Breast cancer: cause and prevention

The ascent in cancer cases is startling, with breast cancer epidemiology extending across the globe. The severity of the aggressiveness of the disease is abetted by many aspects that have a critical impact on breast cancer biology, of which hypoxia is one. There is documentation of its disparate implications that defines its far-reaching impact on the therapeutic regimen. Nanomedicine mediated therapy has been seen to alleviate the adverse impact of hypoxic tumors, which has been backed by many investigative studies. This chapter entails the elemental understanding of hypoxia, its serious fall back on disease presentation, and also acknowledges the resistance to cancer cells along with its consequential impact on the disease prognosis. In this wake, this chapter recapitulates the studies primarily emphasizing the hypoxia-mediated nanoparticle treatment scheme that has advanced to avert this grave condition that makes the cancer therapy cumbersome.

In this work, the effectiveness of the deep learning model is applied for one-dimensional data when converted to images. This work is based on the effective conversion of one-dimensional data to images and designing a stacked ensemble deep learning model that can increase the performance of classification accuracy in comparison to single models. Breast cancer detection from gene expression dataset and breast histopathology images is considered using the proposed ensemble model. The gene expression data is one-dimensional. Using the t-Distributed Stochastic Neighbor embedding technique and Convex Hull algorithm the one-dimensional data is converted to an image. Existing methods are using the datasets directly for training and classification, whereas the proposed method uses the dataset as well as the decomposed forms of the same for improving the performance. It involves two-stage classification. The first stage consists of three Convolutional Neural Networks as the base classifiers. Empirical Wavelet Transform and Variational Mode Decomposition are the two methods used to decompose the dataset so that the models can be trained at the molecular level, making our model robust in comparison to state-of-the-art methods. The first stage classification outcomes are used to train the second stage classifier “Multilayer Perceptron”. The gene expression dataset collected from Mendeley and is used for the generation of two-dimensional synthetic datasets. The synthetic datasets and breast histopathology image datasets are used for the training and validation of the proposedmodel. The improved results obtained in this work show the effectiveness of our method

Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.

There are several evidences supporting the role of 5–10 methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms in breast cancer (BC). Case control association studies on breast cancer have been repeatedly performed over the last two decades, but results are inconsistent. We performed a meta-analysis to confirm the association between MTHFR C677T polymorphism and BC risk.

The articles were retrieved by searching the PubMed, Google Scholar, and Springer Link databases. Crude odds ratios (OR) with 95% confidence intervals (CIs) was used to assess the strength of association between C677T polymorphism and BC. Publication bias was assessed by Egger's and Begg-Mazumdar tests. Meta-analysis was performed with Open Meta Analyst.

Total 75 studies with 31,315 cases and 35, 608 controls were found suitable for the inclusion in the present meta-analysis. The results of meta-analysis suggested that there were moderate significant association between C677T polymorphism and BC risk using overall comparisons in five genetic models (T vs. C: OR = 1.08, 95% CI = 1.03–1.13, p = < 0.001; TT + CT vs. CC: OR = 1.06, 95% CI = 1.02–1.09, p = < 0.001; TT vs. CC: OR = 1.17, 95% CI = 1.06–1.28, p = 0.001; CT vs. CC OR = 1.05, 95% CI = 1.01–1.08, p = 0.005; TT vs. CT + CC: OR = 1.12, 95% CI = 1.03–1.22, p = 0.005). In conclusion, results of present meta-analysis showed modest association between MTHFR C677T polymorphism with breast cancer in total studies. However, sub-group analysis results based on ethnicity showed strong significant association between TT genotype and breast cancer (TT vs. CC; OR°=°1.26; 95% CI: 1.06–1.51; p = 0.009) in Asian population but in Caucasian population such association was not observed (TT vs. CC; OR°=°1.08; 95% CI: 0.99–1.14; p = 0.05).

Steroid growth promoters are commonly administered to beef cattle residing on feedyards, and a portion of these compounds are excreted in manure along with endogenous steroids. Steroids associated with aerosolized particulate matter (PM) can be transported from feedyards via wind. To assess potential androgenic and estrogenic activity of PM extracts, total suspended particulate samples were collected upwind and downwind of feedyards in the Southern High Plains and subjected to in vitro transcriptional activation assays. Androgen-mediated transcriptional activation induced by exposure to extracts from PM collected downwind of feedyards was significantly higher than that induced by exposure to extracts of corresponding upwind samples, whereas estrogen-mediated transcriptional activation was detected after exposure to upwind and downwind PM sample extracts. Detection and quantitation of metabolites of the synthetic androgen trenbolone acetate downwind, and estradiol both upwind and downwind, suggest that synthetic growth promoters contribute to observed in vitro activity. No significant correlations were observed, however, between individual steroid concentrations or total androgen/estrogen concentration and in vitro activity, indicating the contributions of additional, unquantified compounds to observed androgenic and estrogenic activity. Results indicate that steroids affiliated with feedyard PM have the potential to elicit endocrine-modulating effects.