The Sendoel lab investigated the correlation of mRNA and corresponding protein levels in fourteen human cancers using publicly available datasets. They identified two clusters in which the correlations were especially low and mutations in 55 cancer genes which change the mRNA protein correlations. Intriguingly, the loss of post transcriptional control and thereby increased mRNA protein correlations are correlated with shorter patient survival. Their findings have been published in the article "" in Cell Reports.
- Tumor mRNA-protein correlations vary widely but are higher than corresponding healthy tissues
- Two clusters of genes show particularly low mRNA-protein correlations across all cancer types
- 55 cancer gene mutations alter systems-wide mRNA-protein correlations in multiple cancer types
- Higher mRNA-protein correlations are associated with shorter overall cancer patient survival
Understanding the mechanisms underlying cancer gene expression is critical for precision oncology. Posttranscriptional regulation is a key determinant of protein abundance and cancer cell behavior. However, to what extent posttranscriptional regulatory mechanisms impact protein levels and cancer progression is an ongoing question. Here, we exploit cancer proteogenomics data to systematically compare mRNA-protein correlations across 14 different human cancer types. We identify two clusters of genes with particularly low mRNA-protein correlations across all cancer types, shed light on the role of posttranscriptional regulation of cancer driver genes and drug targets, and unveil a cohort of 55 mutations that alter systems-wide posttranscriptional regulation. Surprisingly, we find that decreased levels of posttranscriptional control in patients correlate with shorter overall survival across multiple cancer types, prompting further mechanistic studies into how posttranscriptional regulation affects patient outcomes. Our findings underscore the importance of a comprehensive understanding of the posttranscriptional regulatory landscape for predicting cancer progression.