基于基因表達(dá)譜的結(jié)直腸癌分子分型和預(yù)后評(píng)估
[Abstract]:Background and Objective Colorectal cancer is one of the most common malignant tumors in the world. The morbidity and mortality of colorectal cancer rank the third and fourth in malignant tumors respectively. Colorectal cancer is characterized by high heterogeneity in molecular formation mechanism and pathological morphology, which is a great challenge to the diagnosis, treatment and prognosis of colorectal cancer. The study of tumor gene expression profiles and the selection of characteristic information genes are the most direct means to explain the mechanism of tumorigenesis and development and to find therapeutic targets and prognostic markers. Differentially expressed genes were used to construct the differential diagnosis model of colorectal cancer; unsupervised clustering analysis and genetic analysis were combined to classify colorectal cancer, and the gene expression patterns of different types and their correlation with clinicopathological indexes were analyzed; prognostic index was used to evaluate the prognosis of colorectal cancer patients, and prognostic index was established. Methods 127 patients with colorectal cancer who were diagnosed and treated in a hospital from September 2006 to February 2012 were collected and their postoperative colorectal resection colorectal cancer were collected. Six pairs of colorectal cancer tissues and normal tissues were transcribed and sequenced, and the differentially expressed genes were selected. Seventy-seven genes larger than 10 were selected and validated by enlarged sample. Seventy-five genes with significant difference in expression and consistent direction and sequence were selected for follow-up study. The regression coefficients given by the case risk regression model were used to calculate the prognostic indices of each patient; Logistic regression model was used to analyze the multivariate analysis of binary observation outcome data; _2 test or Fisher's exact test was used to compare the composition ratio among groups; Wilcoxon rank sum test was used to compare and analyze the independent non-normal data; and R. OC curve analysis was used to evaluate the specificity and sensitivity of LASSO regression model and Logistic regression prediction model; ROC curve analysis was used to evaluate the value of prognostic index in survival assessment; unsupervised clustering was used to classify colorectal cancer tissues. The results of this study showed that 75 colorectal cancer tissues and normal groups were consistent with the results of RNA-seq. The overexpression of 13 genes was associated with the prognosis of colorectal cancer patients. The overexpression of CPNE8, LOC646627, CDKN2A, ATP6V1A, CA1, SCARA5, BEST4, SCNN1B, KLF9 was unfavorable to the prognosis of colorectal cancer patients. The overexpression of DNMT3B, ANLN, DNMT1, DNMT3A was beneficial to the prognosis of colorectal cancer patients. Five genes were independently associated with prognosis: high expression of DNMT3B was independently associated with prognosis, and high expression of LOC646627, SCARA5, CDKN2A, ATP6V1A was independently associated with prognosis. LASSO analysis was applied to multiple linear regression model to select 18 colorectal cancer characteristic genes (MLH1, PLOD3, TGM2, ATP6V1A, SQLE, MET, S100P, MT1M, BEST4, C). A7, LOC646627, ANPEP, P2RX1, FOXF2, GAB3, ABI3BP, SCARA5, ADAMDEC1 were used to construct a differential diagnosis model for colorectal cancer. The specificity, sensitivity and accuracy of the model were 96.85%, 98.43% and 97.6% respectively. Seventy-five differentially expressed genes, 13 prognostic-related genes and 5 prognosis-related genes were used in this study. Cluster analysis of 127 patients with colorectal cancer by independent related genes showed that 13 genes and 5 genes could be used to classify the patients into two groups with different prognosis. However, when adjusted for age, sex and TNM stage, the clustering results of 5 genes were still related to the prognosis of patients. DNMT3B was highly expressed in the first type, SCARA5 and LOC were high in the first type. 646627, CDKN2A was highly expressed in the second subtype, and the prognosis of the first subtype was better than that of the second subtype. PI-5 gene was superior to TNM staging in predicting 1 year, 3 years, and 5 years survival. The sub-curve scores of PI-5 gene and TNM staging were 0.719, 0.772 and 0.772, respectively. The prognostic index classification was added to the TNM prognostic prediction model. The actual survival prediction ability of the prognostic index classification was evaluated by the non-risk-based reclassification improvement index (cfNRI). It was found that the prognostic index classification could significantly improve the one-year, three-year, and five-year survival prediction ability of the model. The annual prognostic predictive ability (P 0.001) and cfNRI were 0.381, 0.507 and 0.465, respectively. Conclusion LASSO screening of colorectal cancer characteristic genes, and the construction of colorectal cancer differential diagnosis model, tumor tissue and normal tissue with high accuracy, and the specificity, sensitivity and accuracy of the model are higher than the traditional ones. Logistic regression model. Based on the differential gene expression profiles of colorectal cancer tissues and normal tissues, unsupervised clustering method can be used to classify colorectal cancer. The typing results have some explanatory power for the occurrence and development of colorectal cancer. The prognostic indices were more accurate than TNM staging, and the prognostic evaluation combined with TNM staging and prognostic index grading was more comprehensive and accurate.
【學(xué)位授予單位】:浙江大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2017
【分類(lèi)號(hào)】:R735.34
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