Association between MDR1 C3435T polymorphism and colorectal cancer risk: A meta-analysis

High expression levels and the C3435T SNP of the ABCB1 gene are associated with lower survival in adult patients with acute myeloblastic leukemia in Mexico City

BACKGROUND

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy characterized by different genetic alterations that cause changes in the normal mechanisms of differentiation, which are associated with chemoresistance. The ABCB1 gene is part of a family of ATP-binding cassette (ABC) transporter genes involved in the progression of various types of cancer. The following work aimed to evaluate the expression levels of the ABCB1 gene and the C3435T SNP with the response to first-line treatment and survival in patients with AML.

METHODS

In total 135 samples were taken to isolate total RNA and DNA at the beginning of the treatment. Expression analysis by RT-qPCR and SNP C3435T assessment method were performed for real-time Polymerase chain reaction (qPCR).

RESULTS

The expression levels impact on the survival of patients with AML compared to low or absent levels; the CC genotype was found in 22.9%, the CT genotype was found in 47.4%, and the TT genotype was found in 29.6%, the presence of the C3435T SNP, the TT genotype also impacts with a lower survival compared to CT and CC genotypes. In addition, it was shown that the dominant model significantly impacts survival.

CONCLUSION

In conclusion, we have found that the overexpression of the ABCB1 gene, as well as the presence of the TT genotype of the C3435T SNP, contributes to a worse prognosis in AML.

Effect of ABC transporter expression and mutational status on survival rates of cancer patients

ATP-binding cassette (ABC) transporters mediate multidrug resistance in cancer. In contrast to DNA single nucleotide polymorphisms in normal tissues, the role of mutations in tumors is unknown. Furthermore, the significance of their expression for prediction of chemoresistance and survival prognosis is still under debate. We investigated 18 tumors by RNA-sequencing. The mutation rate varied from 27,507 to 300885. In ABCB1, three hotspots with novel mutations were in transmembrane domains 3, 8, and 9. We also mined the cBioPortal database with 11,814 patients from 23 different tumor entities. We performed Kaplan-Meier survival analyses to investigate the effect of ABC transporter expression on survival rates of cancer patients. Novel mutations were also found in ABCA2, ABCA3, ABCB2, ABCB5, ABCC1-6, and ABCG2. Mining the cBioPortal database with 11,814 patients from 23 different tumor entities validated our results. Missense and in-frame mutations led to altered binding of anticancer drugs in molecular docking approaches. The ABCB1 nonsense mutation Q856* led to a truncated P-glycoprotein, which may sensitize tumors to anticancer drugs. The search for ABC transporter nonsense mutations represents a novel approach for precision medicine.. Low ABCB1 mRNA expression correlated with significantly longer survival in ovarian or kidney cancer and thymoma. In cancers of breast, kidney or lung, ABC transporter expression correlated with different tumor stages and human populations as further parameters to refine strategies for more individualized chemotherapy.

Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).