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Retracing the Process of Translation: Proteome-wide mapping of stable transcriptomic predictors of protein abundance in cancer cell lines — arXiv:2609.24004

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.24004
License (as archived): CC0-1.0
Archived: 2026-10-10T00:53:47.917Z
SHA-256 of the archived text: 0f71ab5558022a136211616b825445b8fed7253caf5943fc2ef8a2397f0bdd11
Stable URL of this page: https://metraipolis.com/documento/dc_bd99007f12e5

How to cite

Retracing the Process of Translation: Proteome-wide mapping of stable transcriptomic predictors of protein abundance in cancer cell lines — arXiv:2609.24004 — arXiv (en), https://arxiv.org/abs/2609.24004 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd99007f12e5 (Metraipolis archive dc_bd99007f12e5, sha256 0f71ab5558022a136211616b825445b8fed7253caf5943fc2ef8a2397f0bdd11)

BibTeX

@misc{dcbd99007f12e5,
  title = {Retracing the Process of Translation: Proteome-wide mapping of stable transcriptomic predictors of protein abundance in cancer cell lines — arXiv:2609.24004},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.24004},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd99007f12e5; sha256 0f71ab5558022a136211616b825445b8fed7253caf5943fc2ef8a2397f0bdd11},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.24004. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Understanding the relationship between gene expression and protein abundance is central to molecular and systems biology. While gene expression reflects transcriptional activity, proteins are the functional molecules that determine cellular phenotypes. However, numerous post-transcriptional and translational regulatory layers complicate this relationship, and prior studies have reported only weak to moderate correlations between RNA and protein levels. Predicting protein abundance from transcriptomic data remains challenging, but it is a valuable goal for biological insight, especially when proteomic data is limited or unavailable. In this study, we applied a large-scale, Ridge regression-based feature selection strategy to identify predictive gene expression features for each of 8,423 proteins across 940 cancer cell lines. To our knowledge, this is the first work to perform such comprehensive protein-wise feature selection at this scale. Our analysis revealed both globally predictive and context-specific gene features. These included biologically meaningful modules such as immune-related genes, HOX transcription factor targets, and cytoskeletal components. The models identified stable candidate gene-protein associations that remained interpretable at the level of individual proteins and recurrent transcriptomic predictor patterns. Our approach enables interpretable modeling of protein expression from transcriptomic data and provides insight into transcriptomic features associated with protein abundance. This framework may support hypothesis generation, protein imputation in incomplete datasets, and deeper understanding of post-transcriptional regulation in cancer biology.

Tema degli esploratori: scienze della vita e salute (retro-catalogo arXiv: annunci del 2026-09-22 in q-bio.QM).
Categorie arXiv: Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2609.24004 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T23:41:52.520Z dalla pagina https://arxiv.org/catchup/q-bio.QM/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).