Mutation impact on mRNA versus protein expression across human cancers
2025

How mutations affect mRNA and protein levels in cancer

Sample size: 953 publication 10 minutes Evidence: moderate

Author Information

Author(s): Yuqi Liu, Abdulkadir Elmas, Kuan-lin Huang

Primary Institution: Icahn School of Medicine at Mount Sinai

Hypothesis

How do somatic mutations affect protein expression in addition to gene expression across different cancer types?

Conclusion

Somatic mutations can have distinct impacts on mRNA and protein levels, highlighting the importance of integrating proteogenomic data for identifying functionally significant cancer mutations.

Supporting Evidence

  • 47.2% of somatic expression quantitative trait loci (seQTLs) were validated for protein-level impacts.
  • TP53 missense mutations were associated with higher protein expression in multiple cancer cohorts.
  • Significant enrichment for truncations was found in protein quantitative trait loci (pQTLs).
  • Concordant effects were observed in 88.9% of significant seQTLs and spQTLs.

Takeaway

This study looks at how changes in genes from cancer can affect both the messages that tell cells to make proteins and the proteins themselves, which can be different.

Methodology

The study used proteogenomic datasets from 953 cancer cases with paired genomics and proteomic profiling across 6 cancer types, applying multiple regression analyses to identify significant gene-cancer pairs.

Potential Biases

Potential biases may arise from the reliance on FDR thresholds and the assumption of linear relationships in regression models.

Limitations

The study does not distinguish between mechanisms leading to discordant effects of mutations on gene and protein expression and is limited by sample sizes and the linear regression model used.

Participant Demographics

The study included diverse cancer types, with specific sample sizes for each type, but detailed demographics were not provided.

Statistical Information

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.1093/gigascience/giae113

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