PANoptosis-Relevant Subgroups Predicts Prognosis and Characterizes the Tumour Microenvironment in Ovarian Cancer - Chen Y; Deng Z; Chen J; Lin J; Zou J; Li S; Sun Y (Journal of Inflammation Research)
Yuwei Chen,1,* Zhibo Deng,2,* Jian Chen,1 Jie Lin,1 Jianping Zou,1 Sang Li,1 Yang Sun1 1Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, People’s Republic of China; 2Department of Orthopedics, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yang Sun, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, 350014, People’s Republic of China, Email sunyang@fjzlhospital.comBackground: Ovarian cancer (OC) poses a significant health burden with high mortality rates among female reproductive malignancies. Variability in treatment responses underscores the need for reliable prognostic markers to refine risk stratification. PANoptosis, a novel form of programmed cell death, plays pivotal roles in cancer pathogenesis and therapy. However, its prognostic relevance in OC remains unclear.Methods: Utilizing data from The Cancer Genome Atlas (TCGA), we analyzed transcriptomic and clinical signatures of OC patients. Through consensus clustering, we delineated molecular subtypes associated with PANoptosis-related genes (PRGs). We constructed and validated prognostic models using LASSO and Cox regression analyses, corroborated with GEO dataset validation. CIBERSORT assessed immune cell infiltration by risk score, and a predictive algorithm evaluated chemotherapy responses. Additionally, we investigated the biological role of the key gene CXCL13 in OC and its response to immunotherapy.Results: Based on 19 PRGs, we identified two OC subtypes (PAN-Cluster1, PAN-Cluster2). Machine learning-derived risk scores using PAN-Cluster differentially expressed genes emerged as an independent prognostic indicator. Distinct risk groups exhibited varying clinical outcomes, immune profiles, drug sensitivities, and mutational landscapes. Notably, we confirmed CXCL13 as a model key gene and explored its role in OC regulation. In OC cells, suppression of CXCL13 expression enhances cell proliferation and migration, while patients with high CXCL13 expression show an improved response to immunotherapy.Conclusion: We initially identified the molecular subtypes associated with PRGs and established a prognostic model related to PRGs to predict survival and drug response in OC patients. Although further validation is required, these findings offer valuable insights into the development of personalized treatment strategies for OC patients.Keywords: PANoptosis, ovarian cancer, tumor microenvironment, drug sensitivity, prognosis