6288

SAA1

serum amyloid A1;SAA1;serum amyloid A1

serum amyloid A protein|serum amyloid A-1 protein|tumor protein p53 inducible protein 4

11p15.1

protein-coding

Lung cancer is recently regarded as an overhealed inflammatory disease. Serum amyloid A (SAA) is known as an acute phase protein, but it is likely involved in the cancer pathogenesis. We identified both SAA1 and SAA2 in the pooled sera of lung cancer patients but not in the healthy control, by LC-MS/MS analysis. We found that about 14-fold higher levels of SAA in lung cancer patients' sera and plasma compared to healthy controls by ELISA using total 350 samples (13.89 +/- 37.18 vs 190.49 +/- 234.70 ug/mL). The SAA levels were also significantly higher than in other pulmonary disease or other cancers. An immunohistochemical study using tissue microarray showed that, unlike other cancer tissues, lung cancer tissues highly express SAA. Further in vitro experiments showed that SAA is induced from lung cancer cells by the interaction with THP-1 monocytes and this, in return, induces MMP-9 from THP-1. In in vivo animal models, overexpressed SAA promoted Lewis lung carcinoma (LLC) cells to metastasize and colonize in the lung. Our data suggest that a higher concentration of SAA can serve as an indicator of lung adenocarcinoma and represents a therapeutic target for the inhibition of lung cancer metastasis.

The synthesis of acute-phase protein serum amyloid A (SAA) is largely regulated by inflammation- associated cytokines and a high concentration of circulating SAA may represent an ideal marker for acute and chronic inflammatory diseases. However, SAA is also synthesized in extrahepatic tissues, e.g. human carcinoma metastases and cancer cell lines. An increasing body of in vitro data supports the concept of involvement of SAA in carcinogenesis and neoplastic diseases. Accumulating evidence suggests that SAA might be included in a group of biomarkers to detect a pattern of physiological events that reflect the growth of malignancy and host response. This review is meant to provide a broad overview of the many ways that SAA could contribute to tumour development, and accelerate tumour progression and metastasis, and to gain a better understanding of this acute-phase reactant as a possible link between chronic inflammation and neoplasia.

BACKGROUND: In metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with overall survival (OS). PATIENTS AND METHODS: Sera from 114 mRCC patients were screened by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS). Identified proteins were related to OS. Three proteins were subsequently validated with enzyme-linked immunosorbent assays and immunoturbidimetry. Prognostic models were statistically bootstrapped to correct for overestimation. RESULTS: SELDI-TOF MS detected 10 proteins associated with OS. Of these, apolipoprotein A2 (ApoA2), serum amyloid alpha (SAA) and transthyretin were validated for their association with OS (P = 5.5 x 10(-9), P = 1.1 x 10(-7) and P = 0.0004, respectively). Combining ApoA2 and SAA yielded a prognostic two-protein signature [Akaike's Information Criteria (AIC) = 732, P = 5.2 x 10(-7)]. Including previously identified prognostic factors, multivariable Cox regression analysis revealed ApoA2, SAA, lactate dehydrogenase, performance status and number of metastasis sites as independent factors for survival. Using these five factors, categorization of patients into three risk groups generated a novel protein-based model predicting patient prognosis (AIC = 713, P = 4.3 x 10(-11)) more robustly than the MSKCC model (AIC = 729, P = 1.3 x 10(-7)). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients. CONCLUSIONS: Proteomics and subsequent validation yielded two novel prognostic markers and survival models which improved prediction of OS in mRCC patients over commonly used risk models. Implementation of these models has the potential to improve current risk stratification, although prospective validation will still be necessary.