Identification of Novel Autoantibodies for Detection of Malignant Mesothelioma

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Identification of Novel Autoantibodies for Detection of Malignant Mesothelioma

Public Library of Science
DOI: 10.1371/journal.pone.0072458
  • Oncology
  • Immunology
  • Microbiology
  • Biology
  • Biochemistry
  • Computational Biology
  • Sequence Analysis
  • Pathology
  • Medicine
  • Virology
  • Proteins
  • Biomarkers
  • Autoimmunity
  • Proteomics
  • Immune System
  • Immune Response
  • Research Article
  • Immunoglobulins
  • Recombinant Proteins
  • General Pathology
  • Mesothelioma
  • Sequencing
  • Viral Vectors
  • Microarrays
  • Immunochemistry
  • Immunologic Techniques
  • Protein Interactions
  • Immune System Proteins
  • Diagnostic Medicine
  • Cancers And Neoplasms
  • Lung And Intrathoracic Tumors
  • Protein Abundance
  • Blood Chemistry
  • Viral Transmission And Infection


Background The malignant mesothelioma (MM) survival rate has been hampered by the lack of efficient and accurate early detection methods. The immune system may detect the early changes of tumor progression by responding with tumor-associated autoantibody production. Hence, in this study, we translated the humoral immune response to cancer proteins into a potential blood test for MM. Methodology/Principal Findings A T7 phage MM cDNA library was constructed using MM tumor tissues and biopanned for tumor-associated antigens (TAAs) using pooled MM patient and normal serum samples. About 1008 individual phage TAA clones from the biopanned library were subjected to protein microarray construction and tested with 53 MM and 52 control serum samples as a training group. Nine candidate autoantibody markers were selected from the training group using Tclass system and logistic regression statistical analysis, which achieved 94.3% sensitivity and 90.4% specificity with an AUC value of 0.89 in receiver operating characteristic analysis. The classifier was further evaluated with 50 patient and 50 normal serum samples as an independent blind validation, and the sensitivity of 86.0% and the specificity of 86.0% were obtained with an AUC of 0.82. Sequencing and BLASTN analysis of the classifier revealed that five of these nine candidate markers were found to have strong homology to cancer related proteins (PDIA6, MEG3, SDCCAG3, IGHG3, IGHG1). Conclusions/Significance Our results indicated that using a panel of 9 autoantibody markers presented a promising accuracy for MM detection. Although the results need further validation in high-risk groups, they provided the potentials in developing a serum-based assay for MM diagnosis.

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