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Protein S100-A8: A potential metastasis-associated protein for breast cancer determined via iTRAQ quantitative proteomic and clinicopathological analysis.

ABSTRACT: Breast cancer is the most common malignancy in females, with metastasis of this type of cancer frequently proving lethal. However, there are still no effective biomarkers to predict breast cancer metastasis. The aim of the present study was, therefore, to analyze breast cancer metastasis-associated proteins and evaluate the association between protein S100-A8 and the prognosis of breast cancer. The isobaric tags for relative and absolute quantitation (iTRAQ) proteomic technique was used to analyze the differential expression of proteins between fresh primary breast tumor (PBT) tissue and fresh paired metastatic lymph nodes (PMLN) tissue. Subsequently, immunohistochemical staining was used to locate and assess the expression of protein S100-A8 in benign breast disease (n=15), primary breast cancer with (n=109) or without (n=83) metastasis, and in paired metastatic lymph nodes (n=109) formalin fixed paraffin embedded (FFPE) tissue. Staining scores were evaluated and the association between protein S100-A8 expression levels and the clinicopathological characteristics of 192 patients with breast cancer were evaluated using the ?2 test. Kaplan-Meier and Cox hazards regression analyses were utilized to investigate the association between the expression of protein S100-A8 and the prognosis of patients with breast cancer. A total of 4,837 proteins were identified using the iTRAQ proteomic technique. Among these proteins, 643 differentially expressed proteins were revealed. Protein S100-A8 expression levels were identified to differ between PBT and PMLN tissues. Immunohistochemical staining suggested a significant difference between NMBT and PMLN (P=0.002), and also between PBT and PMLN (P<0.001). Cox hazards regression model analyses suggested that histological grade (P=0.031) and nodal status (P=0.001) were risk factors for lymph nodes metastasis of breast cancer. Kaplan-Meier analyses revealed no significant relationship between protein S100-A8 expression level and overall survival rate of patients with breast cancer. In conclusion, by using the iTRAQ proteomic technique and immunohistochemistry staining, it was identified that protein S100-A8 may be associated with lymph nodes metastasis of breast cancer and be a marker for progression of breast cancer.

SUBMITTER: Zhong JM

PROVIDER: S-EPMC5840668 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Protein S100-A8: A potential metastasis-associated protein for breast cancer determined via iTRAQ quantitative proteomic and clinicopathological analysis.

Zhong Jing-Min JM Li Jing J Kang An-Ding AD Huang San-Qian SQ Liu Wen-Bin WB Zhang Yun Y Liu Zhi-Hong ZH Zeng Liang L

Oncology letters 20180206 4

Breast cancer is the most common malignancy in females, with metastasis of this type of cancer frequently proving lethal. However, there are still no effective biomarkers to predict breast cancer metastasis. The aim of the present study was, therefore, to analyze breast cancer metastasis-associated proteins and evaluate the association between protein S100-A8 and the prognosis of breast cancer. The isobaric tags for relative and absolute quantitation (iTRAQ) proteomic technique was used to analy ...[more]

PMID: 29552168

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Project description:Metastatic dissemination of colorectal cancer (CRC), the third most common cancer type, is responsible for CRC deaths. Understanding the transition of lymph node metastasis (LNM) from Stage II to Stage III is beneficial in the prognosis and intervention of CRC. In this study, a quantitative proteomic survey was conducted to investigate the LNM-associated proteins and evaluate the clinicopathological characteristics of these target proteins in CRC. By using the LC-MS/MS iTRAQ technology, we analysed the proteomic changes between LMN II and LMN III. Fresh tumours from the CRC specimens consisting of 12 node-negative (Stage II) and 12 node-positive (Stage III) cases were analysed by LC-MS/MS iTRAQ proteome analysis. Subsequently, tissue microarray with immunohistochemistry staining was conducted to access the clinicopathological characteristics of these proteins in 116 paraffin-embedded CRC samples, each for non-LNM and LNM CRC. To study the effects of the differentially expressed proteins on the potential mechanism, Boyden chamber assay, flow cytometry and shRNA-based assessments were conducted to examine the role of the epithelial-mesenchymal transition (EMT) and the invasiveness of CRC cells and others in vivo xenograft mouse model experiments. Forty-eight proteins were found differentially expressed between non-LNM and LNM CRC tissues. Protein abundances of chromogranin-A (CHGA) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) were observed in node-positive CRC (p < 0.05). Knockdown of CHGA and UCHL1 significantly regulate cancer behaviours of HCT-116, including inhibition of cell migration, invasiveness, cell cycle G1/S arrest and reactive oxygen species (ROS) generation. Mechanistically, the CHGA and UCHL1 inactivation displayed decreased levels of UCH-L1, chromogranin A, β-catenin, cyclin E, twist-1/2, vimentin, MMP-9, N-cadherin and PCNA through the activation of the Rho-GTPase/AKT/NFκB pathways. Histone modification of H3K4 trimethylation of CHGA and UCHL1 promoter were increased to activate their transcription through the signalling transduction such as Rho-GTPase, AKT and NFκB pathways. Our results indicated that UCHL1 and chromogranin A are novel regulators in CRC lymph node metastasis to potentially provide new insights into the mechanism of CRC progression and serve as biomarkers for CRC diagnosis at the metastatic stage.

Dataset Information

Protein S100-A8: A potential metastasis-associated protein for breast cancer determined via iTRAQ quantitative proteomic and clinicopathological analysis.

Publications

Protein S100-A8: A potential metastasis-associated protein for breast cancer determined via iTRAQ quantitative proteomic and clinicopathological analysis.

Similar Datasets

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