The CHEK2 gene mutations I157T (c.470T>C) and IVS2+1G>A affecting the forkhead-associated domain (FHA) have been shown to increase the risk of breast cancer development in several populations. We analyzed the CHEK2 gene segment coding for FHA domain in 673 unselected breast cancer patients and 683 controls from the Czech Republic using the denaturant high-performance liquid chromatography. The found frequency of predominant FHA alteration I157T did not differ between breast cancer patients (19/673; 2.82%) and controls (17/683; 2.49%; P=0.71). Besides this mutation we characterized another nine alterations-six located within FHA coding sequence and three occurring in introns 1 or 2). Eight variants occurred once each in patients with breast cancer and two were present in controls. Three alterations found in breast cancer patients were novel missense variants (Y159H, T172A, and L174F) affecting highly conservative residues in FHA domain. Despite the lack of association of I157T mutation with breast cancer development in our population we deduced that the FHA domain is the subject of rare population-specific alterations that might modify risk of various cancers.
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http://dx.doi.org/10.1007/s10549-007-9838-7 | DOI Listing |
Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
This study investigates the potential treatment of breast cancer utilizing Gentiana robusta King ex Hook. f. (QJ) through an integrated approach involving network pharmacology, molecular docking, and molecular dynamics simulation.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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