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.
Highlights
Identifying cancer-specific genes involved in tumorigenesis and cancer progression is one of the major ways to understand the pathophysiologic mechanisms of cancers and to find therapeutic drug targets
Sample analytes are immobilized in the solid phase, and analyte-specific antibodies are used in the solution phase
By using the maximum relevance minimum redundancy method, the 187 features were ranked by importance in the training set
Summary
Identifying cancer-specific genes involved in tumorigenesis and cancer progression is one of the major ways to understand the pathophysiologic mechanisms of cancers and to find therapeutic drug targets. Reverse phase protein array (RPPA) is a powerful and robust antibody-based high-throughput approach for targeted proteomics that allows us to quantitatively assess target protein expression in large sample sets [3]. In this process, sample analytes are immobilized in the solid phase, and analyte-specific antibodies are used in the solution phase. A total of 23 proteins were selected from the training set Their MCC (Matthews Correlation Coefficient) for the training set was 0.904 evaluated by 10-fold cross validation and their MCC on the independent test set was 0.936. Our methods could provide clinicians with knowledge of key distinct biochemical features of cancer types and could shed some new light on the discoveries of specific biomarkers of different types of cancers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
