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

Abstract

Conclusion: 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: positive regulation of chondrocyte differentiation, autoimmune thyroid disease, focal adhesion, linoleic acid metabolism, drug metabolism, organic cation transport, and ECM-receptor interaction. Eight feature genes (PDIA3, MYH11, PDK1, SDC3, RPE65, LAMC3, BTK, and UPK1B) were identified. Their prognostic effect was validated by independent test set as well as survival analysis.

Keywords:

• Laryngeal cancer
• differentially expressed genes
• feature genes
• survival analysis
• support vector machine

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.