Evidence map›Paper›PMID 36180906›Full record

ArticleJournal of translational medicine2022

A genetic map of the chromatin regulators to drug response in cancer cells.

Bo Chen, Pengfei Li, Mingyue Liu, Kaidong Liu, Min Zou, Yiding Geng, Shuping Zhuang, Huanhuan Xu, Linzhu Wang, Tingting Chen and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 44% of its field
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

5 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Review
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

14 authors at 2 institutions in 1 country.

Bo Chen *Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Pengfei Li *Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China.
Mingyue Liu *Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Kaidong LiuDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Min ZouDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yiding GengDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Shuping ZhuangDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Huanhuan XuDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Linzhu WangDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Tingting ChenDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yawei LiDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Zhangxiang ZhaoThe Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Lishuang QiDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. qilishuang7@ems.hrbmu.edu.cn.
Yunyan GuDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. guyunyan@ems.hrbmu.edu.cn.ORCID 0000-0001-5693-4126
Harbin Medical University · CNFirst Affiliated Hospital of Jinan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiverse drug vulnerabilities owing to the Chromatin regulators (CRs) genetic interaction across various cancers, but the identification of CRs genetic interaction remains challenging.

methodsIn order to provide a global view of the CRs genetic interaction in cancer cells, we developed a method to identify potential drug response-related CRs genetic interactions for specific cancer types by integrating the screen of CRISPR-Cas9 and pharmacogenomic response datasets.

resultsTotally, 625 drug response-related CRs synthetic lethality (CSL) interactions and 288 CRs synthetic viability (CSV) interactions were detected. Systematically network analysis presented CRs genetic interactions have biological function relationship. Furthermore, we validated CRs genetic interactions induce multiple omics deregulation in The Cancer Genome Atlas. We revealed the colon adenocarcinoma patients (COAD) with mutations of a CRs set (EP300, MSH6, NSD2 and TRRAP) mediate a better survival with low expression of MAP2 and could benefit from taxnes. While the COAD patients carrying at least one of the CSV interactions in Vorinostat CSV module confer a poor prognosis and may be resistant to Vorinostat treatment.

conclusionsThe CRs genetic interaction map provides a rich resource to investigate cancer-associated CRs genetic interaction and proposes a powerful strategy of biomarker discovery to guide the rational use of agents in cancer therapy.

Indexed as

AdenocarcinomaColonic NeoplasmsBiomarkersChromatinDNA-Binding ProteinsHumansVorinostatBiomarkersChromatinDNA-Binding ProteinsVorinostatCancer therapyChromatin regulatorsDrug responseSynthetic lethalitySynthetic viability

Identifiers

PMID36180906
PMCPMC9523919
OpenAlexW4298141712

What OpenQuestion 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 OpenQuestion graph.