PDF(Pubmed)
Abstract：
Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards \'hot\' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.
摘要：
肺腺癌（LUAD）是一种以高度肿瘤异质性为特征的肿瘤。尽管LUAD有许多预后和免疫治疗选择，缺乏精确的，个体化治疗方案。我们整合了mRNA，lncRNA,microRNA,来自LUADTCGA数据库的甲基化和突变数据。利用十种聚类算法，我们确定了稳定的多组学共识簇(MOCs)。然后将这些数据与十种机器学习方法合并，以开发能够可靠地识别患者预后和预测免疫治疗结果的强大模型。通过十种聚类算法，确定了两个预后相关的MOC，MOC2显示出更有利的结果。随后，我们基于8个MOCs特异性hub基因构建了MOCs相关机器学习模型（MOCM）。以MOCM评分较低为特征的患者表现出更好的总体生存率和对免疫疗法的反应。这些发现在多个数据集中是一致的，与许多以前发表的LUAD生物标志物相比，我们的MOCM评分显示出优异的预测性能。值得注意的是,低MOCM组更倾向于“热”肿瘤，以高水平的免疫细胞浸润为特征。有趣的是，发现GJB3与MOCM评分之间存在显着正相关（R=0.77，p<0.01）。进一步实验证实GJB3显著增强LUAD增殖,入侵和迁移，表明其作为LUAD治疗的关键靶标的潜力。我们开发的MOCM评分可以准确预测LUAD患者的预后，并确定免疫治疗的潜在受益者。提供广泛的临床适用性。
