Optimization and normalization strategies for long term untargeted HILIC-LC-qTOF-MS based metabolomics analysis: Early diagnosis of breast cancer

REÇBER T., NEMUTLU E., Beksac K., CENNET Ö., Kaynaroglu V., AKSOY S., ...More

MICROCHEMICAL JOURNAL, vol.179, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 179
  • Publication Date: 2022
  • Doi Number: 10.1016/j.microc.2022.107658
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Chemical Abstracts Core, Chimica, Food Science & Technology Abstracts, Index Islamicus, Veterinary Science Database
  • Keywords: Breast cancer, Plasma, Untargeted metabolomics, Biomarker, HILIC, Optimization, LC-qTOF-MS, MASS-SPECTROMETRY, LIQUID-CHROMATOGRAPHY, CELL-LINES, PROFILES, SALIVA, BIOMARKERS, TISSUES
  • Hacettepe University Affiliated: Yes


Although breast cancer is one of the main causes of cancer-related female deaths all over the world, more than 95% of patients survive as a result of early diagnosis and effective treatment. The discovery of new biomarkers can enable early diagnosis of breast cancer and treatment without cancer spread. Metabolomic studies are an effective research area for determining biomarkers that can be used for early diagnosis and monitoring of breast cancer. In this study, a HILIC-LC-qTOF-MS method has been developed to find new biomarkers for early detection of breast cancer from plasma. The chromatographic performance of the LC-MS method was tested using different HILIC columns to analyze as many as metabolites in a single run. After determining optimum method parameters, the method in positive and negative ionization modes was applied to a quite large group of plasma samples collected from 105 healthy volunteers, 172 early stage and 92 metastatic breast cancer patients for untargeted metabolomic screening. This long-term metabolomics data was tested with different normalization techniques in order to minimize the biological and analytical variation. The normalized data were evaluated statistically and 35 metabolites have been found significantly altered between the groups. Among these metabolites, Leukotriene A4, PC(24:1(15Z)/24:1(15Z)), TG(17:0/17:0/18:0), Hypoxanthine and cis-ACCP have been suggested as biomarkers based on receiver operating characteristic (ROC) analysis that can be applied in the early diagnosis of breast cancer. Moreover, the cross-validation results showed that the ROC model has a predictive power of over 87.4% in the early diagnosis of breast cancer.