Construction And Validation Of Prognostic Risk Scoring Model For Thyroid Cancer Kinase-related Genes

Objective:Screening kinase genes closely related to prognosis of Papillary Thyroid Carcinoma(P TC)and constructing risk scoring model.Methods:Thyroid Cancer expression profile data and corresponding clinical data were obtained from The Cancer Genome Atlas(TCGA)database,and The data were pressed as 1: A total of 518 known kinase genes(PKGs)were collected,and the expression data and survival information of all kinase genes were extracted.The differentially expressed kinase genes were screened out and survival information was integrated.Differential PKGs were analyzed by univariate Cox regression to obtain prognostic PKGs and functional enrichment analysis was performed.Lasso and multivariate Cox regression were used to construct the best risk prognosis model and validation group data were used to evaluate the efficacy of the model.Using the median risk score,patients were divided into two groups: high risk and low risk.Then,the receiver operating characteristic curve(ROC curve)is drawn and the area under the ROC curve(AUC)is calculated and plotted(Kaplan-Meier,KM)survival curve to evaluate model performance,and finally to validate in the validation group data.Finally,univariate and multivariate Cox regression analysis was used to evaluate the independent prognostic value of the model,and its clinical correlation was analyzed,and the column chart was drawn.Results:A total of 66 differential PKGs were obtained through differential analysis of thyroid cancer and para-cancer tissues,including 34 up-regulated genes and 32down-regulated genes.Univariate cox regression analysis of differential genes was conducted to obtain 17 prognostic PKGs.KEGG and GO pathway enrichment analysis of these prognostic PKGs found that,Prognostic genes are mainly enriched in immune-related pathways,and their functions are mainly related to nutrient metabolism.After further screening,we finally determined 5 PKGs(PLK2,FLT3,TGFBR1,DAPK2 and BRSK2)to construct prognostic model ROC curve,and AUC values were all greater than 0.60 on training and validation data sets.It shows that the prognostic model has good predictive performance.KM analysis in the prognostic model showed that there was significant difference in the prognosis between the training group and the validation group(P<0.05).Univariate Cox regression and multivariate Cox regression showed that risk score was an independent prognostic factor of TC(P<0.05).The established line map combined with prognostic gene characteristics and ConclusionsOur study established a risk prognosis model with 5 gene characteristics and constructed a rograph,which is reliable in predicting the prognosis of TC;It is conducive to individualized treatment and medical decision making.Clinical parameters can effectively predict the prognosis of patients.