Relating transcriptomics to protein abundance reveals self-driven versus interactor-driven proteins

Statistically modeling the interdependence between transcriptomics and protein abundances remains a persistent challenge in bioinformatics research. Transcriptomic data and proteomic abundances typically display a moderate Pearson's correlation of about 0.5, while in tumor conditions, the correlation may decrease to a much weaker correlation of 0.2. Given that transcriptomic datasets are commonly analyzed due to their ample availability, it is imperative to understand this correlation in greater detail and to be aware of potential deficiencies while conducting research connecting transcriptomic data to protein abundances. Recently, the Clinical Proteome Tumor Analysis Consortium has collected several large-scale proteomic datasets containing transcriptomic and proteomic abundances from hundreds of patients in The Cancer Genome Analysis cohorts. Utilizing these data, a detailed analysis of how protein and transcript abundances correspond for individual genes was presented. As variables of linear regression models, transcript levels of each gene and of their known protein-protein interaction partners, taken from the STRING database, are used. Interestingly, this resulted in two distinct classifications of genes, with a continuum present in between: "self-driven" genes, for which protein abundances are mainly determined by the gene's own transcript level, as well as "interaction-driven" genes, for which transcript levels of other genes possess far greater relevance for its protein abundance than its own transcript level. Notably, the former displays significantly shorter mRNA half-lives than the latter. Namely, this is observed in the ribosome, which is encoded by rather long-lived mRNAs and displays protein abundance levels that are determined by the transcript levels of the other protein components, rather than by its own transcript level. These findings suggest that biomarker interpretation and therapeutic intervention should consider whether a gene is self-driven or interactor-driven, as protein abundance in the latter may be regulated primarily through interaction partners rather than the gene's own transcript level.