Evidence map›Paper›PMID 26960563›Full record

ArticleScientific reports2016

A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia.

Xiaoming Liu, Jiasheng Yang, Yi Zhang, Yun Fang, Fayou Wang, Jun Wang, Xiaoqi Zheng, Jialiang Yang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
8.0field-weighted citation impact, top 2% 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

46 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.

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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 at 3 institutions in 3 countries.

Xiaoming LiuDepartment of Mathematics, Shanghai Normal University, Shanghai 200234, P. R. China.
Jiasheng YangDepartment of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore.
Yi ZhangDepartment of Mathematics, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, P. R. China.
Yun FangDepartment of Mathematics, Shanghai Normal University, Shanghai 200234, P. R. China.
Fayou WangDepartment of Mathematics, Shanghai Normal University, Shanghai 200234, P. R. China.
Jun WangDepartment of Mathematics, Shanghai Normal University, Shanghai 200234, P. R. China.
Xiaoqi ZhengDepartment of Mathematics, Shanghai Normal University, Shanghai 200234, P. R. China.
Jialiang YangDepartment of Mathematics, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, P. R. China.
Shanghai Normal University · CNIcahn School of Medicine at Mount Sinai · USHebei University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have studied drug-response associated (DRA) gene expressions by applying a systems biology framework to the Cancer Cell Line Encyclopedia data. More than 4,000 genes are inferred to be DRA for at least one drug, while the number of DRA genes for each drug varies dramatically from almost 0 to 1,226. Functional enrichment analysis shows that the DRA genes are significantly enriched in genes associated with cell cycle and plasma membrane. Moreover, there might be two patterns of DRA genes between genders. There are significantly shared DRA genes between male and female for most drugs, while very little DRA genes tend to be shared between the two genders for a few drugs targeting sex-specific cancers (e.g., PD-0332991 for breast cancer and ovarian cancer). Our analyses also show substantial difference for DRA genes between young and old samples, suggesting the necessity of considering the age effects for personalized medicine in cancers. Lastly, differential module and key driver analyses confirm cell cycle related modules as top differential ones for drug sensitivity. The analyses also reveal the role of TSPO, TP53, and many other immune or cell cycle related genes as important key drivers for DRA network modules. These key drivers provide new drug targets to improve the sensitivity of cancer therapy.

Indexed as

Systems BiologyAge FactorsAntineoplastic AgentsBiomarkers, PharmacologicalCell CycleCell Line, TumorDrug Resistance, NeoplasmFemaleGene Expression RegulationGene Regulatory NetworksHumansMaleNeoplasmsSex FactorsAntineoplastic AgentsBiomarkers, Pharmacological

Identifiers

PMID26960563
PMCPMC4785360
OpenAlexW2296251405

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Registered trials

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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 OpenQuestion graph.