利用生物資訊分析肝癌病患血清蛋白之變異醣抗原

Abstract

現行對於肝細胞癌 (Hepatocellular Carcinoma, HCC) 的診斷除了透過超音波照影及放射照影外，由食品藥物管理署 (Food and Drug Administration, FDA) 審核通過血清中甲型胎兒蛋白 (Alpha-Fetoprotein, AFP)的檢測，為肝癌血液生化檢測的主要方法。甲型胎兒蛋白的表現量上升與肝細胞癌發生有著緊密相關性，然而，高達40%的肝細胞癌病患其甲型胎兒蛋白的表現量呈現正常範圍 (血清中含量約為每毫升2~20奈克以下)，導致偽陰性診斷與檢測靈敏度及專一性不足。此外，含有少量甲型胎兒蛋白(每毫升2奈克)的肝疾病病患，如B型肝炎病毒感染 (Hepatitis B Virus infection, HBV)患者與肝硬化 (Liver Cirrhosis , LC)患者，再診斷的靈敏度也備受挑戰。儘管急性蛋白 (Acute-phase proteins)上的唾液酸醣基化 (Sialylation)、岩藻醣基化 (Fucosylation)、及醣支鏈延伸 (Glycan branching)等異常的醣基化 (Glycosylation) 與癌症具高度相關性，一可用以監測從肝臟疾病轉變為肝細胞癌進程的高靈敏度生物指標 (Biomarker)仍迫切需要，特別是針對那些已進程為癌症但甲型胎兒蛋白數值仍低至無法檢測的病患。 在我們先前的研究中，利用實驗室自行開發的奈米探針質譜法，用以鑑定75位病人血清中血紅素結合蛋白 (Haptoglobin, Hp) 上4個醣基化位點之特定位點醣胜肽。傳統分析方法上，由於透混和多個樣品或實驗上將醣鏈切下，並將醣鏈與胜肽各別分析，難以將醣鏈資訊以及胜肽資訊進行整合，進而造成醣基化定量上的誤差。 在此研究中，我們選擇了血紅素結合蛋白作為異常醣基化的研究標的，血紅素結合蛋白主要由肝臟合成，在血清中有高的表現量，含量約為每毫升1毫克 (~ 1mg/ml)，且血紅素結合蛋上異常的醣基化具有作為肝細胞癌診斷的可行性。我們建立了生物資訊輔助未標定式定量策略並透過自動化統計方法，藉此篩選出在血紅素結合蛋白上顯著變異的特定位點之醣抗原 (Glycotopes)。此研究一共收集了75位病患，包含20例乙型肝炎病毒感染病例、8位乙型肝炎病毒轉變之肝硬化病例、21例肝細胞癌且甲型胎兒蛋白低於每毫升20奈克以及26例肝細胞癌病患為甲型胎兒蛋白高於每毫升20奈克。經由Byonic 軟體進行醣肽分析，一共有39930張圖譜被鑑定為醣肽圖譜。並藉由Progenesis QI for proteomics 軟體進行未標定式定量，根據鑑定到的醣肽序列、醣組成、質荷比、滯留時間進行非線性校準，並透過提取離子色譜圖(extracted ion chromatogram, XIC)進行定量，一共有1388條醣肽被定量。為了找出可提供肝細胞癌診斷的醣肽候選標的，我們透過自行建立的R語言程式 (R programing) 進行數據處理、學生t檢定、變異數分析等統計分析(p-value<0.05)以及統計結果視覺化工作。最後我們從血紅素結合蛋白的醣肽中篩選出19條特定位點的變異醣肽。統計結果進一步透過建立盒鬚圖 (Boxplot) 、比較其四分位距 (Quantile)及熱度圖的建立 (Heatmap)，以了解變異醣肽在各群組樣品的表現量的分布。透過接收者操作特徵分析 (Receiver operating characteristic curve, ROC curve) 評估各個變異醣肽的靈敏度 (Sensitivity) 以及特異性(Specificity)，選出的19條變異醣肽其AUC落在0.70~0.87之間，並進一步評估靈敏度及特異性。最終我們提出14個上升的醣抗原，例如核心岩藻醣基化雙唾液酸化之雙觸角醣型、雙唾液酸化之混和型醣，具有協助肝癌診斷的潛力。 綜上所述，透過我們所建立針對特定位點醣肽的生物資訊分析流程，不僅提供獨立處理各別病人數據的自動化分析策略，同時也提出變異的醣抗原候選標的，希望藉此提高甲型胎兒蛋白表現量低之肝細胞癌病患的診斷精確度及靈敏度。

Except current diagnostic approaches such as ultrasonography and radiologically, serum alpha-fetoprotein (AFP), a FDA-approved biomarker and glycoprotein is common used for diagnosis in hepatocellular carcinoma (HCC). Elevated AFP is commonly associated with HCC, however, about 40% of HCC patients present normal concentration (≤ 2 ng~20 ng/ml in serum) causing false negative diagnosis and lacking satisfactory sensitivity and specificity. Moreover, low level of AFP (≤ 2 ng/ml) in patients with liver diseases, such as hepatitis B virus (HBV) infection and liver cirrhosis (LC), also presents challenges in sensitivity for diagnosis. In spite of aberrant glycosylation including increased sialylation, fucosylation and glycan branching of acute-phase proteins are highly correlated with cancer, a biomarker with better sensitivity to monitor the formation and progression from liver diseases to HCC is instant need, especially for patient cohorts with low AFP. In our previous study, we have applied our lab developed magnetic nanoprobe (MNP)-based affinity mass spectrometry method to identify site-specific glycopeptides from 4 glycosylation site of Haptaglobin in 75 patients individually. By conventional statistical analysis, it is challenging to confidently identify differential expression of glycopepides due to de-sialylation, release glycan and pooled sample for analysis result from hard to link peptides with glycan information and misleading for quantification. In this study, we focus on studying altered glycosylation of haptoglobin (Hp) that mainly produced by liver and had high expression level (~1 mg/ml) in serum and may serve as a reporter of aberrant glycosylation for HCC diagnosis. We developed a bioinformatics-assisted label-free quantitative approach and automatic statistics to filter significant alteration of site-specific glycotopes on Hp. Collecting 75 individual patients (HBV, n=20; LC, n=8; HCC-Low AFP, n=21; and HCC-High AFP, n=26), total of 39930 peptide-to-spectrum matches (PSMs) were identified by using Byonic software for comprehensive site-specific assignment. Applying Progenesis QI for proteomics software for label-free quantitation, 1388 intact glycopeptides were quantified by the extracted ion chromatogram (XIC) and performed by nonlinear alignment using retention time, m/z, peptide sequence and glycan composition. To filter the potential glycopeptides candidates for HCC diagnosis, customized R scripts integrating student-t-test and ANOVA (p < 0.05) was developed and selected 19 of site-specific glycopeptides on Hp from liver diseases, especially in HCC. Moreover, quartile, box plot and heatmap revealed different expression level for altered glycopeptides in each subgroup. ROC and AUC value for selected altered glycopeptides with AUC 0.70~0.87 were introduced to give an index for evaluation of sensitivity and specificity. Finally, 14 up-expressed glycotopes such as core-fucosyl-di-sialo-biantennary and di-sialo-hybrid type glycan, were discovered as candidate glycopeptide biomarker for improving HCC diagnosis. In summary, this bioinformatics-assisted discovery approach for intact glycopeptides not only provides an automatic processing strategy for individual patients but also reveals candidate glycotope to improve accuracy and sensitivity for HCC diagnosis in patients with low AFP.