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Additional file 5. Datapreprocessing and quality control process for GSE150825

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.

Juntuo Zhou,1,* Qiuyu Li,2,* Chengyang Liu,3 Ruifang Pang,1 Yuxin Yin3 1Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, People’s Republic of China; 3Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuxin YinDepartment of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, People’s Republic of ChinaEmail yinyuxin@bjmu.edu.cnBackground: Chronic obstructive pulmonary disease (COPD) is a common disease characterized by persistent respiratory symptoms and airflow restriction. It is usually manifested as airway and/or alveolar abnormalities caused by significant exposure to harmful particulates or gases.Objective: We aim to explore plasma metabolomic changes in the acute exacerbation stage of COPD (AECOPD) and stable stage of COPD (Stable COPD) to identify potential biomarkers for diagnosis or prognosis in clinical practice.Methods: Untargeted metabolomics and lipidomics analyses were performed to investigate dysregulated molecules in blood plasma of AECOPD patients (n=48) and Stable COPD (n=48), and a cohort of healthy people were included as a control group (n=48). Statistical analysis and bioinformatics analysis were performed to reveal dysregulated metabolites and perturbed metabolic pathways. SVM-based multivariate ROC analysis was used for candidate biomarker screening.Results: A total of 142 metabolites and 688 lipids were dysregulated in COPD patients. Pathway enrichment analysis showed that several metabolic pathways were perturbed after COPD onset. Several biomarker panels were proposed for diagnosis of COPD vs healthy control and AECOPD vs Stable COPD with AUC greater than 0.9.Conclusion: Numerous plasma metabolites and several metabolic pathways were detected relevant to COPD disease onset or progression. These metabolites may be considered as candidate biomarkers for diagnosis or prognosis of COPD. The perturbed pathways involved in COPD provide clues for further pathological mechanism studies of COPD.Keywords: metabolomics, lipidomics, COPD, biomarker