Systematic analysis of breast atypical hyperplasia-associated hub genes and pathways based on text mining

Reduction of daily-use parabens and phthalates reverses accumulation of cancer-associated phenotypes within disease-free breast tissue of study subjects

Estrogenic overstimulation is carcinogenic to the human breast. Personal care products (PCPs) commonly contain xenoestrogens (XE), such as parabens and phthalates. Here, we identified the adverse effects of persistent exposure to such PCPs directly within human estrogen responsive breast tissue of subjects enrolled in a regimen of reduced XE use (REDUXE). Pre- and post-intervention fine needle aspirates (FNAs) of the breast were collected from healthy volunteers who discontinued the use of paraben and phthalate containing PCPs over a 28 d period. Based on high-dimensional gene expression data of matched FNA pairs of study subjects, we demonstrate a striking reversal of cancer-associated phenotypes, including the PI3K-AKT/mTOR pathway, autophagy, and apoptotic signaling networks within breast cells of REDUXE compliant subjects. These, and other altered phenotypes were detected together with a significant reduction in urinary parabens and phthalate metabolites. Moreover, in vitro treatment of paired FNAs with 17β-estradiol (E2), displayed a 'normalizing' impact of REDUXE on gene expression within known E2-modulated pathways, and on functional endpoints, including estrogen receptor alpha: beta ratio, and S-phase fraction of the cell cycle. In a paradigm shifting approach facilitated by community-based participatory research, REDUXE reveals unfavorable consequences from exposure to XEs from daily-use PCPs. Our findings illustrate the potential for REDUXE to suppress pro-carcinogenic phenotypes at the cellular level towards the goal of breast cancer prevention.

Text-Mining Approach to Identify Hub Genes of Cancer Metastasis and Potential Drug Repurposing to Target Them

Metastasis accounts for the majority of cancer-related deaths. Despite decades of research, the prevention and suppression of metastasis remain an elusive goal, and to date, only a few metastasis-related genes have been targeted therapeutically. Thus, there is a strong need to find potential genes involved in key driver traits of metastasis and their available drugs. In this study, we identified genes associated with metastasis and repurposable drugs that potentially target them. First, we use text mining of PubMed citations to identify candidate genes associated with metastatic processes, such as invadopodia, motility, movement, metastasis, invasion, wound healing, EMT (epithelial to mesenchymal transition), and podosome. Next, we annotated the top genes involved in each process as a driver, tumor suppressor, or oncogene. Then, a total of 185 unique cancer genes involved in metastasis-related processes were used for hub gene analysis using bioinformatics tools. Notably, a total of 77 hub genes were identified. Further, we used virtual screening data of druggable candidate hub genes involved in metastasis and identified potential drugs that can be repurposed as anti-metastatic drugs. Remarkably, we found a total of 50 approved drugs that have the potential to be repurposed against 19 hub genes involved in metastasis-related processes. These 50 drugs were also found to be validated in different cancer cell lines, such as dasatinib, captopril, leflunomide, and dextromethorphan targeting SRC, MMP2, PTK2B, and RAC1 hub genes, respectively. These repurposed drugs potentially target metastasis, provide pharmacodynamic insight, and offer a window of opportunity for the development of much-needed antimetastatic drugs.

Bufalin: A Systematic Review of Research Hotspots and Antitumor Mechanisms by Text Mining and Bioinformatics

Bufalin is an anticancer drug extract from traditional Chinese medicine. Several articles about bufalin have been published. However, the literature on bufalin has not yet been systematically studied. This study aimed to identify the study status and knowledge structures of bufalin and to summarize the antitumor mechanism. Data were retrieved and downloaded from the PubMed database. The softwares of BICOMB, gCLUTO, Ucinet 6.0, and NetDraw2.084 were used to analyze these publications. The bufalin related genes were recognized and tagged by ABNER software. Then these BF-related genes were performed by Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and protein-protein interaction (PPI) network analysis. A total of 474 papers met the search criteria from 2000 to 2019. By biclustering clustering analysis, the 50 high-frequency main MeSH terms/subheadings were classified into 5 clusters. The clusters of drug therapy and the mechanism of bufalin were hotspot topics. A total of 50 genes were identified as BF-related genes. PPI network analysis showed that inducing apoptosis was the main effect of bufalin, and apoptosis-related gene Caspase 3 was the most reported by people. Bufalin could inhibit the proliferation, invasion, and metastasis of cancer cells through multiple signaling pathways, such as PI3K/AKT, Hedgehog, MAPK/JNK, Wnt/[Formula: see text]-catenin, TGF-[Formula: see text]/Smad, Integrin signaling pathway, and NF-KB signaling pathway via KEGG analysis. Through the quantitative analysis of bufalin literature, we revealed the research status and hot spots in this field and provided some guidance for further research.