Role of SPARC in Bone Remodeling and Cancer‐Related Bone Metastasis

There is a growing socioeconomic recognition that clinical bone diseases such as bone infections, bone tumors and osteoporotic bone loss mainly associated with ageing, are major issues in today0s society. SPARC (secreted protein, acidic and rich in cysteine), a matricellular glycoprotein, may be a promising therapeutic target for preventing or treating bone‐related diseases. In fact, SPARC is associated with tissue remodeling, repair, development, cell turnover, bone mineralization and may also participate in growth and progression of tumors, namely cancer‐related bone metastasis. Yet, the function of SPARC in such biological processes is poorly understood and controversial. The main objective of this work is to review the current knowledge related to the activity of SPARC in bone remodeling, tumorigenesis, and bone metastasis. Progress in understanding SPARC biology may provide novel strategies for bone regeneration and the development of anti‐angiogenic, anti‐proliferative, or counter‐adhesive treatments specifically against bone metastasis.

Telomerase promoter mutations in cancer: an emerging molecular biomarker?

João Vinagre, Vasco Pinto and Ricardo Celestino contributed equally to the manuscript.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Energy analysis of Multi-legged Locomotion Systems

4th International Conference on Climbing and Walking Robots - From Biology to Industrial Applications

Effects of Environmental Organochlorine Pesticides on Human Breast Cancer: Putative Involvement on Invasive Cell Ability

Human exposure to persistent organic pollutants (POPs) is a certainty, even to long banned pesticides like o,p′-dichlorodiphenyltrichloroethane (o,p′-DDT), and its metabolites p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE), and p,p′-dichlorodiphenyldichloroethane (p,p′-DDD). POPs are known to be particularly toxic and have been associated with endocrine-disrupting effects in several mammals, including humans even at very low doses. As environmental estrogens, they could play a critical role in carcinogenesis, such as in breast cancer. With the purpose of evaluating their effect on breast cancer biology, o,p′-DDT, p,p′-DDE, and p,p′-DDD (50–1000 nM) were tested on two human breast adenocarcinoma cell lines: MCF-7 expressing estrogen receptor (ER) α and MDA-MB-231 negative for ERα, regarding cell proliferation and viability in addition to their invasive potential. Cell proliferation and viability were not equally affected by these compounds. In MCF-7 cells, the compounds were able to decrease cell proliferation and viability. On the other hand, no evident response was observed in treated MDA-MB-231 cells. Concerning the invasive potential, the less invasive cell line, MCF-7, had its invasion potential significantly induced, while the more invasive cell line MDA-MB-231, had its invasion potential dramatically reduced in the presence of the tested compounds. Altogether, the results showed that these compounds were able to modulate several cancer-related processes, namely in breast cancer cell lines, and underline the relevance of POP exposure to the risk of cancer development and progression, unraveling distinct pathways of action of these compounds on tumor cell biology.