A major challenge in personalized cancer medicine is to establish a systematic approach to translate huge oncogenomic datasets to clinical situations and facilitate drug discovery for cancers such as endometrial carcinoma. We performed a genome-wide somatic mutation-expression association study in a total of 219 endometrial cancer patients from TCGA database, by evaluating the correlation between ~5,800 somatic mutations to ~13,500 gene expression levels (in total, ~78, 500, 000 pairs). A bioinformatics pipeline was devised to identify expression-associated single nucleotide variations (eSNVs) which are crucial for endometrial cancer progression and patient prognoses. We further prioritized 394 biologically risky mutational candidates which mapped to 275 gene loci and demonstrated that these genes collaborated with expression features were significantly enriched in targets of drugs approved for solid tumors, suggesting the plausibility of drug repurposing. Taken together, we integrated a fundamental endometrial cancer genomic profile into clinical circumstances, further shedding light for clinical implementation of genomic-based therapies and guidance for drug discovery.