Aberrant Methylation of 21 MicroRNA Genes in Breast Cancer: Sets of Genes Associated with Progression and a System of Markers for Predicting Metastasis

Systemic analysis of the relationship between the levels of methylation of 21 microRNA genes and the parameters of breast cancer progression was performed on a representative sample of 91 paired specimens of breast cancer and histologically normal tissues and a system of markers for prediction of metastasis was proposed. A significant association of hypermethylation of 11 genes with late (III-IV) clinical stages was found, and for 6 genes (MIR124-1, MIR127, MIR34B/C, MIR9-3, MIR1258, and MIR339) this association was highly significant (p≤0.001, FDR=0.01). For MIR9-3 and MIR339, an association with tumor size was demonstrated (p<0.001, FDR=0.01). No association of the levels of methylation of the analyzed microRNA genes with the degree of differentiation were found. An association with lymph node metastasis was established for 9 microRNA genes; the most significant association was shown for 6 genes MIR125B-1, MIR127, MIR9-3, MIR339, MIR124-3, and MIR1258 (p<0.005, FDR=0.05). Based on these 6 genes, a marker system for predicting breast cancer metastasis was developed by ROC analysis. This system is characterized by 87% sensitivity and 77% specificity (AUC=0.894). The proposed system may have clinical application in the personalized treatment of breast cancer patients.

Non-invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: The DACHS study

Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non-metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non-metastasized vs. metachronously metastasized stage II CRC patients of the German population-based case-control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow-up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non-metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10-fold cross-validation, by nested cross-validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non-metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10-fold cross-validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non-metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non-invasively stage II CRC patients according to their risk for metachronous metastasis.