Evaluation of E-cadherin (CDH1) gene polymorphism related to gastric cancer in Kurdish population

Abstract: Helicobacter pylori (H.pylori) infection induces inflammation in gastric mucosa that may progress to gastric cancer that causes of much mortality. This cancer is a multistage process involved changes in environmental, genetic and epigenetic factors. Polymorphism in promoter of CDH1 gene is associated with reduced E-cadherin protein expression. Gastric cancer is associated with multiple changes nucleotides in CDH1 gene. Aimed: We were evaluating -160 (C>A) CDH1 gene polymorphism associations with gastric cancer in Kurdish population. Methods: A total of 306 biopsies taken from corpus of 144 gastric cancer patients and 162 nonulcer dyspepsia patients were classified as H.pylori-infected and H.pylori-uninfected. All diagnoses confirmed pathologically and molecularly. Polymorphism in -160(C>A) CDH1 was evaluated by PCR-RFLP. Results: Polymorphism of -160 (C>A) CDH1 in H.pylori-uninfected and H.pylori-infected groups were not associated with gastric cancer (p > 0.05). Also there was not relationship between -160(C>A) CDH1 genotypes and H.pylori infection susceptibility (p > 0.05). We found significant relationship between CC genotype and survival time among gastric cancer patients (p = 0.01). Conclusion: -160(C>A) CDH1 polymorphism may regardless of presence or absence of H.pylori, don’t influences gastric cancer sensibility in Kurdish population. In other hand CC genotype, as a good trait, increases period of life for Kurdish cancer patients

Synthesized Anti-HER2 Trastuzumab-MCC-DM1 Conjugate: An Evaluation of Efficacy and Cytotoxicity

Background: Trastuzumab is a humanized monoclonal antibody that targets site-specifically human epidermal growth factor-2 receptor (HER2) cell surface antigen overexpressed in approximately 20% of human breast carcinomas. Despite its positive therapeutic outcomes, a large proportion of individuals are unresponsive to the treatment with trastuzumab or develop resistance to it.Objective: To evaluate a chemically synthesized trastuzumab-based antibody-drug conjugate (ADC) to improve the trastuzumab therapeutic index.Methods: The current study explored the physiochemical characteristics of the trastuzumab conjugated to a cytotoxic chemotherapy agent DM1 via Succinimidyl 4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC) linker, created in our earlier study, using SDS-PAGE, UV/VIS, and RP-HPLC analyses. The antitumor effects of the ADCs were analyzed using MDA-MB-231 (HER2-negative) and SK-BR-3 (HER2-positive) cell lines utilizing in vitro cytotoxicity, viability, and binding assays. Three different formats of a HER2-targeting agent: trastuzumab, synthesized trastuzumab-MCC-DM1, and commercially available drug T-DM1 (Kadcyla®) were compared.Results: UV-VIS spectroscopic analysis showed that the trastuzumab-MCC-DM1 conjugates, on average, entailed 2.9 DM1 payloads per trastuzumab. A free drug level of 2.5% was determined by RP-HPLC. The conjugate appeared as two bands on a reducing SDS-PAGE gel. MTT viability assay showed that conjugating trastuzumab with DM1 significantly improved the antiproliferative effects of this antibody in vitro. Importantly, the evaluations using LDH release and cell apoptosis assays confirmed that trastuzumab maintains its ability to induce cell death response while conjugating with the DM1. The binding efficiency of trastuzumab-MCC-DM1 was comparable to that of the naked trastuzumab.Conclusion: Trastuzumab-MCC-DM1 was found effective against HER2+ tumors. The potency of this synthesized conjugate brings it closer to the commercially available T-DM1

Tumor necrosis factor‑α in systemic lupus erythematosus: Structure, function and therapeutic implications (Review)

: Tumor necrosis factor‑α (TNF‑α) is a pleiotropic pro‑inflammatory cytokine that contributes to the pathophysiology of several autoimmune diseases, such as multiple sclerosis, inflammatory bowel disease, rheumatoid arthritis, psoriatic arthritis and systemic lupus erythematosus (SLE). The specific role of TNF‑α in autoimmunity is not yet fully understood however, partially, in a complex disease such as SLE. Through the engagement of the TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2), both the two variants, soluble and transmembrane TNF‑α, can exert multiple biological effects according to different settings. They can either function as immune regulators, impacting B‑, T‑ and dendritic cell activity, modulating the autoimmune response, or as pro‑inflammatory mediators, regulating the induction and maintenance of inflammatory processes in SLE. The present study reviews the dual role of TNF‑α, focusing on the different effects that TNF‑α may have on the pathogenesis of SLE. In addition, the efficacy and safety of anti‑TNF‑α therapies in preclinical and clinical trials SLE are discussed

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Analytical solution of nonlinear rolling motion of ship using the method of multiple scales

In this research, the nonlinear rolling motion of ships is studied. After obtaining the governing equation of roll motion, the method of multiple scales perturbation technique is applied to solve the nonlinear differential equation. The ship response is studied with and without harmonic excitation. In order to validate the responses obtained by the method of multiple scales, the response was compared with the numerical solution. Finally, the effects of damping coefficient and restoring arm on the frequency response function and resonance frequency have been studied

Exploiting systems biology to investigate the gene modules and drugs in ovarian cancer: A hypothesis based on the weighted gene co-expression network analysis

Background Ovarian cancer (OC) is one of the worrisome gynecological cancers worldwide. Given its considerable mortality rate, it is necessary to investigate its oncogenesis. Methods In this study, we used systems biology approaches to describe the key gene modules, hub genes, and regulatory drugs associated with serous OC as the novel biomarkers using weighted gene co-expression network analysis (WGCNA). Findings Our findings have demonstrated that the blue module genes (r = 0.8, p-value = 1e-16) are involved in OC progression. Based on gene enrichment analysis, the genes in this module are frequently involved in biological processes such as the Cyclic adenosine monophosphate (cAMP) signaling pathway and the cellular response to transforming growth factor-beta stimulation. The co-expression network has been built using the correlated module's top hub genes, which are ADORA1, ANO9, CD24P4, CLDN3, CLDN7, ELF3, KLHL14, PRSS8, RASAL1, RIPK4, SERINC2, and WNT7A. Finally, a drug-target network has been built to show the interaction of the FDA-approved drugs with hub genes. Conclusions Our results have discovered that ADORA1, ANO9, SERINC2, and KLHL14 are hub genes associated with serous OC. These genes can be considered as novel candidate target genes for treating OC