Construction Of Co-expression Network And Identification Of Biomarkers Of Tongue Squamous Cell Carcinoma

Oral squamous cell carcinoma(OSCC)is the eighth most common malignant tumor in the world with a poor prognosis.The 5-year survival rate of patients is less than 60%.Tongue squamous cell carcinoma(TSCC)is the most common subtype of OSCC,accounting for 41%of OSCC cases.TSCC has much more aggressive clinical behaviors and a worse prognosis than other cancers of the oral cavity,which makes it one of the most lethal cancer types.Unfortunately,even with combined therapy involving surgery,radiation,and chemotherapy,the 5-year survival rate is still unsatisfactory.The molecular mechanism of TSCC is largely unexplored.Exploring gene expression changes in tumor development and metastasis and identifying biological targets of TSCC may help us find the key to improve treatment.Bioinformatics analysis is an effective way to explore the relationship between disease and gene expression regulation.At present,high-throughput sequencing technology is rapidly developing,thus stimulating a large number of gene sequencing data and a series of analysis methods for these data,and also provides a basis for exploring the relationship between gene expression regulation of life sciences.The purpose of this study was to explore the relationship between TSCC and gene expression through a series of bioinformatics analysis,and to screen out gene modules and potential biomarkers related to tumor development and metastasis.First,we downloaded and synthesized RNA sequencing data and clinical data from patients with TSCC from the The Cancer Genome Atlas(TCGA)database,including a total of 126 patient tumor samples and 13 patients with paired normal tissue samples RNA expression information,differentially expressed genes(DEGs)analysis of 13 paired tumor-normal tissue mRNA information was performed by R package"ballgown"(foldchange>1.5 or foldchange<0.67,P value<0.05),and then functional and pathway enrichment analysis of DEGs was applied through DAVID online tool.After that,we used R package"WGCNA"to perform weighted gene co-expression network analysis on the DEGs of 126 tumor samples,and divided the genes according to the expression pattern.Genes with similar expression changes were divided into the same module.Then modules was correlated with the clinical information:tumor pathological stage(stage)and pathological lymph node metastasis(LNM),and the module related to stage and LNM was selected,and the gene co-expression network of the target module was constructed and utilized.Cytoscape web tool was used to visualize the co-expression network and screen out the hub genes of the target module.Finally,hub genes were verified by univariate COX regression analysis and log-rank survival analysis whether they were correlated with survival of TSCC.Through a series of analyses,we obtained a total of 2792 DEGs.The results of gene functional and pathway enrichment analysis showed that the KEGG pathway was mainly enriched in focus adhesion,ECM-receptor interaction and cancer pathway;GO biological process was mainly enriched in Extracellular matrix tissue,collagen catabolism and cell adhesion.In the WGCNA analysis,the DEGs were divided into 8 modules,of which the yellow module was significantly associated with tumor stage and LNM of TSCC.There were 363 genes in the yellow module,and we selected the most central 20 genes as hub genes.The low expression of 6 of 20 core genes in univariate COX regression analysis and log-rank survival analysis was associated with a short overall survival time in patients with TSCC and these 6 genes were considered as key genes:SPRR3,VSIG10L,CYSRT1,CRNN,TGM3,and CYP2C18.This study indicates that the yellow module and six key genes may play an important role in the development and metastasis of TSCC.These key genes may be used as potential biomarkers for prediction and treatment of TSCC.This work also provides a better understanding of the development and metastasis of TSCC and provides direction for future research.