| Summary: | 博士 === 國立中央大學 === 資訊工程學系 === 102 === Based on the diagnosis result of clustering cancer and normal tissues, doctors can go further the cancer treatment for the patients. By means of the medical image recognition, the histopathology of the surgical biopsy, or identifing the statistically differential genes with gene expression profiles to achieve the medical application of clustering cancer and normal tissues.
First medical application of the dissertation, we clustered cancer and normal tissues by K-means and classified cancer and normal tissues by the perceptron model for two datasets of lung tissues, two datasets of pancreas, and one leukemia dataset. We derived out 400 differential genes between cancer and normal tissues in each datasets. Then we clustered and classified the cancer and normal tissues using the the 400 differential genes. The mean accuracy of classifying cancer and normal tissues by the perceptron model was 99.6% which was higher than that of 91.7% mean accuracy by the K-means. Moreover, we improved the mean accuracy of clustering cancer and normal tissues by K-means with the Shannon entropy (93.4%) instead of Euclidean distance (91.7%).
The second medical application, we wanted to find the scientific evidence to prove that psychology influenced health, including the survival time of cancer patients. The psychological factor of loneliness influenced on human survival which was established in the epidemiologically, but genomic research was undeveloped.
We applied statistical methods to get the loneliness-associated genes between the high lonely and low lonely groups. With the loneliness-associated genes, we made use of Cox proportional hazards regression to prove that the psychological factor of loneliness influenced on the survival time of different kinds of cancer patients.
We verified that the high-risk score of cancer patients have shorter mean survival time than the low-risk score of cancer patients. After that we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85, and 191). Kaplan-Meier survival curves of the log-rank test in brain cancer cohorts were significantly separable and had hazard ratio (HR) >1, p-value <0.0001 with log-rank test. Moreover, we testified the loneliness-associated gene signature in the bone cancer cohort, lung cancer cohort, ovarian cancer cohort, and leukemia cohort. The last lymphoma cohort was also proved. The loneliness-associated genes had good survival prediction for different kinds of cancer patients, especially bone cancer patients. In addition, our study furnished the first indication that the psychological factor of loneliness influenced on the survival time in different kinds of cancer patients with genome transcription.
We employed statistical methods of Student's t-test, area under the Receiver Operating Characteristic (ROC) - we called it as AUC, Wilcoxon test, Cheronoff bound, and relative entropy to find out the statistically significant difference genes between cancer and normal tissues in our first medical application. And we found out the loneliness-associated genes with the five statistical methods to predict survival of cancer patients in our second medical application
In our experiments of clustering cancer and normal tissues, the statistical method of Student’s t-test to figure out the genes of significant difference between cancer and normal tissues that resulted 95.3% mean accuracy in clustering cancer and normal tissues, and 93.2% mean accuracy by AUC. Next in the section of psychological factor of loneliness influenced on the survival time of cancer patients, we got the highest average value (3.875) of hazard ratio for 8 cancer corhorts by the AUC method to identify the loneliness-associated genes to predict survival in cancer patients.
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