Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions

Background: Breast cancer is very common and highly fatal in women. Current non-invasive detection methods like mammograms are unsatisfactory. Lipidomics, a promising detection method, may serve as a novel prognostic approach for breast cancer in high-risk patients. Results: According the predictive model, the combination of 15 lipid species had high diagnostic value. In the training set, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the combination of these 15 lipid species were 83.3%, 92.7%, 89.7%, and 87.9%, respectively. The AUC in the training set was 0.926 (95% CI 0.869-0.982). Similar results were found in the validation set, with the sensitivity, specificity, PPV and NPV at 81.0%, 94.5%, 91.9%, and 86.7%, respectively. The AUC was 0.938 (95% CI 0.889-0.986) in the validation set. Methods: Using triple quadrupole liquid chromatography electrospray ionization tandem mass spectrometry, this study was to detect global lipid profiling of a total of 194 plasma samples from 84 patients with early-stage breast cancer (stage 0-II) and 110 patients with benign breast disease included in a training set and a validation set. A binary logistic regression was used to build a predictive model for evaluating the lipid species as potential biomarkers in the diagnosis of breast cancer. Conclusion: The combination of these 15 lipid species as a panel could be used as plasma biomarkers for the diagnosis of breast cancer.