ArticleEBioMedicine2024
Enhancer demethylation-regulated gene score identified molecular subtypes, inspiring immunotherapy or CDK4/6 inhibitor therapy in oesophageal squamous cell carcinoma.
Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Gastrointestinal toxicity associated with cyclin-dependent kinase 4/6 inhibitors in breast cancer patients: insights from a real-world pharmacovigilance analysis.Frontiers in medicine · 2026Article
- Expanding horizons in esophageal squamous cell carcinoma: The promise of induction chemoimmunotherapy with radiotherapy.World journal of clinical oncology · 2025Article
- Integration of scRNA-seq and bulk tissue RNA-seq data to identify cancer-associated fibroblast-related gene RGMA as a potential treatment target for esophageal cancer.Functional & integrative genomics · 2025Article
- Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.World journal of gastroenterology · 2025Article
- PD‑1/PD‑L1 immune checkpoint in bone and soft tissue tumors (Review).Molecular and clinical oncology · 2025Review
- Progressively exploring and assessing the prognosis of bladder urothelial cancer based on the microenvironment through the integration of multiple databases.Frontiers in molecular biosciences · 2025Article
- Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.Journal of cellular and molecular medicine · 2024Article
- Prognostic biomarkers for immunotherapy in esophageal cancer.Frontiers in immunology · 2024Review
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10 authors.
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Abstract
backgroundThe 5-year survival rate of oesophageal squamous cell carcinoma (ESCC) is approximately 20%. The prognosis and drug response exhibit substantial heterogeneity in ESCC, impeding progress in survival outcomes. Our goal is to identify a signature for tumour subtype classification, enabling precise clinical treatments.
methodsUtilising pre-treatment multi-omics data from an ESCC dataset (n = 310), an enhancer methylation-eRNA-target gene regulation network was constructed and validated by in vitro experiments. Four machine learning methods collectively identified core target genes, establishing an Enhancer Demethylation-Regulated Gene Score (EDRGS) model for classification. The molecular function of EDRGS subtyping was explored in scRNA-seq (n = 60) and bulk-seq (n = 310), and the EDRGS's potential to predict treatment response was assessed in datasets of various cancer types.
findingsEDRGS stratified ESCCs into EDRGS-high/low subtypes, with EDRGS-high signifying a less favourable prognosis in ESCC and nine additional cancer types. EDRGS-high exhibited an immune-hot but immune-suppressive phenotype with elevated immune checkpoint expression, increased T cell infiltration, and IFNγ signalling in ESCC, suggesting a better response to immunotherapy. Notably, EDRGS outperformed PD-L1 in predicting anti-PD-1/L1 therapy effectiveness in ESCC (n = 42), kidney renal clear cell carcinoma (KIRC, n = 181), and bladder urothelial carcinoma (BLCA, n = 348) cohorts. EDRGS-low showed a cell cycle-activated phenotype with higher CDK4 and/or CDK6 expression, demonstrating a superior response to the CDK4/6 inhibitor palbociclib, validated in ESCC (n = 26), melanoma (n = 18), prostate cancer (n = 15) cells, and PDX models derived from patients with pancreatic cancer (n = 30).
interpretationIdentification of EDRGS subtypes enlightens ESCC categorisation, offering clinical insights for patient management in immunotherapy (anti-PD-1/L1) and CDK4/6 inhibitor therapy across cancer types.
fundingThis study was supported by funding from the National Key R&D Program of China (2021YFC2501000, 2020YFA0803300), the National Natural Science Foundation of China (82030089, 82188102), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2022-I2M-2-001, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091).
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