列线图
肿瘤科
医学
免疫疗法
比例危险模型
内科学
生存分析
免疫系统
肿瘤微环境
癌症
免疫学
作者
Xiaole Han,Qiuling Tang,Ching‐Lan Cheng,Jianjun Tang
摘要
PURPOSE The aim of this study was to investigate the effect of neutrophil-related genes (NRGs) on prognosis and tumor microenvironment (TME) of patients with gastric adenocarcinoma (GA), to provide a new reference for prognosis evaluation and related mechanism research of GA. METHODS The gene expression data and clinical information of patients with GA were collected from The Cancer Genome Atlas database. NRG data are from the literature. Differential NRGs were obtained by difference analysis and regression analysis for the construction of the prognostic model, which was validated using the GSE84426 data set. The independent prognostic effect of risk score was analyzed by constructing a nomogram. The single-sample gene set enrichment analysis and CIBERSORT methods were used to evaluate differences in TME between a high-risk group (HRG) and a low-risk group (LRG) and to evaluate the differences in response to immunotherapy and sensitivity to different drugs in high and low risk score groups. RESULTS We established a prognostic model on the basis of seven NRGs (NHLRC3, PTPRJ, RTEL1, ST6GALNAC2, HRNR, HP, and MCEMP1) and validated its predictive value. Multivariable Cox regression analysis further demonstrated that the model remained an independent prognostic factor for overall survival, and a nomogram was constructed for clinical practice. Differential analysis of immune cell infiltration levels showed that macrophages, mast cells, and neutrophils were highly infiltrated in HRG compared with LRG. Compared with HRG, LRG was more sensitive to immunotherapy and more sensitive to candidates such as axitinib, cisplatin, and ulixertinib. CONCLUSION In summary, on the basis of expression levels of NRGs, a new prognostic model was established. NHLRC3, PTPRJ, RTEL1, ST6GALNAC2, HRNR, HP, and MCEMP1 were valid candidate biomarkers that may help personalize prognostic predictions and serve as references for clinical studies.
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