ArticleCancer cell international2025
Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4.
Article in Cancer cell international, 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.
- Shared glycosylation-related molecular mechanism and immune infiltration patterns of fetal growth restriction and preeclampsia.Frontiers in immunology · 2026Article
- Combination of Naples prognostic score and pathological factors for evaluating the long-term prognosis of patients with late-onset colorectal cancer: a multicenter machine learning study.Translational cancer research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors of the urinary system. Protein acetylation plays a key role in regulating cellular processes and cancer signaling pathways. This study explores the potential biological mechanisms of ccRCC from the perspective of acetylation.
methodsThis study obtained RNA-seq data and clinical information of ccRCC from TCGA and ICGC, and single-cell RNA sequencing datasets from the GEO database. Ten machine learning algorithms and their 101 combinations were used to analyze the prognostic significance of acetylation-related differentially expressed genes (DEGs) and to construct a prognostic risk model. GSEA was used to analyze the enrichment of different signaling pathways in high-risk and low-risk groups, and the correlation between immune infiltration and risk scores was assessed. Finally, the function of the key gene GCNT4 was verified through cell experiments.
resultsThis study identified 84 acetylation-regulated key genes with significant expression differences between tumor and normal tissues, closely linked to patient prognosis. The LASSO + RSF combination model performed best, and the model could accurately predict patient prognosis. The survival of patients in the high-risk group was significantly worse than that in the low-risk group. High expression of GCNT4 was associated with better survival prognosis and was expressed at higher levels in normal tissues than tumor tissues. Overexpression of GCNT4 significantly inhibited the proliferation, invasion, and migration of renal cancer cells and may affect acetylation by regulating the levels of O-GlcNAc modification in cells.
conclusionThis study constructed a ccRCC acetylation homeostasis model via transcriptome analysis and machine learning, validating GCNT4 as a key gene. High expression of GCNT4 is associated with better survival prognosis and affects acetylation by regulating O-GlcNAc modification levels, inhibiting the proliferation and migration of renal cancer cells, providing a new potential target for the treatment of ccRCC.
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.