Evidence map›Paper›PMID 29642405›Full record

ArticleBiomedicines2018

Transcriptome Analysis of Acute Phase Liver Graft Injury in Liver Transplantation.

Nikki P Lee, Haiyang Wu, Kevin T P Ng, Ruibang Luo, Tak-Wah Lam, Chung-Mau Lo, Kwan Man

Abstract read
In one paragraph

Article in Biomedicines, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

7 authors.

Nikki P LeeDepartment of Surgery, The University of Hong Kong, Hong Kong, China. nikkilee@hku.hk.
Haiyang WuDepartment of Computer Science, The University of Hong Kong, Hong Kong, China. repurpledeep@gmail.com.
Kevin T P NgDepartment of Surgery, The University of Hong Kong, Hong Kong, China. ledodes@hku.hk.
Ruibang LuoDepartment of Computer Science, The University of Hong Kong, Hong Kong, China. rbluo2@hku.hk.
Tak-Wah LamDepartment of Computer Science, The University of Hong Kong, Hong Kong, China. chungmlo@hkucc.hku.hk.
Chung-Mau LoDepartment of Surgery, The University of Hong Kong, Hong Kong, China. kwanman@hku.hk.
Kwan ManDepartment of Surgery, The University of Hong Kong, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLiver transplantation remains the treatment of choice for a selected group of hepatocellular carcinoma (HCC) patients. However, the long-term benefit is greatly hampered by post-transplant HCC recurrence. Our previous studies have identified liver graft injury as an acute phase event leading to post-transplant tumor recurrence.

methodsTo re-examine this acute phase event at the molecular level and in an unbiased way, RNA sequencing (RNA-Seq) was performed on liver graft biopsies obtained from the transplant recipients two hours after portal vein reperfusion with an aim to capture frequently altered pathways that account for post-transplant tumor recurrence. Liver grafts from recurrent recipients (

resultsRNA expression profiles comparison pointed to several frequently altered pathways, among which pathways related to cell adhesion molecules were the most involved. Subsequent validation using quantitative polymerase chain reaction confirmed the differential involvement of two cell adhesion molecules

conclusionThis whole transcriptome strategy unravels the molecular landscape of liver graft gene expression alterations, which can identify key pathways and genes that are involved in acute phase liver graft injury that may lead to post-transplant tumor recurrence.

Indexed as

CD274cell adhesion moleculesHFEintragraft gene expression profilesliver graft injuryLiver transplantation

Identifiers

PMID29642405
PMCPMC6027418

What OpenQuestion holds

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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.