Zingerone induced caspase‐dependent apoptosis in MCF‐7 cells and prevents 7,12‐dimethylbenz(a)anthracene‐induced mammary carcinogenesis in experimental rats

Breast cancer is a prevalent of tumoregenesis in women and reports for the maximum mortality and morbidity in the global. Ginger (Zingiber officinale) is the mainly widespread spice and herbal remedies used in the world. Since antique periods, ginger has been used in Greece, India and China for the curing of upset stomach, nausea, diarrhea, colds, and headaches. The current work was planned to explore the anticancer properties of zingerone (ZO) toward 7,12‐dimethylbenz(a)anthracene (DMBA)‐treated mammary carcinogenesis in Sprague‐Dawley (SD) rats and MCF‐7 mammary cancer cells. The mammary carcinogenesis was produced through a single dosage of DMBA (20 mg/kg bwt) mixed in soya oil (1 mL) administrated intragastrically with a gavage. We found improved concentrations of lipid peroxidation (LOOH and TBARS), carcinoembryonic antigen, lowered levels of enzymatic (CAT, GPx, and SOD), and nonenzymatic (vitamin E, GSH, and vitamin C) antioxidant in mammary tissues and plasma of DMBA‐induced cancer bearing animals. Moreover, augmented concentrations of phase I (Cyt‐b₅ and CYP₄₅₀) and reduced levels of phase II (GR and GST) detoxification microsomal proteins in mammary tissues were noticed. ZO administrations significantly reverted back to all these parameters in this way, showing efficient of anticancer effect. Furthermore, our in vitro study also supported the anticancer effect of the treatment of ZO were noticed loss of cell viability, improved reactive oxygen species formation, and reduced MMP. Furthermore, the status of apoptosis proteins such as Bcl‐2, Bax, and Bid expressions was determined by using Western blot analysis techniques. Overall, these results proposed the anticancer effect of ZO toward DMBA‐induced mammary cancer in SD animals and Michigan cancer foundation‐7 mammary cancer cells.     

Role of Chitosan in the Regulation of the Growth,Antioxidant System and Photosynthetic Characteristics of Maize Seedlings under Cadmium Stress

Russian Journal of Plant Physiology - Cadmium (Cd) pollution was becoming more and more serious; there is an urgent need for an effective solution to inhibit the harm of cadmium stress. Chitosan...     

Development of high‐resolution DNA barcodes for Dioscorea species discrimination and phylogenetic analysis

The genus Dioscorea is widely distributed in tropical and subtropical regions, and is economically important in terms of food supply and pharmaceutical applications. However, DNA barcodes are relatively unsuccessful in discriminating between Dioscorea species, with the highest discrimination rate (23.26%) derived from matK sequences. In this study, we compared genic and intergenic regions of three Dioscorea chloroplast genomes and found that the density of SNPs and indels in intergenic sites was about twice and seven times higher than that of SNPs and indels in the genic regions, respectively. A total of 52 primer pairs covering highly variable regions were designed and seven pairs of primers had 80%–100% PCR success rate. PCR amplicons of 73 Dioscorea individuals and assembled sequences of 47 Dioscorea SRAs were used for estimating intraspecific and interspecific divergence for the seven loci: The rpoB‐trnC locus had the highest interspecific divergence. Automatic barcoding gap discovery (ABGD), Poisson tree processes (PTP), and generalized mixed Yule coalescence (GMYC) analysis were applied for species delimitation based on the seven loci and successfully identified the majority of species, except for species in the Enantiophyllum section. Phylogenetic analysis of 51 Dioscorea individuals (28 species) showed that most individuals belonging to the same species tended to cluster in the same group. Our results suggest that the variable loci derived from comparative analysis of plastid genome sequences could be good DNA barcode candidates for taxonomic analysis and species delimitation.     

DHHC4 and DHHC5 Facilitate Fatty Acid Uptake by Palmitoylating and Targeting CD36 to the Plasma Membrane

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PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq

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Probiotic Therapy (BIO-THREE) Mitigates Intestinal Microbial Imbalance and Intestinal Damage Caused by Oxaliplatin

Probiotics and Antimicrobial Proteins - Gastrointestinal mucositis associated with the use of chemotherapeutic drugs can seriously affect the quality of life of patients. In this study, a probiotic...     

Accurate cancer classification using expressions of very few genes

We aim at finding the smallest set of genes that can ensure highly accurate classification of cancers from microarray data by using supervised machine learning algorithms. The significance of finding the minimum gene subsets is three-fold: 1) it greatly reduces the computational burden and "noise" arising from irrelevant genes. In the examples studied in this paper, finding the minimum gene subsets even allows for extraction of simple diagnostic rules which lead to accurate diagnosis without the need for any classifiers, 2) it simplifies gene expression tests to include only a very small number of genes rather than thousands of genes, which can bring down the cost for cancer testing significantly, 3) it calls for further investigation into the possible biological relationship between these small numbers of genes and cancer development and treatment. Our simple yet very effective method involves two steps. In the first step, we choose some important genes using a feature importance ranking scheme. In the second step, we test the classification capability of all simple combinations of those important genes by using a good classifier. For three "small" and "simple" data sets with two, three, and four cancer (sub)types, our approach obtained very high accuracy with only two or three genes. For a "large" and "complex" data set with 14 cancer types, we divided the whole problem into a group of binary classification problems and applied the 2-step approach to each of these binary classification problems. Through this "divide-and-conquer" approach, we obtained accuracy comparable to previously reported results but with only 28 genes rather than 16,063 genes. In general, our method can significantly reduce the number of genes required for highly reliable diagnosis     

Downregulation of lysyl oxidase-like 4 LOXL4 by miR-135a-5p promotes lung cancer progression in vitro and in vivo

Aberrant microRNAs are widely identified in multiple cancers, including lung cancer. miR-135a-5p can function as a significant tumor regulator in diverse cancers via impacting multiple genes in oncogenic pathways. Nevertheless, the biological role of miR-135a-5p in lung cancer is poorly known. Here, we investigated its function in lung cancer. As exhibited, miR-135a-5p was elevated in lung cancer cells in contrast to BEAS-2B cells. Then, we inhibited miR-135a-5p expression by transfecting LV-anti-miR-135a-5p into lung cancer cells. As displayed, miR-135a-5p was obviously reduced in A549 and H1299 cells. Knockdown of miR-135a-5p repressed lung cancer cell growth and cell proliferation. Meanwhile, cell colony formation capacity was depressed, cell apoptosis was enhanced and cell cycle progression was blocked in G1 phase by inhibition of miR-135a-5p in vitro. Additionally, the migration and invasion of A549 and H1299 cells was strongly depressed by LV-anti-miR-135a-5p. For another, by using informatics analysis, lysyl oxidase-like 4 (LOXL4) was speculated as the downstream target of miR-135a-5p. We validated their direct correlation and moreover, overexpression of miR-135a-5p restrained LOXL4 levels in lung cancer cells. Subsequently, we proved that miR-135a-5p promoted lung cancer development via targeting LOXL4 by carrying out the in vivo assays. Taken these together, our study revealed miR-135a-5p might be indicated as a perspective for lung cancer via targeting LOXL4.