A recurrence model for laryngeal cancer based on SVM and gene function clustering

AbstractConclusion: A prognostic model was obtained for LC. Several critical genes were unveiled. They could be potentially applied for LC recurrence prediction.Objective: Gene expression data of laryngeal cancer (LC) were analyzed to identify critical genes associated with recurrence.Methods: Two gene expression datasets were downloaded from the Gene Expression Omnibus. Dataset GSE27020 is used as the training set, containing 75 non-recurred LC cases and 34 recurred LC cases.Results: A total of 725 DEGs were identified from the training set. A total of 4126 gene pairs showed significant correlations in non-recurred LC only, corresponding to 533 genes. A total of 7235 gene pairs showed significant correlations in recurred LC only, corresponding to 608 genes. Besides, 1694 gene pairs showed significant correlations in both non-recurred and recurred LC, corresponding to 322 genes. Functional enrichment analysis was performed for the three groups of DEGs. Seven overlapping biological functions were revealed:...