Non-small cell lung cancer (NSCLC) is a highly morbid and fatal disease that exhibits individualized differences in prognosis and drug efficacy. Therefore, understanding the molecular mechanism of the occurrence and progression of lung cancer can improve early diagnosis, treatment and prognosis. Macrophages are a crucial component of the tumor microenvironment (TME) due to their high plasticity and heterogeneity. They play a multifaceted role in tumor initiation and progression. In order to elucidate the pathogenesis of tumor-associated macrophages (TAMs) related genes in NSCLC, transcriptomic sequencing, univariate COX regression, LASSO regression and multivariate COX regression analyses were conducted to identify the 11 genes that have the most significant association with prognosis. These genes include FCRLA, LDHA, LMOD3, MAP3K8, NT5E, PDGFB, S100P, SFXN1, TDRD1, TFAP2A and TUBB6. The risk score (RS) was computed, and all samples were split into high- and low-risk groups based on the median RS. The correlation of RS and 11 genes with macrophages was verified by the CIBERSORT deconvolution algorithm. These above results suggest that the risk score developed in this study can be utilized for predicting patients' prognosis and evaluating their immune infiltration status. This study can serve as a guide for subsequent tumor immunotherapy and gene targeting therapy.
非小细胞肺癌(non-small cell lung cancer，NSCLC)是一种高发病率和高死亡率的疾病，其预后和药物治疗效果存在个体化差异。因此,了解肺癌发生和发展的分子机制可以有效提高早期诊断和治疗，改善患者预后。巨噬细胞具有高度可塑性和异质性，是肿瘤微环境(tumor microenvironment, TME)中发挥抗肿瘤作用的关键细胞之一，在肿瘤发生发展过程中起着复杂的作用。为了阐明NSCLC中肿瘤相关巨噬细胞(tumor-associated macrophages, TAMs)相关基因在肺癌中的发生机制，本研究采用转录组测序、单因素COX回归、LASSO回归、多因素COX回归分析方法，筛选出与预后最相关的11个基因(FCRLA、LDHA、LMOD3、MAP3K8、NT5E、PDGFB、S100P、SFXN1、TDRD1、TFAP2A、TUBB6)。计算风险评分(risk score，RS)，根据RS中位数将所有样本分为高风险组和低风险组，并采用CIBERSORT反卷积算法验证RS及11个基因与巨噬细胞的相关性。上述结果表明，本研究所建立的风险评分可用于非小细胞肺癌患者预后预测，还可评估患者的免疫浸润状态，为后续肿瘤免疫治疗及基因靶向治疗提供参考。.
Keywords: non-small cell lung cancer; tumor microenvironment; tumor-associated macrophages.