背景:肺腺癌是一种常见的恶性肿瘤,死亡率高,居世界第二位。据报道,G蛋白偶联受体(GPCRs)在癌症中起重要作用;然而,G蛋白偶联受体相关特征尚未得到充分研究。方法:在本研究中,GPCR相关基因在单细胞和批量转录组水平上基于AUcell筛选,单样本基因集富集分析(ssGSEA)和加权基因共表达网络(WGCNA)分析。并使用包含10种机器学习算法及其多种组合的新机器学习框架构建了一个共有G蛋白偶联受体相关标签(GPCRRS)。在训练集和外部验证集中验证了GPCRRS。我们构建了GPCRRS整合的列线图临床预后预测工具。多组学分析包括基因组学,单细胞转录组学,和批量转录组学,以更全面地了解预后特征。我们评估了风险亚组对免疫治疗的反应,并筛选了针对特定风险亚组的个性化药物。最后,通过RT-qPCR验证关键GPCRRS基因的表达。结果:在这项研究中,我们鉴定了10个GPCR相关基因,这些基因通过单细胞转录组和整体转录组与肺腺癌的预后显著相关.单变量和多变量表明,低风险的生存率高于高风险的生存率。这也表明该模型是LUAD的独立预后因素。此外,我们观察到生物功能的显著差异,突变景观,肿瘤微环境中的免疫细胞浸润介于高危和低危人群之间。值得注意的是,免疫治疗在高危和低危组中也有相关性.此外,确定了针对特定风险亚组的潜在药物.结论:在这项研究中,我们构建并验证了肺腺癌G蛋白偶联受体相关特征,对预测肺腺癌的预后和免疫治疗效果具有重要作用。假设LDHA,GPX3和DOCK4是肺腺癌新的潜在靶点,可以在预测预后方面取得突破,肺腺癌的针对性防治,为抗肿瘤提供重要指导。
Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.