Abstract Background Esophageal squamous cell carcinoma (ESCC), the most common type of esophageal cancer, characterized by low five-year sur
Abstract Background Esophageal squamous cell carcinoma (ESCC), the most common type of esophageal cancer, characterized by low five-year survival rate, and concurrent chemoradiotherapy (CCRT) has been proposed to treat ESCC, while potential biomarkers for prognostic monitoring after optimized CCRT remains unknown. Methods Serum samples from 45 patients with ESCC were collected and categorized into three groups: Control (pre-CCRT), CCRT (during CCRT), and CCRT-1 M (one-month post-CCRT). The therapeutic effect was evaluated using CT imaging and established evaluation criteria. Untargeted metabolomic analysis was performed on the serum samples to identify differential metabolites caused by CCRT treatment, assessing their potential for prognostic monitoring. Results CCRT had significant therapeutic efficacy in patients with ESCC, as indicated by CT imaging and RECIST 1.1 solid tumor evaluation criteria. Notably, several metabolic markers were identified through non-targeted metabolomic analysis, highlighting changes following CCRT treatment. These differential metabolites are involved in the dysregulation of phenylalanine, tyrosine, and tryptophan biosynthesis, as well as histidine, arginine, and proline metabolism, and glycine, serine, and threonine metabolism, suggesting a reduction in glucose metabolism in patients with ESCC after CCRT. Additionally, ROC analysis indicated that the AUC of these metabolites exceeded 0.661, underscoring their diagnostic value for assessing CCRT efficacy and their potential use in prognostic monitoring. Comparative metabolomic analysis identified L-phenylalanine and lysine as promising serum biomarkers for predicting therapeutic outcomes. Conclusions CCRT shows considerable therapeutic benefit in patients with ESCC, with observed reductions in glucose metabolism post-treatment. L-phenylalanine and lysine may serve as potential serum biomarkers to predict CCRT efficacy.
