Leveraging various extracellular matrix levels to assess prognosis and sensitivity to immunotherapy in patients with ovarian cancer

Frontiers in Oncology

Oncology

Xingxing Lei¹  Lisha Sun¹  Jiao Yang¹  Yu Liu²  Youqun Huang³ 

[1] Department of Endocrinology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China;Department of Nephrology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China;Department of Nephrology-2, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China;

关键词: ovarian cancer;    ECMS;    immune landscape;    prognosis;    immunotherapy;   

DOI  :  10.3389/fonc.2023.1163695

 received in 2023-02-11, accepted in 2023-04-18,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundOvarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. Late diagnosis and heterogeneous treatment result in a poor prognosis for patients with OC. Therefore, we aimed to develop new biomarkers to predict accurate prognoses and provide references for individualized treatment strategies.MethodsWe constructed a co-expression network applying the “WGCNA” package and identified the extracellular matrix-associated gene modules. We figured out the best model and generated the extracellular matrix score (ECMS). The ECMS’ ability to predict accurate OC patients’ prognoses and responses to immunotherapy was evaluated.ResultsThe ECMS was an independent prognostic factor in the training [hazard ratio (HR) = 3.132 (2.068–4.744), p< 0.001] and testing sets [HR = 5.514 (2.084–14.586), p< 0.001]. The receiver operating characteristic curve (ROC) analysis showed that the AUC values for 1, 3, and 5 years were 0.528, 0.594, and 0.67 for the training set, respectively, and 0.571, 0.635, and 0.684 for the testing set, respectively. It was found that the high ECMS group had shorter overall survival than the low ECMS group [HR = 2 (1.53–2.61), p< 0.001 in the training set; HR = 1.62 (1.06–2.47), p = 0.021 in the testing set; HR = 1.39 (1.05–1.86), p = 0.022 in the training set]. The ROC values of the ECMS model for predicting immune response were 0.566 (training set) and 0.572 (testing set). The response rate to immunotherapy was higher in patients with low ECMS.ConclusionWe created an ECMS model to predict the prognosis and immunotherapeutic benefits in OC patients and provided references for individualized treatment of OC patients.

【 授权许可】

Unknown   
Copyright © 2023 Huang, Lei, Sun, Liu and Yang