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Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning

British journal of cancer, 2021-08, Vol.125 (3), p.351-357 [Peer Reviewed Journal]

2021. The Author(s), under exclusive licence to Cancer Research UK. ;The Author(s), under exclusive licence to Cancer Research UK 2021. ;The Author(s), under exclusive licence to Cancer Research UK 2021 ;ISSN: 0007-0920 ;EISSN: 1532-1827 ;DOI: 10.1038/s41416-021-01395-w ;PMID: 33953345

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Title:
Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning
Author: Yuan, Yuyao ; Zhao, Zitong ; Xue, Liyan ; Wang, Guangxi ; Song, Huajie ; Pang, Ruifang ; Zhou, Juntuo ; Luo, Jianyuan ; Song, Yongmei ; Yin, Yuxin
Subjects: Aged ; Area Under Curve ; Biomarkers ; Biomarkers, Tumor - blood ; Biomarkers, Tumor - genetics ; Case-Control Studies ; Developing countries ; Early Detection of Cancer ; Esophageal Neoplasms - blood ; Esophageal Neoplasms - diagnosis ; Esophageal Neoplasms - metabolism ; Esophageal Squamous Cell Carcinoma - blood ; Esophageal Squamous Cell Carcinoma - diagnosis ; Esophageal Squamous Cell Carcinoma - metabolism ; Esophagus ; Female ; Gene Expression Profiling - methods ; Gene Expression Regulation, Neoplastic ; Humans ; Invasiveness ; LDCs ; Learning algorithms ; Lipid metabolism ; Lipidomics - methods ; Lipids ; Machine Learning ; Male ; Middle Aged ; Morbidity ; Patients ; Prognosis ; ROC Curve ; Sensitivity and Specificity ; Squamous cell carcinoma ; Support Vector Machine ; Transcriptomes ; Tumors
Is Part Of: British journal of cancer, 2021-08, Vol.125 (3), p.351-357
Description: 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. 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. 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. 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.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 0007-0920
EISSN: 1532-1827
DOI: 10.1038/s41416-021-01395-w
PMID: 33953345
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central
ProQuest Central

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Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning

2021. The Author(s), under exclusive licence to Cancer Research UK. ;The Author(s), under exclusive licence to Cancer Research UK 2021. ;The Author(s), under exclusive licence to Cancer Research UK 2021 ;ISSN: 0007-0920 ;EISSN: 1532-1827 ;DOI: 10.1038/s41416-021-01395-w ;PMID: 33953345

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