In Situ Proteomic Analysis of Human Breast Cancer Epithelial Cells Using Laser Capture Microdissection: Annotation by Protein Set Enrichment Analysis and Gene Ontology
In Situ Proteomic Analysis of Human Breast Cancer Epithelial Cells Using Laser Capture Microdissection: Annotation by Protein Set Enrichment Analysis and Gene Ontology
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DOI：
10.1074/mcp.m110.000398
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发表时间：
2010-11-01
影响因子：
7
通讯作者：
Karger, Barry L.
中科院分区：
文献类型：
--
作者：
Cha, Sangwon;Imielinski, Marcin B.;Karger, Barry L.
识别允许检测从正常乳腺上皮细胞向恶性侵袭细胞转变的分子标记是开发人类乳腺癌诊断、治疗和预防策略的关键组成部分。人们致力于在基因组水平上破译乳腺癌的病因，但只有有限数量的研究出现在蛋白质组水平。在这项工作中，我们结合激光捕获显微解剖(LCM)和定量猎枪蛋白质组学，比较了9例非癌、正常乳腺上皮(NBE)和9例雌激素受体(ER)阳性(腔亚型)、侵袭性恶性乳腺上皮(MBE)样本的个别原位蛋白质组图谱。从18个样品中鉴定出12,970个独特的多肽，并选择1623个蛋白质进行定量分析，使用光谱指数(SPI)作为蛋白质丰度的衡量标准。在95%的置信度水平下，NBE和MBE之间总共有298个蛋白质的差异表达，并且这种差异表达与人类蛋白质图谱(HPA)数据库中报道的免疫组织化学(IHC)结果很好地相关。为了评估观察到的表达变化中的途径水平模式，我们开发了蛋白质集浓缩分析(PSEA)，这是对基因表达分析中著名的方法基因集浓缩分析(GSEA)的改进。与基于单个基因的功能项浓缩分析不同，PSEA只检查高于给定显著阈值的蛋白质的途径过度表达，而PSEA将加权游程和统计应用于整个表达数据，以发现显著丰富的蛋白质组。应用PSEA对本研究中的表达数据进行分析，不仅揭示了众所周知的ER依赖和细胞形态依赖的蛋白质丰度的变化，而且还揭示了多种转录因子(TF)下游靶标的显著变化，表明特定的基因调控途径在乳腺肿瘤的发生中发挥了作用。一项平行的GOMiner分析显示了对PSEA的验证性和互补性数据。这两种注释方法的结合产生了广泛的生物学特征图谱，用于深入分析定量蛋白质组数据。《分子与细胞蛋白质组学》9：2529-2544,2010。
Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data. Molecular & Cellular Proteomics 9:2529-2544, 2010.