Necroptosis‐related regulatory pattern and scoring system for predicting therapeutic efficacy and prognosis in ovarian cancer

Abstract

Abstract Background Ovarian cancer is difficult to treat and is, therefore, associated with a high fatality rate. Although targeted therapy and immunotherapy have been successfully used clinically to improve the diagnosis and treatment of ovarian cancer, most tumors become drug resistant, and patients experience relapse, meaning that the overall survival rate remains low. Aims There is currently a lack of effective biomarkers for predicting the prognosis and/or outcomes of patients with ovarian cancer. Therefore, we used published transcriptomic data derived from a large ovarian cancer sample set to establish a molecular subtyping model of the core genes involved in necroptosis in ovarian cancer. Methods and Results Clustering analysis and differential gene expression analyses were performed to establish the genomic subtypes related to necroptosis and to explore the patterns of regulatory gene expression related to necroptosis in ovarian cancer. A necroptosis scoring system (NSS) was established using principal component analysis according to different regulatory patterns of necroptosis. In addition, this study revealed important biological processes with essential roles in the regulation of ovarian tumorigenesis, including external encapsulating structure organization, leukocyte migration, oxidative phosphorylation, and focal adhesion. Patients with high NSS scores had unique immunophenotypes, such as more abundant M2 macrophages, monocytes, CD4+ memory T cells, and regulatory T cells. Immune checkpoint CD274 had a greater expression in patients with high NSS values. Conclusion This NSS could be used as an independent predictor of prognosis to determine the sensitivity of ovarian cancer to various small‐molecule inhibitors, immune checkpoint inhibitors, and platinum‐based chemotherapy drugs.

Keywords

• biomarkers
• clustering analysis
• necroptosis
• ovarian cancer