Netrank: network-based approach for biomarker discovery

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

BACKGROUND: Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA).

RESULTS: The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank's biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures.

CONCLUSION: In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper.

Details

Original languageEnglish
Article number304
JournalBMC bioinformatics
Volume24
Issue number1
Publication statusPublished - 29 Jul 2023
Peer-reviewedYes

External IDs

PubMedCentral PMC10387193
Scopus 85165937717

Keywords

Sustainable Development Goals

Keywords

  • Humans, Biomedical Research, Algorithms, Area Under Curve, Disease Progression, Gene Library