Department of Cardiothoracic Surgery, Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, China.
Comput Math Methods Med. 2022 Oct 12;2022:1968829. doi: 10.1155/2022/1968829. eCollection 2022.
Lymph node metastasis is an important route of lung cancer metastasis and can significantly affect the survival of lung cancer.
All the analysis was conducted out in the R software. Expression profile and clinical information of lung adenocarcinoma (LUAD) patients were downloaded from The Cancer Genome Atlas database.
In our study, we firstly identified the characteristic genes of lymph node metastasis in LUAD through two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression, and SVM-RFE algorithms. Ten characteristic genes were finally identified, including , , , , , , , , , and . Next, we performed univariate Cox regression, LASSO regression, and multivariate Cox regression sequentially to construct a prognosis model based on , , and , which had a good prognosis prediction efficiency in both training and validation cohorts. Univariate and multivariate analysis indicated that our model is a risk factor independent of other clinical features. Pathway enrichment analysis showed that in the high-risk patients, the pathway of MYC target, unfolded protein response, interferon alpha response, DNA repair, reactive oxygen species pathway, and glycolysis were significantly enriched. Among three model genes, aroused our interest and therefore was selected for further analysis. KM survival curves showed that the patients with higher might have better disease-free survival and progression-free survival. Further, pathway enrichment, genomic instability, immune infiltration, and drug sensitivity analysis were performed to in-deep explore the role of in LUAD.
Results showed that the signature based on , , and is a useful tool to predict prognosis and lung cancer lymph node metastasis.
淋巴结转移是肺癌转移的重要途径，可显著影响肺癌患者的生存。
所有分析均在 R 软件中进行。从癌症基因组图谱数据库中下载肺腺癌 (LUAD) 患者的表达谱和临床信息。
在我们的研究中，我们首先通过两种机器学习算法，最小绝对值收缩和选择算子 (LASSO) 逻辑回归和 SVM-RFE 算法，确定 LUAD 淋巴结转移的特征基因。最终确定了 10 个特征基因，包括、、、、、、、、和。接下来，我们依次进行单因素 Cox 回归、LASSO 回归和多因素 Cox 回归，构建基于、、和的预后模型，该模型在训练和验证队列中均具有良好的预后预测效率。单因素和多因素分析表明，我们的模型是独立于其他临床特征的危险因素。通路富集分析表明，在高危患者中，MYC 靶标、未折叠蛋白反应、干扰素 α 反应、DNA 修复、活性氧途径和糖酵解途径显著富集。在三个模型基因中，引起了我们的兴趣，因此选择其进行进一步分析。KM 生存曲线表明，较高的患者可能具有更好的无病生存和无进展生存。进一步进行通路富集、基因组不稳定性、免疫浸润和药物敏感性分析，以深入探讨在 LUAD 中的作用。
结果表明，基于、、和的特征签名是预测预后和肺癌淋巴结转移的有用工具。
