Leveraging a Necroptosis Pattern to Predict the Prognosis and Drug Sensitivity of Ovarian Cancer
Background: Ovarian Cancer (OC) remains a fatal gynecological malignancy. Necroptosis may be a backup pathway that induces cell death when apoptosis is inhibited. This research aims to develop and validate an OC prognosis model based on necroptosis.
Methods: The Cancer Genome Atlas (TCGA) and Genome Tissue Expression Consortium Project Genome (GTEx) databases were used in obtaining data on OC patients and normal ovarian tissues. Necroptosis related genes were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Differentially Expressed Genes (DEGs) between tumors and normal tissues were screened. COX regression analysis was used in constructing gene signature, which was further tested and validated in the TCGA and GEO cohorts. Based on risk scores and different clinical prognostic features, Nomogram was constructed to predict OS in OC patients. Then, the patients were divided into high- and low-risk groups, and differential genes were identified between the two groups. The potential biological and pathological functions of the differential genes were explored through Gene Ontology (GO) and KEGG analyses. Immunoassays were used to analyze immune status. Immunohistochemistry (IHC) further confirmed the expression levels of core prognostic genes and their correlations with Overall Survival (OS) rates. Drug sensitivity analysis in different risk groups was performed to screen out potential drugs for the treatment of OC. Finally, a consensus clustering analysis was used in subtyping ovarian tumors.
Results: A three gene signature was identified, including JAK1, PYGB, and STAT1. The high-risk group had lower OS than the low-risk group and the risk score was acceptable for predicting prognosis independent of any other clinical prognostic features. The nomogram can accurately predict the 1, 3, and 5 year survival rates of patients with OC. Functional analysis revealed immune related pathways and differences in immune status between the two risk groups. Furthermore, three core prognostic genes involved in model construction were overexpressed in OC versus those in normal ovarian tissues. Patients in the low-risk group were more sensitive to cisplatin and docetaxel. In consensus clustering analysis, OC patients were separated into two subtypes and the survival rate in cluster 1 was better than in cluster 2.
Conclusion: A necroptosis related model based on three cores prognostic DEGs can be used to predict OC prognosis.
Chang-Zhong Li*, Na Su, Ping-Ping Cai, Xiao-Xue Yin, Hong-Min Xu, Zhi-Qiang Dou