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  • Proteogenomic Divergence Between mRNA Abundance and Protein Output in Cancer: Implications for Kinase-Centric Pathway Modeling

  • Alireza Pourrahim,1,*
    1. Student Research Committee, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran


  • Introduction: Transcriptomic profiling has long anchored pathway analysis in oncology, with mRNA expression widely used as a proxy for protein abundance. However, large-scale proteogenomic studies demonstrate systematic discordance between transcript and protein levels across multiple cancer types, driven by post-transcriptional regulation, translational control, and protein turnover. This divergence is particularly consequential for kinase-centric signaling models, where pathway activity predictions depend on accurate quantification of effector proteins and their phosphorylation states.
  • Methods: A comprehensive review of the literature was conducted integrating findings from multi-omic datasets, with emphasis on CPTAC cohorts spanning breast, lung, colon, and ovarian cancers. Evidence was synthesized from ribosome profiling studies, quantitative mass spectrometry-based proteomics, phosphoproteomics datasets, and RNA-binding protein (RBP) regulatory atlases. Discordance between mRNA and protein levels was evaluated for therapeutically relevant kinases including MET, EGFR, CDK6, and MTOR. Pathway model accuracy was assessed by comparing transcriptome-calibrated network predictions against matched proteomic and phosphoproteomic readouts.
  • Results: Across CPTAC cohorts, mRNA-protein correlation coefficients for kinase-encoding transcripts consistently fell below genome-wide medians, with a subset showing near-zero or inverse correlations. Key mechanisms underlying this decoupling include microRNA-mediated translational repression, differential ribosome occupancy, RBP-driven stability regulation, and ubiquitin-proteasome-mediated degradation. Phosphoproteomic data revealed that kinase activity states frequently diverged from cognate mRNA levels, producing systematic misassignment of node activity in transcriptome-derived pathway models. For MET and EGFR specifically, protein abundance and activating phosphorylation were better predicted by proteogenomic integration than by RNA-seq alone.
  • Conclusion: Transcriptome-based pathway models carry inherent and quantifiable error when applied to kinase-driven oncogenic networks. Accurate reconstruction of signaling flux requires multi-layer integration of transcriptomic, translatomic, proteomic, and phosphoproteomic measurements. Resolving mRNA-protein discordance is a prerequisite for identifying genuinely active therapeutic targets and distinguishing them from transcriptional noise in the tumor proteome.
  • Keywords: Proteogenomics, mRNA-protein discordance, translational regulation, kinase signaling

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