Evidence map›Paper›PMID 37301898›Full record

ArticleScientific data2023

GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases.

Ziteng Li, Hena Zhang, Qin Li, Wanjing Feng, Xiya Jia, Runye Zhou, Yi Huang, Yan Li, Zhixiang Hu, Xichun Hu and 2 more

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

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
9.9field-weighted citation impact, top 1% 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

37 citing papers in PubMed, 46 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

12 authors at 2 institutions in 1 country.

Ziteng Li *Department of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.ORCID 0000-0001-9691-2589
Hena Zhang *Department of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Qin LiDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Wanjing FengDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Xiya JiaDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Runye ZhouDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Yi HuangDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Yan LiDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Zhixiang HuDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Xichun HuDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China. huxichun2017@163.com.ORCID 0000-0002-9892-699X
Xiaodong ZhuDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China. xddr001@163.com.
Shenglin HuangDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China. slhuang@fudan.edu.cn.ORCID 0000-0003-1279-0794
Shanghai Medical College of Fudan University · CNFudan University Shanghai Cancer Center · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81872294National Natural Science Foundation of China (National Science Foundation of China) 82072694
6 · The paper itself

Abstract

Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies have been revealing molecular landscape of various liver conditions at bulk and single-cell resolution, however, neither single experiment nor databases enabled thorough investigations of transcriptomic dynamics along the progression of liver diseases. Here we establish GepLiver, a longitudinal and multidimensional liver expression atlas integrating expression profiles of 2469 human bulk tissues, 492 mouse samples, 409,775 single cells from 347 human samples and 27 liver cell lines spanning 16 liver phenotypes with uniformed processing and annotating methods. Using GepLiver, we have demonstrated dynamic changes of gene expression, cell abundance and crosstalk harboring meaningful biological associations. GepLiver can be applied to explore the evolving expression patterns and transcriptomic features for genes and cell types respectively among liver phenotypes, assisting the investigation of liver transcriptomic dynamics and informing biomarkers and targets for liver diseases.

Indexed as

Liver DiseasesTranscriptomeAnimalsGene Expression ProfilingHepatocytesHumansMice

Identifiers

PMID37301898
PMCPMC10257690
OpenAlexW4380203269

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.