ArticleScientific reports2025
m6A regulators-based gene expression pattern is associated with immune microenvironment characteristics in hepatocellular carcinoma.
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.
- A tumor microenvironment-focused signature based on m6A and lactylation modification predicts prognosis and immunotherapy response in hepatocellular carcinoma.Discover oncology · 2026Article
- A six-gene prognostic signature for aflatoxin B1-associated hepatocellular carcinoma identified through integrated bioinformatics and network toxicology.Translational gastroenterology and hepatology · 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
10 authors.
Funding
Abstract
To evaluate the relationship between N6-methyladenosine (m6A) modification-related genes and the immune microenvironment characteristics of hepatocellular carcinoma (HCC), and to evaluate the potential of targeting m6A regulators to sensitize HCC cells to immunotherapy. RNA sequencing data and clinicopathologic data of HCC patients were obtained from TCGA and ICGC database. Based on the expression levels of 23 m6A modification regulators, the HCC samples were divided into three m6A clusters by ConsensusClusterPlus algorithm. Survival analysis and clinical features was explored in different clusters. Next, univariate Cox analysis and LASSO regression analysis were applied to screened out 4 immune-related prognostic genes from the differentially expressed genes (DEGs) in the three clusters, and a risk model was established. Next, Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis were applied to evaluate the prognostic value of risk model. The relationship between the immune microenvironment and the risk model was estimated using the ESTIMATE method and single sample Gene Set Enrichment Analysis (ssGSEA). Finally, immunohistochemistry was applied to explore the correlation between the expression levels of PD-L1 and the key m6A regulators METTL3 and YTHDF1. Most m6A regulators were highly expressed in HCC tissues, and positively correlated with each other. The patients in cluster 3 showed the worse prognosis. 449 of the 710 DEGs were immune regulators, and from the 449 genes, a risk model consisting of 4 m6A-related genes (DNTTIP2, SEPHS1, TCOF1 and TXLNA) was established, which was associated with immune microenvironment characteristics of HCC tissues. The risk model was identified as an independent prognostic factor for the overall survival of HCC patients. Higher levels of YTHDF1 and METTL3 expression in HCC tissues were associated with higher expression of PD-L1. m6A modification participates in regulating immune microenvironment of HCC, and targeting m6A may block the immune escape of HCC cells.
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.