54283184,"identity":"4d501d8f-858f-4e23-9f09-67695b0f019d","added_by":"auto","created_at":"2024-04-08 09:38:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of unknown neuroblastoma genes. \u003c/strong\u003eVenn diagram of 12 overlapped known neuroblastoma genes between the top 15 hub genes and 3109 known neuroblastoma genes. Three neuroblastoma genes not collected in the OMIM database, DisGeNET database, and GeneCards database were screened.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/242c1da0bb9418c797e3c29e.png"},{"id":54283189,"identity":"d70ab1c3-a494-4c06-956c-39b41491badc","added_by":"auto","created_at":"2024-04-08 09:38:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":114943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eqPCR analysis. \u003c/strong\u003eRelative mRNA expressions of TAGLN3 (A), KIF5C (B) and SNAP91(C) in human neuroblastoma SH-SY5Y cell line and ARPE-19 cell line. * * * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 vs. ARPE-19 cells.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/c18c825efbd2e03bbebc6094.png"},{"id":82075623,"identity":"ea60fce2-5f72-41b5-b650-62586fe42e6e","added_by":"auto","created_at":"2025-05-06 13:47:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2237930,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4173002/v1/e6266b2a-5e3d-41a6-80ab-75e735fcda48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Three Novel Neuroblastoma Biomarkers Revealed by Integrative Analysis of GEO data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeuroblastoma is a tumor that develops during childhood is the leading cause of pediatric cancer-related deaths in children aged 1\u0026ndash;5 years. It is the second most common solid tumor in children under the age of 15 worldwide, after central nervous system tumors. Children diagnosed with neuroblastoma account for approximately 13% of all cancer deaths, with 7.5 deaths per 100,000 infants. In addition, 9.0% of all pediatric cancers are accounted by 1.3 new cases per 100,000 children under the age of 15 each year. Furthermore, 90% of children with the disease are diagnosed during the first five years of life (Manfred et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Louis et al., 2015). Neuroblastoma derives from the neural crest and consists of undifferentiated neuroectodermal cells. Neuroblastoma is a type of cancer that typically originates in the adrenal medulla of the abdomen. Around 50% of cases begin in the adrenal gland, with the remainder occurring in the paraspinal sympathetic ganglia of the neck, chest, abdomen, or pelvis (Ayguny, 2018). One of the distinctive features of neuroblastoma is its clinical heterogeneity, which involves different sites of origin and often distant metastasis. Some tumors may regress spontaneously, while others may progress despite aggressive treatment (Mueller \u0026amp;Matthay, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Castel et al., 2007). Early detection, diagnosis and treatment are crucial in improving the cure rate of neuroblastoma. Symptoms are usually absent in the early stages, and it may be difficult to detect a mass during a physical examination. Patients typically seek medical treatment for tumor growth, which compresses adjacent tissues and organs, causing symptoms such as occupation compulsion, eyeball protrusion, irritating cough, shortness of breath, hematuria, constipation, and abnormal urination. Unfortunately, most patients are diagnosed at a late stage (Chung et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dumba et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Infants who are less than 18 months old at diagnosis with non-MYCN-amplified neuroblastoma usually have a better prognosis. However, for high-risk patients with metastasis, despite intensive multimodal therapy, such as standard chemotherapy, surgical resection, radiotherapy, and high-dose chemotherapy, the overall survival (OS) is less than 40% (Ayguny, 2018; Mueller \u0026amp;Matthay, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeuroblastoma usually occurs sporadically, but there are also familial cases, with account for approximately 1\u0026ndash;2% of case (Ayguny, 2018). The molecular mechanism of neuroblastoma is currently unclear. However, research has shown that the abnormal genes expression is closely linked to the grading, treatment and prognosis of neuroblastoma. Additionally, approximately 2% of neuroblastoma patients have a positive family history (Shojaei-Brosseau et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, after the establishment of the International Neuroblastoma Risk Group (INRG) Task Force in 2004, the INRG Biology Committee reviewed data form 8800 patients in the INRG database and identified the most significant neuroblastoma biomarkers. The new INRG risk classification schema now includes MYCN status, 11q23 allele status, and ploidy, as agreed upon by consensus (Ambros et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, numerous molecules and pathways including ALK, the proteasome complex, and the PI3K/AKT/mTOR pathway have been targeted by drugs in preclinical or clinical trials (Pezeshki et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Moreno et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, several studies have shown promising results with immunotherapy drugs such as anti-GD2 antibodies, vaccines, and CAR-T-cell therapy in neuroblastoma patients (Richards et al., 2018; Chan \u0026amp; Chan, 2020). Therefore, it is necessary to screen, and identify biomarkers related to the genesis, development and prognosis of neuroblastoma. Additionally, new effective targets for the diagnosis and treatment of neuroblastoma must be found.\u003c/p\u003e \u003cp\u003eThis study, analyzed differentially expressed genes between tissues and adjacent tissues in two datasets (GSE54720 and GSE78061) obtained from the GEO database using R software. Further exploration of the potential biological functions of co-expressed differentially expressed genes in the two datasets was conducted using GO and KEGG pathway enrichment analysis. The protein interaction network was constructed using the STRING database and hub genes were identified using Cytoscape. Finally, we compared the top 15 hub genes that appeared in the OMIM, DisGeNET, GeneCards databases, and PubMed databases. Neuroblastoma-related genes present mentioned in articles and already validated were excluded. Verified by qPCR, TAGLN3, KIF5C, and SNAP91 were screened. These findings could provide insights into the genesis and progression of neuroblastoma, as well as potential therapeutic targets for future studies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray data\u003c/h2\u003e \u003cp\u003eTwo neuroblastoma-related gene expression datasets GSE54720 (Lavarino et al., 2015) and GSE78061 (Cole et al., 2016) were downloaded from the NCBI Gene Expression Synthesis (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which showed in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.The GSE54720 is based on the GPL13667 platform (Affymetrix Human Genome U219 Array, Agilent Technologies LTD, Santa Clara, CA, USA), and consisted of 20 neuroblastoma tumors and 4 non-pathological tissues (2 fetal brain and 2 adrenal gland) samples. The GSE78061 is based on the GPL6244 platform (Affymetrix Human Gene 1.0 ST Array, Thermo Fisher Scientific, Inc., Waltham, MA, USA), and consists of 25 human neuroblastoma cell lines and 4 retinal pigmented epithelium cell lines.\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\u003eNeuroblastoma-related microarrays datasets in GEO databases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeuroblastoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE54720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL13667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE78061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL6244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Preprocessing and Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eThe datasets were processed using R Studio as follows: The Limma package was used to standardize the matrix data and identify differentially expressed genes (DEGs)with log fold changes\u0026thinsp;\u0026gt;\u0026thinsp;2 and AdjP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 between neuroblastoma and control cells for each dataset (Ritchie et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). DEGs were adjusted by the Benjamini-Hochberg method to handle p-values (Benjamini \u0026amp; Hochberg, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The Venn diagram online web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to obtain co-DEGs between the two datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePPI network construction and Hub genes Identification\u003c/h2\u003e \u003cp\u003eSTRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a database for analyzing known and predicted protein-protein interactions, which shows physical and functional interactions (von Mering et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In this study, we constructed a protein-interaction (PPI) network of 37 DEGs using STRING, with the effective binding score set to \u0026gt;\u0026thinsp;0.4. Subsequently, the PPI network was imported into Cytoscape software (version 3.8.0), and the hub genes were screened using the 12 algorithms (EPC, BottleNeck, EcCentricity, Closeness, Radiality, Betweenness, Stress, Clustering, Coefficient, MCC, DMNC, MNC, and Degree) of Cytohubba in Cytoscape software (Chin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Doncheva et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Szklarczyk et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;).Based on CytoHubba, Maximum Clique Centrality (MCC) were used to screen the top 15 genes (Chin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and the PPI network of hub genes was constructed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis of DEGs with GO and KEGG\u003c/h2\u003e \u003cp\u003eThe clusterProfiler package, org.Hs.eg.db package, ggplot2 package, and enrichplot package of R version 4.2.3 were used for hub gene enrichment analysis of gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) further explained the reliability of the results. The functional annotation of GO included biological process (BP), cell component (CC) and molecular function (MF) (Kanehisa \u0026amp; Goto, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eComparison to Literature\u003c/h2\u003e \u003cp\u003eWe collected the neuroblastoma genes by searching the OMIM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database (Amberger et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the DisGeNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/home/\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database (Pi\u0026ntilde;ero et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the GeneCards(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database (Barshir et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Safran et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The keyword \"neuroblastoma\" was entered into OMIM, DisGeNET, and GeneCards databases, and the target genes in each database were obtained. These three databases\u0026rsquo; targets were merged, duplicate targets were removed, and the remaining targets were the neuroblastoma targets we collected and used in the next study. Then, the Venn diagram online web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to obtain unknown genes associated with neuroblastoma between the top 15 hub genes and 3109 known neuroblastoma genes. Finally, we searched PubMed (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (White, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which has not yet seen articles reporting its expression in neuroblastoma, nor has it been experimentally validated to define it as a novel biomarker of neuroblastoma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of gene expression by Quantitative real-time polymerase chain reaction (qPCR)\u003c/h2\u003e \u003cp\u003eARPE-19 cells (cat. no. CL-0026, Pricella Life Science\u0026amp; Technology Co.,Ltd., Wuhan, China) were cultured in DMEM/F12 medium (Biosharp, Beijing, China) with 10% fetal bovine serum (HAKATA, Shanghai, China), and 1% (v/v) penicillin (100 U/ml)/streptomycin (100 \u0026micro;g /ml). SHSY5Y cells (cat. no. CL-0208, Pricella Life Science\u0026amp; Technology Co.,Ltd., Wuhan, China) were cultured in 1640 medium (Biosharp, Beijing, China) with 10% fetal bovine serum (HAKATA, Shanghai, China), and 1% (v/v) penicillin (100 U/ml)/streptomycin (100 \u0026micro;g /ml). Both cell lines were cultured at 37˚C under a humidified 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere.\u003c/p\u003e \u003cp\u003eTotal RNA was extracted (TRIzol reagent, Wuhan servicebio technologh CO.,LTD), and RNA was reverse-transcribed (RevertAid First Strand cDNA Synthesis Kit, Thermo Fisher Scientific, Inc.) for cDNA synthesis for 5 min at 25\u0026deg;C, followed by 30 min at 42\u0026deg;C, and terminating the reaction by heating at 85\u0026deg;C for 5 seconds. qPCR was conducted (HieffTM qPCR SYBR\u0026reg; Green Master Mix, Shanghai Yeasen BioTechnologies) as follows: 95˚C for 30s; 40 cycles of 95˚C for 15s, 60˚C for 30s, and 72˚C for 30s; followed by 10min at 72˚C. Primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. GAPDH was used as the reference gene. The 2\u003csup\u003e\u0026minus;ΔΔcq\u003c/sup\u003e method was used to calculated relative expression of target genes (Livak and Schmittgen, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). All qPCR experiments were repeated three times and mean values were used.\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\u003ePrimer sequences for the validated genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-TAGLN3-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAGAATCGGAGAGGCTTTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-TAGLN3-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCATCCCGTACCCTGTCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-KIF5C-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATCCCACGAATTGCCCATGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-KIF5C-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCTTTACATACGGGACTCTGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-SNAP91-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGTCCCAGTCAGCACTTCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-SNAP91-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACAGAGGAAAGTGCAGCCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eF, forward; R, reverse.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eR software was used for part of the data analysis and drawing, and the rest of the analysis came from the resource-sharing network data platform. Parameter setting: \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 indicated that the difference was statistically significant.\u003c/p\u003e \u003cp\u003eSPSS 23.0 software was used to analyze the experimental data, the measurement data with normal distribution was expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error, and the differences between groups were evaluated by Student's t-test. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was considered statistically significant. Graphs were obtained using GraphPad Prism9.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMicroarray data information and identification of DEGs\u003c/h2\u003e \u003cp\u003eWe performed background correction and normalization of the neuroblastoma expression microarray datasets GSE54720 and GSE78061. When filtering the GSE54720 dataset through the limma software package in R (AdjP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |LogFC| \u0026gt; 2), 547 DEGs were obtained, including 291 upregulated and 256 downregulated DEGs. Besides, 160 DEGs were screened from the GSE78061 dataset, including 100 upregulated and 60 downregulated DEGs. The R package was used to visualize DEGs. Red represents upregulated DGEs and green represents downregulated DEGs in the volcano plot, which is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and B. In addition, the cluster heatmap of the top 50 DEGs is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D. From red to blue, the expression level of the gene in the sample gradually decreases. Then, the intersection of Venn diagram was used to obtain 37 common DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePPI network construction and hub gene determination\u003c/h2\u003e \u003cp\u003eWe used STRING network-based protein interaction analysis to generate a PPI network from 37 DEGs overlapped in two datasets, confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Following further analysis in Cytoscape, the DEGs were selected by Maximum Clique Centrality (MCC) in CytoHubba and intersected. The top 15 DEGs were identified and visualized as hub genes, namely STMN2, GAP43, TAGLN3, ELAVL4, KIF5C, SNAP91, ISL1, GATA3, PHOX2B, CHGA, HAND2, INA, TUBB2B, CD44 and DDC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG enrichment analysis of overlapped DEGs\u003c/h2\u003e \u003cp\u003eTo understand the molecular functions and pathways involving DEGs, we conducted a functional enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). GO-based BP analysis showed that overlapped DEGs were significantly enriched in cardiac right ventricle morphogenesis, cardioblast proliferation, regulation of cardioblast proliferation, catecholamine metabolic process, catechol-containing compound metabolic process, regulation of cell proliferation involved in heart morphogenesis, catechol-containing compound biosynthetic process, catecholamine biosynthetic process, and so on. GO analysis of CC showed that overlapped DEGs were significantly enriched in the neuronal cell body, growth cone, site of polarized growth, neuronal dense core vesicle, dense core granule, and distal axon. Regarding MF, overlapped DEGs were significantly enriched in structural constituent of cytoskeleton, transcription coregulator binding, and DNA-binding transcription activator activity, RNA polymerase II-specific. In addition, KEGG analysis showed that DEGs were significantly enriched in the phenylalanine metabolism, dopaminergic synapse, tyrosine metabolism, and so on (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison to Literature\u003c/h2\u003e \u003cp\u003eTo identify the novel biomarkers, we downloaded 3109 known neuroblastoma genes from the OMIM database, the DisGeNET database, and the GeneCards database. The intersection of the Venn diagram was used to obtain 12 commonly known genes, we deleted duplicate neuroblastoma genes form three databases, leaving three unknown genes. A further search of PubMed, the 12 of top 15 hub genes has been reported in PubMed, and the expression in neuroblastoma tissues, cell lines or serum has been verified by different experiments, including STMN2, GAP43, ELAVL4, ISL1, GATA3, PHOX2B, CHGA, HAND2, INA, TUBB2B, CD44, and DDC, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Finally, we obtained three neuroblastoma-related genes, TAGLN3, KIF5C, and SNAP91, which were defined as novel markers associated with neuroblastoma. None of these three genes, which we searched in PubMed, have yet been reported in articles for their expression in neuroblastoma, nor have they been experimentally verified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNeuroblastoma-related top 15 hub genes reported in PubMed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTMN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eqRT-PCR \u0026amp; Immunohistochemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAP43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWB \u0026amp; Northern blot analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKim et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELAVL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTissues \u0026amp; cells \u0026amp; cell lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSwerts et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eqRT-PCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTissues \u0026amp; cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGATA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTissues \u0026amp; cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWiles\u0026nbsp;et al., 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHOX2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTissues \u0026amp; cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMa et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT-qPCR \u0026amp; Immunohistochemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell lines \u0026amp; tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBraekeveldt et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAND2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell lines \u0026amp; tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDurbin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWilloughby et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUBB2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT-qPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiu \u0026amp; Li, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell lines \u0026amp; tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVega et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬇\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmunostaining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCombaret et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1997\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬆\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eqRT-PCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell lines \u0026amp; tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCheung et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of gene expression\u003c/h2\u003e \u003cp\u003eqPCR was conducted to test the expressions of DEGs. TAGLN3, KIF5C and SNAP91 were validated, which have not previously been associated with neuroblastoma. Compared with ARPE-19 cells, TAGLN3, KIF5C and SNAP91 exhibited a significantly increased expression level in SH-SY5Y cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Of note, the qPCR results confirmed the bioinformatics analysis of DEGs in the GSE54720 and GSE78061 datasets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, 37 common differentially expressed genes were screened using bioinformatics analysis form GSE54720 and GSE78061 datasets, and the 15 hub genes were finally identified. Then, TAGLN3, KIF5C, and SNAP91 were identified by alignment in PubMed, OMIM, DisGeNET, and GeneCards databases, which have never been reported in the literature or experimentally verified. Additionally, TAGLN3, KIF5C, and SNAP91 were all upregulated in 2 datasets (GSE54720 \u0026amp; GSE78061). Subsequently, TAGLN3, KIF5C and SNAP91 were high expression in the neuroblastoma SH-SY5Y cells by qPCR verified, consistent with our bioinformatics analysis.\u003c/p\u003e \u003cp\u003eIn our study, KEGG pathway analysis showed that 37 common differentially expressed genes were mainly related to dopaminergic synapses, phenylalanine metabolism and Tyrosine metabolism. Most human neuroblastoma cell lines are dopaminergic neuroblastoma cells and exhibit characteristics of dopaminergic neurons (Kovalevich \u0026amp; Langford, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Researchers have been studying the relationship between neuroblastoma and dopamine for more than 20 years. Approximately 80% of patients with neuroblastoma exhibit increased expression levels of catecholamines and their metabolites, including dopamine, vanillylmandelic acid (VMA), and homovanillic acid (HVA), making these molecules promising tumor markers for the diagnosis of neuroblastoma (Candito et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Nakagawara et al., 1988; LaBrosse et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Therefore, the alteration of dopamine expression level mediated by its related signaling pathways may play an important role in the molecular mechanism of neuroblastoma.\u003c/p\u003e \u003cp\u003eAdditionally, both phenylalanine and tyrosine are essential amino acids in our diet. Many nutrients, such as amino acids, are required for the rapid proliferation of tumor cells and are also considered potential biomarkers for malignant diseases (Vettore et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tyrosine, phenylalanine, and tryptophan are reduced in the plasma of patients with esophageal cancer (Lai et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In contrast, tyrosine, phenylalanine, and tryptophan are increased in the urine, gastric contents, and tissues of gastric cancer patients (Wiggins et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, phenylalanine is required for the production of the nonessential amino acid tyrosine (Womack \u0026amp; Rose, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1934\u003c/span\u003e). This conversion is catalyzed by phenylalanine hydroxylase, and it has been shown that the activity of phenylalanine hydroxylase can be altered in inflammation or malignancy (Deng et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, Therefore, changes in phenylalanine and tyrosine metabolism mediated by phenylalanine-related and tyrosine-related signaling pathways may play a role in the initiation and development of neuroblastoma.\u003c/p\u003e \u003cp\u003eGO term enrichment analysis showed that TAGLN3, KIF5C and SNAP91 expression in BP, CC, and MF are mainly associated with axon guidance, neuron projection guidance, axon genesis, axon development, distal axon, site of polarized growth, growth cone, neuronal cell body, and synaptic vesicle transport, indicating that may be involved in protein binding, plasma membrane, membrane composition, nucleus, and other biological functions.\u003c/p\u003e \u003cp\u003eFollowed qPCR, we found that TAGLN3, KIF5C, and SNAP91 were significantly high expression in human neuroblastoma SH-SY5Y cells. Current studies have reported that TAGLN3 belongs to the actin-binding protein family, also known as neuron protein 22, NP22, or NP25, and is only found in highly differentiated neural cells and involved in central nervous system development (Ren et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The amino acid sequence of TAGLN3 shared homology (from 67\u0026ndash;42%) with four other proteins, SM22alpha, calponin, myophilin, and mp20, suggesting a potential interaction of TAGLN3 with cytoskeletal elements and possible mediating regulatory signal transduction pathways in neurons (Ren et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). TAGLN3 is significantly downregulated in the brains of SAD patients and in glioma tissues (Arnaud et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, TAGLN3 is specifically expressed in brain tissue and upregulated in the frontal cortex and hippocampus of chronic alcoholics and rats (Kim et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mori et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Upregulated TAGLN3 inhibits Notch signaling during hypothalamic development (Rati\u0026eacute; et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Studies by Zage et al. found that neuroblastoma tumor cell lines and patient tumors have essentially inactivated Notch signaling, and Notch pathway activation leads to decreased proliferation of neuroblastoma cells (Zage eta l., 2012). Taken together with our study that TAGLN3 expression level was significantly higher in the human neuroblastoma SH-SY5Y cell line than in the ARPE-19 cell line, we hypothesized that upregulated TAGLN3 affects the development and progression of neuroblastoma by blocking Notch activity.\u003c/p\u003e \u003cp\u003eKIF5C, a member of the kinin-1 heavy chain family, which helps transport specific cargoes required for neurite maturation along microtubules, selectively transports molecules from the cell body, and is essential for neuronal development (Kanai et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Poirier et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sch\u0026auml;fer et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Previous studies have shown that KIF5C is downregulated in colon and adenocarcinoma (Tong and Fan, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Aquino et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and highly expressed in mouse and human brain neurons during early developmental stage, and esophageal cancer (Kanai et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mutations in the KIF5C gene can cause common neurodevelopmental disorders in children, including cortical dysplasia, microcephaly, epilepsy, developmental delay/mental retardation, and autism-like features (Sch\u0026auml;fer et al., 2009; Willemsen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, A miRNA has variety of target genes, and a gene can be regulated by multiple miRNAs. He et al. 's study found that KIF5C is a direct target of miR203, and KIF5C overexpression partially counteracted the tumor suppressive effect of miR-203 on esophageal carcinoma cells (He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, Zhao et al. showed that the expression of Sam68 expression was significantly upregulated in neuroblastoma tissues, and that miR-203 has an inhibitory effect on the malignant progression of neuroblastoma by targeting Sam68 (Zhao et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Indeed, miR-203 deregulation has been shown to be associated with various cancer types, including glioma, hepatocellular carcinoma, esophageal carcinoma, and prostate carcinoma, among others (He et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Furuta et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boll et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Our study found that KIF5C is upregulated in neuroblastoma cell line SH-SY5Y, and we speculate that miR-203 target KIF5C is also involved in the development of neuroblastoma progression, but further studies are needed.\u003c/p\u003e \u003cp\u003eSNAP91 (KIAA0656/AP180) encodes a synapse-associated protein with the highest expression in the brain (Ishikawa et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), which is mainly distributed in the polar part of the synapse and is involved in the vesicular transport of neurotransmitters (Schwartz et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Previous studies have shown that SNAP91 is associated with neurological diseases, such as Parkinson's disease, schizophrenia, epilepsy, and Alzheimer's disease (Yemni et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kooet al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Takata et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Previous studies have shown that SNAP91 is significantly reduced in the hippocampus of patients with Alzheimer's disease and low expression in glioma and acute lymphoblastic leukemia (Cao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qi et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, SNAP91 was overexpressed in prostate cancer (PCa), correlated with the metastatic phenotype of PCa, and promoted PCa tumor metastasis (Sun et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, the bioinformatics analysis and qPCR results are shown KIF5C is upregulated in neuroblastoma cell line SH-SY5Y. Thus, SNAP91 may be involved in the development of neuroblastoma.\u003c/p\u003e \u003cp\u003eSo far, no study has mentioned the role of TAGLN3, KIF5C, and SNAP91 in neuroblastoma. In this study, we analyzed data from two datasets from the GEO database and verified by qPCR, and found that Tagln3, KIF5C and Snap91 were upregulated in neuroblastoma. It also provides new ideas and theoretical basis for screening diagnostic, prognostic markers or therapeutic targets of neuroblastoma. However, the novel biomarkers of neuroblastoma screened by bioinformatics methods in this study still need further study.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data GSE54720 and GSE78061 used in this research were obtained from NCBI GEO Datasets (GEO, https://www.ncbi.nlm.nih.gov/gds). This study complies with its data use and publication rules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Grants from the Natural Science Foundation project of Hubei Province (No. 2021CFB158), the Education Research Project of Hubei Province (No. D20222106) and the National Natural Science Foundation of China (No. 32060150).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ X, M X, P Y, J G and W L participated in the conception and design of the study. Z X collected data, did the statistical analysis and created the table and figures. Z X and M X wrote the first draft of the manuscript. W L, J G, P Y, K Y, H X, R Y, P Z, Q L, J Z, Z W and L Z participated in the revision of the manuscript. All authors read and agreed to the final manuscript and authorship arrangement. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmeida J, Costa J, Coelho P, Cea V, Galesio M, Noronha JP, Diniz MS, Prud\u0026ecirc;ncio C, Soares R, Sala C, Fernandes R. 2019. Adipocyte proteome and secretome influence inflammatory and hormone pathways in glioma. Metabolic Brain Disease 34(1):141\u0026ndash;152\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. 2015. OMIM.org: Online Mendelian Inheritance in Man (OMIM\u0026reg;), an online catalog of human genes and genetic disorders. 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Molecular Medicine Reports 12(4):5554\u0026ndash;5560\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":"Neuroblastoma, Bioinformatics analysis, Biomarkers, Differentially expressed genes, qPCR","lastPublishedDoi":"10.21203/rs.3.rs-4173002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4173002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComprehensive bioinformatics analysis was used to identify the differentially expressed genes (DEGs) between neuroblastoma samples and normal samples in GSE54720 and GSE78061 datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on common DEGs. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. The top 15 hub genes were screened out. TAGLN3, KIF5C and SNAP91 were identified by alignment in the PubMed, OMIM, DisGeNET and GeneCards databases and validated by quantitative real-time polymerase chain reaction (qPCR). These three are have never been previously reported in the literature and experimentally validated. We identified a total of 37 commom DEGs from the two microarray databases. The KEGG pathway analysis showed that these DEGs were primarily involved in pathway related to dopaminergic synapses, motor proteins and phenylalanine metabolism related pathways. GO enrichment analysis showed that TAGLN3, KIF5C, and SNAP91 related pathway were mainly concentrated in axon guidance, axon genesis, axon development, distal axon, neuronal cell body, and synaptic vesicle transport, suggesting that they may be involved in biological functions such as protein binding, plasma membrane, membrane composition and nucleus. OMIM, DisGeNET, GeneCards databases, and PubMed have identified that TAGLN3, KIF5C, and SNAP91 were linked to proliferation, migration, and invasion of other tumors. Finally, the expression levels of TAGLN3, KIF5C and SNAP91 were significantly increased in SH-SY5Y cells compared with ARPE-19 cells as verified by qPCR, consistent with our bioinformatics analysis, suggesting that TAGLN3, KIF5C and SNAP91 may be involved in the occurrence and development of neuroblastoma. In this study, some key genes and molecules were identified by bioinformatics methods, revealing the potential pathogenic mechanism of neuroblastoma. These genes can serve as diagnostic indicators and therapeutic biomarkers for neuroblastoma, thereby enhancing our understanding of the molecular mechanisms underlying this disease.\u003c/p\u003e","manuscriptTitle":"Three Novel Neuroblastoma Biomarkers Revealed by Integrative Analysis of GEO data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 09:38:02","doi":"10.21203/rs.3.rs-4173002/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":"9821b92a-af7a-40a0-9402-077be73b3c7c","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30371340,"name":"Biological sciences/Cancer"},{"id":30371341,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-05-06T13:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 09:38:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4173002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4173002","identity":"rs-4173002","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}