Mutation status coupled with RNA-sequencing data can efficiently identify important non-significantly mutated genes serving as diagnostic biomarkers of endometrial cancer.

BACKGROUND

Endometrial cancers (ECs) are one of the most common types of malignant tumor in females. Substantial efforts had been made to identify significantly mutated genes (SMGs) in ECs and use them as biomarkers for the classification of histological subtypes and the prediction of clinical outcomes. However, the impact of non-significantly mutated genes (non-SMGs), which may also play important roles in the prognosis of EC patients, has not been extensively studied. Therefore, it is essential for the discovery of biomarkers in ECs to further investigate the non-SMGs that were highly associated with clinical outcomes.

RESULTS

For the 9681 non-SMGs reported by the mutation annotation pipeline, there were 1053, 1273 and 395 non-SMGs differentially expressed between the patient groups divided by the clinical endpoints of histological grade, histological type as well as the International Federation of Gynecology and Obstetrics (FIGO) stage of ECs, respectively. In the gene set enrichment analysis, the cancer-related pathways, namely neuroactive ligand-receptor interaction signaling pathway, cAMP signaling pathway and calcium signaling pathway, were significantly enriched with the differentially expressed non-SMGs for all the three endpoints. We further identified 23, 19 and 24 non-SMGs, which were highly associated with histological grade, histological type and FIGO stage, respectively, from the differentially expressed non-SMGs by using the variable combination population analysis (VCPA) approach and found that 69.6% (16/23), 78.9% (15/19) and 66.7% (16/24) of the identified non-SMGs had been previously reported to be correlated with cancers. In addition, the averaged areas under the receiver operating characteristic curve (AUCs) achieved by the predictive models with identified non-SMGs as predictors in predicting histological type, histological grade, and FIGO stage were 0.993, 0.961 and 0.832, respectively, which were superior to those achieved by the models with SMGs as features (averaged AUCs = 0.928, 0.864 and 0.535, resp.).

CONCLUSIONS

Besides the SMGs, the non-SMGs reported in the mutation annotation analysis may also involve the crucial genes that were highly associated with clinical outcomes. Combining the mutation status with the gene expression profiles can efficiently identify the cancer-related non-SMGs as predictors for cancer prognostic prediction and provide more supplemental candidates for the discovery of biomarkers.