ArticleBMC gastroenterology2026
Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell carcinoma by machine learning algorithms.
Article in BMC gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.Journal of gastrointestinal oncology · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundEsophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related mortality worldwide, with a high prevalence and poor prognosis in specific regions. Despite advancements in clinical care, the identification of reliable biomarkers for accurate survival prediction remains a significant challenge, hindering personalized treatment.
methodsThis study utilized genomic and clinical data from TCGA and GEO databases, applying ten machine learning algorithms to develop a prognostic model based on lipid metabolism-related genes. The Random Survival Forest (RSF) algorithm was identified as the optimal framework, supplemented by immune infiltration analysis and in vitro functional validation of the key gene ACOT9.
resultsThe resulting 33-gene signature achieved superior performance, with a C-index of 0.708 and survival prediction area under the curve (AUC) values approaching 1.0 in the training cohort. High-risk scores were significantly correlated with advanced histological grade, tumor stage, and increased regulatory T-cell infiltration. Furthermore, silencing of ACOT9 markedly suppressed the proliferation, migration, and invasion of ESCC cells.
conclusionsThis study provides a robust framework for ESCC risk stratification and identifies ACOT9 as a critical oncogenic driver, offering a novel scientific basis for optimizing clinical management strategies.
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