方法
血清樣品制備
無已知惡性腫瘤的健康志愿者（男女均用，年齡23-56歲，見補充表1）和診斷為前列腺癌、乳腺癌或膀胱癌的患者的血清按照標準臨床操作規程取自Sloan-Kettering紀念癌癥中心[29]，有關患者的年齡、性別及病理診斷的詳情參見補充表1。所有采血操作均獲得MSKCC制度審查和隱私局的批準，并得到患者的書面同意書。血樣收集在8.5-ml的BD紅頭玻璃采血管(BD; 366430)中，在室溫下凝固1小時，1,400–2,000 g離心10分鐘。上清液（血清）移至4個4ml冷凍管(Fischer Scientific International, 0566966)中，每管1ml，儲存于–80°C備用[29]。綠頭的肝素抗凝管(BD, 366480)中血漿的制備方法相似，不過在收集血樣后立即進行離心處理。送至MS實驗室后，在冷凍管上貼條形碼。每個樣品取1管，冰上融解，分至9個更小的微量eppendorf管中、貼條形碼、存放于貼條形碼的盒中，置–80°C備用。本實驗中，所有的血清標本均經過兩次凍融，第二次凍融后立即進行多肽提取和MS分析。我們盡了最大努力，使護士、抽血人員、聯絡人員和臨床醫師嚴格按照標準規程操作。
分析化學
自動操作、固相多肽提取、MALDITOF MS、信號處理和質譜圖匹配及常規質譜圖觀察均按照以往作者本實驗室自行開發的過程進行[18,29]。更多細節和串聯MS鑒定選定的血清多肽的方法見補充方法。
統計學
表格程序中含有取自癌癥患者和健康受試者的樣品的質譜圖資料（共106個樣品，651 個m/z值，經過標準化換算的各樣品強度值，> 70,000個數據點）以及前列腺癌試驗組的資料（PR2；41個樣；～27,000個數據點），這些資料均輸入GeneSpring程序(version 7; Agilent Technologies)，采用不同的統計算法如單向ANOVA、主成分分析、分級聚類、k 個最近鄰居分類法或SVM進行分析。GeneSpring編制了不同的試驗代表這些質量值。在這些質量值經數據庫標準化處理之前不對試驗進行標準化處理。在試驗的參數部分，設置了一個名為“癌癥類型”的參數將樣品標明為前列腺癌、膀胱癌、乳腺癌或正常組。未使用交叉基因錯誤模式。
ANOVA
創建試驗后，采用非參數檢驗（曼-懷二氏檢驗即秩和檢驗（二元比較）和克(魯斯卡爾)-瓦(利斯)二氏檢驗即Ｈ檢驗法（多元比較）過濾m/z值(峰) 。 Benjamini-Hochberg法用于校正多元比較的P值[79]。P<0.00001設為有顯著性差異。這些檢驗的目的是發現在臨床各組之間具有顯著性差異的峰。
分級聚類
對651個m/z值（峰）進行平均-連鎖分級聚類分析，以標準相互關系（即皮爾森相互關系）作為距離尺度（GeneSpring程序）。將峰繪成基因數和試驗樹形式，橫軸代表樣品，縱軸代表質量。
分級預測
采用GeneSpring的分級預測工具進行SVM 和 k-NN分析。訓練組采用二元比較(PR1 和 對照組)或多級比較（PR1，乳腺癌、膀胱癌和對照組）。試驗組為PR2。預測參數設置在癌癥類型。基因選擇設為選用經過選擇的不同組的質量（如651、68、26)。k-NN分析中，鄰居數量按照P值判斷截止為1而設為5。SVM分析的訓練組及參數同上，預測PR2組。計算方程為多項式點積（一級方程），斜率為0。
致謝
本工作為NIH基金項目（資助號：1-R21-CA1119425、5-P30-CA08748和5-P50-CA92629），并獲得前列腺癌基金會、Vakil研究基金和Accelerate Brain Cancer Cure的獎勵。感謝Larry Norton和Mark Kris的支持;感謝Richard Robbins、 Mark Robson和Chris Sander對討論的幫助；感謝San San Yi 的多肽合成工作及Lynne Lacomis的文字工作；感謝無償捐助血樣的所有志愿者。
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