ArticleFrontiers in immunology2025
Development and validation of a novel signature to predict the survival and affect the immune microenvironment of esophageal squamous cell carcinoma: epigenetic-related genes.
Article in Frontiers in immunology, 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Esophageal squamous cell carcinoma (ESCC) is a malignancy characterized by extensive epigenetic dysregulation. This study aims to develop a robust prognostic model utilizing epigenetic-related genes (ERGs) to improve survival prediction in ESCC patients, while simultaneously elucidating potential mechanisms underlying immune microenvironment modulation. Methods: This study employed transcriptomic data from The Cancer Genome Atlas (TCGA) as the training cohort and data from GSE53625 in the Gene Expression Omnibus (GEO) as an independent validation cohort. A total of 796 epigenetic regulator genes (ERGs) were curated from the EpiFactors database and intersected with TCGA-ESCC gene expression profiles to identify ESCC-associated ERGs. Differential expression analysis was then conducted to identify differentially expressed ERGs (DE-ERGs). Using univariate Cox and LASSO regression analyses, a prognostic risk model was constructed and thoroughly evaluated through risk stratification curves, survival status distribution maps, risk score heatmaps, survival analysis, ROC curves, and multivariate Cox regression. Further analyses included assessing the prognostic model's association with clinical features and risk stratification. To investigate the immune microenvironment, immune cell infiltration correlation, single-sample gene set enrichment analysis (ssGSEA), and immune checkpoint profiling were performed. Drug sensitivity analysis was also carried out to identify potential therapeutic agents showing differential efficacy between risk subgroups. Finally, the expression patterns of key prognostic ERGs were validated using RT-qPCR. Results: Through comprehensive differential expression analysis, we identified 345 DE-ERGs in ESCC. A robust prognostic signature comprising 13 critical ERGs-PIWIL4, SATB1, GSE1, NCOR1, BUB1, SAP30L, CHEK1, MASTL, ATM, BMI1, DNAJC2, UBE2D1, and SSRP1-was established using univariate Cox regression followed by LASSO penalized regression analysis. The prognostic efficacy of this signature was confirmed through multidimensional assessments using independent GEO datasets. Immunological characterization revealed significant enrichment of CD8 Conclusion: The findings of this study offer clinically relevant insights for prognostic stratification and characterization of the immune microenvironment in ESCC patients. Moreover, these results provide novel perspectives that may contribute to the development of more effective prognostic tools and targeted therapeutic strategies for ESCC management.
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.