Mutation impact on mRNA versus protein expression across human cancers
2025

Mutation Impact on mRNA versus Protein Expression across Human Cancers

Sample size: 953 publication 10 minutes Evidence: high

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 exhibit distinct impacts on mRNA and protein levels, highlighting the importance of integrating proteogenomic data to identify functionally significant cancer mutations.

Supporting Evidence

  • 47.2% of somatic expression quantitative trait loci (seQTLs) were validated at the protein level.
  • TP53 missense mutations were associated with higher protein expression in multiple cancer cohorts.
  • Truncating mutations showed significant enrichment for impacts on protein abundance compared to mRNA levels.

Takeaway

This study looks at how changes in genes can affect the amount of protein made in cancer, showing that some changes affect protein levels without changing the gene's message.

Methodology

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

Potential Biases

The reliance on FDR thresholds could limit the detection of spsQTLs with subtle effects.

Limitations

The study does not distinguish between several potential mechanisms leading to discordant effects of mutations on gene and protein expression and is limited by sample sizes that may not provide sufficient statistical power for all analyses.

Participant Demographics

The study included diverse cancer types, but specific demographic details such as age, gender, and ethnicity were adjusted for in the analyses.

Statistical Information

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.1093/gigascience/giae113

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