Modulation of cyclins, p53 and mitogen-activated protein kinases signaling in breast cancer cell lines by 4-(3,4,5-trimethoxyphenoxy)benzoic acid

Effects of Carbon Nanomaterials and Aloe vera on Melanomas-Where Are We? Recent Updates

Melanoma is an aggressive skin cancer that affects approximately 140,000 people worldwide each year, with a high fatality rate. Available treatment modalities show limited efficacy in more severe cases. Hence, the search for new treatment modalities, including immunotherapies, for curing, mitigating, and/or preventing cancer is important and urgently needed. Carbon nanoparticles associated with some plant materials, such as Aloe vera, have shown appealing antineoplastic activity, derived mainly from the compounds aloin, aloe-emodin, barbaloin acemannan, and octapeptide, thus representing new possibilities as antitumor agents. This systematic review aims to arouse interest and present the possibilities of using Aloe vera combined with carbon-based nanomaterials as an antineoplastic agent in the treatment and prevention of melanoma. Limitations and advances in melanoma treatment using functionalized carbon nanomaterials are discussed here. Moreover, this review provides the basis for further studies designed to fully explore the potential of carbon nanomaterials associated with Aloe vera in the treatment of various cancers, with a focus on melanoma.

Identification of a novel anticancer mechanism of Paeoniae Radix extracts based on systematic transcriptome analysis

Paeoniae Radix (PR) has a great therapeutic value in many clinical applications; however, the presence of various bioactive compounds and its complicated effects on human health makes its precise mechanisms of action unclear. This study investigated the effects of PR at the molecular pathway level by profiling genome-wide gene expression changes following dose-dependent treatment of human lung cancer cells (A549) with PR water extract (WPR), PR ethanol extracts (EPR), as well as their individual components. We found that PR exerts anticancer effects in A549 cells by regulating numerous pathways. Specifically, EPR and two compounds, namely, hederagenin (HG) and oleanolic acid (OA), significantly downregulate the Aurora B pathway. Furthermore, we generated an integrated PR extracts-compounds-target genes network in the Aurora B pathway to understand their interactions. Our findings reinforce that inhibiting Aurora kinase activity is a therapeutic target for treating cancers, providing the potential for novel mechanisms of action for PR and its components against lung cancer.

Enhancing the Anticancer Activity of Antrodia cinnamomea in Hepatocellular Carcinoma Cells via Cocultivation With Ginger: The Impact on Cancer Cell Survival Pathways

Antrodia cinnamomea (AC) is a medicinal fungal species that has been widely used traditionally in Taiwan for the treatment of diverse health-related conditions including cancer. It possesses potent anti-inflammatory and antioxidant properties in addition to its ability to promote cancer cell death in several human tumors. Our aim was to improve the anticancer activity of AC in hepatocellular carcinoma (HCC) through its cocultivation with ginger aiming at tuning the active ingredients. HCC cell lines, Huh-7 and HepG2 were used to study the in vitro anticancer activity of the ethanolic extracts of AC (EAC) alone or after the cocultivation in presence of ginger (EACG). The results indicated that the cocultivation of AC with ginger significantly induced the production of important triterpenoids and EACG was significantly more potent than EAC in targeting HCC cell lines. EACG effectively inhibited cancer cells growth via the induction of cell cycle arrest at G2/M phase and induction of apoptosis in Huh-7 and HepG2 cells as indicated by MTT assay, cell cycle analysis, Annexin V assay, and the activation of caspase-3. In addition, EACG modulated cyclin proteins expression and mitogen-activated protein kinase (MAPK) signaling pathways in favor of the inhibition of cancer cell survival. Taken together, the current study highlights an evidence that EACG is superior to EAC in targeting cancer cell survival and inducing apoptotic cell death in HCC. These findings support that EACG formula can serve as a potential candidate for HCC adjuvant therapy.

The Effects of LW-AFC on the Hippocampal Transcriptome in Senescence-Accelerated Mouse Prone 8 Strain, a Mouse Model of Alzheimer's Disease

The senescence-accelerated mouse prone 8 (SAMP8) strain is considered a robust experimental model for developing preventative and therapeutic treatments for Alzheimer's disease (AD), a neurodegenerative disease which cannot be effectively prevented, halted, or cured. Our previous studies showed that LW-AFC, a new formula derived from the classical traditional Chinese medicinal prescription Liuwei Dihuang decoction, ameliorates cognitive deterioration in PrP-hAβPPswe/PS1ΔE9 transgenic mice and SAMP8 mice. This study aims to investigate the mechanism that mediates how LW-AFC improves cognitive deficit on the basis of the transcriptome. We conducted a genome-wide survey of gene expression in the hippocampus in mice from the senescence accelerated mouse resistant 1 (SAMR1) strain, from SAMP8 and from LW-AFC treated SAMP8. The results showed that LW-AFC reversed the transcriptome in the hippocampus of SAMP8 mice. The specific investigation of altered gene expression in subtypes defined by cognitive profiles indicated that the systemic lupus erythematosus pathway, spliceosomes, amyotrophic lateral sclerosis, and the insulin signaling were involved in the improvement of cognitive ability by LW-AFC. The expression of genes Enpp2, Etnk1, Epdr1, and Gm5900 in the hippocampus were correlated with that of LW-AFC's ameliorating cognitive impairment in SAMP8 mice. Because LW-AFC is composed of polysaccharides, glycosides, and oligosaccharides, we infer that LW-AFC has direct or indirect effects on altering gene expressions and regulating pathways in the hippocampus of SAMP8 mice. These data are helpful for the enhanced identification of LW-AFC as new therapeutic modalities to AD.

Reactive oxygen species mediate Terbufos-induced apoptosis in mouse testicular cell lines via the modulation of cell cycle and pro-apoptotic proteins

Terbufos (S-t-butylthiomethyl-O,O-diethyl phosphorodithioate) is a highly toxic organophosphate which is extensively used as an insecticide and nematicide. Chronic exposure to terbufos causes neuronal injury and predisposes to neurodegenerative diseases. Accumulating evidence has shown that the exposure to terbufos, as an occupational risk factor, may also cause reproductive disorders. However, the exact mechanisms of reproductive toxicity remain unclear. The present study aimed to investigate the toxic effect of terbufos on testicular cells and to explore the mechanism of toxicity on a cellular level. The cytotoxic effects of terbufos on mouse immortalized spermatogonia (GC-1), spermatocytes (GC-2), Leydig (TM3), and Sertoli (TM4) cell lines were assessed by MTT assays, caspase activation, flow cytometry, TUNEL assay, Western blot, and cell cycle analysis. The exposure to different concentrations of terbufos ranging from 50 to 800 μM for 6 h caused significant death in all the used testicular cell lines. Terbufos increased reactive oxygen species (ROS) production, reduced mitochondrial membrane potential, and initiated apoptosis, which was confirmed by a dose-dependent increase in the number of TUNEL-positive apoptotic cells. Blocking ROS production by N-acetyl cysteine (NAC) protected GC-1 cells from terbufos-induced cell death. The results demonstrated that terbufos induces ROS, apoptosis, and DNA damage in testicular cell lines and it should be considered potentially hazardous to testis. Together, this study provided potential molecular mechanisms of terbufos-induced toxicity in testicular cells and suggests a possible protective measure. © 2015 Wiley Periodicals, Inc. Environ Toxicol 31: 1888-1898, 2016.

Design, synthesis and antitumor activity of novel pyrazolo[3,4-d]pyrimidine derivatives as EGFR-TK inhibitors

A novel series of 2-(3,6-dimethyl-1-phenyl-1H-pyrazolo[3,4-d]pyrimidin-4-yloxy)-N-(4-substitutedbenzylidene)acetohydrazide (12a-g) was prepared and their structures were confirmed by spectral and elemental analyses. The cytotoxic activity of the newly synthesized compounds was evaluated against breast carcinoma (MCF-7), non-small cell lung cancer (A549) and human colorectal adenocarcinoma (HT-29) cell lines using MTT and colony formation assays. The tested compounds showed a marked anticancer activity against all the tested cell lines, especially compound 12g, which was the most potent anticancer agent with half maximal inhibitory concentrations (IC50) between 5.36 and 9.09μM. Docking studies into ATP binding site of EGFR protein tyrosine kinase were performed to predict their scores and mode of binding to amino acids, In addition, the inhibitory activity of the target compounds against epidermal growth factor receptor tyrosine kinase (EGFR-TK) was evaluated. Results indicated the ability of the target compounds to inhibit EGFR-TK with half maximal inhibitory concentrations (IC50) in the range of 4.18-35.88μM. Furthermore, The most active compounds 12g, 12c and 12d were assayed against Fibroblast Growth Factor Receptor (FGFR), Insulin Receptor (IR) and Vascular Endothelial Growth Factor Receptor (VEGFR). The activity of the reported compounds warrants further optimization as novel members in cancer treatment protocols.

A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer

Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g. P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value<0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.

With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed early on for more personalized treatment and management. Towards this goal we propose in this study a novel pathway-based prognosis prediction model, which emphasizes on individualized pathway-based risk measurement using the pathway dysregulation score (PDS). In combination with the L1-LASSO penalized feature selection and the COX-Proportional Hazards regression model, we have identified fifteen cancer relevant pathways using the pathway-based genomic model that successfully differentiated the relapse in the training set as well as three diversified test sets. Moreover, given the debate whether higher-order representative features, such as GO sets, pathways and network modules are superior to the gene-level features in the genomic models, we demonstrate that pathway-based genomic models consistently performed better than gene-based models in all four data sets. Last but not least, we show strong evidence that models that combine genomic information with clinical information improves the prognosis prediction significantly, in comparison to models that use either genomic or clinical information alone.