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基于肿瘤微环境基因的肺腺癌相关研究
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Abstract:
目的：肿瘤微环境(TME)在各种癌症的发生发展中起着关键作用，本研究旨在探讨肺腺癌(LUAD)中TME相关基因的作用。方法：研究数据来源于TCGA及GEO数据库。首先提取出TME相关的差异表达基因(DEGs)，然后采用非负矩阵分解(NMF)聚类方法识别不同亚型。通过单因素Cox回归分析和Lasso回归分析筛选具有预后意义的基因，构建预后模型。最后，采用受试者工作特征曲线(ROC)和决策曲线分析(DCA)对模型进行验证。结果：高危组患者的生存时间明显更短。单因素和多因素Cox回归分析证实，风险评分是影响LUAD患者预后的独立危险因素。模型的预测稳定性不受年龄及性别的影响，且能反映TME相关免疫特征。结论：我们构建的包含5个TME相关基因的预后模型的预测性能较为稳定、准确，未来可能为肺癌的个体化治疗提供新的方向和依据。
Objective: Tumor microenvironment (TME) plays a key role in the occurrence and development of various cancers. This study aims to investigate the role of TME-related genes in LUAD (lung adenocarcinoma). Methods: The study data were obtained from the TCGA and GEO databases. DEGs (differentially expressed genes) related to TME were first extracted. Then, NMF (nonnegative matrix factorization) clustering was applied to identify different subtypes. Univariate Cox regression analysis and Lasso regression analysis were performed to screen genes with prognostic significance to construct the prognostic characteristics. Finally, ROC (receiver operating characteristic) curve and DCA (decision curve analysis) were used to verify the MODEL. Results: Patients in the high-risk group had a significantly shorter survival time. Univariate analysis and multivariate Cox regression analysis confirmed that the risk score was an independent risk factor for the outcome of LUAD patients. The prediction stability of the model was not affected by age and sex and could reflect TME-related immune characteristics. Conclusion: We constructed a prognostic model containing five TME-related genes, and the prediction performance of the model was relatively stable and accurate, which might provide a new direction and basis for the individualized treatment of lung cancer in the future.
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