Patients diagnosed preoperatively with ductal carcinoma (DCIS) breast cancer have the potential to develop invasive ductal carcinoma (IDC). The present study investigated the usefulness of exosome-encapsulated microRNA-223-3p (miR-223-3p) as a biomarker for detecting IDC in patients initially diagnosed with DCIS by biopsy. The potential association between miR-223-3p and clinicopathological characteristics was examined in patients with breast cancer. Exosomes of 185 patients with breast cancer were separated from plasma by ultracentrifugation. Initially a microRNA (miRNA) microarray was examined to reveal the invasion specific miRNAs using exosomes collected from 6 patients with breast cancer, including 3 DCIS patients, 3 IDC patients and 3 healthy controls. In the miR microarray analysis the miR-223-3p levels of IDC patients demonstrated the highest fold-change compared with those in the DCIS patients and healthy controls. The potential of miR-223-3p for cell proliferation and cell invasion were examined using MCF7 cells transfected with the miR-223-3p gene. MCF7 cells transfected with the miR-223-3p gene significantly promoted cell proliferation and cell invasive ability (P<0.05). The plasma exosomal miR-223-3p levels of the other 179 patients with breast cancer and 20 healthy controls were measured using TaqMan miR assays. The exosomal miR-223-3p levels of the patients with breast cancer were significantly increased compared with the healthy controls (P<0.01). A statistically significant association was observed between the exosomal miR-223-3p levels and histological type, pT stage, pN stage, pathological stage, lymphatic invasion and nuclear grade (P<0.05). The exosomal miR-223-3p levels of IDC patients (stage I) and upstaged IDC patients (stage I) were significantly higher compared with the DCIS patients (P<0.05). These results suggest that exosomal miR-223-3p may be a useful preoperative biomarker to identify the invasive lesions of DCIS patients diagnosed by biopsy. In addition, plasma exosome-encapsulated miR-223-3p level was significantly associated with the malignancy of breast cancer.

No abstract

3135 Background: Saliva is non-invasively accessible and informative biological fluid which has high potential for the early diagnosis of various diseases. The aim of this study is to develop machine learning methods and to explore new salivary biomarkers to discriminate breast cancer patients from healthy controls. Methods: We conducted a comprehensive metabolite analysis of saliva samples obtained from 101 patients with invasive carcinoma (IC), 23 patients with ductal carcinoma in situ (DCIS) and 42 healthy controls, using capillary electrophoresis and liquid chromatography with mass spectrometry to quantify hundreds of hydrophilic metabolites. Saliva samples were collected under 9h fasting and were split into training and validation data. Conventional statistical analyses and artificial intelligence-based methods were used to access the discrimination abilities of the quantified metabolite. Multiple logistic regression (MLR) model and an alternative decision tree (ADTree)-based machine learning methods were used. The generalization abilities of these mathematical models were validated in various computational tests, such as cross-validation and resampling methods. Results: Among quantified 260 metabolites, amino acids and polyamines showed significantly elevated in saliva from breast cancer patients, e.g. spermine showed the highest area under the receiver operating characteristic curves (AUC) to discriminate IC from C; 0.766 (95% confidence interval [CI]; 0.671 – 0.840, P < 0.0001). These metabolites showed no significant difference between C and DICS, i.e., these metabolites were elevated only in the samples of IC. The MLR yielded higher AUC to discriminate IC from C; 0.790 (95% CI; 0.699 – 0.859, P < 0.0001). The ADTree with ensemble approach showed the best AUC; 0.912 (95% CI; 0.838 – 0.961, P < 0.0001). In the comparison of these metabolites in the analysis of each subtype, seven metabolites were significantly different between Luminal A-like and Luminal B-like while, but few metabolites were significantly different among the other subtypes. Conclusions: These data indicated the combination of salivary metabolomic profiles including polyamines showed potential ability to screening breast cancer in a non-invasive way.

No abstract

No abstract