GenHITS: A network science approach to driver gene detection in human regulatory network using gene's influence evaluation.

Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs). In this regard, several methods have been previously developed for identifying cancer driver genes. Most of these methods are computational-based, which use the concept of mutation to predict CDGs. In this research, a method has been proposed for identifying CDGs in the transcription regulatory network using the concept of influence diffusion and by modifying the Hyperlink-Induced Topic Search algorithm based on the diffusion concept. Due to the type of these networks and the processes of abnormality progression in cells and the formation of cancerous tumors, high-influence genes can be the most likely considered as the driver genes. Therefore, we can use the influence diffusion concept as an acceptable theory to identify these genes. Recently, a method has been proposed to detect CDGs with the concept of the influence maximization. One of the challenges in these types of networks is finding the power of regulatory interaction between genes. Moreover, we have proposed a novel method to calculate the weight of regulatory interactions, based on the concept of diffusion. The performance of the proposed method was compared with other seventeen computational and network tools. Correspondingly, three cancer types were used as benchmarks as follows: breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC). In addition, to determine the accuracy of the detected drivers using each method, CGC (Cancer Gene Census) and Mut-driver gene lists were utilized as gold standard. The results show that GenHITS performs better compared to the most of the other computational and network methods. Besides, it is also able to identify genes that have been identified by none of the other methods yet.

Forecasting the Amount of Blood Ordered in the Obstetrics and Gynaecology Ward with the Data Mining Approach.

Preoperative blood ordering is frequently used in the obstetrics and gynecology ward of university hospitals in Iran, even for surgeries that rarely require blood transfusions. This routine procedure is an inefficient use of resources and rising costs, wasting time and cause shortage for essential patients. So this study was carried out to propose a new optimal system based on data mining techniques for ordering blood. This cross-sectional study examined the number of units cross-matched and transfused during surgery in the obstetrics and gynecology ward from 2013 to 2015. Data was collected for 1097 patients. Statistical analyzing was applied on data to prove that; the current blood ordering was not optimal. So with use of blood indices, C/T ratio, the new blood ordering variable was introduced. Then decision tree was applied on data with use of Rapid miner. Decision tree evaluation measures were rMSE and accuracy. A total of 1097 patients were examined for which 9747 units of blood were ordered. There was a significant difference between the number of cross-matched and transfused units according to all variables. The new method reduced the cross-matched units about 71.50%. The accuracy of proposed decision tree based on new blood ordering variable (according to C/T index) was 96.10%. The effective variables of blood ordered were type of surgery, blood group and amount of hemoglobin. The recent blood ordering variable prevent blood shortages, reduce costs. Excessive blood ordering is common in the obstetrics and gynecology department. According to proper results of new ordering variable, we suggest to apply this procedure in all hospitals in order to reduce extra costs and the optimal management of blood ordering.

Relationship between serum anti-heat shock protein 27 antibody levels and obesity.

BACKGROUND: Heat shock protein 27 (HSP27) is an intracellular molecular chaperone that is expressed at high levels following the exposure of cells to environmental stressors such as heat, toxins, and free radicals. High levels of HSP antigens and antibody titers have been reported in several conditions including cardiovascular disease and cancers. We measured serum anti-HSP27 antibody levels in 993 subjects and assessed the associations between serum anti-HSP27 antibody levels and demographic characteristics including coronary risk factors. METHODS: A total of 993 subjects were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) cohort study. Demographic, clinical, and biochemical parameters and serum anti-HSP27 antibody titers were determined in all the subjects. RESULTS: Serum anti-HSP27 antibody levels increased with increasing age in men. No significant differences in levels were detected between men and women. Serum anti-HSP27 antibody levels were significantly higher in obese subjects than in nonobese subjects (P=0.046); however, no significant influence of smoking status was observed. Moreover, serum anti-HSP27 antibody titers were positively associated with age, body mass index, waist/hip ratio, the presence of diabetes mellitus, nonsmoking habit, serum triglycerides, cholesterol, and high-sensitivity c-reactive protein. CONCLUSION: We have found that serum anti-HSP27 antibody titers are related to several cardiovascular risk factors, necessitating further studies on the value of this emerging marker for risk stratification.

Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand Gene Polymorphisms and Hepatitis B Virus Infection.

BACKGROUND: Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is an apoptotic molecule with a key role in the apoptosis of tumors and virus-infected cells. The association of 1525G/A and 1595C/T polymorphisms in the region of 3' UTR on the TRAIL gene has been shown in many cancers and diseases. Polymorphism at the positions of 1525G/A and 1595C/T might influence the clearance of hepatitis B virus (HBV). OBJECTIVES: This study was carried out to determine the role of the TRAIL gene polymorphisms in clinical outcome of HBV infection. PATIENTS AND METHODS: Polymerase chain reaction-based restriction fragment length polymorphism (PCR-RFLP) was applied to genotype TRAIL polymorphisms at positions 1525G/A and 1595C/T. To evaluate the TRAIL gene polymorphism in the 3' UTR region at position 1525G/A and 1595C/T, 147 patients with HBV infection were divided into three different groups of chronic hepatitis (n = 52), cirrhosis (n = 33), and carrier (n = 62) and there was a group of 101 healthy controls. RESULTS: Our data showed that genotypes 1525G/A and 1595C/T were in complete linkage disequilibrium and the genotype frequencies at the two positions were the same. No significant differences in frequencies of genotype and alleles at positions 1525G/A and 1595C/T were observed between all the three groups (P value > 0.05). CONCLUSIONS: According to our result, 1525G/A and 1595C/T were in strong linkage disequilibrium and the polymorphisms of the TRAIL gene in the 3' UTR region were not associated with the outcome of HBV infection.