The Study Of Protein Profiling Of Pancreatic Cancer And Correlation Of CCR7 With Lymph Node Metastasis Of Pancreatic Cancer

一. Background and objectivePC is one of the most malignant tumors in digestive system, due to its rising incidence, difficulty in early diagnosis, rapid progress and poor prognosis.Currently, there are no ideal methods for the early detection of PC. Only 10-20% of patients (5-7.5% can be under radical correction) are resectable at the time of diagnosis and 5-year survival rate after operation is still 15-40%.The early diagnosis and right treatment are closely related to death. The preoperative assessment is one key factor of the choice of treatment, the prediction of survival and the plan of postoperative follow-up and local recurrence or distant metastasis confirms the failure of treatment. It's important for the improving of prognosis to achieve the early and right diagnosis and to monitor the recurrence and metastasis. CA19-9 is the most commonly used tumor marker but value little for the early diagnosis, prognosis evaluation and detection of recurrence. So it's necessary to seek for new biomarkers.By now, PC is proved not a single disease, but an accumulation of sophisticated biological evolutions include polygene, muti-procedure and multistage under the role of environment and heredity. Also in the procedure exists the function lost of cancer suppressor gene and activation of oncogene. Therefore, the attempt of early diagnosis, prognosis evaluation and monitoring for local recurrence and/or metastasis by the detection of single or several factors has unavoidablely the conflict between the sensitivity and specificity. Protein is the production of gene expression and tumor is believed to be a disease of protein's pitfall. Numerous of proteins changed during the included different modifications after protein translation as well as the expression level, resulted in the change of protein expression profiles of tumor tissue or body fluid. Therefore there is much need in the proteome level for further research of the development of malignant tumor. It detects the functional units of expressed genes using proteomic methods to analyze celluar proteins and provide a protein fingerprint. The proteomic reflects both the intrinsic genetic programme of the cell and the impact of the immediate environment and is therefore valuable in biomarker discovery. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is a new method of proteomics developed in recent years. Not only because of its high sensitivity and high throughput, but also its ability of reflecting the full protein profiles of tested sample and eventually finding the protein biomarkers, it is a promising method for early diagnosis, prognosis evaluation and monitoring of local recurrence and metastasis of tumorous diseases.Serum samples were applied to strong anionic exchange chromatography (SAX2) protein chips for protein profiling by SELDI-TOF-MS to distinguish PC, Pancreatic benign disease and normal healthy control. Statistical Methods in Bioinformatics were used to analyze the multiple protein peaks. The valuable protein peak was identified using SELDI Immunoassay and enzyme linked immunosorbent assay (ELISA) and its tissues expression was further confirmed.MethodsSerum samples obtained from subjects with PC (n=58), Pancreatic benign diseases (n=18) and normal healthy control (n=51) were applied to SAX2 chips for protein profiling by SELDI-TOF-MS to screen multiple serum biomarkers. The decision tree or logistic regression classification models were constructed to diagnose PC, evaluate the prognosis and curative effect, compared with CA19-9. The valuable protein peak was identified using SELDI Immunoassay and further confirmed by ELISA and immunohistochemical analysis.Results1. Sixty-one qualified protein peaks between 2 000 and 30 000 m/z ratios were detected and significant differences were detected in the levels of 26 serum protein biomarkers between PC patients (n=58) and the healthy controls (n=51) (P<0.001). The top 10 decision trees with the highest correct validation rate were chosen to establish the classification tree model, which had the high positive predictive value of 0.929, a sensitivity of 0.833, and a specificity of 1.000. It was significantly better to use a combination assay of seven protein biomarkers (AUC,0.976; P<0.001) in diagnostic power, resulting in a specificity of between 0.922 and 1.000 and a corresponding sensitivity of between 0.914 and 0.776. Application of this logistic model classification using combinations of the seven protein biomarkers gave diagnostic accuracies of up to 0.857 in the independent testing set.2. Significant differences were detected in the levels of 16 serum protein biomarkers between PC patients and Pancreatic benign disease patients (P<0.05). The top 10 decision trees with the highest correct validation rate between 0.90 and 0.95 were chosen to establish the classification tree model, which had the high positive predictive value of 0.929, with a sensitivity of 0.833 and a specificity of 1.000. It was significantly better to use a combination assay p of three protein biomarkers (AUC,0.933; P<0.001) in diagnostic power, resulting in a specificity of between 0.769 and 1.000 and a corresponding sensitivity of between 0.958 and 0.708. Application of this logistic model classification using combinations of the three protein biomarkers gave diagnostic accuracies of up to 0.857. However, there was a strong trend for a superior discrimination of PC from Pancreatic benign disease combining SELDI profiling and CA19-9 (P<0.001). The combination p1 of CA19-9 and the discriminating peaks had an AUC of 0.976 (P<0.001), resulting in a specificity of between 0.923 and 1.000 and a corresponding sensitivity of between 0.958 and 0.750, and diagnostic accuracies of up to 0.929. So the classification tree model was superior to CA19-9 in the discrimination of PC from Pancreatic benign disease. The latter had a sensitivity of 0.813 and a specificity of 0.770.3. The five most discriminating protein biomarkers were detected through the comparison of the PC group and the noncancer group (i.e. healthy controls and the Pancreatic benign disease group), which yielded an AUC of 0.763 between the group of PC and healthy controls and 0.865 between another group of PC and Pancreatic benign disease. There was a strong trend for a superior discrimination of PC from Pancreatic benign disease when combining SELDI profiling and CA19-9 (P<0.001). The combination p of CA19-9 and the five discriminating peaks had an AUC of 0.971 resulting in a sensitivity of between 0.923 and 1.000 and a specificity of between 0.927 and 0.917.4. A panel of the six most discriminating protein biomarkers could classify patients with PC in different stages (P<0.01). There was a significant improvement when predicting different PC stages by combining the SELDI six protein peaks. The AUC were 0.897 (between stage I and stageⅡ),0.978 (between stageⅡand stageⅢ), and 0.792 (between stageⅢand stageⅣ) (P<0.05) in the diagnosis of different PC stages resulting in a sensitivity of between 0.839-0.871/0.903-1.000/0.750-0.833 and a specificity of 1.000-0.400/0.968-0.833/0.938-0.500.5. There was a down-regulated trend (P<0.05) in the most discriminating protein biomarker(M4 016)through analyzing the pre-operative cancer group and the postoperative cancer group (1,2,4 weeks, and 6 months after operation).6. We performed a SELDI-based immunoassay with a specific anti-C14orf166 monoclonal antibody on 10 pancreatic serum samples:five for which the 28 068 Da peak was present and five for which a 28 068 Da peak was nearly absent on the SAX2 chip. We found that a specific peak of mean mass at 28 068 Da with an intensity of 3.33±1.76 was present in all five samples that displayed a peak on the SAX2 chip, and the peak had an intensity of 0.60±0.43 in the other five samples.7. In 127 individual serum samples including 58 PC,18 Pancreatic benign diseases, and 51 healthy controls, C14orf166 levels were detected. C14orf16 serum concentrations were significantly higher in patients with PC (24.21±10.42μg/ml) than in patients with Pancreatic benign diseases (9.11±4.57μg/ml) and healthy controls (7.78±3.69μg/ml) (P<0.001). There was no statistically significant difference when comparing levels in patients with Pancreatic benign diseases and healthy controls (P= 0.34). The sensitivity and specificity of a serum C14orf166 level of 14.56μg/ml for predicting PC in healthy controls was 0.828/0.922 and 0.828 /0.889 compared with benign disease, respectively, which yielded an AUC of 0.938/0.917 (P＜0.001).8. We analyzed C14orf166 expression by immunohistochemistry in patients undergoing pancreatectomy including 35 PC. Semi-quantitative scoring showed that C14orf166 expression levels of cancer cells (5.05±3.0) were significantly higher than in the normal pancreas (0.83±0.95, P<0.001). C14orf166 protein was localized in the cytoplasm of tumor cells.Conclusions1. SELDI-TOF-MS was an ideal technological platform for proteomic research because of high reproducibility and stability. Quality control and standardization conditions could be key issue for the reliability of outcome.2. The results suggest that SELDI-TOF-MS serum profiling is helpful to the diagnostic, prognostic or therapeutic effects of PC, which is superior to CA19-9.3. SELDI immunoassay is useful for the identification of the significant protein peak screened by SELDI-TOF-MS.4. The identified protein biomarker C14orf166 is a potential biomarker of PC and lays the foundation for the pathogenesis research. Objective:To investigate the relationship of chemokine receptor CCR7, CXCR4, VEGF-C and the lymph node metastasis of PC. Methods:The transcription levels of CCR7, CXCR4 and VEGF-C in PC(n=24) were measured by Real-time PCR, the expressions of CCR7, CXCR4 and VEGF-C were measured by immuohistochemistry(n=65). The professional software of pathological image manipulation (Image Pro Plus 6.0, IPP 6.0)was used to quantitate the results of the immunohistochimical staining including mean density and integrated option density (IOD). Results:The transcription of CCR7 and VEGF-C in PC with lymph node metastasis increased compared with PC without lymph node metastasis (P<0.05), but that of CXCR4 was irrelevant to lymph node metastasis (P>0.05). The transcription of CCR7, CXCR4 and VEGF-C in PC increased compared with the normal pancreas (P<0.05). The mean dentisy of CCR7 in PC with lymph node metastasis increased compared with PC without lymph node metastasis(P<0.05), but that of CXCR4 and VEGF-C was irrelevant to lymph node metastasis(P>0.05). The IOD of CCR7 and VEGF-C in PC with lymph node metastasis increased compared with PC without lymph node metastasis (P<0.05), but that of CXCR4 was irrelevant to lymph node metastasis (P>0.05). The mean density and IOD of CCR7 and CXCR4 in PC decreased compared with the adjacent pancreas (P<0.05) but there was no diffirence in both group for VEGF-C(P>0.05). Conclusions:CCR7 and VEGF-C not CXCR4 seem to play a pivotal role in lymph node metastasis of PC.