In silico analysis of the wild-type and mutant-type of BRCA2 gene.

BACKGROUND: The aim of this study was to conduct an in silico analysis of a novel compound heterozygous variant in breast cancer susceptibility gene 2 (BRCA2) to clarify its structure-function relationship and elucidate the molecular mechanisms underlying triple-negative breast cancer (TNBC). METHODS: A tumor biopsy sample was obtained from a 42-year-old Chinese woman during surgery, and a maxBRCA™ test was conducted using the patient's whole blood. We obtained an experimentally determined 3D structure (1mje.pdb) of the BRCA2 protein from the Protein Data Bank (PDB) as a relatively reliable reference. Subsequently, the wild-type and mutant structures were predicted using SWISS-MODEL and AlphaFold, and the accuracy of these predictions was assessed through the SAVES online server. Furthermore, we utilized a high ambiguity-driven protein-protein docking (HADDOCK) algorithm and protein-ligand interaction profiler (PLIP) to predict the pathogenicity of the mutations and elucidate pathogenic mechanisms that potentially underlies TNBC. RESULTS: Histological examination revealed that the tumor biopsy sample exhibited classical pathological characteristics of TNBC. Furthermore, the maxBRCA™ test revealed two compound heterozygous BRCA2 gene mutations (c.7670 C > T.pA2557V and c.8356G > A.pA2786T). Through performing in silico structural analyses and constructing of 3D models of the mutants, we established that the mutant amino acids valine and threonine were located in the helical domain and oligonucleotide binding 1 (OB1), regions that interact with DSS1. CONCLUSION: Our analysis revealed that substituting valine and threonine in the helical domain region alters the structure and function of BRCA2 proteins. This mutation potentially affects the binding of proteins and DNA fragments and disrupts interactions between the helical domain region and OB1 with DSS1, potentially leading to the development of TNBC. Our findings suggest that the identified compound heterozygous mutation contributes to the clinical presentation of TNBC, providing new insights into the pathogenesis of TNBC and the influence of compound heterozygous mutations in BRCA2.

Predictive Model of Oxaliplatin-induced Liver Injury Based on Artificial Neural Network and Logistic Regression.

Background and Aims: Identifying potential high-risk groups of oxaliplatin-induced liver injury (OILI) is valuable, but tools are lacking. So artificial neural network (ANN) and logistic regression (LR) models will be developed to predict the risk of OILI. Methods: The medical information of patients treated with oxaliplatin between May and November 2016 at 10 hospitals was collected prospectively. We used the updated Roussel Uclaf causality assessment method (RUCAM) to identify cases of OILI and summarized the patient and medication characteristics. Furthermore, the ANN and LR models for predicting the risk of OILI were developed and evaluated. Results: The incidence of OILI was 3.65%. The median RUCAM score with interquartile range was 6 (4, 9). The ANN model performed similarly to the LR model in sensitivity, specificity, and accuracy. In discrimination, the area under the curve of the ANN model was larger (0.920>0.833, p=0.019). In calibration, the ANN model was slightly improved. The important predictors of both models overlapped partially, including age, chemotherapy regimens and cycles, single and total dose of OXA, glucocorticoid drugs, and antihistamine drugs. Conclusions: When the discriminative and calibration ability was given priority, the ANN model outperformed the LR model in predicting the risk of OILI. Other chemotherapy drugs in oxaliplatin-based chemotherapy regimens could have different degrees of impact on OILI. We suspected that OILI may be idiosyncratic, and chemotherapy dose factors may be weakly correlated. Decision making on prophylactic medications needs to be carefully considered, and the actual preventive effect needed to be supported by more evidence.