Zhang Yi, Wang Yuzhi, Qian Haitao
Department of Laboratory Medicine, Guang'an People's Hospital, Guang'an, Sichuan, China.
Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, China.
Front Immunol. 2024 Nov 28;15:1497300. doi: 10.3389/fimmu.2024.1497300. eCollection 2024.
Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.
Data on clinical and pathological characteristics, genetic alterations, DNA methylation patterns, and the expression of mRNA, lncRNA, and microRNA, along with somatic mutations in LUAD patients, were gathered from the TCGA and GEO datasets. A computational workflow was utilized to merge multi-omics data from LUAD patients through 10 clustering techniques, which were paired with 10 machine learning methods to pinpoint detailed molecular subgroups and refine a prognostic risk model. The disparities in somatic mutations, copy number alterations, and immune cell infiltration between high- and low-risk groups were assessed. The effectiveness of immunotherapy in patients was evaluated through the TIDE and SubMap algorithms, supplemented by data from various immunotherapy groups. Furthermore, the Cancer Therapeutics Response Portal (CTRP) and the PRISM Repurposing dataset (PRISM) were employed to investigate new drug treatment approaches for LUAD. In the end, the role of SLC2A1 in tumor dynamics was examined using RT-PCR, immunohistochemistry, CCK-8, wound healing, and transwell tests.
By employing multi-omics clustering, we discovered two unique cancer subtypes (CSs) linked to prognosis, with CS2 demonstrating a better outcome. A strong model made up of 17 genes was created using a random survival forest (RSF) method, which turned out to be an independent predictor of overall survival and showed reliable and impressive performance. The low-risk group not only had a better prognosis but also was more likely to display the "cold tumor" phenotype. On the other hand, individuals in the high-risk group showed a worse outlook and were more likely to respond positively to immunotherapy and six particular chemotherapy medications. Laboratory cell tests demonstrated that SLC2A1 is abundantly present in LUAD tissues and cells, greatly enhancing the proliferation and movement of LUAD cells.
Thorough examination of multi-omics data offers vital understanding and improves the molecular categorization of LUAD. Utilizing a powerful machine learning system, we highlight the immense potential of the riskscore in providing individualized risk evaluations and customized treatment suggestions for LUAD patients.
肺腺癌（LUAD）是一种异质性肿瘤，具有多种基因和分子改变。迫切需要开发一种基于多组学的LUAD分类系统，以增进对其生物学特性的理解。
从TCGA和GEO数据集中收集LUAD患者的临床和病理特征、基因改变、DNA甲基化模式以及mRNA、lncRNA和microRNA的表达数据，以及体细胞突变数据。利用一个计算工作流程，通过10种聚类技术合并LUAD患者的多组学数据，并将其与10种机器学习方法相结合，以确定详细的分子亚组并完善一个预后风险模型。评估高风险组和低风险组之间体细胞突变、拷贝数改变和免疫细胞浸润的差异。通过TIDE和SubMap算法评估免疫疗法对患者的有效性，并辅以来自各个免疫疗法组的数据。此外，利用癌症治疗反应门户（CTRP）和PRISM重新利用数据集（PRISM）来研究LUAD的新药物治疗方法。最后，使用RT-PCR、免疫组织化学、CCK-8、伤口愈合和transwell试验研究SLC2A1在肿瘤动态中的作用。
通过多组学聚类，我们发现了两种与预后相关的独特癌症亚型（CSs），其中CS2显示出更好的预后。使用随机生存森林（RSF）方法创建了一个由17个基因组成的强大模型，该模型被证明是总生存的独立预测因子，表现出可靠且令人印象深刻的性能。低风险组不仅预后较好，而且更有可能表现出“冷肿瘤”表型。另一方面，高风险组的个体预后较差，更有可能对免疫疗法和六种特定的化疗药物产生阳性反应。实验室细胞试验表明，SLC2A1在LUAD组织和细胞中大量存在，极大地增强了LUAD细胞的增殖和迁移能力。
对多组学数据的深入研究提供了重要的认识，并改善了LUAD的分子分类。利用强大的机器学习系统，我们强调了风险评分在为LUAD患者提供个性化风险评估和定制治疗建议方面的巨大潜力。
