Applying Untargeted Lipidomics to Evaluate the Efficacy of Combined Neoadjuvant Chemotherapy and Immunotherapy for Esophageal Squamous Carcinoma Treatment.

Abstract

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignant tumor with a poor prognosis due to insidious symptoms that make early diagnosis difficult. Despite the combination of multiple treatment modalities, the recurrence and mortality rates of ESCC remain high. Neoadjuvant chemotherapy combined with immunotherapy is an emerging treatment modality that improves the prognosis of patients with ESCC. However, owing to the presence of hyperprogression and pseudoprogression, the currently used methods cannot accurately evaluate the efficacy of this therapy in patients, thus creating an evaluation bias and depriving these patients of the opportunity to benefit. We used untargeted lipidomics to identify the differences in lipid composition between cancer specimens and normal tissue specimens in the neoadjuvant chemotherapy combined with the immunotherapy group and the surgery-alone group of esophageal cancer patients and constructed a prediction model based on sphingomyelin 12:1;2O/30:0 and triglyceride (TG) 60:3 | TG 18:0_24:1_18 using a machine learning approach, which helps to better evaluate the neoadjuvant efficacy of combination therapy and better guide the treatment of ESCC.