基于GEO数据库的肝癌相关基因的筛选及验证

肝细胞癌(hepatocellular carcinoma,HCC)是中国高发的恶性肿瘤之一,识别肝细胞癌发生发展相关基因,对于深入研究肝癌发病机制和开发诊疗靶点均具有重要意义.本研究利用GEO2R工具从基因表达汇编数据库(Gene Expression Omnibus Database,GEO)筛选5个数据集中共有的差异表达基因作为潜在的肝癌相关基因.利用Metascape网站,对差异表达基因进行功能富集及信号通路分析.结合GEPIA(Gene Expres-sion Profiling Interaction Analysis)网站筛选具有临床意义的基因.利用荧光定量PCR技术验证与肝癌预后相关的差异表达基因,候选肝癌相关基因,为后续的深入研究奠定扎实的基础.结果显示,从5个数据集中共发现94个共有的差异表达基因.文献检索后发现24个基因与肝癌发生发展的关系少见文献报道,属于肝癌中未知功能基因.利用GEPIA分析癌症基因组图谱(the cancer genome atlas,TCGA)中数据后发现,GINS1在肝癌组织中高表达,与肝癌患者生存期呈负相关;CFHR4和DNASE1L3在肝癌组织中显著低表达,与肝癌患者生存期呈正相关.荧光定量PCR技术证实GINS1在81.3％的肝癌组织中呈现高表达,CFHR4和DNAS-E1L3分别在71.9％和93.8％肝癌组织中低表达.因此,本研究发现GINS1、CFHR4和DNASE 1L3在肝癌组织中显著差异表达,与肝癌患者的预后密切相关,可能作为潜在的判断肝癌患者预后的分子标志物和研发肝癌治疗的潜在靶标.     

内质网应激相关基因能预测骨肉瘤预后并与肿瘤免疫微环境相关

以内质网应激相关基因构建骨肉瘤患者的风险模型,探索其与肿瘤免疫微环境的关系。采用生物信息学分析法,训练集的转录组数据及临床数据下载于UCSC Xena数据库,验证集的相应数据下载于GEO数据库(GSE21257,GSE39058)。采用单因素COX回归分析、LASSO回归分析及多因素COX回归分析提取风险特征基因构建风险模型,使用决策曲线分析、受试者工作特征曲线分析验证模型的准确性,随后构建列线图进一步预测骨肉瘤患者预后;根据风险评分将患者分为高、低风险组,使用Kaplan-Meier生存曲线评估高、低风险组间的生存差异,对差异表达基因(Differentially expressed genes, DEGs)进行GO/KEGG联合富集分析、基因集富集分析(Gene set enrichment analysis, GSEA)及基因集变异分析(Gene set variation analysis, GSVA);采用ESTIMATE算法、微环境种群计数器(Microenvironment cell population counter, MCP counter)方法、单样本基因集富集分析(Single sample gene set enrichment analysis, ssGSEA)进行免疫分析;最终在验证集中验证上述结果。6个风险特征基因中VEGFA、PTGIS及SERPINH1与骨肉瘤患者的不良预后相关,而TMED10、MAPK10及TOR1B与与骨肉瘤患者的良好预后相关,高、低风险组患者之间具有显著生存差异;GO/KEGG联合富集分析、GSVA、GSEA结果表明DEGs与免疫状态相关;免疫分析显示高风险组具有更低的免疫评分及免疫景观;列线图进一步准确地预测了骨肉瘤患者的预后。内质网应激相关基因构建的风险模型能准确预测骨肉瘤患者预后,并与肿瘤免疫微环境相关。     

肝细胞癌相关差异基因的生物信息学及预后分析

肝细胞癌是全球癌症相关死亡的主要原因,目前对肝细胞癌的发病机制研究尚不完善,探索肝细胞癌发生、发展相关的分子标志物及其预后具有重要意义。从GEO数据库获得肝细胞癌组织和非癌组织的基因表达阵列数据GSE84402,利用GEO2R筛选差异表达基因;采用DAVID数据库对差异基因进行GO富集分析和KEGG通路分析;通过STRING数据库和Cytoscape软件构建差异表达基因对应的蛋白质相互作用网络,并从网络中筛选出核心基因(hub genes);结合KM plotter数据库的临床信息对hub genes进行预后分析。结果显示:共得到1 307个差异表达基因,其中上调基因741个,下调基因566个,这些差异表达基因主要涉及细胞分裂、细胞周期、DNA复制及物质代谢等生物学过程及生物通路。通过GO、KEGG及蛋白质相互作用网络筛选出BUB1、BUB1B、CCNA2、CCNB1、CCNB2、CDC20、CDK1、MAD2L1、PLK1等9个hub genes,进一步分析发现hub genes均与细胞周期的调控相关,表明细胞周期的调控失常在肝细胞癌的发生、发展过程中具有重要作用。生存分析显示9个hub genes在肝细胞癌患者中均为表达上调的基因,且与患者预后不良相关,这为寻找肝细胞癌患者预后相关生物标志物的研究提供了线索。     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

基于生物信息学分析的肝癌核心基因的筛选及验证

本研究基于GEO数据库,选取由慢性乙型肝炎诱导的肝细胞癌芯片数据GSE121248为研究对象,利用GEO2R软件分析数据,筛选出差异表达基因,利用DAVID数据库进行GO分析和KEGG pathway富集分析.利用STRING数据库构建PPI网络,分析筛选核心基因.利用GEPIA对核心基因的表达进行验证,Kaplan Meier Plotter在线分析工具对核心基因与患者生存情况的相关性进行验证.通过上述方法筛选出309个DEGs,其中上调基因94个,下调基因215个.差异基因功能分析显示上调的DEGs主要参与细胞周期和卵母细胞减数分裂通路等途径,下调的DEGs则在补体和凝血级联、代谢途径以及咖啡因代谢途径富集.筛选出15个具有高度关联性的核心基因(BUB1,BUB1B,BIRC5,CCNB1,CCNB2,CDC20,CDK1,KIF-20A,MAD2L1,NCAPG,ZWINT,PBK,BTL,TTK和NUSAP1),它们与肝癌患者的总体生存率具有明显相关性,并为其构建了miRNA调控网络.本研究通过生物信息学方法有效分析了肝细胞癌发生、发展相关的差异表达基因,筛选出15个核心基因,分析其生物学相关功能,以期探索肝细胞癌发病机制,并为临床诊断标志物的改进以及筛选提供一定的理论基础.     

HPX基因在肝癌中表达及与肿瘤细胞增殖的关系

[目的]探究HPX基因在肝细胞癌中的表达水平,阐述其表达水平与患者预后的关系以及其表达对肝癌细胞增殖的影响.[方法]利用Oncomine数据集探索HPX基因在肝癌肿瘤组织及癌旁正常组织的表达差异;用Kaplan-Meier法分析HPX表达水平对肝癌患者总生存期及无复发生存期的影响;实时荧光定量PCR法检测正常肝细胞和肝...     

生物信息学分析筛选结直肠癌靶基因及评估预后价值

为寻找与结直肠癌发展和预后相关的潜在关键基因及信号通路.从美国国立信息中心NCBI的GEO数据库获得结直肠癌基因表达数据集GSE106582,通过PCA对样本进行分组,利用GEO2R进行综合分析,筛选结直肠癌与癌旁对照组的差异表达基因;通过DAVID在线工具对差异表达基因进行GO本体分析和KEGG通路富集分析,初步分析...     

强直性脊柱炎差异表达基因的生物信息学分析

目的探讨强直性脊柱炎(AS)患者差异表达基因,并基于差异基因探讨强直性脊柱炎发病相关的可能生物学过程和信号通路。方法检索基因表达谱数据库(GEO)并筛选AS相关基因表达谱数据集。应用GEO在线分析功能GEO2R分析AS组和正常对照组的差异表达基因,用Cytoscape软件clueGO插件进行基因本体论和京都基因与基因组百科全书分析,采用String蛋白-蛋白相互作用(PPI)数据库分析差异表达基因编码蛋白间的相互作用;应用Cytoscape绘制蛋白相互作用网络图,并软件筛选信号通路关键基因分析。结果选取AS患者全血表达数据集GSE25101为研究对象,分析获得差异表达基因72个。72个差异表达基因分子功能主要为参与高迁移率族盒染色体蛋白1(HMGB1)转导机制;生物学过程主要富集于巨噬细胞迁移、骨髓细胞凋亡过程、线粒体呼吸链复合体装配、ATP合成偶联电子传输、线粒体ATP合成耦合电子输运等;细胞成分主要富集于呼吸链复合体、线粒体呼吸体等。信号通路富集于氧化磷酸化信号通路和帕金森综合征相关信号通路。PPI网络经过cytohubba插件筛选,ATP5J、NDUFS4、UQCRB、UQCRH、NDUFB3、COX7B、LSM3、ATP5EP2、ENY2、PSMA4被筛选为网络中的核心基因。结论通过生物信息学方法进行预测了AS的潜在机制,并筛选出10个潜在的与AS相关的重要分子,其中氧化磷酸化可能在AS发病机制中发挥了重要的作用。     

联合TCGA和GEO数据库筛选乳腺癌易感基因

本研究是利用公共基因芯片数据库筛选乳腺癌的预后基因,预测和探索这些基因在乳腺癌进展中的可能机制和临床价值.首先,我们筛选了公共基因芯片数据库(gene expression omnibus,GEO)GSE22820和癌症基因组图谱(the cancer genome atlas,TCGA)乳腺癌数据库的重叠差异表达基因,联合R语言分析乳腺癌组织与癌旁正常组织差异表达的基因;其次,基于STRING数据库及Cytoscape软件构建蛋白质相互作用网络图,分析并识别了中枢基因和前3个模块;之后进行了更多的功能分析,包括基因本体(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路分析以及基因集富集分析(gene set enrichment analysis,GSEA),以研究这些基因的作用以及潜在的潜在机制;最后进行了Kaplan-Meier分析和Cox比例风险分析,以阐明这些基因的诊断和预后效果.相关数据分析表明15个基因的表达水平与生存预后相关,高表达基因患者的总生存时间短于低表达患者(P＜0.05);Cox比例风险分析表明UBE2T、ER-CC6L和RAD51这3个基因是预后生存的独立因素(P＜0.05);GSEA分析表明在UBE2T、ERCC6L和RAD51基因中细胞周期、基础转录因子和卵母细胞减数分裂明显富集.最终,我们得出结论,这3种基因标志物的高表达是乳腺癌预后不良因素,可作为预测乳腺癌患者转移和预后的有效生物标志物.     

基于TCGA数据分析研究TMEM206在HCC中的表达及其与预后的相关性

[目的]探讨TMEM206表达在肝细胞癌(hepatocellular carcinoma,HCC)诊断和预后中的价值。[方法]从肿瘤基因组图谱(The Cancer Genome Atlas,TCGA)数据库下载肝细胞癌数据集表达谱及临床病理特征资料。采用KaplanMeier和Cox分析,观察TMEM206表达对肝细胞癌患者生存和预后的影响。使用TCGA数据集进行基因集富集分析(GSEA)。[结果]TMEM206在HCC肿瘤组织中的表达高于癌旁组织(P 0.000 1),并且TMEM206的表达与HCC的肿瘤分级、肿瘤分期都有显著相关。生存分析结果表明TMEM206表达越高,HCC患者的总体生存时间越短。单因素和多因素Cox分析表明,TMEM206 mRNA的表达可能是判断HCC预后的有效生物标志物。GSEA鉴定了TMEM206在HCC中的高表达可能与泛素介导的蛋白质水解、胞吞作用、RNA降解、小细胞肺癌、癌症通路、胰腺癌、胞质DNA传感途径、慢性粒细胞白血病、自然杀伤细胞介导的细胞毒性、细胞周期等反应有关。[结论]TMEM206 mRNA高表达是肝细胞癌的诊断和预后中的有效生物标志物。     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

目的:筛选肝细胞癌(HCC)预后不良相关基因,并探讨其临床意义。方法:在基因表达综合数据库(GEO)中获取符合分析条件的肝细胞癌全基因组表达谱数据并分析得到差异表达基因(DEGs),再运用生物学信息注释及可视化数据库 (DAVID) 和蛋白相互作用数据库 (String) 分别进行功能富集分析和蛋白质互作用网络的构建。利用癌症基因组图谱数据库(TCGA)和Cox比例风险回归模型对相关差异基因进行预后分析。结果:找到一个符合条件的人类HCC数据库 (GSE84402),共筛选出1141个差异表达基因(DEGs),其中上调基因720个,下调基因421个。基因功能富集分析和蛋白质互作用分析结果显示CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11和CYP2B6为HCC预后的关键基因。TCGA数据库和Cox回归模型分析显示CDC6、PIK3R1、RACGAP1和KIF11的表达升高,CENPE的表达降低与HCC预后不良密切相关。结论:CDC6、CENPE、PIK3R1、RACGAP1和KIF11可能和HCC的预后不良相关,可作为未来HCC预后研究的参考标志物。     

数据挖掘探究肝-肾-肺纤维化的关键基因

当前用于纤维化治疗的方法很少且疗效有限,为进一步了解纤维化的消退机制以发现潜在的治疗靶点。从Gene Expression Omnibus(GEO)数据库中选取了三个具有代表性的小鼠肝、肾、肺纤维化样本的mRNA数据集,使用GEO2R工具和Venn分析识别了差异表达基因(Differentially Expressed Genes, DEGs)。通过Webgestalt在线工具对DEGs进行基因功能富集。蛋白质-蛋白质相互作用(Protein-Protein Interactions, PPI)网络是由STRING数据库生成的。然后利用CytoHubba插件探索了关键基因,分别选取了三器官共有DEG和肝特异性DEG中MCC (Maximal Clique Centrality)得分最高的前10个作为关键基因。研究中整合分析了基于小鼠模型的肝-肾-肺纤维化的数据集,GSE36066和GSE97546用于第一轮的DEG分析,由于研究除了探究三种器官纤维化共有差异基因,也进一步探究了肝纤维化特有关键基因,所以引入另外一个肝纤维化数据集GSE55747用于验证分析。结果识别出58个肝肺肾纤维化共有DEG和 85个肝纤维化特异性DEG。功能富集分析表明,共有DEG主要在免疫反应和炎症反应过程中富集。肝纤维化特有DEG与脂质和脂肪酸代谢过程有关。之后通过PPI网络选择了20个关键基因。为了评估这些关键基因成为潜在靶点的可能性,进一步分析了它们的成药性(Druggability)。得出以下结论:TYROBP,FCGR3B,ALOX5AP和CD14或可成为纤维化治疗的潜在靶点,CYP8B1和UGT2A3则可能与非酒精性脂肪性肝病(Non-alcoholic Fatty Liver Disease, NAFLD)或非酒精性脂肪性肝炎(Non-Alcoholic Steato-Hepatitis , NASH)相关。对于基因FCGR1B,C1QB,LY86和CD53,目前没有直接的研究证据表明其与纤维化相关,需要进一步验证它们在纤维化发生过程中的功能。     

Correlation of zinc finger protein 2, a prognostic biomarker,with immune infiltrates in liver cancer

Purpose: The expression and clinical value of zinc finger protein 2 gene (ZIC2) in hepatocellular carcinoma (HCC) were analyzed by mining gene information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.Methods: Gene chip data sets were retrieved from GEO and TCGA and screened for differentially expressed genes in HCC. Gene expression profile interaction analysis (GEPIA) and Kaplan–Meier curves were used to analyze the relationship between differentially expressed genes (DEGs) and survival and prognosis in patients with HCC. Moreover, the Genecards database was used to extract ZIC2-related proteins and to analyze the physiological process of protein enrichment. Furthermore, the relationships between ZIC2 gene and tumor cell immune invasion and that between immune cell infiltration and the 5-year survival rate were studied using the tumor immune evaluation resource (TIMER) database.Results: Datasets from GEO and TCGA revealed that ZIC2 was differentially expressed in HCC tissues and normal tissues (P<0.05). High ZIC2 expression was associated with overall survival (OS) and progress-free survival in HCC patients. Overall, 25 ZIC2 related proteins, including Gli3, PRKDC, and rnf180 were identified and protein enrichment analysis indicated these were associated with four types of cell components, six types of cell functions, and eight types of biological processes. ZIC2 was positively correlated with immune infiltration cells in patients with HCC, and higher expression of ZIC2 mRNA CD4+T cells is associated with a better 5-year survival.Conclusion: ZIC2 gene may be used as an immune response marker in liver cancer to predict the prognosis of HCC.     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis

PurposeThe prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.Patients and methodsThe gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.ResultsA total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.ConclusionOur study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.     

IGF2BP2 maybe a novel prognostic biomarker in oral squamous cell carcinoma

Aim: The main of the present study was to investigate the role of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) in oral squamous cell carcinoma (OSCC) with the overarching of providing new biomarkers or potential therapeutic targets for OSCC.Methods: We combined datasets downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and samples collected from the clinic to evaluate the expression of IGF2BP2 in OSCC. IGF2BP2 survival analysis was respectively performed based on TCGA, GEO, and clinical samples. Correlations between IGF2BP2 expression and clinicopathological parameters were then analyzed, and signaling pathways associated with IGF2BP2 expression were identified using gene set enrichment analysis (GSEA 4.1.0). Moreover, an IGF2BP2 co-expressed gene network was constructed, followed by gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on IGF2BP2 co-expressed genes. Finally, TIMER and CIBERSORT were used to analyze the correlations among IGF2BP2, IGF2BP2-coexpressed genes, and tumor-infiltrating immune cells (TICs).Results: IGF2BP2 was highly expressed in OSCC and significantly correlated with overall survival of OSCC patients (P<0.01). High IGF2BP2 expression correlated with poor overall survival. The GSEA results showed that cell apoptosis-, tumor-, and immune-related pathways were significantly enriched in samples with high IGF2BP2 expression. Furthermore, GO and KEGG enrichment analyses results of IGF2BP2 co-expressed genes indicated that these genes are mainly associated with immunity/inflammation and tumorigenesis. In addition, IGF2BP2 and its co-expressed genes are associated with TICs (P<0.01).Conclusion: IGF2BP2 may be a potential prognostic biomarker in OSCC and correlates with immune infiltrates.