Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy.
Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy.
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DOI：
10.1016/j.isci.2023.107058
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发表时间：
2023-07-21
期刊：
影响因子：
5.8
通讯作者：
Hong, Shaodong
中科院分区：
文献类型：
--
作者：
He, Li-Na;Li, Haifeng;Du, Wei;Fu, Sha;Luo, Linfeng;Chen, Tao;Zhang, Xuanye;Chen, Chen;Jiang, Yongluo;Wang, Yixing;Wang, Yuhong;Yu, Hui;Zhou, Yixin;Lin, Zuan;Zhao, Yuanyuan;Huang, Yan;Zhao, Hongyun;Fang, Wenfeng;Yang, Yunpeng;Zhang, Li;Hong, Shaodong
肿瘤微环境（TME）内的免疫和基质结构与癌细胞相互作用，共同决定疾病过程和治疗反应。我们的目的是建立一个基于TME相关基因的风险评分模型，以预测患者的预后和免疫反应。通过探索与免疫评分和间质评分相关的基因来鉴定TME相关基因。采用LASSO-Cox回归模型建立TME相关风险评分（TMErisk）模型。建立了包含6个基因的TME风险模型。在LUSC患者中，高TME风险与不利的OS相关，这种相关性在多个NSCLC数据集中得到了验证。参与免疫抑制微环境相关途径的基因在高TME风险组中富集。具有高TME风险的肿瘤显示免疫抑制细胞浸润增加。高TME风险预示着多种癌症的免疫应答和预后较差。TME风险模型可作为预测OS和免疫应答的稳健生物标志物。基于六个免疫/基质相关基因建立了TME风险模型高TME风险预测多种癌症的不良预后和免疫治疗疗效高TME风险与免疫抑制性肿瘤微环境相关TME风险在预测免疫治疗疗效方面优于PD-L1和TMB生物科学;免疫学;癌症;机器学习
The immune and stromal contexture within the tumor microenvironment (TME) interact with cancer cells and jointly determine disease process and therapeutic response. We aimed at developing a risk scoring model based on TME-related genes of squamous cell lung cancer to predict patient prognosis and immunotherapeutic response. TME-related genes were identified through exploring genes that correlated with immune scores and stromal scores. LASSO-Cox regression model was used to establish the TME-related risk scoring (TMErisk) model. A TMErisk model containing six genes was established. High TMErisk correlated with unfavorable OS in LUSC patients and this association was validated in multiple NSCLC datasets. Genes involved in pathways associated with immunosuppressive microenvironment were enriched in the high TMErisk group. Tumors with high TMErisk showed elevated infiltration of immunosuppressive cells. High TMErisk predicted worse immunotherapeutic response and prognosis across multiple carcinomas. TMErisk model could serve as a robust biomarker for predicting OS and immunotherapeutic response. A TMErisk model was established based on six immune/stroma-related genes High TMErisk predicts poor prognosis and immunotherapy efficacy in multiple cancers High TMErisk correlated with immune-suppressive tumor micro-milieu TMErisk outperformed PD-L1 and TMB in predicting immunotherapy efficacy Biological sciences; Immunology; Cancer; Machine learning
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影响因子：
9.5
作者：
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影响因子：
12.4
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WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
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影响因子：
64.5
作者：
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Schreiber SL
影响因子：
82.9
作者：
Binnewies M;Roberts EW;Kersten K;Chan V;Fearon DF;Merad M;Coussens LM;Gabrilovich DI;Ostrand-Rosenberg S;Hedrick CC;Vonderheide RH;Pittet MJ;Jain RK;Zou W;Howcroft TK;Woodhouse EC;Weinberg RA;Krummel MF
通讯作者：
Krummel MF