Human breast fibroblasts have been shown to express urokinase-type plasminogen activator (uPA). This suggests that fibroblasts are actively involved in the process of uPA-directed breast tumor proteolysis. To investigate a possible role for the insulin-like growth factors (IGFs) in regulating uPA expression in human breast fibroblasts, we correlated the expression of uPA with the expression of IGF-1 and IGF-2 in a paired panel of normal and tumor tissue-derived human breast fibroblasts in vitro. Analysis of reverse transcribed polymerase chain reaction (RT-PCR) amplified mRNA revealed that the tumor-derived fibroblast strain expressed significantly more basal uPA mRNA and significantly less IGF-1 mRNA when compared to their normal counterpart. The expression of basal IGF-2 mRNA did not differ between these cultures. For both normal and tumor tissue-derived fibroblasts, cytokine- and growth factor-induced steady-state levels of uPA and IGF-1 mRNA were inversely related. No such correlation was found for uPA and IGF-2 mRNA. While exogenously added IGF-1 decreased the amount of uPA mRNA transcripts similarly in both normal and tumoral fibroblasts, exogenously added uPA decreased the amount of IGF-1 mRNA transcripts only in tumor tissue-derived fibroblasts. These data suggest that in human breast fibroblasts IGF-1 controls the expression of uPA and that, possibly due to an altered sensitivity to uPA, tumor-associated fibroblasts have escaped this local control mechanism.
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Sci Rep
December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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View Article and Find Full Text PDFthe evolution of axillary management in breast cancer has witnessed significant changes in recent decades, leading to an overall reduction in surgical interventions. There have been notable shifts in practice, aiming to minimize morbidity while maintaining oncologic outcomes and accurate staging for newly diagnosed breast cancer patients. These advancements have been facilitated by the improved efficacy of adjuvant therapies.
View Article and Find Full Text PDFthe axillary reverse mapping (ARM) procedure aims to preserve the lymphatic drainage structures of the upper extremity during axillary surgery for breast cancer, thereby reducing the risk of lymphedema in the upper limb. Material and this prospective study included 57 patients with breast cancer who underwent SLNB and ARM. The sentinel lymph node (SLN) was identified using a radioactive tracer.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
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