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

Abstract

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.

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

“…We also compared the performance of our method with that of Zhang’s method [ 8 ] using the same data sets and the same SVM model. Among the seven types of cancer used in our study, comparison was made for four types of cancer because the four cancer types are common to both studies.…”

“…For example, the study of Okugawa et al [ 7 ] suggested that the expression of KiSS1 is closely related to lymph node metastasis in colorectal cancer. Zhang et al [ 8 ] predicted lymph node metastasis using differentially expressed mRNAs and noncoding RNAs. Dihge et al predicted lymph node metastasis using gene expressions combined with clinicopathological characteristics [ 9 ].…”

Feedforward Deep Learning Optimizer-based RNA-Seq Women's cancers Detection with a hybrid Classification Models for Biomarker Discovery

“…Zhang et al [16], proposed a SVM classifiers based on various features selection to forecast lymphatic metastasis in a range of malignancies. Such classifiers were applied to identify differentially expressed signatures in lymph node metastatic and non-metastatic cancer groups.…”