Abstract
Glioma is a family of brain tumors with three main types exhibiting different progressions, which lack effective therapeutic options and specific molecular biomarkers. In this work, we propose a pipeline for multi-omics integrated analysis aimed at identifying features that could impact the development of different gliomas, assigned according to the latest classification guidelines. We estimate networks of genes and proteins based on human data, via the graphical lasso, as a network-based step towards variable selection. The estimated glioma networks were compared to disclose molecular relations that can be important for the development of a certain tumor type. Our outcomes were validated both mathematically, and through principal component analysis to determine if the selected subset of variables carries enough biological information to distinguish the three glioma types in a reduced dimensional subspace. The results highlight an overall agreement in variable selection across the two omics. Features exclusively selected by each glioma type appear as more representative of the pathological condition, making them suitable as potential diagnostic biomarkers. The comparison between glioma-type networks and with known protein-protein interactions reveals the presence of molecular relations that could be associated to a pathological condition. The 59 features identified by our analysis will be further considered to extend our work by integrating targeted biological evaluation.
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Notes
- 1.
DeltaCon distance was computed by a modified version of the delta_con R function (package rdsg) [6].
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Acknowledgment
These results are based on data from the TCGA Research Network: https://www.cancer.gov/tcga.
This work is part of the MONET project PTDC/CCI-BIO/4180/2020, supported by FCT, with references CEECINST/00042/2021, UIDB/00297/2020, UIDP/00297/2020 (NOVA Math), UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI).
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Coletti, R., Lopes, M.B. (2023). Multi-omics Data Integration and Network Inference for Biomarker Discovery in Glioma. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_20
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