ArticleScientific reports2025
Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Sample size and power analysis for ROC AUC differences in diagnostic tests: a methodological evaluation of the Obuchowski-McClish and Hanley-McNeil methods.BMC medical research methodology · 2026Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression and treatment response. This study aims to investigate the role of PCD-related genes in hepatocellular carcinoma (HCC), identifying potential prognostic biomarkers and therapeutic targets to enhance patient outcomes. Data from the GEO, TCGA, and ICGC databases were analyzed to identify differentially expressed genes associated with PCD in HCC. A cell death signature (CDS) model was constructed based on seven key PCD genes using machine learning techniques, including Random Survival Forest and Cox regression models. The model was validated across multiple cohorts to evaluate its predictive accuracy for clinical outcomes, immune infiltration, and therapeutic response, and further validation of the relationship between NNMT overexpression and clinical prognosis using tumor tissue microarray data, and in vitro experiments to confirm the impact of NNMT overexpression on cell proliferation and apoptosis. A total of 183 differentially expressed genes were identified, leading to the construction of a CDS model that incorporates seven key PCD-related genes (PRGs). The CDS showed significant associations with overall survival, immune cell infiltration, and therapeutic response in HCC patients. High CDS scores were linked to poorer prognosis, increased tumor immune exclusion, and decreased efficacy of immunotherapy and conventional treatments. The model demonstrated strong predictive performance across independent validation cohorts, underscoring its potential as a valuable prognostic tool. Additionally, NNMT overexpression promotes HepG2 proliferation, inhibits apoptosis, and correlates with poor prognosis in HCC patients. This study established a prognostic model for HCC based on PCD, and the CDS holds promise as a powerful tool for personalized risk assessment and treatment planning in HCC. Moreover, the model gene NNMT may serve as a potential therapeutic target for HCC.
Indexed as
Identifiers
What OpenQuestion holds
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.