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Colorectal Cancer Serum Protein Fingerprint Analysis and Diagnosis Model and Prognostic Model Research

Author: LiXiaoQiong
Tutor: WangKaiZheng
School: Luzhou Medical College
Course: Internal Medicine
Keywords: Colorectal cancer SELDI-TOF-MS technology Artificial Neural Networks Prognosis Proteomics
CLC: R735.34
Type: Master's thesis
Year: 2011
Downloads: 28
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Abstract


Objective: colorectal cancer (Colorectal cancer) is one of the common malignant tumors in China, tops in the forefront of morbidity and mortality in Europe and the United States and China, and has remained the. In addition, colorectal cancer has been dropping, and symptoms of occult fast progression of the disease, most patients are diagnosed with colorectal cancer, is already in the late, resulting in the treatment of patients with colorectal cancer and poor prognosis, the long-term outcome after the effect of the judgment more difficult. Therefore, early detection, early diagnosis, early treatment that is \The main reason lies in the lack of early diagnosis is difficult and long-term prognosis of colorectal Evaluation of the lack of objective indicators of high sensitivity, specificity of methods for early diagnosis. Fecal occult blood test, cytology, digital rectal examination, endoscopy, imaging studies, and tumor markers can be used for the diagnosis of colorectal cancer, the efficacy evaluation, prognosis and recurrence and metastasis tracking, etc. However, these checks The sensitivity, specificity, and accuracy to be improved, and various scholars have reported mixed. There is an excellent serum tumor markers for colorectal cancer can be used clinically as distinguishing criteria of detection. The etiology and pathogenesis of colorectal cancer is still in the research stage, and all aspects of research involving gene expression, transcriptional regulation, cell restructuring and cytoskeleton, signal transduction, protein synthesis and overlap the identification of the molecular level studies of cancer patients hot spots. The cancer cells in the process of growth and metabolism, will leave traces, is only limited to the sensitivity of the detection means in general rather difficult to find. To provide experimental data in order to find traces of the serum of patients with colorectal cancer iconic differential proteome exploration colorectal carcinoma with protein gene transcription regulation mechanisms, as well as early diagnosis and Next gene therapy. The laboratory Surface laser desorption ionization time-of-flight mass spectrometry (Surface enhanced laser desorption ionization time of flight mass spectrometry, SELDI-TOF-MS)-binding protein chip screening differentially expressed proteins, combined with the technology of artificial neural network (artificial neural network, ANN) data mining, diagnostic and prognostic models, and examine the diagnostic efficiency of the model to the prognosis of patients with colorectal cancer, as well as help develop individualized medical measures and to explore its etiology and pathogenesis. Methods: 251 serum samples using SELDI-TOF-MS technology and its supporting gold chip (Gold Chip) detection: including colorectal cancer before surgery group (45 cases), postoperative good prognosis group (postoperative recurrence and metastasis-free 14 cases), postoperative poor prognosis group (postoperative recurrence or metastasis 13 cases), benign intestinal disease group (24 cases) and normal group (155 cases), protein fingerprints and get protein spectra Ciphergen Protein Chip 3.0 software calibration and analysis of the data using Ciphergen Biomaker Wizard 3.1 software screening differentially expressed proteins, and artificial neural network diagnostic model and prognostic models. Results: filter out seven express the marker proteins significant difference (P lt; 0.01), molecular weight 4955Da, 5325Da, 5890Da, 6615Da, 7739Da, 8109Da, 8575Da. The 7 signs proteins to establish the diagnosis of colorectal artificial neural network model, re-use molecular weight 4955Da, 5325Da 5890Da 6615Da 7739Da five differentially expressed proteins establish artificial neural network model of the prognosis of colorectal cancer. Diagnostic model sensitivity and specificity of diagnosis of colorectal cancer were 82.22% and 80.45%, negative predictive value of 94.74%, a positive predictive value of 51.39%, accuracy of 80.80%; prognostic model diagnosis of recurrent or metastatic colorectal cancer in line with the rate of 62.96%. Swiss-Prot protein database searches, preliminary identification of beta-defensin 38 Unchara-the-terized protein ypaB ypaBUPF0181 protein Probable replicative DNA helicase, 50S ribosomal protein L350, Secindependent protein translocase protein tatA / E homolog and Acyl carrier protein. Conclusion: The model of colorectal cancer using the screened logo proteomics to create, for the diagnosis and prognosis of patients with colorectal cancer have a certain significance, and colorectal cancer molecular pathology research new ideas.

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CLC: > Medicine, health > Oncology > Gastrointestinal Cancer > Intestinal neoplasms > Colorectal tumors
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