Inferring cancer common and specific gene networks via multi-layer joint graphical model

Cancer is a complex disease caused primarily by genetic variants. Reconstructing gene networks within tumors is essential for understanding the functional regulatory mechanisms of carcinogenesis. Advances in high-throughput sequencing technologies have provided tremendous opportunities for inferring gene networks via computational approaches. However, due to the heterogeneity of the same cancer type and the similarities between different cancer types, it remains a challenge to systematically investigate the commonalities and specificities between gene networks of different cancer types, which is a crucial step towards precision cancer diagnosis and treatment. In this study, we propose a new sparse regularized multi-layer decomposition graphical model to jointly estimate the gene networks of multiple cancer types. Our model can handle various types of gene expression data and decomposes each cancer-type-specific network into three components, i.e., globally shared, partially shared and cancer-type-unique components. By identifying the globally and partially shared gene network components, our model can explore the heterogeneous similarities between different cancer types, and our identified cancer-type-unique components can help to reveal the regulatory mechanisms unique to each cancer type. Extensive experiments on synthetic data illustrate the effectiveness of our model in joint estimation of multiple gene networks. We also apply our model to two real data sets to infer the gene networks of multiple cancer subtypes or cell lines. By analyzing our estimated globally shared, partially shared, and cancer-type-unique components, we identified a number of important genes associated with common and specific regulatory mechanisms across different cancer types.

Impact of GSTT1 and GSTM1 Polymorphisms in the Susceptibility to Philadelphia Negative Chronic Myeloid Leukaemia

BACKGROUND

Our research aimed to clarify the role of genetic polymorphisms in GST (T1 and M1) in the development of Ph-ve CML.

MATERIALS AND METHODS

We report on a case-control study with 126 participants, divided into 26 patients with Ph-ve CML (57.7% male, 42.3% female) and 100 healthy volunteers (51% male, 49% female) with no medical history of cancer as a control population. All Ph-ve CML patients were diagnosed according to standard hematologic and cytogenetic criteria based on CBC, confirmed by Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) to determine the presence or absence of the BCRABL gene, followed by bone marrow (BM) examination.

RESULTS

Of the 26 studied cases, 50% had the GSTT1 null genotype against 21% of the control group, a statistically significant difference (CI= 1.519 - 9.317; p-value= 0.004). The GSTM1 null genotype was detected in 23.1% of cases and 35% of controls, a difference not statistically significant (OR= 0.557; CI= 0.205-1.515; p-value= 0.252). The distribution of GSTT1 and GSTM1 polymorphisms was also examined according to gender, age and ethnic grouping; these findings revealed no statistically significant differences.

CONCLUSION

Our study reveals a strong correlation between GSTT1 polymorphism and Ph-ve CML, whereas the data for GSTM1 polymorphisms indicates no role in the initial development of the disease. More studies are required to further clarify these and other genes' roles in disease development.

M2PP: a novel computational model for predicting drug-targeted pathogenic proteins

BACKGROUND

Detecting pathogenic proteins is the origin way to understand the mechanism and resist the invasion of diseases, making pathogenic protein prediction develop into an urgent problem to be solved. Prediction for genome-wide proteins may be not necessarily conducive to rapidly cure diseases as developing new drugs specifically for the predicted pathogenic protein always need major expenditures on time and cost. In order to facilitate disease treatment, computational method to predict pathogenic proteins which are targeted by existing drugs should be exploited.

RESULTS

In this study, we proposed a novel computational model to predict drug-targeted pathogenic proteins, named as M2PP. Three types of features were presented on our constructed heterogeneous network (including target proteins, diseases and drugs), which were based on the neighborhood similarity information, drug-inferred information and path information. Then, a random forest regression model was trained to score unconfirmed target-disease pairs. Five-fold cross-validation experiment was implemented to evaluate model's prediction performance, where M2PP achieved advantageous results compared with other state-of-the-art methods. In addition, M2PP accurately predicted high ranked pathogenic proteins for common diseases with public biomedical literature as supporting evidence, indicating its excellent ability.

CONCLUSIONS

M2PP is an effective and accurate model to predict drug-targeted pathogenic proteins, which could provide convenience for the future biological researches.

GSTM1 and GSTP1 Genetic Polymorphisms and Their Associations With Acute Lymphoblastic Leukemia Susceptibility in a Jordanian Population

The genetic variations between different individuals in the xenobiotic detoxifying enzyme activity were shown to change susceptibility to acute lymphoblastic leukemia (ALL). The current study aimed to assess the association of GSTM1 and GSTP1 genetic polymorphisms with the susceptibility of ALL. This case-control study (N=264) involved 88 Jordanian ALL children and 176 healthy controls from an ethnically homogenous Jordanian children population. The polymerase chain reaction assay was used to genotype GSTM1 (null/present) and the polymerase chain reaction-restriction fragment length polymorphism technique was also applied to detect the genetic polymorphisms of GSTP1 (Ile105Val) at the rs1695 position. The biallelic analysis revealed that there was no association between GSTM1 double-null genotype and ALL (P=0.57). However, there was a strong association between GSTP1 (Ile105Val) polymorphism genotypes and alleles within GSTP1 gene and ALL (P=0.00049 and 0.000044, respectively). A combination between GSTM1 double-null genotype and rs1695 also showed an association with ALL (P=0.042). This study showed that the rs1695 single nucleotide polymorphism within the GSTP1 gene is strongly implicated in ALL among Jordanian children with ALL. These results indicate that genetic variants of GSTP1 gene influence the risk of developing ALL in the Jordanian children of Arab ancestry.

Biomarkers of susceptibility following benzene exposure: influence of genetic polymorphisms on benzene metabolism and health effects

Benzene is a ubiquitous occupational and environmental pollutant. Improved industrial hygiene allowed airborne concentrations close to the environmental context (1-1000 µg/m(3)). Conversely, new limits for benzene levels in urban air were set (5 µg/m(3)). The biomonitoring of exposure to such low benzene concentrations are performed measuring specific and sensitive biomarkers such as S-phenylmercapturic acid, trans, trans-muconic acid and urinary benzene: many studies referred high variability in the levels of these biomarkers, suggesting the involvement of polymorphic metabolic genes in the individual susceptibility to benzene toxicity. We reviewed the influence of metabolic polymorphisms on the biomarkers levels of benzene exposure and effect, in order to understand the real impact of benzene exposure on subjects with increased susceptibility.

Effect of interaction of glutathione S-transferases (T1 and M1) on the hematologic and cytogenetic responses in chronic myeloid leukemia patients treated with imatinib

The glutathione S-transferases (GSTs) are phase II xenobiotic metabolizing enzymes known to be involved in the detoxification of carcinogens and anticancer drugs. Individual genetic variation linked to inherited polymorphisms of GSTT1 and GSTM1 leading to a complete loss of enzyme activity could expose subjects to develop cancer or to induce drug resistance. Indeed, despite the impressive results obtained with the imatinib, some patients with chronic myeloid leukemia (CML) fail to achieve the expected results or develop resistance. The present study aimed to examine the impact of GSTT1 and GSTM1 polymorphisms on the response to imatinib in patients with CML. Multiplex polymerase chain reaction was used to detect the genotypes of GSTT1 and GSTM1 in 60 CML patients. We found that side effects were more frequent in patients carrying GSTT1 null when compared to GSTT1 present carriers (31 vs. 16.6 %; χ (2) = 6.2; p = 0.013). The loss of hematologic response was statistically greater in patients carrying the combined genotype GSTT1 present/GSTM1 present (26.3 %) when compared to GSTT1 null/GSTM1 present (12.8 %), GSTT1 present/GSTM1 null (8.3 %) and GSTT1 null/GSTM1 null (0 %), (χ (2) = 18.85; p < 0.001). The complete cytogenetic response was higher in patients harboring the GSTT1 null/GSTM1 null (75 %) compared with GSTT1 null/GSTM1 present (55.6 %), GSTT1 present/GSTM1 null (50 %) and GSTT1 present/GSTM1 present (47.8). On the other hand, the frequency of none cytogenetic responders was more common in patients carrying GSTT1 present/GSTM1 present (34.8 %) when compared to other genotype combinations (χ (2) = 20.99; p = 0.05). Moreover, the GSTT1 present/GSTM1 present appeared to be associated with a final dose of 600 or 800 mg of imatinib, but not significantly. Based on these findings, we find that the interaction between GSTT1 and GSTM1 seems to influence treatment outcome in patients with CML. Therefore, further investigations are required to confirm these results, for better genotype-phenotype correlation.