Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma - Wang, Pan Pan; Song, Xin; Zhao, Xue Ke; Wei, Meng Xia; Gao, She Gan; Zhou, Fu You; Han, Xue Na; Xu, Rui Hua; Wang, Ran; Fan, Zong Min; Ren, Jing Li; Li, Xue Min; et al. (Frontiers in Oncology, 2022)
Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. Absence of specific early symptoms and biomarkers lead to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites (All-Trans-13,14-Dihydroretinol, (±)-Myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, Carnitine C10:1, Carnitine C10:1 Isomer1, Carnitine C14-OH, Carnitine C16:2-OH, Carnitine C9:1, Formononetin, Hyodeoxycholic acid, Indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1) and Resolvin E1) was developed by logistic regression after lasso and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in discovery and validation groups (accuracies > 89%). In addition, levels of 4 downregulated metabolites above (Hyodeoxycholic acid, (2S, 3S)-3-methylphenylalanine, Carnitine C9:1 and Indole-3-carboxylic acid) were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariable Cox regression analyses with and without clinical indicators: high level of MG(20:4)isomer, low level of 9,12-octadecadienoic acid and L-Isoleucine correlated with an unfavorable prognosis. And the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for early detection, risk stratification for clinical management and potentially improve outcomes of ESCC.