2 research outputs found
Five microRNAs in Serum Are Able to Differentiate Breast Cancer Patients From Healthy Individuals
Breast cancer is the cancer with the most incidence and mortality in women. microRNAs
are emerging as novel prognosis/diagnostic tools. Our aim was to identify a serum
microRNA signature useful to predict cancer development. We focused on studying
the expression levels of 30 microRNAs in the serum of 96 breast cancer patients vs.
92 control individuals. Bioinformatic studies provide a microRNA signature, designated
as a predictor, based on the expression levels of five microRNAs. Then, we tested the
predictor in a group of 60 randomly chosen women. Lastly, a proteomic study unveiled
the overexpression and downregulation of proteins differently expressed in the serum of
breast cancer patients vs. that of control individuals. Twenty-six microRNAs differentiate
cancer tissue from healthy tissue, and 16 microRNAs differentiate the serum of cancer
patients from that of the control group. The tissue expression of miR-99a, miR-497,
miR-362, and miR-1274, and the serum levels of miR-141 correlated with patient survival.
Moreover, the predictor consisting of miR-125b, miR-29c, miR-16, miR-1260, and
miR-451 was able to differentiate breast cancer patients from controls. The predictor was
validated in 20 new cases of breast cancer patients and tested in 60 volunteer women,
assigning 11 out of 60 women to the cancer group. An association of low levels of miR-16
with a high content of CD44 protein in serum was found. Circulating microRNAs in serum
can represent biomarkers for cancer prediction. Their clinical relevance and the potential
use of the predictor here described are discussed
Five microRNAs in Serum Are Able to Differentiate Breast Cancer Patients From Healthy Individuals
Breast cancer is the cancer with the most incidence and mortality in women. microRNAs
are emerging as novel prognosis/diagnostic tools. Our aim was to identify a serum
microRNA signature useful to predict cancer development. We focused on studying
the expression levels of 30 microRNAs in the serum of 96 breast cancer patients vs.
92 control individuals. Bioinformatic studies provide a microRNA signature, designated
as a predictor, based on the expression levels of five microRNAs. Then, we tested the
predictor in a group of 60 randomly chosen women. Lastly, a proteomic study unveiled
the overexpression and downregulation of proteins differently expressed in the serum of
breast cancer patients vs. that of control individuals. Twenty-six microRNAs differentiate
cancer tissue from healthy tissue, and 16 microRNAs differentiate the serum of cancer
patients from that of the control group. The tissue expression of miR-99a, miR-497,
miR-362, and miR-1274, and the serum levels of miR-141 correlated with patient survival.
Moreover, the predictor consisting of miR-125b, miR-29c, miR-16, miR-1260, and
miR-451 was able to differentiate breast cancer patients from controls. The predictor was
validated in 20 new cases of breast cancer patients and tested in 60 volunteer women,
assigning 11 out of 60 women to the cancer group. An association of low levels of miR-16
with a high content of CD44 protein in serum was found. Circulating microRNAs in serum
can represent biomarkers for cancer prediction. Their clinical relevance and the potential
use of the predictor here described are discussed