Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non‐small cell lung cancer

• Published in: Cancer medicine (Malden, MA) Vol. 12; no. 18; pp. 19245 - 19259
• Main Authors: Sun, Runbin, Fei, Fei, Wang, Min, Jiang, Junyi, Yang, Guangyu, Yang, Na, Jin, Dandan, Xu, Zhi, Cao, Bei, Li, Juan
• Format: Journal Article
• Language: English
• Published: Bognor Regis John Wiley & Sons, Inc 01.09.2023; John Wiley and Sons Inc; Wiley
• Subjects: Acids; Algorithms; Artificial intelligence; Biomarkers; Cancer therapies; Chemotherapy; Data analysis; Discriminant analysis; Disease; Drug efficacy; Drug resistance; Efficiency; Epidemics; Folic acid; Hospitals; Infections; Learning algorithms; Lung cancer; Machine learning; Mathematical models; Metabolism; Metabolites; Metabolomics; Methods; Non-small cell lung carcinoma; NSCLC; Patients; pemetrexed; pharmacometabolomics; Pyruvic acid; Small cell lung carcinoma; Threonine; Tricarboxylic acid cycle; Tryptophan; Tumors; Vitamin B; China

Background Anti‐folate drug pemetrexed is a vital chemotherapy medication for non‐small cell lung cancer (NSCLC). Its response varies widely and often develops resistance to the treatment. Therefore, it is urgent to identify biomarkers and establish models for drug efficacy evaluation and prediction...