ArticleNucleic acids research2021
Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed, 9 citations in OpenAlex.
- DORSSAA: Drug-Target interactOmics Resource Based on Stability/Solubility Alteration Assay.Molecular & cellular proteomics : MCP · 2026Article
- Informative Missingness in Nominal Data: A Graph-Theoretic Approach to Revealing Hidden Structure.Computational and structural biotechnology journal · 2026Article
- Integrated MicroRNA-mRNA Sequencing Analysis Identifies Regulators and Networks Involved in Feline Hypertrophic Cardiomyopathy.International journal of molecular sciences · 2025Article
- PhenoMultiOmics: an enzymatic reaction inferred multi-omics network visualization web server.Bioinformatics (Oxford, England) · 2024Article
- Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores.Scientific reports · 2024Article
- Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens.Life science alliance · 2024Article
- SL-scan identifies synthetic lethal interactions in cancer using metabolic networks.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 7 institutions in 3 countries.
Funding
Abstract
Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.
Indexed as
Identifiers
What OpenQuestion holds
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.