Master regulators governing protein abundance across ten human cancer types
Wang, Z.; Wojciechowicz, M.; Rosen, J.; Elmas, A.; Song, W.-M.; Liu, Y.; Huang, K.-l.
Show abstract
Protein abundance correlates only moderately with mRNA levels, and are modulated post-transcriptionally by a network of regulators including ribosomes, RNA-binding proteins (RBPs), and the proteasome. Here, we identified Master Protein abundance Regulators (MaPRs) across ten cancer types by devising a new computational pipeline that jointly analyzed transcriptomes and proteomes from 1,305 tumor samples. We identified 232 to 1,394 MaPRs per cancer type, mediating up to 79% of post- transcriptional regulatory networks. MaPRs exhibit high network connectivity, strong genetic dependency in cancer cells, and significant enrichment for RBPs. Combining tumor up-regulation, druggability, and target network analyses identified cancer-specific vulnerabilities. MaPRs predict tumor proteomic subtypes more accurately than other proteins. Finally, significant portions of RBP MaPR-target relationships were validated by experimental evidence from eCLIP binding and knockdown assays. Our findings uncover central MaPRs that govern post-transcriptional networks, highlighting diverse processes underlying human proteome regulation and identifying key regulators in cancer biology.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genetic dependencies associated with transcription factor activities in human cancer cell lines 97%
- RNA Splicing Junction Landscape Reveals Abundant Tumor-Specific Transcripts in Human Cancer 96%
- Interrogation of cancer gene dependencies reveals novel paralog interactions of autosome and sexchromosome encoded genes 96%
Similar papers in this journal
- Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer 96%
- NDC80 status pinpoints mitotic kinase inhibitors as emerging therapeutic options in clear cell renal cell carcinoma 96%
- Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.