1. Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning
- Author
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Yuyao Yuan, Juntuo Zhou, Guangxi Wang, Jianyuan Luo, Yongmei Song, Huajie Song, Zitong Zhao, Liyan Xue, Yuxin Yin, and Pang Ruifang
- Subjects
Male ,Oncology ,Cancer Research ,medicine.medical_specialty ,Support Vector Machine ,Esophageal Neoplasms ,Sensitivity and Specificity ,Article ,Machine Learning ,Transcriptome ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,Lipidomics ,Biomarkers, Tumor ,medicine ,Humans ,Basal cell ,Early Detection of Cancer ,Aged ,business.industry ,Gene Expression Profiling ,Cancer ,Diagnostic marker ,Lipid metabolism ,Middle Aged ,Lipidome ,Prognosis ,medicine.disease ,Serum samples ,Gene Expression Regulation, Neoplastic ,ROC Curve ,Area Under Curve ,Case-Control Studies ,030220 oncology & carcinogenesis ,Female ,Esophageal Squamous Cell Carcinoma ,business - Abstract
Background Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients. Methods A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation. Results Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism. Conclusions We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.
- Published
- 2021