The integration of differentially expressed genes based on multiple microarray datasets for prediction of the prognosis in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a common human malignancy. However, its pathogenesis and prognostic information are poorly elucidated. In the present study, we aimed to probe the most significant differentially expressed genes (DEGs) and their prognostic performance in OSCC. Multiple microarray datasets from the Gene Expression Omnibus (GEO) database were aggregated to identify DEGs between OSCC tissue and control tissue. Least absolute shrinkage and selection operator (LASSO) Cox model was constructed to determine the prognostic performance of the aggregated DEGs based on The Cancer Genome Atlas (TCGA) OSCC cohort. Ten datasets with 341 OSCC samples and 283 control samples were included. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed that the integrated DEGs were enriched in the IL-17 signaling pathway, viral protein interactions with cytokines and cytokine receptors, and amoebiasis, among others. Our LASSO Cox model was able to discriminate two groups with different overall survival in the training cohort and test cohort (p < 0.001). The time-dependent receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) values at one year, three years, and five years were 0.831, 0.898, and 0.887, respectively. In the testing cohort, the time-dependent ROC curve showed that the AUC values at one year, three years, and five years were 0.696, 0.693, and 0.860, respectively. Our study showed that the integrated DEGs of OSCC might be applicable in the evaluation of prognosis in OSCC. However, further research should be performed to validate our findings.

Automated summary

This study identified significant differentially expressed genes in oral squamous cell carcinoma (OSCC) and constructed a prognostic model based on these genes. The results suggest that these genes may have value in prognostic evaluation for OSCC.