| Summary: | 博士 === 國立臺灣大學 === 電機工程學研究所 === 99 === Cancer markers can be utilized as therapeutic targets, diagnostic markers, and prognostic markers. Although lots of methods to identify marker genes or proteins were identified as cancer markers by directly compare the expression of the cancer tissues with its normal tissue of origin, the expression in other normal tissues and the functional impact were usually unconsidered. This thesis described three novel approaches to find out lung cancer markers based on different clinical and/or basic research purposes. In the first approaches, lung cancer associated plasma membrane protein coding genes were identified by considered with the sub-cellular location of proteins and the overall expression in the vital organs. The markers exploited by the approaches can be used as lung cancer therapeutic targets. The candidate genes were further examined for their expression in 13 kinds of normal tissues. The results were consistent with the results attained from bioinfomatic analysis. The study also delineated the enriched molecular functions and biological processes and the association of the markers to patients’ survival. The results demonstrated that the markers identified in this study may have great potential to serve as therapeutic and diagnostic targets.
In the second approaches, this thesis proposed an integrated system for high-throughput analysis to identify splicing events and transcript variants. The system resolves individual splicing events and elucidates transcript variants by integrated bioinformatic analysis, high-throughput transcript variant amplification, and high-resolution capillary electrophoresis. This novel system not only facilitates the validation of putative transcript variants and the detection of novel transcript variants, it also semi-quantitatively measures the transcript variant expression levels of each gene. The result demonstrate the system’s capability, the system used it to resolve transcript variants yielded by single and multiple splicing events, and to decipher the exon connectivity of long transcripts. The result indicated the system could be utilized to identify lung cancer associated transcript variants.
In the third approaches, this study employed gene sets to decipher the active oncogene in squamous cell carcinoma and the prognostic markers of lung adenocarcinoma. The analysis of gene set showed higher cell cycling and the IGF1R gene set were more active in squamous cell carcinoma. It indicates that the IGF1R pathway is significant in the proliferation signaling of squamous cell carcinoma. The method uncovered the signaling pathway accountable for the fast growth rate of squamous cell lung cancer and suggests that the IGF1R identified in this study may have great potential to serve as a therapeutic target of squamous cell lung cancer.
The gene set analysis was also utilized to measure the activity of the cell cycle, nutrient metabolism and the response to hypoxia in lung adenocarcinoma, and evaluate the accuracy for prognosis. Five gene sets were used to reflect the status of the three activities. Kaplan-Meier estimation, Cox hazard regression, and gene set based Cox model, a novel method developed in this study, were used to evaluate the capability of the gene sets for prognosis,. The results revealed that these gene sets have the power of risk assessment in lung adenocarcinoma, and are applicable to early stage patients. The data implicated that tumor with higher proliferation activity, greater metabolic rate, and more responsive to hypoxia is more malignant, even its size is still small. Based on the findings, 12 prognostic marker genes were used to present the status of the three processes and also applied for prognosis. The derived marker genes are valuable in risk assessment for early stage lung adenocarcinoma patients.
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