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Published on: April 22, 2019
Serum metabolomics profiling in head and neck squamous cell carcinoma: a multicentre diagnostic study
Hexin Ma1, Xinran Zhao1, Zhihui Li2
1Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology; Shanghai Center of Head and Neck Oncology Clinical and Translational Science, Shanghai, China; Digital Diagnosis and Treatment Innovation Center for Cancer, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Background:
Head and neck squamous cell carcinoma (HNSCC) is frequently diagnosed at advanced stages, when outcomes are poor. Biomarkers for HNSCC detection remain needed.
Methods:
We performed serum metabolomics profiling in 938 participants, including 457 patients with HNSCC, 451 controls, and 30 patients with oral potentially malignant disorders (OPMDs). Discovery and external validation cohorts were analysed using LC-MS/MS. Machine learning-based feature selection was used to develop an 8-metabolite diagnostic model. Disease specificity was assessed in an independent third-centre cohort. Multi-omics analyses integrating TCGA transcriptomics and spatial transcriptomics data were used to explore molecular heterogeneity.
Study Registration:
ChiCTR-BRC-17014040.
Findings:
Serum metabolomics profiling showed altered arginine metabolism, characterised by arginine decrease and ornithine accumulation. The 8-metabolite model achieved an internal validation AUC of 0.990, with 91.3% sensitivity and 97.1% specificity. In the multicentre external validation cohort, the model achieved an AUC of 0.901, with 91.1% sensitivity and 80.2% specificity, and maintained performance in Stage I disease, with an AUC of 0.927. In the independent disease-control cohort, the model distinguished HNSCC from healthy controls with an AUC of 0.980 and showed moderate discrimination between HNSCC and OPMDs, with an AUC of 0.710. Multi-omics analyses identified two candidate molecular subtypes with distinct immune features, prognosis, TP53 mutation status, and predicted treatment sensitivities. Spatial transcriptomics supported an inverse association between metabolic score and immune infiltration.
Interpretation:
This multicentre study identifies a serum metabolomics signature associated with HNSCC detection and molecular stratification. The findings support further prospective validation in clinically representative populations.
Funding:
National Natural Science Foundation of China; Shanghai Committee of Science and Technology; Innovative Research Team of High-level Local Universities in Shanghai; Shanghai Jiao Tong University Trans-med Awards Research; The Explorers Program of Shanghai; National Major Science and Technology Infrastructure for Translational Medicine, Shanghai; Beijing Municipal Natural Science Foundation; Beijing High-Level Innovation and Entrepreneurship Talent Support Program Young Top Talent Projects.
