Phenotype Relevant Network-based Biomarker Discovery Integrating Multiple Omics Data (EMT Network-based Lung Cancer Prognosis Prediction)

Biological networks have been employed as prior knowledge to identify robust molecular signatures. Many studies integrate protein-protein interaction(PPI) network with gene expression data, where the network is often used to guide the search in the feature space to select the important nodes or subnetworks as signatures. As multiple types of omics data become more and more available, it brings the questions of which data types contain better molecular signatures and whether it is beneficial to use multiple omics data simultaneously. We investigated this topic with the task of cancer prognosis prediction. After conducting a comprehensive literature review, we selected 10 representative feature selection algorithms. Five of them integrate network information and the other five algorithms use only omics data. On single-omics levels, we performed feature selection alternatively on mRNA and miRNA expression data, DNA methylation data, and copy number alteration data. Then we evaluated the prediction performance of these features, as well as their stability, network properties, and biological interpretation. To obtain multi-omics signatures, we first combined the selected features from single-omics levels and evaluated the prediction performance of different combinations. Then we extended network-based feature selection algorithms to incorporate multi-omics data using a multiplex structure. For the feature selection algorithms that do not use network information, we employed them on concatenated data. Last but not least, we evaluated the predictive performance of both single-omics and multi-omics signatures on independent lung cancer multi-omics data. One of the major challenges in biomarker discovery is the curse of dimensionality, which contributes to the low reproducibility of many proposed molecular signatures. Even with network-based feature selection algorithms, significant improvements are hard to achieve as the networks are often of large sizes. To avoid this issue and find biologically meaningful signatures, we propose to construct phenotype relevant gene regulatory networks based on Epithelial Mesenchymal Transition (EMT), which is demonstrated as highly relevant to the metastasis and prognosis of epithelial cancers. We integrated different types of omics data with EMT networks to select prognostic signatures. Although the dimensionality was reduced to less than 2.5% of the original, EMT-based feature selection gave even better prediction performance than selecting features from the original data. Frequently selected features achieved average AUC value of 0.83. The features were able to stratify patients into significantly different prognostic groups on both the training data and the independent testing data. Using combinations of single-omics signatures and using multiplex-based feature selection further improved the prediction performance. Since biological data have large volume, high velocity, and wide varieties, it becomes necessary to employ database systems that meet the need of integrative omics data analysis. Therefore, we tested the performance of a few relational and non-relational databases for storing and retrieving omics data. Based on the results, we provided a few advices on building scalable omics data infrastructures.