摘要
基于基因表达数据库(Gene Expression Omnibus,GEO)筛选差异基因,建立白内障预测模型并对其进行评价。首先,采用生物信息学方法从GEO中筛选出与白内障相关的芯片数据,并采用GEO2R软件和NetworkAnalyst工具分析得到最显著的差异表达基因。然后,依托我院健康管理中心白内障筛查队列,采用Cox比例风险回归构建白内障发病风险预测模型,绘制列线图,通过C指数、校准曲线、受试者操作特征(receiver operating characteristic,ROC)曲线、决策曲线分析(decision curve analysis,DCA)评价模型的区分度、校准度、预测能力和获益情况。结果显示,在GSE5645、GSE193629和GSE161701数据集中,鱼精蛋白1(protamine 1,PRM1)为高表达基因,五羟色胺2C受体(serotonin 2C receptor,HTR2C)为低表达基因;白内障组和非白内障组在年龄、体重、收缩压、对比敏感度(contrast sensitivity,CS)、客观散射指数(objective scatter index,OSI)、调制传递函数截止频率(modulation transfer function cut off,MTF cut off)、斯特列尔比(Strehl ratio,SR)、动态视力、PRM1、HTR2C和CX46等指标的差异均有统计学意义(P<0.05);预测模型最终纳入年龄、OSI、MTF cut off、PRM1和HTR2C共5个变量(P<0.05),建立的预测模型为log[h(t)/h_(0)(t)]=2.6892+0.012×年龄+1.320×OSI-0.041×MTF cut off+0.029×PRM1-6.549×HTR2C;模型C指数为0.875,置信区间为0.862~0.886;模型的预测概率与实际概率接近;ROC曲线下面积(area under the curve,AUC)为0.904(95%CI:0.884~0.923),灵敏度和特异度分别为82.4%和92.3%;平均AUC为0.911;当模型的高风险阈值为0.25~0.75时,净收益率>0。本研究建立的白内障临床预测模型具有很好的区分度、校准度、预测能力、内部有效性和临床效益,具备较高的临床应用价值。
A cataract prediction model was established and evaluated by screening differential genes based on the Gene Expression Omnibus(GEO)database.Firstly,the microarray data related to cataract were screened from GEO by bioinformatics method and analyzed by GEO2R and NetworkAnalyst software to obtain the most significant differentially expressed genes.Then,based on the cataract screening cohort of the Health Management Center of our hospital,a cataract risk prediction model was constructed and the nomogram was drawn by using Cox proportional hazards regression.The degrees of differentiation and calibration,predictive ability and benefit of the model were evaluated through C-index,calibration curve,the receiver operating characteristic(ROC)curve and decision curve analysis(DCA).In GSE5645,GSE193629 and GSE161701 datasets,protamine 1(PRM1)is a high expression gene,and serotonin 2C receptor(HTR2C)a low expression gene.There were statistically significant differences(P<0.05)between the cataract and non-cataract groups in the age,body mass,systolic blood pressure,contrast sensitivity(CS),objective scatter index(OSI),modulation transfer function(MTF)cut off,Strehl ratio(SR),dynamic vision,PRM1,HTR2C and CX46.Five variables including age,OSI,MTF cut off,PRM1 and HTR2C were finally included in the prediction model(P<0.05).The final prediction model was log[h(t)/h_(0)(t)]=2.6892+0.012×age+1.320×OSI-0.041×MTF cut off+0.029×PRM1-6.549×HTR2C.The C-index of the model was 0.875,confidence interval(CI)was 0.862~0.886,and the predicted probability was close to the actual probability.The area under the curve(AUC)was 0.904(95%CI:0.884~0.923),and the sensitivity and specificity were 82.4%and 92.3%,respectively.The average AUC by the ten-fold crossover method was 0.911.The DCA diagram showed that,when the high risk threshold of the model was 0.25~0.75,the net return rate would be greater than 0.The cataract clinical prediction model established in this study proved to have good differentiation,calibration,prediction ability,internal effectiveness and clinical benefit,and would possess high clinical application value.
作者
郭志强
张立友
许利娟
宫美娜
韩笑
GUO Zhiqiang;ZHANG Liyou;XU Lijuan;GONG Meina;HAN Xiao(The Fourth Hospital of Cangzhou City(Nanpi County People’s Hospital),Cangzhou 061500,Hebei,China;Cangzhou Eye Hospital,Cangzhou 061000,Hebei,China)
出处
《生命科学研究》
CAS
2023年第5期447-454,共8页
Life Science Research
基金
河北省2020年度医学科学研究课题计划(20200343)。
