ArticleDiscover oncology2025
Construction and validation of risk models of prognostic genes associated with parthanatos in papillary thyroid carcinoma based on bioinformatics.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectThis study aimed to elucidate the role of parthanatos-related genes (PRGs) in papillary thyroid carcinoma (PTC) and construct a prognostic risk model to guide personalized treatment.
methodsUsing the GSE33630 dataset, differentially expressed PRGs were identified and analyzed via weighted gene co-expression network analysis (WGCNA) to pinpoint key module genes. Regression analysis selected seven prognostic genes for risk model construction. The model's performance was validated, and a nomogram was developed for survival prediction. Further analyses included clinical feature correlations, immune infiltration, drug sensitivity, gene set enrichment analysis (GSEA), and experimental validation via RT-qPCR.
resultsSeven prognostic genes (TSHZ3, SERGEF, AKAP12, SGPP2, ASGR1, AK1, PELI2) were identified. The risk model demonstrated robust predictive accuracy, stratifying patients into high- and low-risk groups with significant survival differences. GSEA revealed 29 enriched pathways (e.g., ribosome, focal adhesion), while immune infiltration analysis highlighted CD56 + NK cells and AK1 as key immune correlates. Drug sensitivity screening identified 111 differential therapeutics. Functional analysis indicated AKAP12 had the strongest functional similarity among prognostic genes.
conclusionThis study comprehensively mapped PRGs in PTC, established a validated risk model, and provided insights into immune-microenvironment interactions and therapeutic targets, advancing precision oncology for PTC.
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.