Metastasis-Associated in Colon Cancer 1 Is a Novel Survival-Related Biomarker for Human Patients with Renal Pelvis CarcinomaReportar como inadecuado




Metastasis-Associated in Colon Cancer 1 Is a Novel Survival-Related Biomarker for Human Patients with Renal Pelvis Carcinoma - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Metastasis-associated in colon cancer 1 MACC1 has recently been identified as a novel independent prognostic indicator for metastasis occurrence, overall survival and cancer-free survival for patients with colon cancer and other solid tumors. In this study, we investigated the role of MACC1 in the development and progression of renal pelvis carcinoma, a form of upper tract urothelial carcinomas. MACC1 protein has been found in the cytoplasm as well as in the nucleus of the transitional epithelial cells of the normal renal pelvis in immunohistochemical IHC assays. Quantitative IHC examinations revealed that MACC1 abnormal abundance in cancerous tissues might represent a biological indicator clinically suggestive of tumor malignancy in the renal pelvis. Furthermore, investigation of the association of MACC1 protein levels with clinicopathological parameters in this study has suggested a correlation of MACC1 expression with tumor-node-metastasis stage and histopathological grade of patients with renal pelvis carcinoma, with elevated MACC1 protein levels frequently associated with higher aggressiveness of the disease. Moreover, both disease-free survival and overall survival for the patients in the high MACC1 expression group were significantly lower than those in the low expression group. Multivariate analysis with a Cox proportional-hazards model suggested that MACC1 is indeed an independent prognostic indicator of overall survival and cancer-free survival for patients with renal pelvis carcinoma. Thus, MACC1 may represent a promising prognostic biomarker candidate, as well as a potential therapeutic target for this disease.



Autor: Hailong Hu , Dawei Tian , Tao Chen, Ruifa Han, Yan Sun, Changli Wu

Fuente: http://plos.srce.hr/



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