Chen, Tzu-Yi | Search NASA

DOE OSTI2018

CHD4-mediated loss of E-cadherin determines metastatic ability in triple-negative breast cancer cells

Highlights: • CHD4 is associated with poor prognosis in triple-negative breast cancer patients. • Increased CHD4 is positively correlated with cell motility and drug resistance of TNBC cells. • CHD4 should be a potential prognostic biomarker in TNBC patient survival. Triple-negative breast cancer (TNBC) is a subtype of cancer with aggressive behaviors (high recurrence and metastasis rate) and poor prognosis. Therefore, studying the determining factors that lead to malignant TNBCs is necessary to develop personalized therapy and improve survival rates. In this study, we first analyzed levels of chromodomain helicase DNA binding protein 4 (CHD4) in 60 TNBC patients by immunohistochemical staining. We then clarified the role of CHD4 in TNBC and non-TNBC cell lines. Our clinical data indicated that higher CHD4 expression is positively correlated with metastatic stage, tumor recurrence, and survival status. Consistent with the clinical analytical data, our in vitro data also indicated that high level of CHD4 is positively correlated with malignant behaviors in TNBC cells, such as cell motility and mortality. For further analyses, we found that E-cadherin, N-cadherin and fibronetin are involved in CHD4-mediated epithelial-mesenchymal transition (EMT). Silencing of CHD4 also increased drug sensitivity to cisplatin and PARP1 inhibitor, especially in TNBC cells. Altogether, our findings showed that CHD4 is not only a potential prognostic biomarker for TNBC patient survival, but is also a powerful candidate in the development of new anti-cancer agents in TNBC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

A new augmentation based algorithm for extracting maximal chordal subgraphs

If every cycle of a graph is chordal length greater than three then it contains an edge between non-adjacent vertices. Chordal graphs are of interest both theoretically, since they admit polynomial time solutions to a range of NP-hard graph problems, and practically, since they arise in many applications including sparse linear algebra, computer vision, and computational biology. A maximal chordal subgraph is a chordal subgraph that is not a proper subgraph of any other chordal subgraph. Existing algorithms for computing maximal chordal subgraphs depend on dynamically ordering the vertices, which is an inherently sequential process and therefore limits the algorithms’ parallelizability. In our paper we explore techniques to develop a scalable parallel algorithm for extracting a maximal chordal subgraph. We demonstrate that an earlier attempt at developing a parallel algorithm may induce a non-optimal vertex ordering and is therefore not guaranteed to terminate with a maximal chordal subgraph. We then give a new algorithm that first computes and then repeatedly augments a spanning chordal subgraph. After proving that the algorithm terminates with a maximal chordal subgraph, we then demonstrate that this algorithm is more amenable to parallelization and that the parallel version also terminates with a maximal chordal subgraph. That said, the complexity of the new algorithm is higher than that of the previous parallel algorithm, although the earlier algorithm computes a chordal subgraph which is not guaranteed to be maximal. Finally, we experimented with our augmentation-based algorithm on both synthetic and real-world graphs. We provide scalability results and also explore the effect of different choices for the initial spanning chordal subgraph on both the running time and on the number of edges in the maximal chordal subgraph.

97 MATHEMATICS AND COMPUTING↗