Department of Critical Care Medicine, Affiliated Hospital of Chifeng University, Chifeng 024000, Inner Mongolia Autonomous Region, China.
Department of Chronic Disease, Chifeng Center for Disease Control and Prevention, Chifeng 024000, Inner Mongolia Autonomous Region, China.
Aging (Albany NY). 2024 Oct 14;16(20):13104-13116. doi: 10.18632/aging.206122.
There is no golden noninvasive and effective technique to diagnose lymph node metastasis (LNM) for esophageal squamous cell carcinoma (ESCC) patients. Here, a classifier was proposed consisting of miRNAs to screen ESCC patients with LNM from the ones without LNM.
miRNA expression and clinical data files of 93 ESCC samples were downloaded from TCGA as the discovery set and 119 ESCC samples with similar dataset GSE43732 as the validation set. Differentially expressed miRNAs (DE-miRNAs) were analyzed between patients with LNM and without LNM. LASSO regression was performed for selecting the DE-miRNA pair to consist the classifier. To validate the accuracy and reliability of the classifier, the SVM and AdaBoost algorithms were applied. The CCK-8 and wound healing assay were used to evaluate the role of the miRNA in ESCC cells.
There were 43 DE miRNAs between the LNM+ group and LNM- group. Among them, miR-224-5p, miR-99a-5p, miR-100-5p, miR-34c-5p, miR-503-5p, and miR-452-5p were identified by LASSO to establish the classifier. SVM and AdaBoost showed that the model could classify the ESCC patients with LNM from the ones without LNM precisely and reliably in 2 data sets. miR-224-5p in the classifier as the top contributor to discriminate the two groups of patients based on AdaBoost, promoted ESCC cell proliferation and migration .
The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully.
目前尚无用于诊断食管鳞癌（ESCC）患者淋巴结转移（LNM）的无创、有效的金标准技术。本研究构建了一个基于 miRNA 的分类器，用于筛选有 LNM 的 ESCC 患者和无 LNM 的 ESCC 患者。
从 TCGA 下载 93 例 ESCC 样本的 miRNA 表达谱和临床数据作为发现集，从 GSE43732 下载 119 例具有相似数据集的 ESCC 样本作为验证集。分析 LNM 阳性和 LNM 阴性患者间的差异表达 miRNA（DE-miRNA）。采用 LASSO 回归选择 DE-miRNA 对以构建分类器。应用 SVM 和 AdaBoost 算法验证分类器的准确性和可靠性。采用 CCK-8 和划痕愈合实验评估 miRNA 在 ESCC 细胞中的作用。
LNM+组和 LNM-组间存在 43 个 DE-miRNA。其中，miR-224-5p、miR-99a-5p、miR-100-5p、miR-34c-5p、miR-503-5p 和 miR-452-5p 通过 LASSO 被鉴定用于构建分类器。SVM 和 AdaBoost 表明，该模型能够在 2 个数据集内准确、可靠地对有 LNM 的 ESCC 患者和无 LNM 的 ESCC 患者进行分类。基于 AdaBoost，miR-224-5p 作为分类器中对两组患者区分贡献最大的 miRNA，促进 ESCC 细胞增殖和迁移。
基于这 6 个 miRNA 的分类器可成功地对有 LNM 的 ESCC 患者和无 LNM 的 ESCC 患者进行分类。
