现代妇产科进展2024，Vol.33Issue(7)：517-524,8.DOI:10.13283/j.cnki.xdfckjz.2024.07.007
基于糖代谢相关基因的卵巢癌预后模型的构建及验证
Construction and validation of an ovarian cancer prognostic model based on glycome-tabolism-related gene signature
摘要
Abstract
Objective:To establish a prediction model for the prognosis and drug re-sponse of ovarian cancer related to glucose metabolism and explore its clinical significance.Methods:The gene expression profile and clinical characteristics data of ovarian cancer patients were obtained from the ICGC database and GSE26712 data set,and glycometabolism-related genes were extracted and collected from MSigDB and intersected with them to obtain glycome-tabolism-related genes.An algorithm is used to screen out prognosis-related genes to build a model.Survival analysis,gene function enrichment analysis and drug response prediction were performed on the risk model.The cBioPortal online tool was used to present the genetic informa-tion of prognosis-related genes.Cytoscape software was used to display the network of prognosis-related genes and glucose metabolism co-expression genes.Finally,in normal ovarian tissue Dif-ferential verification of prognosis-related gene expression in cells and epithelial ovarian cancer tissue cells.Results:21 genes related to glucose metabolism were obtained for LASSO regres-sion analysis,and further multivariable Cox regression analysis was performed to establish a gene including LHPP(HR=1.51,95%CI:1.24～1.83,P<0.001),PCK2(HR=0.72,95%CI:0.57～0.92,P=0.009),PPP3CA(HR=1.35,95%CI:1.08～1.69,P=0.008)and NADK(HR=0.68,95%CI:0.52～1.89,P=0.005)Optimal risk model.Using cBioPortal to explore the genetic information of these 4 genes in 398 ovarian cancer samples suggested that changes in the gene domain may affect the function of the protein.Kaplan-Meier survival analy-sis showed that the overall survival rate of the high-risk group was lower.Group difference(P<0.0001),the ROC curve indicates that the model has good predictive performance(2-year AUC=0.773,3-year AUC=0.839,4-year AUC=0.852).The risk model is further verified through GSE26712 and GSE9891 to be an independent risk for the prognosis of ovarian cancer patients.Factors,and based on the clinical information of the GSE9891 data set,multi-factor Cox regression analysis was performed on risk score,pathological grade,FIGO stage,age and other factors,and it was found that the risk score was an independent prognostic factor(P<0.001).KEGG enrichment analysis suggested the biological functions mediated by the high-risk score group include cell cycle,TNF,Hedgehog,DNA repair and other pathways.Among them,the cell cycle pathway is significantly enriched(P<0.001).Glucose metabolism-related genes mainly play a role in the development of ovarian cancer through the cell cycle pathway.Play a key role in the occurrence and progression of inflammatory bowel disease;in the high-risk group,most chemotherapy drugs are less sensitive,and research based on cell cycle checkpoint inhibitors found 4 drugs(CGP-60474,BI-2536,CGP-082996 and GW843682X)It has high re-sponse sensitivity for high-risk groups;RT-qPCR results show that LHPP,PPP3CA and NADK are significantly up-regulated in ovarian cancer cell lines(SKOV-3)compared with normal o-varian cell lines(OSE),and PCK2 is significantly up-regulated in SKOV3 cell lines.Signifi-cantly down-regulated;the HPA database confirmed that the immunohistochemistry of these four genes in ovarian cancer tissues also showed the same trend as the RT-qPCR results.Conclu-sion:This study established an ovarian cancer risk model based on 4 glycometabolism-related genes,which is helpful to predict the prognosis,biological characteristics and potential therapeu-tic drugs of ovarian cancer patients.The model has good stability and predictive ability,and is a good tool for ovarian cancer.Molecular markers and therapeutic targets are provided for cancer prognosis.关键词
卵巢癌/糖代谢/预后模型/药敏预测/生物信息学Key words
Ovarian cancer/Glycometabolism-related/Prognostic model/Drug sensitiv-ity prediction/Bioinformatics分类
医药卫生引用本文复制引用
刘欣悦,韩妍,姚卉,王君霞,索玉平..基于糖代谢相关基因的卵巢癌预后模型的构建及验证[J].现代妇产科进展,2024,33(7):517-524,8.基金项目
国家自然科学基金项目(No:61975105) （No:61975105）
山西省基础研究计划项目(No:202303021212366) （No:202303021212366）
