Expression of uPAR in tumor-associated stromal cells is associated with colorectal cancer patient prognosis: a TMA study

肿瘤相关基质细胞中 uPAR 的表达与结直肠癌患者预后相关:一项 TMA 研究

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作者:Martin C Boonstra, Floris P R Verbeek, Andrew P Mazar, Hendrica A J M Prevoo, Peter J K Kuppen, Cornelis J H van de Velde, Alexander L Vahrmeijer, Cornelis F M Sier

Background

The receptor for urokinase-type plasminogen activator (uPAR) is associated with cancer development and progression. Within the tumor microenvironment uPAR is expressed by malignant cells as well as tumor-associated stromal cells. However, the contribution of uPAR expression in these stromal cells to malignancy and patient survival in colorectal cancer is still unclear. This study compares the association of uPAR expression in both colorectal tumor-associated stromal cells and neoplastic cells with clinico-pathological characteristics and patient survival using tissue micro arrays (TMA).

Conclusion

This study demonstrates a significant independent negative association between colorectal cancer patient survival and uPAR expression in especially tumor-associated stromal cells.

Methods

Immunohistochemical staining of uPAR expression was performed on tumor tissue from 262 colorectal cancer patients. Kaplan-Meier, log rank, and uni- and multivariate Cox's regression analyses were used to calculate associations between uPAR expression and patient survival.

Results

In the colorectal tumor-associated stromal microenvironment, uPAR is expressed in macrophages, (neoangiogenic) endothelial cells and myofibroblasts. uPAR expression in tumor-associated stromal cells and neoplastic cells (and both combined) were negatively associated with overall survival (OS) and Disease Free Survival (DFS). Uni- and multivariate Cox's regression analysis for combined uPAR expression in tumor-associated stromal and neoplastic cells showed significant and independent negative associations with OS and DFS. Only uPAR expression in tumor-associated stromal cells showed independent significance in the uni- and multivariate analysis for DFS.

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