Association of a genetic variant in ATP-binding cassette sub-family B member 1 gene with poor prognosis in patients with squamous cell carcinoma of the esophagus

Advances in the structure, mechanism and targeting of chemoresistance-linked ABC transporters

Cancer cells frequently display intrinsic or acquired resistance to chemically diverse anticancer drugs, limiting therapeutic success. Among the main mechanisms of this multidrug resistance is the overexpression of ATP-binding cassette (ABC) transporters that mediate drug efflux, and, specifically, ABCB1, ABCG2 and ABCC1 are known to cause cancer chemoresistance. High-resolution structures, biophysical and in silico studies have led to tremendous progress in understanding the mechanism of drug transport by these ABC transporters, and several promising therapies, including irradiation-based immune and thermal therapies, and nanomedicine have been used to overcome ABC transporter-mediated cancer chemoresistance. In this Review, we highlight the progress achieved in the past 5 years on the three transporters, ABCB1, ABCG2 and ABCC1, that are known to be of clinical importance. We address the molecular basis of their broad substrate specificity gleaned from structural information and discuss novel approaches to block the function of ABC transporters. Furthermore, genetic modification of ABC transporters by CRISPR-Cas9 and approaches to re-engineer amino acid sequences to change the direction of transport from efflux to import are briefly discussed. We suggest that current information regarding the structure, mechanism and regulation of ABC transporters should be used in clinical trials to improve the efficiency of chemotherapeutics for patients with cancer.

SNMRS: An advanced measure for Co-expression network analysis

The challenge of identifying modules in a gene interaction network is important for a better understanding of the overall network architecture. In this work, we develop a novel similarity measure called Scaling-and-Shifting Normalized Mean Residue Similarity (SNMRS), based on the existing NMRS technique [1]. SNMRS yields correlation values in the range of 0 to +1 corresponding to negative and positive dependency. To study the performance of our measure, internal validation of extracted clusters resulting from different methods is carried out. Based on the performance, we choose hierarchical clustering and apply the same using the corresponding dissimilarity (distance) values of SNMRS scores, and utilize a dynamic tree cut method for extracting dense modules. The modules are validated using a literature search, KEGG pathway analysis, and gene-ontology analyses on the genes that make up the modules. Moreover, our measure can handle absolute, shifting, scaling, and shifting-and-scaling correlations and provides better performance than several other measures in terms of cluster-validity indices. Also, SNMRS based module detection method results in interesting biologically relevant patterns from gene microarray and RNA-seq dataset. A set of crucial genes having high relevance with the ESCC are also identified.

Detection of Tri-allelic Single Nucleotide Polymorphisms of ABCB1 and CRP Genes by Penta-Primer Amplification Refractory Mutation System-Polymerase Chain Reaction

Objective: The standard methods for tri-allelic single nucleotide polymorphism (SNP) genotyping require special equipment and are costly to perform. The aim of this study was to establish a fast, simple, and low-cost method to differentiate among tri-allelic SNPs in general laboratories. Methods: Based on the tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS-PCR) typing of bi-allelic SNPs, we developed a penta-primer amplification refractory mutation system-polymerase chain reaction (P-ARMS-PCR) method characterize tri-allelic SNPs. The two most studied tri-allelic SNPs, rs2032582 and rs3091244, were genotyped using P-ARMS-PCR in 110 volunteers, and the results were verified by direct DNA sequencing. Results: For rs2032582, there were 20 samples (18.18%) with the GG genotype, 3 (2.73%) with the AA genotype, 24 (21.82%) with the TT genotype, 43 (39.09%) with the GT genotype, 11 (10.00%) with the AG genotype, and 9 (8.18%) with the AT genotype. For rs3091244, there were 67 samples (60.91%) with the CC genotype, 1 (0.91%) with the AA genotype, 8 (7.27%) with the CT genotype, 31 (28.18%) with the CA genotype, and 3 (2.73%) with the AT genotype. The genotypic distributions of rs2032582 (p = 0.482) and rs3091244 (p = 0.492) were in Hardy-Weinberg equilibrium. The DNA sequencing results were entirely consistent with the results of P-ARMS-PCR. Conclusion: P-ARMS-PCR is an accurate, rapid, simple, and low-cost characterization method for tri-allelic SNP genotyping of rs2032582 and rs3091244.

Single nucleotide polymorphisms as the efficient prognostic markers in breast cancer

BACKGROUND

Breast cancer (BC) is known as the most common malignancy in women. Environmental and genetic factors are associated with BC progression. Genetic polymorphisms have been reported as important risk factors of BC prognosis and drug response. Main body: Therefore, in the present review, we have summarized all single nucleotide polymorphisms (SNPs) which have been significantly associated with drug response in BC patients around the world. We have also categorized the reported SNPs based on their related genes functions to clarify the molecular biology of drug responses in BC.

CONCLUSION

The majority of SNPs were reported in detoxifying enzymes, which introduced such genes as the main genetic risk factors during BC drug responses. This review paves the way for introducing a prognostic panel of SNPs for the BC patients in the world.

The association between genetic variants in the genes for cytochrome P450 B1 and ATP-binding cassette transporter genes and breast cancer risk

Breast cancer is among the most common malignancies in women. Recent studies have shown that polymorphisms in genes involved in the metabolism and transport of anticancer drugs are associated with outcomes of several malignancies, e.g., breast cancer. In this study we evaluate whether CYP1B1/rs1056836 and ABCB1/rs2032582 gene variants are associated with breast cancer. Eighty eight cases and 200 controls, were genotyped for polymorphisms of the CYP1B1 and ABCB1 genes using Taqman®-based methods. Logistic regression was also used to test the associations between breast cancer risk and the various genotypes involved. The GG genotype of rs2032582 locus had a frequency of 43.5% with 0.38 MAF; while the GT and TT genotypes in the control group were 40% and 16.5%, respectively. The GG, GT and TT genotype frequencies in the patients with breast cancer were 45.5%, 12.5% and 26.1%, respectively. An association was observed between the TT genotype of ABCB1/rs2032582 locus and a larger breast cancer tumor size (P < 0.05). However, neither the relationship between the CYP1B1 polymorphism and breast cancer type nor the risk of breast cancer were statistically significant. Our data suggest a potential association of the ABCB1 genetic variant with breast cancer tumor size, however further investigation in a larger population is necessary to show its value as a risk stratification biomarker.