基于LASSO回归的胃癌失巢凋亡相关基因预后风险评分模型构建与分析

Construction and analysis of a prognostic risk scoring model for gastric cancer anoikis-related genes based on LASSO regression

目的基于最小绝对收缩和选择算子(LASSO)回归构建胃癌(GC)失巢凋亡相关基因(ARGs)的预后风险评分模型,分析失巢凋亡与胃癌预后之间的关系及该模型在胃癌患者免疫治疗及化疗中的应用意义。方法通过癌症基因组图谱(TCGA)数据库和基因集数据库(MSigDB)筛选在胃癌组织和癌旁组织中差异表达的预后失巢凋亡相关基因(ARGs);基于LASSO回归分析选出关键失巢凋亡基因构建预后风险评分模型,并以风险评分的中位值作为分界点,将患者分为高风险组和低风险组。通过实时荧光定量PCR(RT-qPCR)检测胃癌临床样本与胃癌细胞中基因表达水平;采用Kaplan-Meier(KM)生存曲线、单因素和多因素Cox回归分析验证预后风险评分模型对胃癌患者预后的预测效率;采用CIBERSORT和ESTIMATE算法对不同风险组患者免疫细胞浸润水平进行分析;通过R软件“ggplot2”、“ggExtra”数据包分析胃癌患者风险评分与免疫检查点表达水平的相关性,并评估肿瘤突变负荷(tumor mutation burden,TMB)与风险评分之间的相关性;采用化疗药物敏感性分析评估构建的预后风险评分模型在胃癌化疗中的价值。结果基于LASSO回归分析筛选出6个关键ARGs(VCAN、FEN1、BRIP1、CNTN1、P3H2、DUSP1),并构建预后风险评分模型。RT-qPCR检测显示VCAN、FEN1、BRIP1基因在胃癌组织和胃癌细胞中均高表达(P<0.05),CNTN1、P3H2和DUSP1基因均低表达(P<0.05);Kaplan-Meier生存曲线分析发现,高风险组患者的生存率显著低于低风险组(P<0.001),胃癌患者生存率和TMB水平在高、低风险组患者间差异有统计学意义(P<0.05);单因素和多因素分析结果显示胃癌患者肿瘤分期、年龄和风险评分与生存显著相关(P<0.05);免疫细胞浸润分析显示高风险组患者的肿瘤免疫基质评分明显高于低风险组(P<0.05),构建的预后风险评分模型可作为肿瘤免疫微环境(TME)状态的指标;通过R软件“ggplot2”、“ggExtra”数据包分析显示,构建模型的风险评分与免疫检查点TIM3、VISTA、TIGIT、BTLA和B7-H3(r=0.26、0.40、0.16、0.26、0.21,P均<0.05)的表达上调相关,TMB水平与风险评分呈负相关(R=–0.4,P<0.05);构建的预后风险评分模型可用于指导胃癌患者的化疗。结论基于失巢凋亡相关基因构建的预后风险评分模型可用于预测胃癌患者的疾病预后;模型的风险基因可作为胃癌治疗的潜在靶点,为胃癌的个体化治疗提供参考依据。

Objective To construct a prognostic risk scoring model for gastric cancer(GC)anoikis-related genes(ARGs)based on least absolute shrinkage and selection operator(LASSO)regression,and analyze the relationship between anoikis and prognosis of gastric cancer as well as the significance of the model in immunotherapy and chemotherapy of gastric cancer patients.Methods Differentially expressed prognostic anoikis-related genes(ARGs)in gastric cancer and adjacent tissues were screened through The Cancer Genome Atlas(TCGA)database and the Molecular Signatures Database(MSigDB);key anoikis genes were selected based on LASSO regression analysis to construct a prognostic risk scoring model,and patients were divided into high-risk and low-risk groups with the median risk score as the cutoff point.Gene expression levels in gastric cancer clinical samples and cells were detected by real-time quantitative PCR(RT-qPCR);Kaplan-Meier(KM)survival curves,univariate and multivariate Cox regression analyses were used to verify the predictive efficiency of the prognostic risk scoring model for the prognosis of gastric cancer patients;CIBERSORT and ESTIMATE algorithms were used to analyze the immune cell infiltration levels in patients with different risk groups;the correlation between risk scores and immune checkpoint expression levels in gastric cancer patients was analyzed using the R package"ggplot2"and"ggExtra",and the correlation between tumor mutation burden(TMB)and risk scores was assessed;chemotherapy drug sensitivity analysis was used to evaluate the value of the constructed prognostic risk scoring model in gastric cancer chemotherapy.Results Six key ARGs(VCAN,FEN1,BRIP1,CNTN1,P3H2,DUSP1)were screened out based on LASSO regression analysis,and a prognostic risk scoring model was constructed.RT-qPCR detection showed that VCAN,FEN1,and BRIP1 genes were highly expressed in gastric cancer tissues and cells(P<0.05),while CNTN1,P3H2,and DUSP1 genes were lowly expressed(P<0.05);Kaplan-Meier survival curve analysis found that the survival rate of patients in the high-risk group was significantly lower than that in the low-risk group(P<0.001),and there were statistically significant differences in survival rate and TMB levels between high-and low-risk group patients(P<0.05);univariate and multivariate analyses showed that tumor stage,age,and risk score were significantly associated with survival in gastric cancer patients(P<0.05);immune cell infiltration analysis showed that the tumor immune matrix score was significantly higher in the high-risk group than in the low-risk group(P<0.05),and the constructed prognostic risk scoring model can be used as an indicator of the tumor immune microenvironment(TIME)status;analysis using the R package"ggplot2"and"ggExtra"showed that the risk score of the constructed model was positively correlated with the upregulated expression of immune checkpoints TIM3,VISTA,TIGIT,BTLA,and B7-H3(r=0.26,0.40,0.16,0.26,0.21,P<0.05),and TMB level was negatively correlated with risk score(R=–0.4,P<0.05);the constructed prognostic risk scoring model can be used to guide chemotherapy for gastric cancer patients.Conclusion The prognostic risk scoring model constructed based on anoikis-related genes can be used to predict the prognosis of gastric cancer patients;the risk genes in the model can serve as potential targets for gastric cancer treatment,providing a reference for individualized treatment of gastric cancer.