Mechanistic insights expatiating the biological role and regulatory implications of estrogen and HER2 in breast cancer metastasis

Anticancer Activities of Natural and Synthetic Steroids: A Review

Steroids have demonstrated a wide field of research on the subject of anticancer compounds, particularly antiproliferative with cell lines, with special emphasis on the historical link between steroids and cancer and the use of in silico technologies to understand the impact of natural and synthetic steroids on cancer cells focused on finding common denominators of the type of structural changes that give antiproliferative and/or cytotoxic properties, both in control and cancer cell lines. Through this review and classification by origin and/or synthesis, it is found that steroidal saponins are highly cytotoxic, although with low selectivity against control cells, while on the part of the aglycone the presence of heteroatoms such as nitrogen and oxygen increases the antiproliferative activity, mainly via cell cycle arrest and the induction of apoptosis, mechanisms that have been partially proven, using semisynthetic derivatives, as well as bioconjugates between saponins and nitrogenous steroids with now a high cytotoxicity and selectivity against control cell lines. This gives rise to the idea that steroids as a study model for the design of anticancer agents are an excellent template with a wide field of study.

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.