Abstract：
Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.
摘要：
肺腺癌(LUAD)具有高度侵袭性并且易于转移的恶性特征。仍然缺乏合适的生物标志物来促进基于精确的治疗方案的改进。我们使用10种已知的聚类算法和来自4个维度的组学数据的组合来鉴定LUAD的高分辨率分子亚型。随后，基于亚型相关基因和包含10种机器学习算法的集成程序框架,开发了共识机器学习相关预后签名(CMRS).从肿瘤微环境的角度分析了CMRS的效率，基因组景观，免疫疗法，药物敏感性,和单细胞分析。在结果方面,通过多组学聚类，我们确定了2种综合组学亚型（CSs），其中CS1患者的生存结局较差，更高的侵略性，mRNAsi和突变频率。随后，我们基于CS1中上调的13个关键基因开发了CMRS。CMRS的预后预测效率优于大多数已建立的LUAD预后特征。CMRS显示出与肿瘤微环境特征变异和基因组不稳定性产生的强相关性。关于临床表现，高CMRS组的患者更有可能从免疫治疗中获益，而低CMRS更有可能从化疗和靶向药物治疗中获益.此外,我们评估了neratinib等药物，寡霉素A,和其他人可能是高CMRS组患者的候选人。单细胞分析显示CMRS相关基因主要在上皮细胞中表达。在这项研究中基于多组学数据确定的新分子亚型可以为LUAD的分层治疗提供新的见解，而CMRS的发展可以作为LUAD精准治疗和免疫治疗获益程度的候选指标.
