Background: Triple negative breast cancer (TNBC) is an aggressive phenotype of breast cancer with reduced survival and poor prognosis. Increased VEGF-A, IGF-I, IGF-IR and TGF-β1 expressions were detected in breast cancer. However, little is known about their prognostic and predictive roles in TNBC.
Aim: We assessed the possible prognostic and predictive values of VEGF-A, IGF-I/IGF-IR and TGF-β1 in TNBC cases by measuring their protein and mRNA expression in TNBC and non-TNBC cases.
Methods: VEGF-A, IGF-I, IGF-IR and TGF-β1 RNA and their corresponding proteins were assessed in 43 TNBCs, 53 non-TNBCs and 30 normal breast tissues (NBT) by real time PCR (qPCR) and immunohistochemistry (IHC); respectively. Results were related to clinico-pathological factors, response to treatment and survival rates.
Results: Increased mRNA expression of VEGF-A, IGF-I, and IGF-IR was significantly higher in TNBC (65.1%, 65.1%, and 72.1%) than non-TNBC (28.1%, 33.96% and 28.3%) and NBT (0.00%) (P<0.001). Similarly, TNBC patients were significantly associated with high expression of VEGF-A, IGF-I, and IGF-IR proteins (67.44%, 62.79% and 83.72%) than non-TNBC (20.75%, 35.86% and 20.75%) and NBT (0.00%) (P<0.001). Protein and RNA expression levels of all studied markers showed high concordance in all investigated patients (correlation coefficient exceeding 0.5 and 0.4, respectively). In the TNBC group, metastasis and poor response to treatment were significantly associated with VEGF-A (P<0.001, P=0.007, respectively), IGF-I (P<0.001, P<0.001, respectively), IGF-IR (P=0.001, P=0.015, respectively) and TGF-β1 (P<0.001, P=0.007, respectively) protein levels. Multivariate logistic regression showed that IGF-I was the only independent prognostic factor for reduced OS (P=0.034) and DFS (P=0.026) in the TNBC patients.
Conclusions: VEGF-A, IGF-I and IGF-IR play an important role in the development and progression of TNBC compared to non-TNBC. Therefore, they could be used as prognostic and predictive biomarkers as well as candidates for targeted therapy. However, only IGF-I can predict survival in those patients.
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http://dx.doi.org/10.1016/j.yexmp.2015.07.014 | DOI Listing |
Cancer Cell Int
December 2024
Department of Ultrasound, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China.
Gas therapy represents a promising strategy for cancer treatment, with nitric oxide (NO) therapy showing particular potential in tumor therapy. However, ensuring sufficient production of NO remains a significant challenge. Leveraging ultrasound-responsive nanoparticles to promote the release of NO is an emerging way to solve this challenge.
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December 2024
MKA Breast Cancer Clinic, Tepe Prime, Ankara, Turkey. Electronic address:
Trends Mol Med
December 2024
Cancer Signaling and Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA, USA. Electronic address:
Genetic and epigenetic defects of the p53 system have previously been associated with resistance to CDK4/6 inhibitors in women with HR breast cancer. Recent data from Kudo et al. demonstrate that CDK2-targeting agents may offer an effective strategy to circumvent such resistance by enforcing cellular senescence downstream of RBL2 dephosphorylation.
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December 2024
Breast Cancer Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China. Electronic address:
Am J Pathol
December 2024
Department of Computer Science, Faculty of Engineering Sciences, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
Understanding the tumor hypoxic microenvironment is crucial for grasping tumor biology, clinical progression, and treatment responses. This study presents a novel application of AI in computational histopathology to evaluate hypoxia in breast cancer. Weakly Supervised Deep Learning (WSDL) models can accurately detect morphological changes associated with hypoxia in routine Hematoxylin and Eosin (H&E) whole slide images (WSI).
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