Evidence map›Paper›PMID 35547744›Full record

ReviewTheranostics2022

CRISPR-Cas9 library screening approach for anti-cancer drug discovery: overview and perspectives.

Yau-Tuen Chan, Yuanjun Lu, Junyu Wu, Cheng Zhang, Hor-Yue Tan, Zhao-Xiang Bian, Ning Wang, Yibin Feng

Abstract readReview
In one paragraph

Review in Theranostics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yau-Tuen ChanSchool of Chinese Medicine, The University of Hong Kong.
Yuanjun LuSchool of Chinese Medicine, The University of Hong Kong.
Junyu WuSchool of Chinese Medicine, The University of Hong Kong.
Cheng ZhangSchool of Chinese Medicine, The University of Hong Kong.
Hor-Yue TanSchool of Chinese Medicine, Hong Kong Baptist University.
Zhao-Xiang BianSchool of Chinese Medicine, Hong Kong Baptist University.
Ning WangSchool of Chinese Medicine, The University of Hong Kong.
Yibin FengSchool of Chinese Medicine, The University of Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CRISPR-Cas9 is a Nobel Prize-winning robust gene-editing tool developed in the last decade. This technique enables a stable genetic engineering method with high precision on the genomes of all organisms. The latest advances in the technology include a genome library screening approach, which can detect survival-essential and drug resistance genes via gain or loss of function. The versatile machinery allows genomic screening for gene activation or inhibition, and targets non-coding sequences, such as promoters, miRNAs, and lncRNAs. In this review, we introduce the emerging high-throughput CRISPR-Cas9 library genome screening technology and its working principles to detect survival and drug resistance genes through positive and negative selection. The technology is compared with other existing approaches while focusing on the advantages of its variable applications in anti-cancer drug discovery, including functions and target identification, non-coding RNA information, actions of small molecules, and drug target discoveries. The combination of the CRISPR-Cas9 system with multi-omic platforms represents a dynamic field expected to advance anti-cancer drug discovery and precision medicine in the clinic.

Indexed as

Antineoplastic AgentsNeoplasmsCRISPR-Cas SystemsGene EditingGenetic EngineeringHumansAntineoplastic AgentsCancer therapyCRISPR-Cas9Drug discoveryExperimental modelsLibrary screening

Identifiers

PMID35547744
PMCPMC9065202

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.