Identifying key genes in retinoblastoma by comparing classifications of several kinds of significant genes.

OBJECTIVE

The objective of this paper was to investigate key genes in retinoblastoma using a novel method which is mainly based on five kinds of genes, differentially expressed genes (DEGs), differential pathway genes (DPGs), seed genes (common genes between DEGs and DPGs), hub genes and informative genes (common genes of hub genes and DEGs), and support vector machines (SVM) model.

MATERIALS AND METHODS

In the proposed method, the first step was to identify five types of significant genes. DEGs were identified using linear models for microarray data (Limma) package (The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia). DPGs were originated from differential pathways based on attract method. Hub genes of mutual information network which is constructed by the context likelihood of relatedness algorithm were obtained according to topological degree centrality analysis. For the second step, SVM model was implemented to assess the classification performance of DEGs, DPGs, seed genes, hub genes, and informative genes, depending on its induces the area under the receiver operating characteristics curve (AUC), true negative rate (TNR), true positive rate (TPR) and the Matthews coefficient correlation classification (MCC).

RESULTS

We detected 479 DEGs, 747 DPGs, 29 seed genes, 34 hub genes, and 7 informative genes in total for retinoblastoma. The classification performance of informative genes was the best of all with AUC = 1.00, TNR = 1.00, TPR = 1.00, and MCC = 1.00, hence they were considered to key genes which included EPARS1, FN1, HLA-DPA1, HLA-DPB1, HLA-DRA, CFI, and transforming growth factor, beta receptor II.

CONCLUSIONS

We have successfully identified seven key genes, which might be potential biomarkers for detection and therapy of retinoblastoma for current and future study.