在癌症治療中，病人的存活率與階段性的診斷有很大關係。儘管許多癌症治療方法已開發且獲得證明，但因診斷普遍較晚，並不總是成功，因此迫切需要對診斷做更進一步的推進。一個好的生物標記物可以作為癌症的早期診斷、助於預後和生存。普遍認為生物標記物的發現是透過對DNA、RNA、醣類、代謝物和蛋白質等分子的分析，客觀地分析出體液或組織樣品中健康捐贈者癌症患者的特徵。在生物標記物的開發中，其過程涉及到開發至後面驗證的實驗設計。隨著質譜技術的發展，全圖譜方法已成為開發生物標記物最有力的工具。質譜技術與液相層析的結合（LC-MS）提供了更大的分析特異性，這提供了多體學研究進展最主要的動力，在我的研究中將利用多體學的角度去進行腫瘤標記之開發。
醣基化是一種轉譯後修飾，其功能已被報導參與調控蛋白質的功能、穩定、運送和循環。 N-glycan的變化也被證實與許多癌症類型的發展有顯著的關聯性，包括腫瘤的進展、細胞爬行、侵襲、血管新生以及轉移等等。在醣質體學研究中，我們將其醣蛋白利用PNGaseF將N-glycan切除釋放，並通過C18固相萃取法純化。醣鏈在未知相關蛋白的情況下透過MALDI-TOF的分析，發現與健康對照組相比，口腔鱗狀細胞（OSCC）癌患者血清中的N-glycan產生了變化。在我們的研究中，健康捐贈者和口腔鱗狀細胞癌患者的血清N-glycan被釋放、純化後再進行甲基化後進行分析後發現有7種N-glycan結構的相對表現在OSCC的血清中有呈現減少或增加的趨勢，診斷準確性大於75%。在OSCC的血清N-glycan中，具有不同程度的岩藻醣基化和矽藻醣基化的三分支醣鏈結構和四分支醣鏈結構的相對表現也有所增加。我們另外申請48 位OSCC患者血清作為驗證組，依據cutoff值計算，發現高分子量的血清N-glycan結構表現出明顯的高敏感性和特異性。
蛋白質體學研究主要是辨別和量化人體生物樣本中表現差異的蛋白質或胜肽，並將有可能性的表徵作為潛在的癌症生物標記物。由於蛋白質有複雜的轉譯後修飾，且蛋白質又具有高度動態性，這意味著後續的驗證非常重要。近十多年來，儘管許多經過驗證的蛋白質生物標記物已應用於臨床，但只有少數具有足夠的特異性和敏感性。因此，臨床上迫切需要開發新穎且具敏感和特異性的生物標記物用於早期診斷癌症並改善預後，特別是在高死亡率腫瘤中。在我們的研究中，我們將肺癌患者與健康受試者的血漿樣本進行分析，並涵蓋了四個不同分期。 為了探索肺癌患者的蛋白差異，我們應用 TMT 標記蛋白質體學和通過 Q-Orbitrap-LTQ Tribrid MS (Thermo Fusion Lumos) 分析的血漿蛋白質組。目的在於希望能鑑定出做為診斷的特異蛋白，並研究特定蛋白的表現是否與腫瘤進展相關。原始數據透過 Progenesis QI (WATERS) 做數據處理及蛋白質鑑定。在我們的方法中，健康組和肺癌血漿中檢測到 34,489 個肽並鑑定出 479 個蛋白質。 我們觀察到肺癌患者血漿中的 CRISP3 和 IGFBP2 表現上升，而 KAL 和 SEMA3C 表現下降。接著我們另外申請不同批次的肺癌癌症患者血漿，透過 ELISA 技術，我們找到的標記物獲得驗證並定量計算其cut-off值。即便ROC曲線分析顯示，CRISP3、IGFBP2、KAL和SEMA3C的診斷表現差強人意，但透過邏輯回歸的組合仍展現出不錯的專一性及特異性，其AUC值達到0.837。
在代謝體學研究中，我們利用UPLC/Orbitrap QE Plus MS對健康對照組和大腸直腸癌患者血漿中的代謝物進行了分析。應用隨機森林演算法對大數據的非靶向代謝物方法訊號進行分類，結果顯示有1,007個特徵被挑選為對分類有幫助。 當中有58個特徵在ROC曲線中有顯著表現，透過MassBank資料庫比對鑑定到8個代謝物，包括茶鹼、亞麻酸、L-亮氨酸、DL-正亮氨酸、delta-Valerolactam（2-哌啶酮）、N-甲基胞嘧啶、（-）Asarinin和Kobusone。為了進一步驗證和量化已知代謝物，當中有5個標準品得以購得應用於後續驗證，包括茶鹼、亞麻酸、DL-正亮氨酸、2-哌啶酮和N-甲基胞嘧啶。驗證組包括了來自E-DA醫院的46名大腸癌患者的血漿樣本及另外招募了47名健康的捐助者進行驗證。研究結果顯示驗證組呈現出與開發組相同的趨勢。這些代謝物對癌細胞的細胞功能的影響隨後在不同的大腸癌細胞系中被觀察到。綜合以上，在我們的研究中，我們試圖用多體學方法建立一個全面的腫瘤生物標誌物數據庫，即便尚未完整，但仍引出建立多體學腫瘤標誌物及大數據演算法應用的重要性，可望能提高臨床上癌症診斷和預後檢測的準確性和精確性。

In cancer therapy, the patient survival rate is broadly related to diagnosis at stages. Despite many cancer treatments that have been developed and proven, they are not always successful due to late diagnosis. Thus, advanced screening for early-stage diagnosing is needed. A good biomarker can improve the early diagnosis, prognosis, and survival of cancers. Biomarker discovery is objectively analyzed characteristic that identifies a healthy donor or cancer patient in body fluid or tissue sample by profiling molecules including DNA, RNA, glycan, metabolite, and protein. In biomarker development, the processes involve linking initial discovery and validation. With Mass spectrometry advanced, the Omics approach has been a powerful tool for biomarker discovery. Mass spectrometry technique with liquid chromatography-mass spectrometry (LC-MS) provides much greater analytical specificity which is the major driving force behind progress in Omics research.
Glycosylation is a post-translational modification that governs protein functionality, stability, trafficking, and degradation. Alterations in N-glycan structures have been demonstrated to exhibit a notable correlation with the progression of various cancer types, influencing key aspects such as cell migration, invasion, angiogenesis, and metastasis. In the glycomics study, N-glycans were harvested by PNGaseF and purified through a C18 cartridge column from the serum specimen. The N-glycan structure was analyzed by MALDI-TOF which was altered in serum specimens from disease states compared to healthy donors without prior knowledge of the related protein. In our study, serum N-glycan was released, purified, and permethylated from healthy donors and oral squamous cell carcinoma (OSCC) patients, and analyzed by MALDI-TOF-Mass spectrometry. We found that the serum of OSCC patients displayed changes in the relative abundances of seven N-glycans, with a diagnostic accuracy exceeding 75%. Moreover, there was a rise in the proportional prevalence of tri-antennary and tetra-antennary glycans exhibiting diverse degrees of fucosylation and sialylation. In a separate validation group consisting of 48 OSCC patients, most of the high-molecular-weight serum N-glycans demonstrated high sensitivity and specificity based on the identified cutoff values.
The proteomics study is focused on identifying and quantifying the molecular level of proteins or peptides differentially expressed in the human sample and making it possible to characterize them as a potential cancer biomarker. Proteins expressed are highly dynamic because of complex post-translational regulation, and it means well validation seems quaintly important. For a decade, many proven protein biomarkers have been applied in clinical, but only a few of them show adequate specificity and sensitivity. Therefore, there is an urgent need to discover novelty, sensitive, and specific biomarkers for the diagnosis of cancer early and improve prognosis, specifically in high-mortality tumors. In our study, we conducted an analysis of the plasma samples from patients with lung cancer across four stages of disease progression. To explore the differential proteins of lung cancer and healthy group, we performed TMT labeling proteomics and the plasma proteome of cancer analyzed by Q-Orbitrap-LTQ Tribrid MS (Thermo Fusion Lumos) and aimed to identify unique proteins for diagnosing and to investigate whether the expression of specific proteins correlates with tumor progression. The raw spectrum was normalized, aligned, and identified by Progenesis QI (WATERS) software. In our method, 34,489 peptides were detected, and 479 proteins were identified in plasma from healthy donors and five cancer groups. We observed an upregulation of CRISP3 and IGFBP2 levels in lung cancer patients, while KAL and SEMA3C levels were downregulated. In addition, another batch of cancer patient specimens was also validated and quantified by the ELISA platform for candidate biomarkers, and their cut-off values were calculated. Despite the ROC curve analysis indicating poor individual diagnostic performance for CRISP3, IGFBP2, KAL, and SEMA3C, the combination derived from logistic regression exhibited robust sensitivity and specificity, achieving an AUC value of 0.837.
In the metabolomics study, we utilized UPLC/Orbitrap QE Plus MS with pattern recognition techniques to profile metabolites in the plasma of healthy controls and colorectal cancer patients. Random Forest algorithm was used to classify the large-scale untargeted metabolite approach, which resulted in 1007 features selected as critical for classification. 58 features were significant in the ROC curve and 8 metabolites were identified in the MassBank database, including Theophylline, Linolenic acid, L-Leucine, DL-norleucine, delta-Valerolactam (2-Piperidone), N-methylcytisine, (-) Asarinin, and Kobusone. To further validate and quantify the identified metabolites, we searched for available metabolite standards for purchase and obtained a total of 5 standards for follow-up testing, including Theophylline, Linolenic acid, DL-norleucine, 2-Piperidone, and N-methylcytisine. The validation cohort consisted of 46 CRC plasma samples obtained from E-DA Hospital, while 47 healthy donors were recruited for validation purposes. The validation cohort comprised 46 plasma samples from individuals with colorectal cancer (CRC) obtained from E-DA hospitals. Additionally, 47 healthy donors were recruited for validation. The findings demonstrated a consistent trend with the development cohort. Moreover, the cellular functional impacts of these metabolites on cancer cells were subsequently observed in various colorectal cancer cell lines. In summary, our study aimed to construct a comprehensive tumor biomarker database through a multi-omics approach. Even though it is not yet complete, this underscores the significance of establishing a comprehensive multi-omics approach to tumor markers and the utilization of big data algorithms This approach may substantially enhance the accuracy and precision of cancer diagnosis and prognostic testing in clinical settings.

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