Comparative analyses of gene networks mediating cancer metastatic potentials across lineage types

Sheng Wang, Emily K. Stroup, Ting You Wang, Rendong Yang, Zhe Ji*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein-protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inf lammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.

Original languageEnglish (US)
Article numberbbae357
JournalBriefings in Bioinformatics
Volume25
Issue number4
DOIs
StatePublished - Jul 1 2024

Funding

This work was supported by grants to Z.J.: the National Institutes of Health (R35GM138192, R01HL161389, and R00CA207865) and the Lynn Sage Scholar fund. E.S. was supported by the Predoctoral Training Program in Biomedical Data Driven Discovery (T32LM012203). T.W. and R.Y. were supported by the National Institutes of Health R01CA259388.

Keywords

  • cancer metastasis
  • drug response
  • gene network
  • lineage types

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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