ArticleFrontiers in endocrinology2023
Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- The development and validation of a novel programmed cell death-related signature based on machine learning analysis of 15 programmed cell death patterns for predicting prognosis and therapeutic response in high-grade serous ovarian carcinoma.Translational cancer research · 2025Article
- In-depth exploration of programmed cell death-related subtypes and development of a prognostic signature model in lung adenocarcinoma.Scientific reports · 2025Article
- Development of a machine learning-derived programmed cell death index for prognostic prediction and immune insights in colorectal cancer.Discover oncology · 2025Article
- A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer.Journal of ovarian research · 2024Article
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6 authors at 1 institution in 1 country.
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Abstract
Background: Programmed cell death (PCD) is an overwhelming factor affecting tumor cell metastasis, but the mechanism of PCD in ovarian cancer (OV) is still uncertain. Methods: To define the molecular subtypes of OV, we performed unsupervised clustering based on the expression level of prognosis related PCD genes in the Cancer Genome Atlas (TCGA)-OV. COX and least absolute shrinkage and selection operator (LASSO) COX analysis were used to identify the OV prognostic related PCD genes, and the genes identified according to the minimum Akaike information criterion (AIC) were the OV prognostic characteristic genes. According to the regression coefficient in the multivariate COX analysis and gene expression data, the Risk Score of OV prognosis was constructed. Kaplan-Meier analysis was conducted to assess the prognostic status of OV patients, and receiver operating characteristic (ROC) curves were conducted to assess the clinical value of Risk Score. Moreover, RNA-Seq date of OV patient derived from Gene Expression Omnibus (GEO, GSE32062) and the International Cancer Genome Consortium (ICGC) database (ICGC-AU), verifying the robustness of the Risk Score Results: 9-gene composition Risk Score system was finally determined by COX and LASSO COX analysis. Patients in the low Risk Score group possessed improved prognostic status, immune activity. PI3K pathway activity was increased in the high Risk Score group. In the chemotherapy drug sensitivity analysis, we found that the high Risk Score group might be more suitable for treatment with PI3K inhibitors Taselisib and Pictilisib. In addition, we found that patients in the low-risk group responded better to immunotherapy. Conclusion: Risk Score of 9-gene composition of PCD signature possesses promising clinical potential in OV prognosis, immunotherapy, immune microenvironment activity, and chemotherapeutic drug selection, and our study provides the basis for an in-depth investigation of the PCD mechanism in OV.
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