| ObjectiveOral Squamous Cell Carcinoma(OSCC)is one of the most common malignant tumors in the mouth.It is highly invasive and progresses rapidly.Patients with poorly differentiated OSCC often have low 5-year survival rate and poor prognosis Tumor markers can be used as detection indicators to reflect tumor development,progression and therapeutic effect,which can provide help for the early diagnosis of OSCC.With the increase of OSCC incidence and fatality rate year by year,the search for high efficiency specific markers has become the focus of OSCC research.Bioinformatics,as a new subject,can be used as an important tool to explore tumor markers and their implications.This project aims to explore the diagnostic significance and relationship between the potential OSCC marker SPRR3 and the prognosis of diseases by using high-throughput sequencing technology and bioinformatics methods,and to evaluate its value as a marker for diagnosis and survival prediction,so as to provide a preliminary reference for the diagnosis and treatment of OSCC.Method1.Collection,treatment and analysis of clinical samples(1)We collected three pairs of tumor and adjacent oral normal specimens from three patients diagnosed with OSCC.All the three patients,coming from Stomatological Hospital of Shandong University,were considered as candidates for curative surgical resection.(2)We extracted total RNA from the three pairs of tissues we collected mentioned above by Trizol,and removed ribosomal RNA.Paired-end reads alignment to the human reference genome hg19.229 DEGs were obtained through high-throughput RNA-seq.(3)To determine the functions of respective DEGs,we initially performed a GO enrichment analysis.The enrichment of different pathways was mapped using the KEGG pathway analysis2.Data processing in a public database(1)The GEPIA server provides online bioinformatic analysis for obtaining differential expression data,between carcinoma and normal tissues in TCGA database head and neck squamous cell carcinoma dataset.(2)the original files and platform files of GSE3524,GSE30784,and GSE42743 datasets from the GEO database were downloaded,and bioinformatic methods were used to obtain standardized gene expression data.(3)The gene expression data of OSCC samples and their control samples from the TCGA database were downloaded,and the standardized gene expression data were obtained by bioinformatic methods.3.Construction of protein-protein network and module analysisTo assess the inter-relationships among DEGs at protein level,the inter-relation among DEGs was utilized to design a PPI network.The molecular complex detection plugin was used to select meaningful modules in the PPI network4.Statistical method(1)Univariate Cox regression model was performed on the prognostic factors and candidate genes that may affect overall survival of OSCC patients in TCGA and GEO databases.Subsequently,we used significant factors to conduct the LASSO regression analysis to select and regularize the variables.The positive factors of the LASSO regression analysis were incorporated into the multivariant Cox regression model,which confirmed the feasibility of these factors as independent predictors of OSCC.We later verified the effect(s)in the TCGA database using this same methodology.(2)The Kaplan Meier survival analysis was performed to examine the relationship between the survival time of OSCC patients and the expression level of respective DEGs.We calculated and then indicated the hazard ratio with 95%confidence intervals and log-rank P-value along the plot.(3)To evaluate the discriminatory accuracy of a selected gene between two groups,its expression level was included in the Receiver Operating Characteristic curve analysis.The value for the area under ROC curve corresponds to the ability of one gene to distinguish tumor tissues from adjacent ones.?2 test was utilized to verify the correlation between gene expression levels and clinicopathologic factors.The correlation coefficient between two genes was calculated by Pearson correlation analysis.For expression data comparison,student's t test and ANOVA were performed.5.The detection of the target molecule expression with immunohistochemical stainingTissue microarray chips consisted of 61 OSCC samples and 10 samples of normal control was constructed.Immunohistochemical staining was used to detect the protein expression level of the target gene in OSCC and adjacent tissues.6.Gene-set enrichment analysisThe GSEA enrichment score(ES)reflects the degree to which a particular set is over-represented at the extremes(top or bottom)of the entire ranking list.In this study,the data of OSCC was respectively obtained from our own RNA-seq microarray data and TCGA database.Result1.DEGs search and analysisAccording to the sequencing data here available,a total of 229 DEGs were identified in OSCC samples,from which 85 genes were up-regulated while 144 were down-regulated.2.The enrichment results of the DEGs(1)Based on the GO enrichment analysis,the commonly enriched categories were keratinization,cell adhesion,migration,proliferation and so on.In addition,DEGs were enriched in alcohol and drug metabolic processes.(2)KEGG and GSEA enrichment analysis also showed that SPRR3 was enriched in processes closely related to the tumorigenesis and development of OSCC.3.Identification of module clusters via PPI network(1)A global PPI network was designed after establishing the inter-relationships among the annotated DEGs.(2)MCODE plugin was applied for module analysis,where two modules were chosen for further analysis according to the inter-connectivity and density between nodes in PPI networks.Module clusters1 and 2 consisted.4.Validation of DEGs in independent OSCC datasetsThe RNA expression level of DEGs in module cluster 1 was analyzed by GEPIA in the HNSCC dataset.Five of the DEGs were significantly down-regulated in tumor tissues when compared with their normal counterparts.To further demonstrate that these five genes are differentially expressed in OSCC.we utilized the GSE30784 and GSE3524 datasets to confirm potential disease-related DEGs.5.Identification of DEG-related prognostic value6.(1)To properly select the predictive factors associated with the overall survival of OSCC patients,the five above-mentioned genes and clinical disease characteristics were introduced into univariate Cox regression model using the GSE42743 dataset.(2)The most statistically significant DEGs and N staging were concomitantly examined by LASSO method.As a result,only SPRR3 and N staging were confidently sorted out(Figs.5A-B).To further determine whether low SPRR3 expression could be an independent predictor of OSCC prognosis,a Cox multivariate regression model was executed.(3)Statistical platforms including univariate Cox regression model,LASSO method and Cox multivariate regression model were also approached to validate the SPRR3's prognostic value,based on the TCGA database.The result is the same as above(4)The SPRR protein family has been reported to be functional in a variety of tumors.Still,the expression and function of SPRR3 in OSCC remain largely unclear.Thus,we have presently proposed SPRR3 as an OSCC-related gene and attempted to explore its role in tumorigenesis and development of this condition.7.Validation of the target gene's effect(1)Kaplan-Meier survival analysis was carried out using the GSE42743 dataset and TCGA-OSCC dataset to verify whether SPRR3 could act as a prognostic marker.In both datasets,Log-Rank P<0.05,SPRR3 was preliminarily identified as a potential prognostic marker for OSCC.(2)ROC analysis was then performed to confirm the statistical relevance of these results.For this,AUC was calculated to identify the diagnostic specificity and sensitivity of SPRR3 expression.Based on the GSE30784 dataset and the TCGA database,down regulated SPRR3 levels yielded an AUC of 0.920 and 0.731,respectively.Thus.SPRR3 could act as a potential diagnostic indicator for OSCC.(3)Student's t-test(two-tailed)and one-way ANOVA were performed and show that low SPRR3 expression was in the subgroups of patients with high alcohol consumption,poor differentiation,non NO staging,positive lymphovascular invasion,and positive perineural invasion(Figs.6D-H)(4)The ?2 test was also utilized to figure out the correlation between SPRR3 expression of and the clinicopathological characteristics of OSCC.Consistent with previous tests,SPRR3 expression was significantly correlated with alcohol consumption,histologic grade,N staging,lymphovascular invasion and perineural invasion.These results reiterated that SPRR3 may act as a factor related to tumor progression and/or metastasis.(5)IHC staining suggests that the SPRR3 protein levels in OSCC tissues vary according to the histologic grade of the cancer.Specifically,SPRR3 levels were lower in tumor tissues from patients at higher histologic grade when compared to those at lower grade.(6)GSEA enrichment analysis showed that SPRR3 is enriched in a variety of metabolic processes.In addition,SPRR3 is also enriched in the biological process of epithelial formation and is related to the down-regulation of the K-Ras signaling pathway and the signaling pathways such as VEGF.(7)Using the gene expression data in GSE30784 and TCGA-OSCC dataset,Pearson correlation analysis showed that there was positive correlation between the gene expression in module 1 and SPRR3,and negatively correlated with module 2 gene(8)SPRR3 was co-expressed with the key factors in cell adhesion,tumor metastasis,the process of EMT,and alcohol metabolism,the results further demonstrated the role SPRR3 played in OSCC tumor occurrence and developmentConclusionIn our research,the expression of SPRR3 was dysregulated in OSCC by means of gene sequence,and further was verified by Bioinformatics methods.In addition,SPRR3 could be a potential bio-marker to identify OSCC from normal mucosa and its under-expression may predict the prognosis of patients with OSCC.Therefore,the present findings will bring a brand-new direction and strategy for diagnosis and prognosis of OSCC. |
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