Date of Award
4-21-2015
Document Type
Dissertation
Degree Name
Molecular Biosciences, Ph.D.
First Advisor
Xiuzhen Huang
Committee Members
Carole Cramer; Elizabeth Hood; Gail McClure; Jie Miao; Shuzhong Zhang
Call Number
LD 251 .A566d 2015 J45
Abstract
Lung Cancer is the leading cause of cancer-related deaths in the United States. The key to surviving this disease is early discovery and treatment. Precise diagnosis is critical when selecting the treatment for the patient. Diagnosis of the patient will depend upon the location of the tumor, the stage of the tumor, and the class of the tumor. Clinically, lung cancer is divided into two classes: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Non small-cell lung cancer accounts for 85% of all lung cancer. NSCLC can be separated into three major subtypes: adenocarcinoma, squamous-cell, and large-cell carcinoma. Of these, adenocarcinoma comprises 40% of NSCLCs. Adenocarcinoma poses the most risk to the patient due to its tendency to rapidly metastasize to other organs. Traditionally oncologists utilize the histology of a tumor in order to classify it. Global gene expression profiling using microarray technologies has helped to improve our understanding of non-small cell lung cancer. However, it is very challenging for the comprehensive molecular study of the heterogeneity of lung cancer to identify potential gene markers for classifying tumor samples with significantly different survival outcomes. Current clustering approaches for molecular subtyping have limitations. We are proposing a new clustering framework for lung cancer molecular subtyping and facilitating new gene marker discovery. This framework will also be applicable to the comprehensive molecular study of other cancers.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Johnson, Daniel L., "Determining Lung Cancer Subtypes, Classifications, and Potential Gene Markers" (2015). Student Theses and Dissertations. 715.
https://arch.astate.edu/all-etd/715
