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Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell
Peng Wang1, Zhenyuan Feng1, Weigang Chen2
1The First Affiliated Hospital of Shihezi University, Shihezi City, Xinjiang, 832000, China.
Background:
Esophageal 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.
Methods:
This 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.
Results:
The 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.
Conclusions:
This 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.
