Detection and identification of potential biomarkers of breast cancer

PurposeNoninvasive and convenient biomarkers for early diagnosis of breast cancer remain an urgent need. The aim of this study was to discover and identify potential protein biomarkers specific for breast cancer.MethodsTwo hundred and eighty-two (282) serum samples with 124 breast cancer and 158 controls were randomly divided into a training set and a blind-testing set. Serum proteomic profiles were analyzed using SELDI-TOF-MS. Candidate biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays and western blot technique.ResultsA total of 3 peaks (m/z with 6,630, 8,139 and 8,942 Da) were screened out by support vector machine to construct the classification model with high discriminatory power in the training set. The sensitivity and specificity of the model were 96.45 and 94.87%, respectively, in the blind-testing set. The candidate biomarker with m/z of 6,630 Da was found to be down-regulated in breast cancer patients, and was identified as apolipoprotein C-I. Another two candidate biomarkers (8,139, 8,942 Da) were found up-regulated in breast cancer and identified as C-terminal-truncated form of C3a and complement component C3a, respectively. In addition, the level of apolipoprotein C-I progressively decreased with the clinical stages I, II, III and IV, and the expression of C-terminal-truncated form of C3a and complement component C3a gradually increased in higher stages.ConclusionsWe have identified a set of biomarkers that could discriminate breast cancer from non-cancer controls. An efficient strategy, including SELDI-TOF-MS analysis, HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification, has been proved very successful.

[1]  Ding‐Shinn Chen,et al.  Identification of complement C3a as a candidate biomarker in human chronic hepatitis C and HCV‐related hepatocellular carcinoma using a proteomics approach , 2006, Proteomics.

[2]  S. Hanash,et al.  Mining the plasma proteome for cancer biomarkers , 2008, Nature.

[3]  Jianwen Fang,et al.  Feature Selection in Validating Mass Spectrometry Database Search Results , 2008, J. Bioinform. Comput. Biol..

[4]  Jing Wang,et al.  Proteomic studies of early-stage and advanced ovarian cancer patients. , 2008, Gynecologic oncology.

[5]  S. Meri,et al.  Ascitic complement system in ovarian cancer , 2005, British Journal of Cancer.

[6]  Jiekai Yu,et al.  Diagnosis of Pancreatic Adenocarcinoma Using Protein Chip Technology , 2008, Pancreatology.

[7]  Lucila Ohno-Machado,et al.  Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality , 2007, J. Biomed. Informatics.

[8]  Richard Baumgartner,et al.  Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions , 2003, Bioinform..

[9]  Weijian Zhu,et al.  WT1, monoclonal CEA, TTF1, and CA125 antibodies in the differential diagnosis of lung, breast, and ovarian adenocarcinomas in serous effusions , 2007, Diagnostic cytopathology.

[10]  A. Langer,et al.  Exploration des seins denses en mammographie : techniques et limites , 2008 .

[11]  John D Lambris,et al.  Structure and biology of complement protein C3, a connecting link between innate and acquired immunity , 2001, Immunological reviews.

[12]  P. Meleady,et al.  Proteomic approaches for serum biomarker discovery in cancer. , 2007, Anticancer research.

[13]  Jiekai Yu,et al.  SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer. , 2005, Breast.

[14]  D. Chan,et al.  Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. , 2002, Clinical chemistry.

[15]  Michael Brady,et al.  Evaluating the Effectiveness of Using Standard Mammogram Form to Predict Breast Cancer Risk: Case-Control Study , 2008, Cancer Epidemiology Biomarkers & Prevention.

[16]  Yue Hu,et al.  [Diagnostic application of serum protein pattern and artificial neural network software in breast cancer]. , 2005, Ai zheng = Aizheng = Chinese journal of cancer.

[17]  Hermann Brenner,et al.  Blood Markers for Early Detection of Colorectal Cancer: A Systematic Review , 2007, Cancer Epidemiology Biomarkers & Prevention.

[18]  S. Bao,et al.  The activation of Akt/PKB signaling pathway and cell survival , 2005, Journal of cellular and molecular medicine.

[19]  John D Lambris,et al.  C3a and C3b Activation Products of the Third Component of Complement (C3) Are Critical for Normal Liver Recovery after Toxic Injury1 , 2004, The Journal of Immunology.

[20]  Emanuel F. Petricoin,et al.  Serum Proteomic Analysis Identifies a Highly Sensitive and Specific Discriminatory Pattern in Stage 1 Breast Cancer , 2007, Annals of Surgical Oncology.

[21]  P. Stattin,et al.  SELDI‐TOF MS versus prostate specific antigen analysis of prospective plasma samples in a nested case–control study of prostate cancer , 2007, International journal of cancer.

[22]  A. Jemal,et al.  Cancer Statistics, 2008 , 2008, CA: a cancer journal for clinicians.

[23]  O. John Semmes,et al.  SELDI-TOF Serum Profiling for Prognostic and Diagnostic Classification of Breast Cancers , 2004, Disease markers.

[24]  N. Tomosugi.  [Discovery of disease biomarkers by ProteinChip system; clinical proteomics as noninvasive diagnostic tool]. , 2004, Rinsho byori. The Japanese journal of clinical pathology.

[25]  M. Redondo,et al.  Monitoring indicators of health care quality by means of a hospital register of tumours. , 2008, Journal of evaluation in clinical practice.