Shen et al
|Available at http:// ISSN 2161-2609 |
Expression of IGF-IR in breast cancer tissue before and after neoadjuvant chemotherapy
William Welch1, Paul Wooley2, Yunjie Zeng1, *
1Department of Pathology, Sun Yat-sen Mmemorial Hospital of Sun Yat-Sen University, City, Postcode, Country
2Department of Surgery, Orthopedics, University of Kansas School of Medicine, City, Postcode, USA
Abstract: Neoadjuvant chemotherapy (NAC) is a common therapeutic schedule in treatment of breast cancer. In order to discover the mechanism of IGF-IR in breast cancer tissue and its influence on cell proliferation and CAM angiogenesis and NAC effect. 160 breast cancer patients in II and III period were selected and tested the expression of IGF-IR. All the patients were diagnosed according to specimen taken with needle biopsy and treated with NAC (Duoxitasai, epirubicin and cyclophosphamide) for 2 months, had mastectomy and tested expression of IGF-IR. The results showed that positive expression rate of IGF-IR in breast cancer patients were73.125% and the expression of IGF-IR is closely related with the periods of tumor and Axillary node metastases (P=0.01) but not related with tumor size (P=0.21). After treatment, IGF-IR in 65 patients had changed from high-expression to low expression (P=0.001) and negative conversion rate was 55.56%.
Keywords: IGF-IR; Neoadjuvant chemotherapy; Breast cancer; Proliferation; Angiogenesis
|Received 15 May 2016, Revised 20 May 2016, Accepted 22 May 2016 |
|*Corresponding Author: Yunjie Zeng, 222222@ |
Introduction
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Papers should clearly describe the background of the subject, the authors work, including the methods used, results and concluding discussion on the importance of the work. Papers are to be prepared in English and SI units must be used[1,2].
[pic]
Figure 1. Androgen receptor signaling pathway.
Methods
2.1. Wavelet Neural Network Model
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Results and Discussion
In this section, authors must give the key positive results of the study, and the description of the results need be provided. Discussion is the good way for authors to found the profound mechanism.
Table 1. The application of TLRs agonists in nonresolving inflammation-related
|Cancer type |Nonresolving |Agent |Target |
| |inflammation | | |
| | |BCG |TLR2 |
| | | |TLR4 |
|Melanoma |Nonresolving | | |
| |dermatitis | | |
| | |Hiltonol |TLR3 |
| | |Resiquimod |TLR7, TLR8|
| | |IPH 3102 |TLR3 |
| | |852A |TLR7 |
|Pancreatic cancer |Nonresolving |Hiltonol |TLR3 |
| |pancreatitis | | |
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References
1] Wang T, Ma K, Li X C, et al. QSPR Model for Predicting Retention Time Basis on Gene Expression Programming[J]. Advanced Materials Research, 2014, 926-930:3153-3156.
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