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CancerHubs: a systematic data mining and elaboration approach for identifying novel cancer-related protein interaction hubs

I. Ferrari,Federica De Grossi,Giancarlo Lai,S. Oliveto,Giorgia Deroma,Stefano Biffo,N. Manfrini

Published 2024 in Briefings Bioinform.

ABSTRACT

Abstract Conventional approaches to predict protein involvement in cancer often rely on defining either aberrant mutations at the single-gene level or correlating/anti-correlating transcript levels with patient survival. These approaches are typically conducted independently and focus on one protein at a time, overlooking nucleotide substitutions outside of coding regions or mutational co-occurrences in genes within the same interaction network. Here, we present CancerHubs, a method that integrates unbiased mutational data, clinical outcome predictions and interactomics to define novel cancer-related protein hubs. Through this approach, we identified TGOLN2 as a putative novel broad cancer tumour suppressor and EFTUD2 as a putative novel multiple myeloma oncogene.

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