Circulating microRNAs have drawn a great deal of attention as promising novel biomarkers for breast cancer. However, to date, the results are mixed. Here, we performed a three-stage microRNA analysis using plasma samples from breast cancer patients and healthy controls, with efforts taken to address several pitfalls in detection techniques and study design observed in previous studies. In the discovery phase with 122 Caucasian study subjects, we identified 43 microRNAs differentially expressed between breast cancer cases and healthy controls. When those microRNAs were compared with published data from other studies, we identified three microRNAs, including miR-148b, miR-133a and miR-409-3p, whose plasma levels were significantly higher in breast cancer cases than healthy controls and were also significant in previous independent studies. In the validation phase with 50 breast cancer cases and 50 healthy controls, we validated the associations with breast cancer detection for miR-148b and miR-133a (P = 1.5×10^-6 and 1.3×10^-10, respectively). In the in-vitro study phase, we found that both miR-148b and miR-133a were secreted from breast cancer cell lines, showing their secretory potential and possible tumor origin. Thus, our data suggest that both miR-148b and miR-133a have potential use as biomarkers for breast cancer detection.

A few studies in the last several years have shown that metabolomics, the study of metabolites and small intermediate molecules, may help better understand the breast carcinogenesis. However, breast cancer is a heterogeneous disease with different subtypes. Additionally, there is a significant racial difference in terms of breast cancer incidence and mortality. Few, if any, metabolomics studies in breast cancer have considered race and tumor subtypes in the study design. We performed a global metabolomic profiling using mass spectrometry and samples from 60 breast cancer cases and 60 matched controls. A total of 375 named metabolites were observed, with 117 metabolites whose levels were significantly different between African American and Caucasian American women (P < .05 and q < 0.10) and 78 that differed between breast cancer cases and healthy controls (P < .05 and q < 0.10). Most of those differentiated metabolites belong to amino acids, fatty acids, and lysolipids. In the pathway-based analysis, we found that plasma levels of many amino acids were statistically significantly lower in patients with breast cancer, especially those with triple-negative breast cancer, than healthy controls. However, plasma levels of many FAs related to β-oxidation were statistically significantly higher in patients with breast cancer than healthy controls, suggesting the possibility of altered FA β-oxidation in patients with breast cancer. Because of small sample size, the clinical usage of the metabolites from this study is unclear. Further validation of those significant metabolites is warranted, especially with the consideration of racial difference.

Because circulating microRNAs (miRNAs) have drawn a great deal of attention as promising novel cancer diagnostics and prognostic biomarkers, we sought to identify serum miRNAs significantly associated with outcome in glioblastoma patients. To do this, we performed global miRNA profiling in serum samples from 106 primary glioblastoma patients. The study subjects were randomly divided into two sets: set one (n = 40) and set two (n = 66). Using a Cox regression model, 3 serum miRNAs (miR-106a-5p, miR-182, and miR-145-5p) and 5 serum miRNAs (miR-222-3p, miR-182, miR-20a-5p, miR-106a-5p, and miR-145-5p) were identified significantly associated with 2-year patient overall survival and disease-free survival (P < 0.05) in both sets and the combined set. We then created the miRNA risk scores to assess the total impact of the significant serum miRNAs on survival. The high risk scores were associated with poor patient survival (overall survival: HR = 1.92, 95% CI: 1.19, 10.23, and disease-free survival: HR = 2.03, 95%CI: 1.24, 4.28), and were independent of other clinicopathological factors. Our results suggest that serum miRNAs could serve as prognostic predictors of glioblastoma.

Metabolomics profiling in plasma samples from glioma patients correlates with tumor phenotypes Metabolomics profiling in plasma samples from glioma patients correlates with tumor phenotypes

Living in a disadvantaged neighborhood is associated with adverse clinical outcomes among breast cancer patients, but the underlying pathway is still unclear. Limited evidence has suggested that accelerated biological aging may play an important role. In this study, using a sub-sample of 906 women with newly diagnosed breast cancer at M.D. Anderson, we examined whether levels of selected markers of biological aging (e.g., allostatic load, telomere length, and global DNA methylation) were affected by neighborhood disadvantage. The Area Deprivation Index was used to determine the neighborhood disadvantage. Based on the median ADI at the national level, the study population was divided into low and high ADI groups. Overall, breast cancer patients from the high ADI group were more likely to be younger and non-Hispanic Black than those from the low ADI group (P < 0.001, respectively). They were also more likely to have higher grade and poorly differentiated breast tumors (P = 0.029 and 0.019, respectively). For the relationship with markers, compared to the low ADI group, high ADI group had higher median levels of allostatic load (P = 0.046) and lower median levels of global DNA methylation (P < 0.001). Compared to their counterparts, those from the high ADI group were 20% more likely to have increased allostatic load and 51% less likely to have increased levels of global DNA methylation. In summary, we observed that levels of allostatic load and global DNA methylation are influenced by neighborhood disadvantage among breast cancer patients.