ArticleScientific reports2025
A Comprehensive multi-network analysis of ceRNAs and transcription factors for papillary thyroid carcinoma diagnosis and prognosis.
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 1 paper.
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
1 citing paper in PubMed.
- Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response.Cancer immunology, immunotherapy : CII · 2026Article
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
Papillary thyroid carcinoma (PTC) is a malignancy with an ambiguous etiology. The competitive endogenous RNA (ceRNA) hypothesis provides a framework for clarifying the molecular mechanisms that drive carcinogenesis. In this study, we constructed a novel ceRNA network to identify reliable diagnostic and prognostic indicators applicable across all stages of PTC. Transcriptome analysis was performed to identify stage-specific hub genes using the MCC, IVI, and MCODE algorithms. A novel five-layer ceRNA network and its associated regulatory network (DE-TF) were constructed. Receiver operating characteristic curves were used to evaluate the diagnostic performance of elements within both networks. A risk assessment model was developed by identifying key genes from the ceRNA and DE-TF components through univariable Cox regression and LASSO regression analyses. RNA-seq findings were validated by RT-qPCR. The correlations between gene expression levels and blood calcium levels were examined. The ceRNA and DE-TF networks contained 33 and 21 components, respectively. Logistic regression analysis identified PKMYT1, E2F1, NFATC1, STAT6, E2F3, LINC02910, GAS5, and TK1 as reliable diagnostic markers for PTC, achieving an AUC of 96.9%. Among these, PKMYT1 and GAS5 were stage-specific markers, showing significant upregulation in highly aggressive PTC tumors compared to less aggressive ones. Both genes demonstrated strong diagnostic value in differentiating high- from low-aggressive tumors, with AUCs of 0.81 and 0.87, respectively. circMET, which was overexpressed in both low- and high-aggressive tumors, showed diagnostic potential in distinguishing low-aggressive tumors from normal adjacent tissues (AUC = 0.81). GAS5 expression demonstrated an association with blood calcium levels. The SERN prognostic model, including STAT6, E2F1, RMI2, and NR4A1, illustrates the importance of these four genes as reliable prognostic markers for overall survival in PTC. Three components of the ceRNA network-PKMYT1, GAS5, and circMET-were significantly associated with PTC aggressiveness. PKMYT1 and GAS5 demonstrated strong diagnostic value in distinguishing high-aggressive from low-aggressive tumors, while circMET showed notable diagnostic efficacy in differentiating low-aggressive PTC tumors from adjacent normal tissues. Furthermore, GAS5 expression levels were correlated with blood calcium levels.
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.