A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma
Résumé
Background: Integrating genome-wide gene expression patient profiles with
regulatory knowledge is a challenging task because of the inherent heterogeneity,
noise and incompleteness of biological data. From the computational side, several
solvers for logic programs are able to perform extremely well in decision problems
for combinatorial search domains. The challenge then is how to process the
biological knowledge in order to feed these solvers to gain insights in a biological
study. It requires formalizing the biological knowledge to give a precise
interpretation of this information; currently, very few pathway databases offer this
possibility.
Results: The presented work proposes an automatic pipeline to extract
automatically regulatory knowledge from pathway databases and generate novel
computational predictions related to the state of expression or activity of
biological molecules. We applied it in the context of hepatocellular carcinoma
(HCC) progression, and evaluate the precision and the stability of these
computational predictions. Our working base is a graph of 3,383 nodes and
13,771 edges extracted from the KEGG database, in which we integrate 209
differentially expressed genes between low and high aggressive HCC across 294
patients. Our computational model predicts the shifts of expression of 146 initially
non-observed biological components. Our predictions were validated at 88% using
a larger experimental dataset and cross-validation techniques. In particular, we
focus on the protein complexes predictions and show for the first time that
NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large
dimension of the reconstructed models, our analyses over the computational
predictions discover a well constrained region where KEGG regulatory knowledge
constrains gene expression of several biomolecules. These regions can offer
interesting windows to perturb experimentally such complex systems.
Conclusion: This new pipeline allows biologists to develop their own predictive
models based on a list of genes. It facilitates the identification of new regulatory
biomolecules using knowledge graphs and predictive computational methods. Our
workflow is implemented in an automatic python pipeline which is publicly
available at https://github.com/LokmaneChebouba/key-pipe and contains
as testing data all the data used in this paper.
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Origine | Fichiers produits par l'(les) auteur(s) |
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Commentaire | Additional tables, figures and explanations |
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Commentaire | Additional information to generate differentially expressed genes from ICGC database |
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Commentaire | Input data and results of the application of our pipeline regarding hepatocellular carcinoma progression |
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