Table_2_Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures.XLSX

Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96–92.19%), 81.97% (70.83–95.24%), and 80.78% (69.61–90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.

精准预测癌症淋巴结转移，可为制定靶向临床干预方案、改善患者预后提供关键依据。目前，各类分子谱（信使RNA（mRNA）与非编码RNA（non-coding RNAs））已被广泛用于构建癌症预测分类器，例如用于判定肿瘤起源、组织良恶性状态及癌症亚型。然而，鲜有研究基于此类分子谱开展淋巴结转移风险评估相关工作，且不同分子谱对应的分类器性能也尚未得到系统对比。本研究将淋巴结转移组与非转移组间的差异表达信使RNA（mRNA）、微小RNA（miRNAs）及长链非编码RNA（lncRNAs）鉴定为分子标记物，用于构建多种癌症的淋巴结转移预测分类器。采用统一的特征选择策略，本研究对基于不同分子谱构建的支持向量机（SVM）分类器的预测性能开展了系统对比。针对9种代表性癌症类型，基于独立的mRNA、miRNA及lncRNA数据集构建的分类器，分别取得了81.00%（67.96%~92.19%）、81.97%（70.83%~95.24%）与80.78%（69.61%~90.00%）的综合预测准确率，且所需生物标志物数量极少（平均分别为6个、12个与4个）。综上，本研究所提出的特征选择策略经济高效，可精准筛选出可用于构建高性能癌症淋巴结转移预测分类器的生物标志物。本研究还搭建了一款易用的在线服务器，研究人员可提交不同来源的基因表达谱，借助该工具完成淋巴结转移风险评估。