鑑定肺癌相關生物標記

Abstract

癌症標記在醫學與研究的運用相當廣泛，可以作為治療標靶，診斷標記，或者是癒後風險評估指標等等不同的用途。雖然過去的學術研究已經發表為數不少尋找癌症標記的研究方法，但是大多數找尋到的標記只單純考慮癌症細胞與相對應細胞的表達量，忽略該標記在身體其他重大器官表達量以及在生物機能上扮演角色的探討。針對這幾個方向，本論文研發了三種創新的生物資訊演算法來尋找及鑑定肺癌相關生物標記。在第一種方法當中，本研究建立一個用以尋找肺癌相關膜蛋白以作為治療標靶的演算法。此方法結合了身體重大器官的基因表達量測以及蛋白質於細胞體中的位置兩種生物資訊來找尋標記。利用此方法鑑定出的肺癌表面特異高度表達的膜蛋白可作為肺癌治療的標靶蛋白。將經生物資訊分析鑑定之標記經由實驗驗證與人體十三個正常組織相比較，驗證結果顯示與生物資訊分析的結果相吻合。本研究更進一步地探討這些癌症標記相關的生物機轉過程以及分子層級功能，也同時分析這些生物標記在病人檢體的表現量與其存活周期的關聯。這些結果證實了在此次分析中得到的生物標記基因可作為治療肺癌的標靶蛋白。此成果對於未來發展治療肺癌的方法上將會有很大的幫助。 在第二種方法，本論文研究建立一套整合性高通量分析系統用以量測選擇性剪接反應 (splicing event)以及用來鑑定轉錄變體 (transcript variant)。此系統整合了生物資訊、高通量轉錄變體放大技術、與高解析度毛細管電泳來分析各個選擇性剪接反應以及轉錄變體。這個創新的系統不僅可用於驗證已知的轉錄變體，並可用於偵測未知的轉錄變體。此系統並可半定量地量測某個基因所有轉錄變體的表達量。此套系統成功地偵測了具有單個或多個選擇性剪接反應的轉錄變體。此結果證明此系統可用於偵測肺癌相關轉錄變體標記。 在第三種方法中，本論文使用基因組的概念來探討肺鱗狀細胞癌相關致癌基因以及肺腺癌相關癒後評估。基因組的分析結果顯示與肺腺癌相比，肺鱗狀細胞癌擁有較高的生命週期運轉的活動力，IGF1R的基因組在鱗狀細胞癌中也呈現高度表達。此結果顯示IGF1R對於鱗狀細胞癌的異常增殖功能是顯著重要的。在此次分析中發現的IGF1R可作為肺鱗狀細胞癌治療的標靶基因。 本論文亦運用基因組方法分析肺癌細胞週期、新陳代謝、以及缺氧反應等三種生物機轉的活性。運用相關的五個基因組來分析量測肺腺癌患者在此三種功能上的活動力，並探討運用其結果作為病患存活週期的預測指標的可行性。此次研究結合了傳統存活分析，並且研發了一種基因組專用Cox存活分析模型來分析基因組對於病患預後分析的能力。計算分析結果顯示此五個基因組在腫瘤有比較高的分數，此結果應與腫瘤異常增生有關。更進一步，存活分析的結果亦證實了此五個基因組可用於預測肺腺癌患者之預後，尤其是針對早期患者。結果指出當體積小可是其細胞週期、新陳代謝、以及缺氧反應活動力強的腫瘤仍然是惡性、預後較差的癌症。根據這樣的發現，此十二個標記基因可來測量此三種生物機轉過程的程度，並運用其分數來預測病人的預後。此發現顯示這十二個標記基因將有益於早期肺腺癌患者醫囑上的評判。

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.