Abstract
The constantly and rapidly increasing amount of the biological data gained from many different high-throughput experiments opens up new possibilities for data- and model-driven inference. Yet, alongside, emerges a problem of risks related to data integration techniques. The latter are not so widely taken account of. Especially, the approaches based on the flux balance analysis (FBA) are sensitive to the structure of a metabolic network for which the low-entropy clusters can prevent the inference from the activity of the metabolic reactions. In the following article, we set forth problems that may arise during the integration of metabolomic data with gene expression datasets. We analyze common pitfalls, provide their possible solutions, and exemplify them by a case study of the renal cell carcinoma (RCC). Using the proposed approach we provide a metabolic description of the known morphological RCC subtypes and suggest a possible existence of the poor-prognosis cluster of patients, which are commonly characterized by the low activity of the drug transporting enzymes crucial in the chemotherapy. This discovery suits and extends the already known poor-prognosis characteristics of RCC. Finally, the goal of this work is also to point out the problem that arises from the integration of high-throughput data with the inherently nonuniform, manually curated low-throughput data. In such cases, the over-represented information may potentially overshadow the non-trivial discoveries.
Highlights
Observing the technological progress of the recent decades one can notice that it facilitated an access to vast biomedical data resources describing different molecular levels.our comprehension of biological processes becomes more profound as well as reliable.These facts open up a broad field of data integration, that aims to infer from various data collection platforms taking into account known biological dependencies between them.Motivations
Using the proposed approach we provide a metabolic description of the known morphological renal cell carcinoma (RCC) subtypes and suggest a possible existence of the poor-prognosis cluster of patients, which are commonly characterized by the low activity of the drug transporting enzymes crucial in the chemotherapy
We report an interesting phenomenon related to the analysis of individual metabolic networks in the context of transcriptomic data
Summary
Observing the technological progress of the recent decades one can notice that it facilitated an access to vast biomedical data resources describing different molecular levels (so-called -omics data). Here, we focus on a summary of what type of biomedical outcomes they have provided so far Using this type of models, where a general metabolic network becomes context-specific through integration with a transcriptome of specific tissue or organism few groups of researchers have already reported some interesting discoveries. Li et al by integrating transcriptomic knowledge with human metabolic network suggest a supervised method to predict novel drug-target interaction [21] In their work they predict related metabolic reactions and enzyme targets for approved cancer drugs, and predict drug targets with statistically high confidence rate. The preliminary version of this study was published as an extended abstract in the proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 96–101; Madrid, Spain
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