ArticleMethods in molecular biology (Clifton, N.J.)2026
Gain-of-Functional Genomic Screening for Microproteins Essential for Tumorigenesis.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Microproteins in Human Physiology and Pathology.Biochemistry · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mounting studies suggest that the protein-coding potential of the human genome is underestimated, and previously unannotated open reading frames (ORFs) have been revealed with technological advances. Although the coding potential of many non-canonical ORFs (ncORFs) is now recognized, their functional prevalence remains to be characterized, partly due to the technical difficulty and labour intensity in screening numerous ncORFs at once. Here, we describe a gain-of-function genomic screen to identify functional ncORF-encoded proteins responsible for breast cancer tumorigenesis. This method is thought to improve the efficiency of characterizing previously neglected ncORFs and reveal potential targets for cancer treatment.
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Registered trials
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 OpenQuestion graph.