Background: The effects of plant products on cancer cells has become a field of major importance. Many substancesmay induce apoptosis in anti-cancer treatment. Pectins, a family of complex polysaccharides, and their degradation products may for exasmple exert apoptotic effects in cancer cells. Apples and citrus fruits are the main sources of pectin which can be applied for anti-cancer research. The present study concerned an intact form of pectic-oligoshaccharide named pectic acid (poly galactronic acid). Materials and Methods: Inhibition of cell proliferation assays (MTT), light microscopy, fluorescence microscopy (acridin orange/ethidium bromide), DNA fragmentation tests, cell cycle analysis, annexin PI and Western blotting methods were applied to evaluate apoptosis. Results: The results indicated that pectic acid inhibited cell growth and reduced cell attachment after 24h incubation. This did not appear to be due to necrosis, since morphological features of apoptosis were detected with AO/EB staining and cell cycling was blocked in the sub-G1 phase. Annexin/PI and DNA fragmentation findings indicated that apoptosis frequency increased after 24h incubation with pectic acid. In addition, the data showed pectic acid induced caspase-dependent apoptosis. Conclusions: These data indicate that apple pectic acid without any modification could trigger apoptosis in MDA-MB-231 human breast cancer cells and has potential to improve cancer treatment as a natural product.

Background Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways. Results In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes. Conclusions The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of “Renin-angiotensin system” pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far. Electronic supplementary material The online version of this article (10.1186/s12859-019-2635-6) contains supplementary material, which is available to authorized users.

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