山东医药2023，Vol.63Issue(35)：19-23,5.DOI:10.3969/j.issn.1002-266X.2023.35.005
肺腺癌预后相关的炎症反应关键基因筛选及其预后预测模型建立
Screening of key genes of inflammatory response related to prognosis in lung adenocarcinoma and establishment of prognostic predictive model
摘要
Abstract
Objective To screen the key genes of inflammatory response related to prognosis in lung adenocarcino-ma(LUAD),and to construct a prognostic prediction model based on these genes.Methods LUAD tissue data were downloaded from the TCGA database as a training set,and normal lung tissue data were downloaded from the GTEx data-base as a normal control for the training set to screen for differentially expressed genes(DEGs).A list of inflammatory re-sponse-related genes was downloaded from the Molecular Characteristics Database.Univariate COX regression was used to analyze the genes that were associated with prognosis.The intersection with DEGs was then taken to obtain the inflammato-(RSF)algorithms were applied to screen key genes for prognosis-related inflammatory response in LUAD and to establish a model for prognostic risk.Internal validation was conducted using the training set,while external validation was carried out by obtaining the LUAD data from the GEO database as the validation set.Receiver operating characteristic(ROC)curve of this model in predicting the 1-year,3-year,and 5-year survival rates of patients was drawn.We divided the pa-tients into high-risk and low-risk groups based on cut-off values and compared their overall survival(OS).Univariate and multifactorial COX regression analyses were conducted to examine the association between risk scores and OS in patients from both the training and validation sets.We incorporated all independent prognostic factors and constructed a nomogram to predict 1-,3-,and 5-year survival in patients from the training set.Results There were 48 DEGs between the LUAD tissues and normal lung tissues.Additionally,50 genes for inflammatory response and prognosis were identified,and 11 genes for prognosis-related inflammatory response in LUAD were obtained after taking the intersection.Nine key genes were identified through LASSO regression and RSF algorithm screening:adrenomedullin(ADM),LCCL domain-contain-ing protein 2(DCBLD2),interleukin 7 receptor(IL-7R),MAX dimerization protein 1(MXD1),Neuromedin-U receptor 1(NMUR1),protocadherin 7(PCDH7),phosphoinositide-3-kinase regulatory subunit 5(PIK3R5),scavenger receptor type F family member 1(SCARF1),and serine protease inhibitor clade E member 1(SERPINE1).The results of internal and external validation showed that the AUC of the risk score in predicting 1-,3-,and 5-year survival of patients in the training set was 0.73,0.64,and 0.68,respectively,and the AUC in predicting 1-,3-,and 5-year survival of patients in the validation set was 0.578,0.602,and 0.581,respectively,with OS being shorter in the high-risk group than in the low-risk group(all P＜0.01).Results of univariate and multivariate COX regression analyses showed that the risk score was an independent factor for OS in patients in both the training and validation sets(training set HR=2.99,95%CI:2.11-4.23,P＜0.01;validation set HR=2.47,95%CI:1.43-4.28,P＜0.01).We constructed a nomogram containing all inde-pendent prognostic factors(age,gender,tumor stage,risk score),with an overall consistency index of 0.710,indicating a high level of predictive accuracy for the model.The calibration and standard curves overlapped well.Conclusions Nine key genes of inflammatory response related to prognosis of LUAD are screened out,namely ADM,DCBLD2,IL-7R,MXD1,NMUR1,PCDH7,PIK3R5,SCARF1 and SERPINE1.The prognostic risk model based on key genes of inflammatory response is helpful to judge the prognosis of patients with LUAD.关键词
炎症反应相关基因/预后预测/机器学习/生物信息学/肺腺癌Key words
inflammatory response-related genes/prognostic prediction/machine learning/bioinformatics/lung adenocarcinoma分类
医药卫生引用本文复制引用
胥婉婷,加依娜·拉兹别克,刘新亚,文保锋,曹明芹..肺腺癌预后相关的炎症反应关键基因筛选及其预后预测模型建立[J].山东医药,2023,63(35):19-23,5.基金项目
新疆维吾尔自治区自然科学基金面上项目(2022D01C288). （2022D01C288）
