ArticleFrontiers in oncology2023
A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Bioinformatics Analysis Identifies Lipid Droplet-Associated Gene Signatures as Promising Prognostic and Diagnostic Models for Endometrial Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- SCENE: Signature Collection for Endometrial Cancer Prognosis.Journal of cellular and molecular medicine · 2025Review
- Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms.Frontiers in immunology · 2025Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model. Methods: The RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods. Results: We identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters Conclusion: We identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.
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Registered trials
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.