AB070. 59. Investigating how the extracellular matrix directs gene expression in breast cancer metastasis

2018 ◽  
Vol 2 ◽  
pp. AB070-AB070
Author(s):  
Joanne Nolan ◽  
Maeve Kiely ◽  
Aoife J. Lowery ◽  
Colum P. Dunne ◽  
Patrick A. Kiely
Molecules ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 236 ◽  
Author(s):  
Karolina Chrabaszcz ◽  
Katarzyna Kaminska ◽  
Karolina Augustyniak ◽  
Monika Kujdowicz ◽  
Marta Smeda ◽  
...  

This work focused on a detailed assessment of lung tissue affected by metastasis of breast cancer. We used large-area chemical scanning implemented in Fourier transform infrared (FTIR) spectroscopic imaging supported with classical histological and morphological characterization. For the first time, we differentiated and defined biochemical changes due to metastasis observed in the lung parenchyma, atelectasis, fibrous, and muscle cells, as well as bronchi ciliate cells, in a qualitative and semi-quantitative manner based on spectral features. The results suggested that systematic extracellular matrix remodeling with the progress of the metastasis process evoked a decrease in the fraction of the total protein in atelectasis, fibrous, and muscle cells, as well as an increase of fibrillar proteins in the parenchyma. We also detected alterations in the secondary conformations of proteins in parenchyma and atelectasis and changes in the level of hydroxyproline residues and carbohydrate moieties in the parenchyma. The results indicate the usability of FTIR spectroscopy as a tool for the detection of extracellular matrix remodeling, thereby enabling the prediction of pre-metastatic niche formation.


2011 ◽  
Vol 8 (2) ◽  
pp. 222-238 ◽  
Author(s):  
Erik van den Akker ◽  
Bas Verbruggen ◽  
Bas Heijmans ◽  
Marian Beekman ◽  
Joost Kok ◽  
...  

Summary Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.


PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63146 ◽  
Author(s):  
Arvind P. Pathak ◽  
Stephen McNutt ◽  
Tariq Shah ◽  
Flonne Wildes ◽  
Venu Raman ◽  
...  

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