一种新型结直肠癌预后模型的建立与治疗预测  

A novel prognostic model for predicting the outcomes and therapeutic efficacy of colorectal cancer

作　　者：张虎 毛春蓉 练云[1,2] 庞洁 吴红雁[1] 杜欣娜 ZHANG Hu;MAO Chunrong;LIAN Yun;PANG Jie;WU Hongyan;DU Xinna(School of Basic Medical Sciences,Jiangsu Vocational College of Medicine,Yancheng 224005;Department of Obstetrics and Gynecology,Dongtai People’s Hospital,Yancheng 224200,China)

机构地区：[1]江苏医药职业学院基础医学部,江苏盐城224005 [2]东台市人民医院妇产科,江苏盐城224200

出　　处：《南京医科大学学报（自然科学版）》2024年第9期1217-1226,共10页Journal of Nanjing Medical University(Natural Sciences)

基　　金：国家自然科学基金(82274339);江苏省青蓝工程项目(2021);江苏省产学研合作项目(FZ20210156);江苏省教育厅科研项目(22KJD310001);江苏省高校优秀科技创新团队(2023);江苏省心脑血管与癌症防控工程研究中心(2022);校企横向合作项目(2021320906000215、2021320906000004、2023320906000197);校级科研项目(20214401、20238316);盐城市工程中心建设项目(YC2022808)。

摘　　要：目的:构建一种新的预测结直肠癌(colorectal cancer,CRC)预后和治疗的模型。方法:利用单因素分析以及最小绝对收缩和选择运算(least absolute shrinkage and selection operator,LASSO)-Cox回归分析训练队列GSE39582,构建CRC预后标签(prognostic signature of colorectal cancer,PSCRC),并利用外部队列CRC_TCGA和GSE17536验证PSCRC。评估PSCRC与临床指标、肿瘤免疫微环境和免疫细胞浸润的相关性,基因富集分析(gene set enrichment analysis,GSEA)PSCRC的潜在功能。整合PSCRC和临床分期等7个因素绘制预后列线图,并通过决策曲线分析(decision curve analysis,DCA)评估预后效果。最后,预测免疫治疗和化疗疗效。结果:本研究构建了PSCRC,2个外部队列证实其预后灵敏度和特异度较高。TNM分期均显著影响PSCRC风险评分(P均<0.001);PSCRC与肿瘤微环境基质评分、免疫评分和ESTIMATE评分均显著正相关(P均<0.001),与中性粒细胞浸润显著正相关(P均<0.05),与活化记忆性CD4+T细胞浸润显著负相关(P均<0.01)。进一步地GSEA分析显示,PSCRC可能参与氧化磷酸化、血管新生、缺氧和炎症应答等过程。重要的是,新构建模型显示出较好的预后能力,C指数为0.765,95%置信区间为0.747~0.783(P <0.001)。最后,在3个队列中治疗预测均显示低风险评分组免疫治疗响应率更高(P均<0.001);PSCRC与伊马替尼、达沙替尼、帕唑帕尼等化疗药半抑制浓度(half maximal inhibitory concentration,IC50)显著负相关(P均<0.001),与二甲双胍、索拉非尼等药物IC50显著正相关(P均<0.001)。结论:本研究构建了PSCRC,为CRC预后提供了良好的模型,还为预测治疗提供了潜在标志物。Objective:To construct a new model for predicting the outcomes and therapeutic efficacy of colorectal cancer(CRC).Methods:Firstly,the univariate analysis and the least absolute shrinkage and selection operator(LASSO)-Cox regression analysis were used to train the GSE39582 dataset to construct a prognostic signature of colorectal cancer(PSCRC),and external datasets CRC_TCGA and GSE17536 were used to validate PSCRC.The correlations of PSCRC with clinical indicators,tumor immune microenvironment and immune cells infiltration were evaluated,and the molecular function of PSCRC was analyzed by gene set enrichment analysis(GSEA).Next,seven factors,such as PSCRC and clinical stage,were integrated to draw a prognostic nomogram,and the prognostic effect was evaluated by the decision curve analysis(DCA).Finally,the efficacy of immunotherapy and chemotherapy was predicted.Results:We constructed a PSCRC,which was validated by two external datasets,confirming its high prognostic sensitivity and specificity.TNM staging significantly affected the risk scores of PSCRC(all P < 0.001);PSCRC showed significantly positively correlations with tumor microenvironment matrix(TME)score,immune score,and ESTIMATE score(all P <0.001),significantly positive correlations with infiltrations such as neutrophils(all P < 0.05),and significantly negative correlations with infiltrations like activated memory CD4+T cells(all P < 0.01).In addition,the GSEA analysis indicated that PSCRC might participate in oxidative phosphorylation,angiogenesis,hypoxia and inflammatory response.Importantly,the newly constructed model showed a good prognostic ability,with a C-index of 0.765 and a 95% confidence interval(CI)of 0.747 to 0.783(P < 0.001).Finally,in the three datasets,the therapy prediction results revealed that the low-risk scoring group had a high response rate to immunotherapy(all P < 0.001);PSCRC was significantly negatively correlated with the half inhibitory concentration(IC50)of chemotherapy drugs such as imatinib,dasatinib,and pazopanib(all P < 0.0

关 键 词：结直肠癌 预后模型 LASSO-Cox回归 免疫治疗 化疗 

分 类 号：R735.35[医药卫生—肿瘤]