Application Of Bioinformatics Methods In The Identification Of Biomarkers Of Nasopharyngeal Carcinoma And The Risk Prediction Model Of Head And Neck Squamous Cell Carcinoma

Objective Nasopharyngeal carcinoma(NPC)lacks biomarkers for early diagnosis,and many patients with NPC are already in advanced stages of NPC by the time they are diagnosed.Therefore,it is necessary to identify candidate biomarkers for NPC and thus find an effective diagnostic indicator and make a better therapeutic strategy.MethodsIn the GEO(Gene Expression Omnibus)database,three microarray data sets GSE12452,GSE53819 and GSE64634 about nasopharyngeal carcinoma were downloaded,and R3.6.0 software was used to identify differentially expressed genes(DEGs).On this basis,GO(Gene Ontology)analysis,KEGG(Kyoto Encyclopedia of Genes and Genomes)enrichment analysis,differential gene volcano map and heat map were performed on these differentially expressed genes.Construct a Protein-Protein Interaction(PPI)network of differential genes through the STRING online website,and use Cytoscape3.7.1 software to analyze the subsequent network modules and determine the hub gene.In addition,draw the receiver operating characteristic curve(ROC)for the selected hub genes,and judge their diagnostic value according to the area under the curve(AUC).ResultsThe 3 microarray data sets(GSE12452,GSE53819 and GSE64634)included 61 NPC tissue samples 32 healthy control samples.In these 3 microarray data sets,836 DEGs were identified,including 349 up-regulated genes and 487 down-regulated genes.Using STRING online website combined with the MCODE tool in Cytoscape 3.7.1software,four important gene modules were identified.According to the Cytohubba tool in Cytoscape 3.7.1 software,six hub genes(CDK1,CCNB1,CCNB2,CCNA2,BUB1 B and KIF11)were identified among these 4 gene modules.The ROC curve shows that the AUC value of these six genes as a combined diagnostic indicator is 0.958,95% CI:0.926-0.973,the sensitivity value is 0.951,and the specificity value is 0.813,indicating that these six genes have good diagnostic value.ConclusionBased on bioinformatics analysis,we identified 6 hub genes(CDK1,CCNB1,CCNB2,CCNA2,BUB1 B and KIF11),which can be used as potential biomarkers for the diagnosis of nasopharyngeal carcinoma.However,further studies are needed to confirm the role of these six genes in the occurrence and development of NPC and their relationship with the clinical indicators of NPC.ObjectiveWe aim to use the Human Cancer Genome Atlas(TCGA)database to construct a multi-gene prognostic model for predicting the prognosis of head and neck squamous cell carcinoma(HNSCC)and explore the association between the multi-gene prediction model and clinicopathological features of HNSCC.MethodsThe TCGA database contains transcriptome data of 502 HNSCC patients.We randomly divided 502 patients into a training set(70%,N = 352)and a testing set(30%,N = 150)according to a fixed ratio.The R 3.6.0 software was used to identify differentially expressed genes(DEGs)between HNSCC tissue samples and adjacent normal tissue samples.Univariate Cox regression was used to screen DEGs related to patient prognosis,and a multi-gene prediction model was constructed based on Lasso regression and multivariate Cox regression.Calculate the prognostic risk score of each HNSCC patient based on the constructed prediction model.The median risk score of352 HNSCC patients in the training set is used as a critical value.HNSCC patients above this critical value are classified into the high-risk group,below the critical value are divided into low-risk group,the survival curve and ROC curve of the training set are drawn with R 3.6.0 software,and verified in the testing set.Then the clinical information of these 502 HNSCC patients(mainly: age,gender,drinking history,smoking history,tumor stage,HPV status,radiotherapy,overall survival-OS and recurrence-free survival-RFS)combined with risk score for univariate and multivariate COX regression analysis to explore the factors affecting the HNSCC prognosis.Finally,the Chi-square test and the Kaplan-Meier(KM)method were used to explore the relationship between the risk score of the prediction model and the clinicopathological features of HNSCC patients,and to evaluate the predictive effect of the prediction model in the prognosis of HNSCC patients in each subgroup.ResultsAfter differential expression analysis,1842 DEGs were identified,and 18 DEGs were finally selected to construct a multi-gene COX prediction model through univariate COX regression and Lasso regression.In the training set and testing set,the survival curve was statistically significant(P < 0.05).The AUC value in the ROC curve in the training set was 0.816,and the AUC value in the testing set was 0.724.Both univariate and multivariate Cox regression analysis showed that the factors affecting the prognosis of HNSCC patients include the prediction model risk score,pathological stage and radiotherapy.Chi-square test showed that the prediction model risk score was related to the age,pathological stage and HPV status of HNSCC patients.In addition,in each subgroup,the overall survival rate and recurrence-free survival rate of patients in the low-risk group were higher than those in the high-risk group(P < 0.05).It indicates that the multi-gene prediction model has better predictive ability.ConclusionBased on bioinformatics analysis,our study is expected to provide new insights for HNSCC.On the basis of clinical characteristics and treatment,the 18-gene prediction model may be beneficial to guide the clinical individualized treatment of HNSCC patients and promote the prognosis.