Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer - Liu, Xuan; Liu, Chuan; Liu, Jie; Song, Ying; Wang, Shanshan; Wu, Miaoqing; Yu, Shanshan; Cai, Luya (Frontiers in Oncology, 2021)
Background: Endometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients survival. And growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers. Methods: We downloaded the clinical information and mRNA expression profiles of 543 tumour tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we contructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment. Results: The prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group with notably better. What’s more, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient's ESTIMATE score and the higher the infiltration of immune cells. Conclusions: We used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature combined with tumor microenvironment (TME) and AS events, which has a deeper understanding of the immune status of EC patients, and also provides a new insights for clinical patients with EC.