New, fast and cheap prediction tests for BRCA1 gene mutations identification in clinical samples

Abstract

Despite significant progress in cancer therapy, cancer is still the second cause of mortality in the world. The necessity to make quick therapeutic decisions forces the development of procedures allowing to obtain a reliable result in a quick and unambiguous manner. Currently, detecting predictive mutations, including BRCA1, is the basis for effectively treating advanced breast cancer. Here, we present new insight on gene mutation detection. We propose a cheap BRCA1 mutation detection tests based on the surface plasmon resonance (SPR) or quartz crystal microbalance with energy dissipation (QCM-D) response changes recorded during a hybridization process of an oligonucleotide molecular probe with DNA fragments, with and without the BRCA1 mutation. The changes in the morphology of the formed DNA layer caused by the presence of the mutation were confirmed by atomic force microscopy. The unique property of the developed SPR and QCM tests is really short time of analysis: ca. 6 min for SPR and ca. 25 min for QCM. The proposed tests have been verified on 22 different DNA extracted from blood leukocytes collected from cancer patients: 17 samples from patients with various BRCA1 gene mutation variants including deletion, insertion and missense single-nucleotide and 5 samples from patients without any BRCA1 mutation. Our test is a response to the need of medical diagnostics for a quick, unambiguous test to identify mutations of the BRCA1 gene, including missense single-nucleotide (SNPs).

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

“…These results demonstrate the effectiveness of our computational method in unveiling important molecular mechanisms pertaining to breast cancer pathogenesis and metastasis. [46][47][48][49][50]. TFs such as ETS1 and STAT3 have been reported as potential therapeutic targets in breast cancer [51].…”

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

“…These results demonstrate the effectiveness of our computational method in unveiling important molecular mechanisms pertaining to breast cancer pathogenesis and metastasis. processes in breast cancer [38][39][40][41][42]. TFs such as ETS1 and STAT3 have been reported as potential therapeutic targets in breast cancer [43] .…”