An EMT-based gene signature enhances the clinical understanding and prognostic prediction of patients with ovarian cancers

• Main Authors: Qi-jia Li (Author), Zi-liang Wu (Author), Juan Wang (Author), Jing Jiang (Author), Bing Lin (Author)
• Format: Book
• Published: BMC, 2023-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s13048-023-01132-2; 1757-2215
• Keywords: Ovarian cancer; EMT; Prognosis; Immune infiltration; Chemotherapy; Gynecology and obstetrics; RG1-991
• Source: Journal of Ovarian Research, Vol 16, Iss 1, Pp 1-13 (2023)

Abstract Background Ovarian cancer (OC) is one of the most common gynecological cancers with malignant metastasis and poor prognosis. Current evidence substantiates that epithelial-mesenchymal transition (EMT) is a critical mechanism that drives OC progression. In this study, we aspire to identify pivotal EMT-related genes (EMTG) in OC development, and establish an EMT gene-based model for prognosis prediction. Methods We constructed the risk score model by screening EMT genes via univariate/LASSO/step multivariate Cox regressions in the OC cohort from TCGA database. The efficacy of the EMTG model was tested in external GEO cohort, and quantified by the nomogram. Moreover, the immune infiltration and chemotherapy sensitivity were analyzed in different risk score groups. Results We established a 11-EMTGs risk score model to predict the prognosis of OC patients. Based on the model, OC patients were split into high- and low- risk score groups, and the high-risk score group had an inevitably poor survival. The predictive power of the model was verified by external OC cohort. The nomogram showed that the model was an independent factor for prognosis prediction. Moreover, immune infiltration analysis revealed the immunosuppressive microenvironment in the high-risk score group. Finally, the EMTG model can be used to predict the sensitivity to chemotherapy drugs. Conclusions This study demonstrated that EMTG model was a powerful tool for prognostic prediction of OC patients. Our work not only provide a novel insight into the etiology of OC tumorigenesis, but also can be used in the clinical decisions on OC treatment.