Metabolite Early Warning Model for the Risk of Early Progression in Esophageal Squamous Cell Carcinoma

doi:10.13560/j.cnki.biotech.bull.1985.2025-0418

Abstract

Abstract:

Objective An early warning model for the risk of esophageal squamous cell carcinoma (ESCC) was constructed based on metabolomics to identify high-risk populations accurately. Method Eighty-four patients with low-grade intraepithelial neoplasia (LGIN) were included, and serum was collected at baseline. They were divided into a progress group (N=28) and a non-progress group (N=56) according to whether they progressed to high-grade intraepithelial neoplasia (HGIN) or ESCC during follow-up. Untargeted metabolomics analysis was performed using reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography combined with high-resolution mass spectrometry. Differences in metabolic profiles between groups were assessed by combining univariate and multivariate analyses, and pathway enrichment analyses were performed for differential metabolites. The samples were divided into training and test sets in the ratio of 7:3. In the training set, univariate logistic regression combined with least absolute shrinkage and selection operator (LASSO) regression was used to screen key metabolites associated with disease progression, and a risk warning model was constructed based on the regression coefficients. Model performance was assessed by the receiver operating characteristic curve and area under the curve (AUC). Result A total of 1 431 metabolites from 10 classes were identified. Differential metabolites were significantly enriched in steroid hormone biosynthesis, primary bile acid synthesis, and linoleic acid metabolic pathways. Finally, 18 key metabolites closely related to the progression of the disease were selected, including sn-glycero-3-phosphocholine, hexadecanoic acid, xanthurenic acid, and N-amidino-aspartate. The risk warning model showed good predictive ability in the test set (AUC=0.812). Conclusion Based on the prospective follow-up cohort, multiple key metabolites and metabolic pathways are identified, and metabolite early warning models for the risk of early progression of ESCC are constructed. The model has good prediction robustness and generalization ability and may provide theoretical support for early risk assessment and intervention strategy optimization in people at high risk of ESCC.

Key words: esophageal squamous cell carcinoma, metabolomics, precision medicine, least absolute shrinkage and selection operator, risk prediction model, early screening

ZHANG Ya-qi, WANG Qin-qin, SHEN Xia, LI Xu-miao, GAO Min, LI Jun, LI Chen, WANG Hui. Metabolite Early Warning Model for the Risk of Early Progression in Esophageal Squamous Cell Carcinoma[J]. Biotechnology Bulletin, 2025, 41(9): 335-344.

Figures/Tables 10

Table 1 Baseline characteristics of the subjects in this study

指标 Index	无进展组 Non-progress group （N=56）	进展组 Progress group （N=28）	P值 P value
年龄（岁） Age （years）	60.02 ± 6.31	61.32 ± 6.21	0.372
性别 Sex			1
男 Male	36 （64.3%）	18 （64.3%）
女 Female	20 （35.7%）	10 （35.7%）
身体质量指数 BMI （kg/m²）	23.86 ± 2.16	23.34 ± 1.87	0.279

Fig. 1 Plot of PCA scores for metabolic profiles of samples

Fig. 2 Distribution of metabolite species

Fig. 3 PLS-DA score plot of metabolic profiles between progress and non-progress groups

Fig. 4 Volcano plot of differential metabolites between progress and non-progress groups

Fig. 5 Top 10 most significantly upregulated and downregulated metabolites

Fig. 6 Pathway enrichment analysis of differential metabolites

Fig. 7 LASSO cross-validation error curve

Fig. 8 Heatmap of metabolite weights from LASSO regression for disease progression prediction

Fig. 9 Test set ROC curves for risk scoring models

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