Association of microRNA-652 Expression with Radiation Response of Colorectal Cancer: A Study from Rectal Cancer Patients in a Swedish Trial of Preoperative Radiotherapy to Public Data Analysis and in Vitro Investigation

Abstract

Abstract Purpose: Radiotherapy (RT) is a standard adjuvant therapy in progressive rectal cancer patients , but many patients are resistant to RT, leading to poor prognosis. Our study identified microRNA-652 (miR-652) value on RT response and outcome in rectal cancer patients. Methods: miR-652 expression was determined by RT-PCR in primary rectal cancer from 48 patients with and 53 patients without RT. The relationship of miR-652 with biological factors and prognosis were examined. The biological function of miR-652 was identified through TCGA and GEPIA database search. Two human colon cancer cell lines (HCT116 p53+/+ and p53-/-) were used for in vitro study. Results: miR-652 expression was augmented significantly in cancer than normal mucosa in non-RT patients ( P =0.044). High miR-652 expression in non-RT patients was related to more apoptosis ( P =0.036), ATM ( P =0.010) and DNp73 expression ( P =0.009). High miR-652 expression was related to worse disease-free survival of non-RT patients, independent of gender, age, tumor stage and differentiation ( P =0.028; HR=7.398, 95% CI 0.217-3.786). The biological functional analysis further identified the prognostic value and potential relationship of miR-652 with the apoptosis in rectal cancer. In RT patients, miR-652 expression was notably decreased in cancers when compared to non-RT cases ( P =0.047), and miR-652 expression in cancers was negatively related to WRAP53 expression ( P =0.022). After miR-652 inhibition, the estimation of reactive oxygen species, caspase activity and apoptosis in HCT116 p53+/+ cells were significantly increased compared with HCT116 p53-/- cells after radiation. Conclusions Our findings suggest the potential value of miR-652 expression as a marker for the prediction of radiation response and clinical outcome in rectal cancer patients.

Type
Xueli Zhang
Xueli Zhang
PhD of Bioinformatics, Assistant Professor

My research interest is to explore the comorbidity relationship of diseases based on complex networks and to find new combination markers, and has constructed multiple biomarker databases and prediction models.