Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Jiangxi Key Laboratory of Molecular Medicine, Nanchang, Jiangxi, China.
Front Immunol. 2024 Apr 4;15:1354339. doi: 10.3389/fimmu.2024.1354339. eCollection 2024.
Lymphangiogenesis (LYM) has an important role in tumor progression and is strongly associated with tumor metastasis. However, the clinical application of LYM has not progressed as expected. The potential value of LYM needs to be further developed in lung adenocarcinoma (LUAD) patients.
The Sequencing data and clinical characteristics of LUAD patients were downloaded from The Cancer Genome Atlas and GEO databases. Multiple machine learning algorithms were used to screen feature genes and develop the LYM index. Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore the correlation of LYM index with immune profile and anti-tumor therapy.
We screened four lymphangiogenic feature genes (PECAM1, TIMP1, CXCL5 and PDGFB) to construct LYM index based on multiple machine learning algorithms. We divided LUAD patients into the high LYM index group and the low LYM index group based on the median LYM index. LYM index is a risk factor for the prognosis of LUAD patients. In addition, there was a significant difference in immune profile between high LYM index and low LYM index groups. LUAD patients in the low LYM index group seemed to benefit more from immunotherapy based on the results of TIDE algorithm.
Overall, we confirmed that the LYM index is a prognostic risk factor and a valuable predictor of immunotherapy response in LUAD patients, which provides new evidence for the potential application of LYM.
淋巴管生成（LYM）在肿瘤进展中起着重要作用，与肿瘤转移密切相关。然而，LYM 的临床应用并没有像预期的那样进展。LYM 在肺腺癌（LUAD）患者中的潜在价值需要进一步开发。
从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载 LUAD 患者的测序数据和临床特征。使用多种机器学习算法筛选特征基因并构建 LYM 指数。免疫细胞浸润、免疫检查点表达、肿瘤免疫功能障碍和排除（TIDE）算法和药物敏感性分析用于探讨 LYM 指数与免疫特征和抗肿瘤治疗的相关性。
我们筛选了四个淋巴管生成特征基因（PECAM1、TIMP1、CXCL5 和 PDGFB），基于多种机器学习算法构建了 LYM 指数。我们根据中位数 LYM 指数将 LUAD 患者分为高 LYM 指数组和低 LYM 指数组。LYM 指数是 LUAD 患者预后的危险因素。此外，高 LYM 指数组和低 LYM 指数组之间的免疫特征存在显著差异。根据 TIDE 算法的结果，低 LYM 指数组的 LUAD 患者似乎更受益于免疫治疗。
总体而言，我们证实 LYM 指数是 LUAD 患者的预后风险因素和免疫治疗反应的有价值的预测因子，为 LYM 的潜在应用提供了新的证据。
