The Study Of Differential Proteomics In Pancreatic Cancer

Pancreatic cancer is one of the most malignant tumors in digestive system, due to its rising incidence, difficulties in early diagnosis, low rate of radical cure and poor survival. Unfortunately, its mechanism is not clear yet. Few effective tumor markers are used for early diagnosis. In recent years, the development of proteomic technology has expanded a new path for the study of pancreatic cancer. In our study, proteomic technologies were used to screen proteins which were differently expressed in serum and tissue of pancreatic cancer. A model of serum protein patterns was founded to differentiate patients between cancer and normal controls. The differential expressed proteins in cancer tissue were identified and compared with corresponding mRNA using cDNA microarray. Several proteins were proved to be relative with the development of pancreatic cancer.In PART I, SELDI ProteinChip technology was applied to screen abnormally expressed proteins in pancreatic cancer. Using the data of the training group, a predictive model was founded by bioinformation methods. The data of testing group was imported to this model to verify by blind method. In addition, sensitivity and specificity of both diagnosis model and CA19-9 were analyzed to evaluate the model in diagnosis of pancreatic cancer. The results showed that ProteinChip was an excellent platform for serum proteomics because of its good reproducibility and stability. IMAC3-Cu chip was a useful tool in serum proteomics of pancreatic cancer. Using IMAC3-Cu chip, 12 peaks of different expressed proteins were discovered. Six of those had more significant value in diagnosis of pancreatic cancer. With the principle of decision tree, a model was founded to diagnose pancreatic cancer, which was composed of 4 decisive nodes and 5 terminal nodes. With the blind test, the sensitivity of the model was 90.7% (39/43) and the specificity was 89.6% (43/48) in differentiating cancer and normal. The modal was better than CA19-9. Serial tests could raise the specificity to 97.9% (47/48), while parallel test could raise the sensitivity to 95.3% (41/43).In PART II, 2-DE and MS technology were applied to separate and identify the differential expressed proteins among the tissue of cancer, corresponding distant pancreas, normal pancreas and benign tumor. The improved 2-DE map had good reproducibility and resolution. 12 pairs of cancer and corresponding distant pancreas, 3 pairs of benign tumors and 3 normal pancreas were analyzed by 2-DE. 30 proteinswere found to be differently expressed between cancer tissues and normal tissues. Furthermore, 24 proteins were identified by MALDI-TOF-MS/MS, 15 proteins were up-regulation in cancer tissue, while 9 proteins were down-regulation. These proteins had relationships with pancreatic cancer and maybe became biomarkers or target proteins in the future treatments.In PART III, cDNA microarray was applied to analyze the difference of gene expression between the tissue of pancreatic cancer and corresponding distant pancreas. The data of genes and proteins were compared to discover the character of their difference. As a result, there were 572 abnormal genes between the tissues of cancer and corresponding distant pancreas. 178 genes were up-regulation, while 394 genes were down-regulation. Compared with data of proteins, 8 genes were found to have same regulation. Both down-regulation proteins were Calgizzarin, Actin alpha 1 skeletal muscle protein, Stathmin, Transgelin-2 and Transthyretin precursor; both up-regulation proteins were Annexin V, ATP synthase alpha chain and Ribosomal protein S12. These proteins were relative with pancreatic cancer. Conclusions: proteomic technologies, such as SELDI-ProteinChip 2-DE MS, had clinical significance and important applications in the study of pancreatic cancer. These technologies could help to screen biomarkers and illuminate mechanism. SELDI-based predictive model had better sensitivity and specificity than classic tumor marker CA19-9. The model should be improved to increase the ability of screening asymptomatic crowd. Many proteins were abnormally expressed in the tissues of pancreatic cancer and they altered in mRNA level simultaneously. These proteins had significant relationship with pancreatic cancer and should be further studied.