Backgrounds Polycystic ovary syndrome affects 7% of women of reproductive ages. Poor-quality oocytes, along with lower cleavage and implantation rates, reduce fertilization. Objective This study aimed to determine crucial molecular mechanisms behind PCOS pathogenesis and repurpose new drug candidates interacting with them. To predict a more in-depth insight, we applied a novel bioinformatics approach to analyze interactions between the drug-related and PCOS proteins in PCOS patients. Methods The newest proteomics data was retrieved from 16 proteomics datasets and was used to construct the PCOS PPI network using Cytoscape. The topological network analysis determined hubs and bottlenecks. The MCODE Plugin was used to identify highly connected regions, and the associations between PCOS clusters and drug-related proteins were evaluated using the Chi-squared/Fisher's exact test. The crucial PPI hub-bottlenecks and the shared molecules (between the PCOS clusters and drug-related proteins) were then investigated for their drug-protein interactions with previously US FDA-approved drugs to predict new drug candidates. Results The PI3K/AKT pathway was significantly related to one PCOS subnetwork and most drugs (metformin, letrozole, pioglitazone, and spironolactone); moreover, VEGF, EGF, TGFB1, AGT, AMBP, and RBP4 were identified as the shared proteins between the PCOS subnetwork and the drugs. The shared top biochemical pathways between another PCOS subnetwork and rosiglitazone included metabolic pathways, carbon metabolism, and citrate cycle, while the shared proteins included HSPB1, HSPD1, ACO2, TALDO1, VDAC1, and MDH2. We proposed some new candidate medicines for further PCOS treatment investigations, such as copper and zinc compounds, reteplase, alteplase, gliclazide, Etc. Conclusion Some of the crucial molecules suggested by our model have already been experimentally reported as critical molecules in PCOS pathogenesis. Moreover, some repurposed medications have already shown beneficial effects on infertility treatment. These previous experimental reports confirm our suggestion for investigating our other repurposed drugs (in vitro and in vivo). Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s40199-021-00413-9.
Background: Breast cancer is the most common malignancy worldwide. Doxorubicin is an anthracycline used to treat breast cancer as the first treatment choice. Nevertheless, the molecular mechanisms underlying the response to Doxorubicin and its side effects are not comprehensively understood so far. We used systems biology and bioinformatics methods to identify essential genes and molecular mechanisms behind the body response to Doxorubicin and its side effects in breast cancer patients. Methods: Omics data were extracted and analyzed to construct the protein-protein interaction and gene regulatory networks. Network analysis was performed to identify hubs, bottlenecks, clusters, and regulatory motifs to evaluate crucial genes and molecular mechanisms behind the body response to Doxorubicin and its side effects. Results: Analyzing the constructed PPI and gene-TF-miRNA regulatory network showed that MCM3, MCM10, and TP53 are key hub-bottlenecks and seed proteins. Enrichment analysis also revealed cell cycle, TP53 signaling, Forkhead box O (FoxO) signaling, and viral carcinogenesis as essential pathways in response to this drug. Besides, SNARE interactions in vesicular transport and neurotrophin signaling were identified as pathways related to the side effects of Doxorubicin. The apoptosis induction, DNA repair, invasion inhibition, metastasis, and DNA replication are suggested as critical molecular mechanisms underlying Doxorubicin anti-cancer effect. SNARE interactions in vesicular transport and neurotrophin signaling and FoxO signaling pathways in glucose metabolism are probably the mechanisms responsible for side effects of Doxorubicin. Conclusion: Following our model validation using the existing experimental data, we recommend our other newly predicted biomarkers and pathways as possible molecular mechanisms and side effects underlying the response to Doxorubicin in breast cancer requiring further investigations.
Feature subset selection and/or dimensionality reduction is an essential preprocess before performing any data mining task, especially when there are too many features in the problem space. In this paper, a clusteringbased feature subset selection (CFSS) algorithm is proposed for discriminating more relevant features. In each level of agglomeration, it uses similarity measure among features to merge two most similar clusters of features. By gathering similar features into clusters and then introducing representative features of each cluster, it tries to remove some redundant features. To identify the representative features, a criterion based on mutual information is proposed. Since CFSS works in a filter manner in specifying the representatives, it is noticeably fast. As an advantage of hierarchical clustering, it does not need to determine the number of clusters in advance. In CFSS, the clustering process is repeated until all features are distributed in some clusters. However, to diffuse the features in a reasonable number of clusters, a recently proposed approach is used to obtain a suitable level for cutting the clustering tree. To assess the performance of CFSS, we have applied it on some valid UCI datasets and compared with some popular feature selection methods. The experimental results reveal the efficiency and fastness of our proposed method.
Objectives: Endometrial receptivity is a complex event that occurs during the midluteal phase of the menstrual cycle known as the "window of implantation". During this period, the endometrium develops characteristics that allow the adhesion and invasion of the embryo to the uterine epithelium. Accordingly, the expressions of miR-31 and its target gene were evaluated to study the effect of miR-31 on FOPX3 gene expression in recurrent implantation failure (RIF) patients and normal fertile women. More precisely, the aim of this study was to understand the expression of miR-31 as one of the important regulators of the FOXP3 gene in the endometrium of RIF patients versus receptive endometria from fertile patients. Materials and Methods: This case-control study was conducted on 20 endometrial tissue samples of normal fertile women and RIF patients in order to evaluate miR-31 and its target gene expression. Results: According to the results of this study, a significant difference existed between RIF patients and normal fertile women (control group). The expression of the FOXP3 gene was more significant in the control group. miR-31 was also significantly expressed, which was due to the endometrial immunological disorder leading to the decreased expression of its target gene (FOXP3). Conclusions: In general, implant abnormalities and recurrent abortions were observed in RIF patients due to the decreased expression of the FOXP3 gene resulting from the inhibitory effects of miR-31.
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