ArticleDiscover oncology2026
Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.
Article in Discover oncology, 2026. 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.
- Integrated multi-omic and functional profiling reveals a ZDHHC16-associated palmitoylation-proteostasis state in hepatocellular carcinoma.Discover oncology · 2026Article
- A palmitoylation-related signature predicts prognosis and immune microenvironment features in diffuse large B-cell lymphoma.Frontiers in immunology · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims to construct a prognostic model for hepatocellular carcinoma (HCC) based on palmitoylation-related genes and explore its molecular mechanisms through multi-dimensional analyses.
methodsThe research integrated single-cell transcriptome data (GSE189903) with bulk transcriptome data (TCGA-LIHC, GEO datasets), focusing on palmitoylation-related genes in HCC epithelial cells. The scAB deconvolution algorithm was used to analyze the association between epithelial cell subsets and patient survival, and hdWGCNA was combined to construct a gene co-expression network. Through differential expression analysis, univariate Cox regression, and LASSO penalized regression, 7 key genes (SERPINE1, FMO3, ALDH2, CPS1, SLCO1B1, ACAT1, ACADS) were identified to build a prognostic risk model.
resultsValidation results showed that the model could effectively distinguish the survival prognosis of high-risk and low-risk patients (AUC values for 1/3/5 years in the TCGA cohort were 0.676, 0.656, and 0.642, respectively; those in the GSE14520 validation set were 0.702, 0.658, and 0.654, respectively), and the risk score was an independent prognostic factor. Further analyses revealed that the risk score was associated with tumor staging, immune cell infiltration (e.g., T cells, monocytes), response to immunotherapy, and drug sensitivity. Functional enrichment analysis indicated that the high-risk group was enriched in cell cycle regulation and oncogenic signaling pathways, while the low-risk group was related to metabolic pathways.
conclusionThis study is the first to analyze the regulatory network of palmitoylation in HCC epithelial cells by combining single-cell and bulk transcriptomes, providing new molecular targets and methodological references for HCC prognosis evaluation and precision therapy.
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.