ABSTRACT
BACKGROUND: In the Next Generation Sequencing (NGS) era a large amount of biological data is being sequenced, analyzed, and stored in many public databases, whose interoperability is often required to allow an enhanced accessibility. The combination of heterogeneous NGS genomic data is an open challenge: the analysis of data from different experiments is a fundamental practice for the study of diseases. In this work, we propose to combine DNA methylation and RNA sequencing NGS experiments at gene level for supervised knowledge extraction in cancer. METHODS: We retrieve DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas (TCGA), focusing on the Breast Invasive Carcinoma (BRCA), the Thyroid Carcinoma (THCA), and the Kidney Renal Papillary Cell Carcinoma (KIRP). We combine the RNA sequencing gene expression values with the gene methylation quantity, as a new measure that we define for representing the methylation quantity associated to a gene. Additionally, we propose to analyze the combined data through tree- and rule-based classification algorithms (C4.5, Random Forest, RIPPER, and CAMUR). RESULTS: We extract more than 15,000 classification models (composed of gene sets), which allow to distinguish the tumoral samples from the normal ones with an average accuracy of 95%. From the integrated experiments we obtain about 5000 classification models that consider both the gene measures related to the RNA sequencing and the DNA methylation experiments. CONCLUSIONS: We compare the sets of genes obtained from the classifications on RNA sequencing and DNA methylation data with the genes obtained from the integration of the two experiments. The comparison results in several genes that are in common among the single experiments and the integrated ones (733 for BRCA, 35 for KIRP, and 861 for THCA) and 509 genes that are in common among the different experiments. Finally, we investigate the possible relationships among the different analyzed tumors by extracting a core set of 13 genes that appear in all tumors. A preliminary functional analysis confirms the relation of part of those genes (5 out of 13 and 279 out of 509) with cancer, suggesting to focus further studies on the new individuated ones.
ABSTRACT
BACKGROUND: The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed of genes, and their relation to the investigated disease. State of the art rule-based classifiers are designed to extract a single classification model, possibly composed of few relevant genes. Conversely, we aim to create a large knowledge base composed of many rule-based models, and thus determine which genes could be potentially involved in the analyzed tumor. This comprehensive and open access knowledge base is required to disseminate novel insights about cancer. RESULTS: We propose CamurWeb, a new method and web-based software that is able to extract multiple and equivalent classification models in form of logic formulas ("if then" rules) and to create a knowledge base of these rules that can be queried and analyzed. The method is based on an iterative classification procedure and an adaptive feature elimination technique that enables the computation of many rule-based models related to the cancer under study. Additionally, CamurWeb includes a user friendly interface for running the software, querying the results, and managing the performed experiments. The user can create her profile, upload her gene expression data, run the classification analyses, and interpret the results with predefined queries. In order to validate the software we apply it to all public available RNA sequencing datasets from The Cancer Genome Atlas database obtaining a large open access knowledge base about cancer. CamurWeb is available at http://bioinformatics.iasi.cnr.it/camurweb . CONCLUSIONS: The experiments prove the validity of CamurWeb, obtaining many classification models and thus several genes that are associated to 21 different cancer types. Finally, the comprehensive knowledge base about cancer and the software tool are released online; interested researchers have free access to them for further studies and to design biological experiments in cancer research.
