ArticleOncoTargets and therapy2020
A Comprehensive Risk Assessment Model for Ovarian Cancer Patients with
Article in OncoTargets and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- IL-31: State of the Art for an Inflammation-Oriented Interleukin.International journal of molecular sciences · 2022Pooled it
- The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLCBiomolecules · 2025Review
- The Associations and Causal Relationships of Ovarian Cancer - Construction of a Prediction Model.International journal of women's health · 2024Article
- A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOvarian carcinoma is a malignant tumor with a high mortality rate and a lack of effective treatment options for patients at advanced stages. For improving outcomes and helping patients with poor prognosis, choose a suitable therapy and an excellent risk assessment model and new treatment options are needed. MATERIALS AND
methodsOvarian cancer gene expression profile of GSE32062 was downloaded from the NCBI GEO database for screening differentially expressed genes (DEGs) between well and poor prognosis groups using limma package in R (version 3.4.1). Prognosis-related genes and clinical prognostic factors were obtained from univariate and multivariate Cox regression analyses, and a comprehensive risk assessment model was constructed using a Pathway Dysregulation Score (PDS) matrix, Cox-Proportional Hazards (Cox-PH) regression, as well as L1-least absolute shrinkage and selection operator (L1-LASSO) penalization. Then, significant DEGs were converted to pathways and optimal prognosis-related pathways were screened. Finally, risk prediction models based on pathways, genes involved in pathways, and comprehensive clinical risk factors with pathways were built. Their prognostic functions were assessed in verification sets. Besides, genes involved in immune-pathways were checked for immune infiltration using immunohistochemistry.
resultsA superior risk assessment model involving 9 optimal combinations of pathways and one clinical factor was constructed. The pathway-based model was found to be superior to the gene-based model.
conclusionWe have generated a comprehensive risk assessment model consisting of a clinical risk factor and pathways that showed a possible bright foreground. The set of significant pathways might play as a better prognosis model which is more accurate and applicable than the DEG set. Besides,
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.