Establishment of a 10-gene prognostic model for gastric cancer based on the tumor immune microenvironment

作者全名："Yu, Jun; Li, Tong; Han, Huaxin; Zeng, Feng; Wu, Zhouxuan; Zhang, Jianbo; Chen, Yi; Sheng, Bo; Deng, Shijiang; Zhu, Peng"

作者地址："[Yu, Jun; Li, Tong; Zhang, Jianbo; Chen, Yi; Sheng, Bo; Deng, Shijiang; Zhu, Peng] Chongqing Med Univ, Dept Gastrointestinal Surg, Affiliated Hosp 2, Chongqing 400000, Peoples R China; [Han, Huaxin] Guizhou Chishui Peoples Hosp, Dept Gen Surg, Chishui 564700, Guizhou, Peoples R China; [Zeng, Feng] Chongqing Xiushan Peoples Hosp, Dept Gastrointestinal Surg, Chongqing 409900, Peoples R China; [Wu, Zhouxuan] Chongqing Xiushan Peoples Hosp, Dept Anorectal Surg, Chongqing 409900, Peoples R China; [Zhu, Peng] Chongqing Med Univ, Dept Gastrointestinal Surg, Affiliated Hosp 2, 288 Tianwen Ave, Chongqing 400000, Peoples R China"

通信作者："Zhu, P (通讯作者)，Chongqing Med Univ, Dept Gastrointestinal Surg, Affiliated Hosp 2, 288 Tianwen Ave, Chongqing 400000, Peoples R China."

来源：ANALYTICAL BIOCHEMISTRY

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:000841161600002

JCR分区：Q2

影响因子：2.9

年份：2022

卷号：654

期号：　

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结束页：　

文献类型：Article

关键词：Gastric cancer; Immune; The Cancer Genome Atlas; Biomarker; Single -sample gene set enrichment analysis

摘要："Gastric cancer seriously affects the health of modern people. The immune microenvironment of gastric cancer tissue is key to gastric cancer progression. We downloaded training and validation sets data from The Cancer Genome Atlas and Gene Expression Omnibus. Single-sample gene set enrichment analysis was used to sort patients into high, middle, and low immunity groups, of which immune infiltration in the high immunity group was substantially higher than of other two groups. Genes in high and low immunity groups expressed prominent differences. Further, the enrichment of differentially expressed genes was found mainly in immune-related pathways. Subsequently, an immune-related prognostic model was established, composed of ten prognosisrelated genes identified by univariate risk regression, least absolute shrinkage and selection operator Cox, and multivariate risk regression. Survival analysis and receiver operating characteristic curves suggested good diagnostic efficacy of this model, and feature genes were linked to the degree of immune infiltration. An independent test suggested that the risk score could independently determine patient outcomes. We combined all clinical information and risk scores to establish a nomogram that could predict patient's prognosis. A prognostic model composed of 10 prognosis-related genes was generated with good diagnostic efficacy in predicting prognoses of gastric cancer patients."

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