ArticleBMC cancer2025
Integrated bioinformatics analysis of differences between EAC and ESCC.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Associations of PD-L1 expression status, tumor histology and patient ethnic background with immunotherapy outcomes in patients with advanced esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Pooled it
- Neoadjuvant nedaplatin and paclitaxel regimen with concurrent radiotherapy for esophageal squamous cell carcinoma: a single-arm phase II clinical trial.Future oncology (London, England) · 2026Trial
- METTL3 in esophageal cancer: Current insights into molecular mechanisms, subtype heterogeneity and targeted therapy prospects (Review).International journal of oncology · 2026Review
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
Abstract
backgroundAs the major subtypes of esophageal cancer (EC), esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC) exhibit distinct etiological mechanism, epidemiology, tumor biology, and prognoses. We performed bioinformatics analysis on specific genes related to pathological subtypes to identify potential biomarkers and therapeutic targets.
methodsDifferentially expressed genes (DEGs) between tumor and normal tissues were identified from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) was subsequently employed to screen genes associated with pathological subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations, along with gene set enrichment analysis (GSEA), were performed to elucidate the biological roles of these DEGs. A protein‒protein interaction (PPI) network was constructed to explore the hub genes. Furthermore, the prognostic genes were identified via LASSO Cox regression. The differences between EAC and ESCC in terms of immune cell infiltration, somatic mutations, copy number variations (CNVs), and drug sensitivity were subsequently analyzed.
resultsA total of 131 genes were identified as EAC-specific DEGs, whereas 49 genes were recognized as ESCC-specific DEGs. Enrichment analysis revealed significant enrichment of the extracellular matrix (ECM)-related pathway, along with the cell cycle, epithelial‒mesenchymal transition (EMT), and hypoxia signaling pathways, in ESCC. Conversely, EAC was associated with alterations in tumor metabolism, particularly glycolysis and gluconeogenesis. For EAC, a prognostic risk model was constructed, including RHOV, SYTL1, MT1X, PRRG4, KCNK5, and CCL20, which demonstrated robust predictive capabilities for patient outcomes. In ESCC, TUSC3 was identified as a prognostic biomarker and was validated in tissue samples. Furthermore, an in-depth analysis revealed distinct patterns in immune cell infiltration, somatic mutations, CNVs, and drug sensitivity between EAC and ESCC.
conclusionsIn this study, pathologically specific genes were identified in EAC and ESCC. These findings might provide valuable insights into the molecular mechanisms and potential therapeutic targets for these two distinct subtypes.
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.