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Plakoglobin is a tumor suppressor gene in lung cancer; however, the mechanism by which it is downregulated in lung cancer is largely unknown. The aim of the present study was to investigate whether histone deacetylases (HDACs) regulate plakoglobin expression in lung cancer. The effects of overexpression or knockdown of HDAC7 on plakoglobin were determined using stably transfected lung cancer cell lines. Chromatin immunoprecipitation assays were performed to elucidate the mechanisms underlying the HDAC7-induced suppression of plakoglobin. A Cell Counting Kit-8 and Transwell assays were performed, and a nude mouse in vivo model was established to investigate the role of the HDAC7/plakoglobin pathway in cell migration, invasion and metastasis. Ectopic expression of HDAC7 was identified to suppress mRNA and protein levels of plakoglobin in lung cancer cells, whereas silencing HDAC7 with short hairpin RNA increased the expression of plakoglobin. HDAC7 was proposed to suppressed plakoglobin by directly binding to its promoter. Overexpression or knockdown of HDAC7 promoted or inhibited cell proliferation, migration and invasion, respectively. Furthermore, knockdown of HDAC7 significantly suppressed tumor growth and metastasis in vivo. In addition, overexpression of plakoglobin significantly reduced the enhanced cell proliferation, migration and invasion induced by ectopic HDAC7. In conclusion, suppression of plakoglobin by HDAC7 promoted the proliferation, migration, invasion and metastasis in lung cancer. This novel axis of HDAC7/plakoglobin may be valuable in the development of novel therapeutic strategies for treating patients with lung cancer.

The effects of the LDH-A depletion via shRNA knockdown on three murine glioma cell lines and corresponding intracranial (i.c.) tumors were studied and compared to pharmacologic (GNE-R-140) inhibition of the LDH enzyme complex, and to shRNA scrambled control (NC) cell lines. The effects of genetic-shRNA LDH-A knockdown and LDH drug-targeted inhibition (GNE-R-140) on tumor-cell metabolism, tumor growth, and animal survival were similar. LDH-A KD and GNE-R-140 unexpectedly increased the aggressiveness of GL261 intracranial gliomas, but not CT2A and ALTS1C1 i.c. gliomas. Furthermore, the bioenergetic profiles (ECAR and OCR) of GL261 NC and LDH-A KD cells under different nutrient limitations showed that (a) exogenous pyruvate is not a major carbon source for metabolism through the TCA cycle of native GL261 cells; and (b) the unique upregulation of LDH-B that occurs in GL261 LDH-A KD cells results in these cells being better able to: (i) metabolize lactate as a primary carbon source through the TCA cycle, (ii) be a net consumer of lactate, and (iii) showed a significant increase in the proliferation rate following the addition of 10 mM lactate to the glucose-free media (only seen in GL261 KD cells). Our study suggests that inhibition of LDH-A/glycolysis may not be a general strategy to inhibit the i.c. growth of all gliomas, since the level of LDH-A expression and its interplay with LDH-B can lead to complex metabolic interactions between tumor cells and their environment. Metabolic-inhibition treatment strategies need to be carefully assessed, since the inhibition of glycolysis (e.g., inhibition of LDH-A) may lead to the unexpected development and activation of alternative metabolic pathways (e.g., upregulation of lipid metabolism and fatty-acid oxidation pathways), resulting in enhanced tumor-cell survival in a nutrient-limited environment and leading to increased tumor aggressiveness.

Background/Aim: Mathematical models have long been considered as important tools in cancer biology and therapy. Herein, we present an advanced non-linear mathematical model that can predict accurately the effect of an anticancer agent on the growth of a solid tumor. Materials and Methods: Advanced non-linear mathematical optimization techniques and human-to-mouse experimental data were used to develop a tumor growth inhibition (TGI) estimation model. Results: Using this mathematical model, we could accurately predict the tumor mass in a human-to-mouse pancreatic ductal adenocarcinoma (PDAC) xenograft under gemcitabine treatment up to five time periods (points) ahead of the last treatment. Conclusion: The ability of the identified TGI dynamic model to perform satisfactory short-term predictions of the tumor growth for up to five time periods ahead was investigated, evaluated and validated for the first time. Such a prediction model could not only assist the pre-clinical testing of putative anticancer agents, but also the early modification of a chemotherapy schedule towards increased efficacy.

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Heparanase (Hpa1) is expressed by tumor cells and cells of the tumor microenvironment and functions to remodel the extracellular matrix (ECM) and regulate the bioavailability of ECM-bound factors that support tumor growth. Heparanase expression is upregulated in human carcinomas, sarcomas, and hematological malignancies, correlating with increased tumor metastasis, vascular density, and shorter postoperative survival of cancer patients, and encouraging the development of heparanase inhibitors as anti-cancer drugs. Among these are heparin/HS mimetics, the only heparanase-inhibiting compounds that are being evaluated in clinical trials. We have synthesized dicarboxylated oxy-heparins (DCoxHs) containing three carboxylate groups per split residue (DC-Hep). The resulting lead compound (termed XII) was upscaled, characterized, and examined for its effectiveness in tumor models. Potent anti-tumorigenic effects were obtained in models of pancreatic carcinoma, breast cancer, mesothelioma, and myeloma, yielding tumor growth inhibition (TGI) values ranging from 21 to 70% and extending the survival time of the mice. Of particular significance was the inhibition of spontaneous metastasis in an orthotopic model of breast carcinoma following resection of the primary tumor. It appears that apart from inhibition of heparanase enzymatic activity, compound XII reduces the levels of heparanase protein and inhibits its cellular uptake and activation. Heparanase-dependent and -independent effects of XII are being investigated. Collectively, our pre-clinical studies with compound XII strongly justify its examination in cancer patients.

The current paper concerns to develop a new numerical formulation to simulate the tumor growth. The used numerical method is based on the meshless Galerkin technique in which the test and trial functions have been selected from the shape functions of moving Taylor approximation. The main mathematical model to describe the tumor growth is defined as a nonlinear system of equations. Thus, to get acceptable results from the Galerkin weak form, a two-grid algorithm is employed. The first step of the two-grid algorithm computes the corresponding approximated scheme in a coarse mesh by solving a nonlinear algebraic system of equations. Then, the obtained solution in the previous step has been used to solve the corresponding approximated scheme in a fine mesh, such that in the second step, a linear algebraic system of equations is solved. On the other hand, to access more accurate results, the number of nodes in the computational domain must be increased which causes the matrix to become larger. Therefore, the proper orthogonal decomposition is used to reduce size of the algebraic system of equations. Finally, some test problems are tested to confirm the efficiency and accuracy of the proposed numerical formulation.

Ovarian cancer cell dissemination can lead to the mortality of patients with advanced ovarian cancer. Complete surgery for no gross residual disease contributes to a more favorable prognosis than that of patients with residual disease. HCFs have highly regular porous structures and their 3D porous structures act as scaffolds for cell adhesion. HCFs are fabricated from biodegradable polymers and have been widely used in tissue engineering. This study aimed to show that HCFs suppress tumor growth in an in vivo ovarian cancer model. The HCF pore sizes had a significant influence on tumor growth inhibition, and HCFs induced morphological changes that rounded out ovarian cancer cells. Furthermore, we identified gene ontology (GO) terms and clusters of genes downregulated by HCFs. qPCR analysis demonstrated that a honeycomb structure downregulated the expression of CXCL2, FOXC1, MMP14, and SNAI2, which are involved in cell proliferation, migration, invasion, angiogenesis, focal adhesion, extracellular matrix (ECM), and epithelial–mesenchymal transition (EMT). Collectively, HCFs induced abnormal focal adhesion and cell morphological changes, subsequently inhibiting the differentiation, proliferation and motility of ovarian cancer cells. Our data suggest that HCFs could be a novel device for inhibiting residual tumor growth after surgery, and could reduce surgical invasiveness and improve the prognosis for patients with advanced ovarian cancer.

Assessing the efficacy of anticancer agents in animal models remains a necessary step in the development of new treatment options and plays an important role in their optimization and comparison. Often, however, interpretation of the results is flawed by excessive trust in scores traditionally handed down, but whose origin and limitations have been lost. Here I examine the theories and assumptions underlying the most common rating scales, suggesting improvements to the old scores and proposing the adoption of multi-parameter analysis and interpretation of the results, considering different time-windows. I examined case examples of different scenarios of antiproliferative effects induced by treatment, demonstrating that common scores fail to distinguish between completely different responses to treatment or, in other circumstances, indicate a different outcome when the response is the same. I found that a combination of parameters, including the percent tumor growth between the start and end of treatment, the relative tumor burden at nadir and the absolute growth delay, may distinguish among the different cases and support a correct interpretation of the antitumor response. All these parameters can be derived from individual tumor growth curves in a simple way, without any change to common experimental procedures.

A central aspect of in vivo experiments with anticancer therapies is the comparison of the effect of different therapies, or doses of the same therapeutic agent, on tumor growth. One of the most popular clinical endpoints is tumor growth delay, which measures the effect of treatment on the time required for tumor volume to reach a specific value. This effect has been analyzed through a variety of statistical methods: conventional descriptive analysis, linear regression, Cox regression, etc. We propose a new approach based on stochastic modeling of tumor growth and the study of first-passage time variables. This approach allows us to prove that the time required for tumor volume to reach a specific value must be determined empirically as the average of the times required for the volume of individual tumors to reach said value instead of the time required for the average volume of the tumors to reach the value of interest. In addition, we define several measures in random environments to compare the time required for the tumor volume to multiply k times its initial volume in control, as well as treated groups, and the usefulness of these measures is illustrated by means of an application to real data.

To identify a novel and effective therapy for allergic rhinitis and asthma (ARA), the present study focused on treatment with tumor growth factor (TGF)-β1 neutralizing antibody. In the present study, four medications were administered to mice with ovalbumin-induced allergic inflammation. Allergic symptoms in the lungs and nasal mucosa were evaluated by detecting the secretion of cytokines from helper T cells (Th) in the peripheral blood, nasal lavage fluid and bronchoalveolar lavage fluid using ELISA. Defects in regulatory T (Treg) cells in peripheral blood mononuclear cells were also detected using flow cytometry. Furthermore, the expression of TGF-β1 and activation of Smad2/3 pathways were assessed using immunohistochemical staining, reverse transcription-quantitative polymerase chain reaction, and western blotting. It was observed that TGF-β1 neutralizing antibody inhibited symptoms of inflammation in the upper and lower airways. TGF-β1 neutralizing antibody also restored the Th1/Th2 balance and ameliorated Treg cell defects induced by ARA. Furthermore, the therapeutic effects of TGF-β1 neutralizing antibody were related to its inhibitory effects on TGF-β1 expression and Smad2/3 signaling in nasal and lung tissues. Therefore, TGF-β1 neutralizing antibody may be an effective medicine for the treatment of ARA.