Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma

Frontiers in Oncology

Jia Li¹  Xue Min Li¹  Xin Song¹  Liu Yu Li¹  Meng Xia Wei¹  Rui Hua Xu¹  Fu You Zhou¹  Kan Zhong¹  Miao Miao Yang¹  Ran Wang²  Ya Jie Chen³  Yao Chen⁴  Zong Min Fan⁵  Jia Jia Ji⁶  Xian Zeng Wang⁷  Jing Feng Hu⁷  Xue Ke Zhao⁷  She Gan Gao⁷  Pan Pan Wang⁷  Jing Li Ren⁷  Ling Ling Lei⁷  Xue Na Han⁷  Yuan Ze Yang⁷  Li Dong Wang⁷ 

[1] Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China;Department of Oncology, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;Department of Pathology, Hebei Provincial Cixian People’s Hospital, Cixian, China;Department of Pathology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China;Department of Thoracic Surgery, Anyang Tumor Hospital, Anyang, China;Department of Thoracic Surgery, Linzhou People’s Hospital, Linzhou, China;;State Key Laboratory of Esophageal Cancer Prevention &

关键词: biomarkers;    metabolic profiles;    early detection;    esophageal carcinoma;    prognosis;   

DOI  :  10.3389/fonc.2022.790933

来源: DOAJ

【 摘 要 】

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads 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 the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [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 multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; 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 that 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 the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.

【 授权许可】

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