ArticlePLoS computational biology2022
Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Evaluating a Glioma Transcriptomic Signature Against a Clinical Reference Model and a Random-Signature Null Distribution: A Leakage-Controlled Internal Audit and a Survey of the Field.Diagnostics (Basel, Switzerland) · 2026Article
- Multi-omic profiling defines three distinct molecular subtypes of urothelial carcinoma with implications for precision therapy.Clinical and translational medicine · 2026Article
- Spatial FBA reveals heterogeneous Warburg niches in renal tumors and lactate consumption in colorectal cancer.NPJ systems biology and applications · 2026Article
- RANBP9 and RANBP10 cooperate in regulating non-small cell lung cancer proliferation.Journal of experimental & clinical cancer research : CR · 2025Article
- The intrinsic dimension of gene expression during cell differentiation.Nucleic acids research · 2025Article
- Polymerase theta expression is correlated with proliferative capacity but not with DNA repair deficiency status in solid tumors.NPJ precision oncology · 2025Article
- Genes driving three-dimensional growth of immortalized cells and cancer.Cell death & disease · 2025Article
- Brain-body mitochondrial distribution patterns lack coherence and point to tissue-specific regulatory mechanisms.Life metabolism · 2025Article
- Quantitative Proteomics and Phosphoproteomics Analysis of Patient-Derived Ovarian Cancer Stem Cells.Molecular & cellular proteomics : MCP · 2025Article
- Article
- BRAFClinical and translational medicine · 2025Article
- Trophic and temporal dynamics of macrophage biology in human inner ear organogenesis.Frontiers in immunology · 2025Article
- Single-cell RNA sequencing of the holothurian regenerating intestine reveals the pluripotency of the coelomic epithelium.bioRxiv : the preprint server for biology · 2024Article
- Patient-derived response estimates from zero-passage organoids of luminal breast cancer.Breast cancer research : BCR · 2024Article
- Long-term breast cancer response to CDK4/6 inhibition defined by TP53-mediated geroconversion.Cancer cell · 2024Article
- Brain-body mitochondrial distribution patterns lack coherence and point to tissue-specific and individualized regulatory mechanisms.bioRxiv : the preprint server for biology · 2024Article
- Bhlhe40 Regulates Proliferation and Angiogenesis in Mouse Embryoid Bodies under Hypoxia.International journal of molecular sciences · 2024Article
- Identification of unique cell type responses in pancreatic islets to stress.Nature communications · 2024Article
- PGC-1α regulates the interplay between oxidative stress, senescence and autophagy in the ageing retina important in age-related macular degeneration.Journal of cellular and molecular medicine · 2024Article
- Patient-derived response estimates from zero-passage organoids of luminal breast cancer.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.