ArticlePloS one2015
Classifying ten types of major cancers based on reverse phase protein array profiles.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Antibody reliability influences observed mRNA-protein correlations in tumour samples.Life science alliance · 2023Article
- A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.Life (Basel, Switzerland) · 2023Article
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- Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis.Laboratory investigation; a journal of technical methods and pathology · 2020Article
- Identification and Analysis of the Blood lncRNA Signature for Liver Cirrhosis and Hepatocellular Carcinoma.Frontiers in genetics · 2020Article
- Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing.Frontiers in bioengineering and biotechnology · 2020Article
- Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas.Frontiers in genetics · 2020Article
- Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale.Frontiers in genetics · 2020Article
- The Methylation Pattern for Knee and Hip Osteoarthritis.Frontiers in cell and developmental biology · 2020Article
- The Blood Gene Expression Signature for Kawasaki Disease in Children Identified with Advanced Feature Selection Methods.BioMed research international · 2020Article
- Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.International journal of molecular sciences · 2019Article
- Identification of the copy number variant biomarkers for breast cancer subtypes.Molecular genetics and genomics : MGG · 2019Article
- The early detection of asthma based on blood gene expression.Molecular biology reports · 2019Article
- The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing.Journal of Cancer · 2019Article
- The blood transcriptional signature for active and latent tuberculosis.Infection and drug resistance · 2019Article
- Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine.Frontiers in genetics · 2019Article
- Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.Frontiers in bioengineering and biotechnology · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method. A careful feature selection procedure was employed to select 23 important proteins from the total of 187 proteins by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) on the training set. By using the 23 proteins, we successfully classified the ten cancer types with an MCC (Matthews Correlation Coefficient) of 0.904 on the training set, evaluated by 10-fold cross-validation, and an MCC of 0.936 on an independent test set. Further analysis of these 23 proteins was performed. Most of these proteins can present the hallmarks of cancer; Chk2, for example, plays an important role in the proliferation of cancer cells. Our analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification. We also believe our methods and findings may shed light on the discoveries of specific biomarkers of different types of cancers.
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