ArticleMicroarrays (Basel, Switzerland)2015
Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies.
Article in Microarrays (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
- Validation of a model of rheumatoid arthritis using mice reconstituted with patient peripheral blood mononuclear cells.Disease models & mechanisms · 2025Article
- Patients with Systemic Juvenile Idiopathic Arthritis (SJIA) Show Differences in Autoantibody Signatures Based on Disease Activity.Biomolecules · 2023Article
- A Bayesian hierarchical model for signal extraction from protein microarrays.Statistics in medicine · 2023Article
- FLU-LISA (fluorescence-linked immunosorbent assay): high-throughput antibody profiling using antigen microarrays.Immunology and cell biology · 2023Article
- Antibody Profiling and In Silico Functional Analysis of Differentially Reactive Antibody Signatures of Glioblastomas and Meningiomas.International journal of molecular sciences · 2023Article
- Temporal reproducibility of IgG and IgM autoantibodies in serum from healthy women.Scientific reports · 2022Article
- Functional Analysis of Autoantibody Signatures in Rheumatoid Arthritis.Molecules (Basel, Switzerland) · 2022Article
- Autoantibodies as diagnostic markers and potential drivers of inflammation in ulcerative colitis.PloS one · 2020Article
- IgG based immunome analyses of breast cancer patients reveal underlying signaling pathways.Oncotarget · 2019Article
- Distribution based Fuzzy Estimate Spectral Clustering for Cancer Detection with Protein Sequence and Structural MotifsAsian Pacific journal of cancer prevention : APJCP · 2018Article
- PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization.BMC research notes · 2018Article
- The Immunome of Colon Cancer: Functional In Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling.Genomics, proteomics & bioinformatics · 2018Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New minimal invasive diagnostic methods for early detection of lung cancer are urgently needed. It is known that the immune system responds to tumors with production of tumor-autoantibodies. Protein microarrays are a suitable highly multiplexed platform for identification of autoantibody signatures against tumor-associated antigens (TAA). These microarrays can be probed using 0.1 mg immunoglobulin G (IgG), purified from 10 µL of plasma. We used a microarray comprising recombinant proteins derived from 15,417 cDNA clones for the screening of 100 lung cancer samples, including 25 samples of each main histological entity of lung cancer, and 100 controls. Since this number of samples cannot be processed at once, the resulting data showed non-biological variances due to "batch effects". Our aim was to evaluate quantile normalization, "distance-weighted discrimination" (DWD), and "ComBat" for their effectiveness in data pre-processing for elucidating diagnostic immune‑signatures. "ComBat" data adjustment outperformed the other methods and allowed us to identify classifiers for all lung cancer cases versus controls and small-cell, squamous cell, large-cell, and adenocarcinoma of the lung with an accuracy of 85%, 94%, 96%, 92%, and 83% (sensitivity of 0.85, 0.92, 0.96, 0.88, 0.83; specificity of 0.85, 0.96, 0.96, 0.96, 0.83), respectively. These promising data would be the basis for further validation using targeted autoantibody tests.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.