ArticleJournal of ovarian research2023
Development of a necroptosis-related gene signature and the immune landscape in ovarian cancer.
Article in Journal of ovarian research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Development of a PANoptosis-Related Pathomics Prognostic Model in Ovarian Cancer: A Multi-Omics Study.Journal of cellular and molecular medicine · 2025Article
- Arginine methylation patterns in LUAD: defining prognostic subtypes and relevance to immunotherapy.Discover oncology · 2025Article
- Development of CSOARG: a single-cell and multi-omics-based machine learning model for ovarian cancer prognosis and drug response prediction.Frontiers in oncology · 2025Article
- Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer.Frontiers in immunology · 2024Article
- Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer.Journal of cellular and molecular medicine · 2024Article
- Genetic alterations in the neuronal development genes are associated with changes of the tumor immune microenvironment in pancreatic cancer.Annals of pancreatic cancer · 2023Article
- Editorial: Molecular landscapes of human papillomavirus-related squamous cell carcinoma.Frontiers in oncology · 2023Article
- Construction and Identification of an NLR-Associated Prognostic Signature Revealing the Heterogeneous Immune Response in Skin Cutaneous Melanoma.Clinical, cosmetic and investigational dermatology · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundNecroptosis is a novel type of programmed cell death distinct from apoptosis. However, the role of necroptosis in ovarian cancer (OC) remains unclear. The present study investigated the prognostic value of necroptosis-related genes (NRGs) and the immune landscape in OC.
methodsThe gene expression profiling and clinical information were downloaded from the TCGA and GTEx databases. Differentially expressed NRGs (DE-NRGs) between OC and normal tissueswere identified. The regression analyses were conducted to screen the prognostic NRGs and construct the predictive risk model. Patients were then divided into high- and low-risk groups, and the GO and KEGG analyses were performed to explore bioinformatics functions between the two groups. Subsequently, the risk level and immune status correlations were assessed through the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden (TMB) and the drug sensitivity were also analyzed based on the two-NRG signature in OC.
resultsTotally 42 DE-NRGs were identified in OC. The regression analyses screened out two NRGs (MAPK10 and STAT4) with prognostic values for overall survival. The ROC curve showed a better predictive ability in five-year OS using the risk score. Immune-related functions were significantly enriched in the high- and low-risk group. Macrophages M1, T cells CD4 memory activated, T cells CD8, and T cells regulatory infiltration immune cells were associated with the low-risk score. The lower tumor microenvironment score was demonstrated in the high-risk group. Patients with lower TMB in the low-risk group showed a better prognosis, and a lower TIDE score suggested a better immune checkpoint inhibitor response in the high-risk group. Besides, cisplatin and paclitaxel were found to be more sensitive in the low-risk group.
conclusionsMAPK10 and STAT4 can be important prognosis factors in OC, and the two-gene signature performs well in predicting survival outcomes. Our study provided novel ways of OC prognosis estimation and potential treatment strategy.
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