Evidence map›Paper›PMID 34350138›Full record

ArticleJournal of hepatocellular carcinoma2021

DNA and RNA Sequencing Recapitulated Aberrant Tumor Metabolism in Liver Cancer Cell Lines.

Yihong Sun, Xia Tang, Bo Ye, Keyue Ding

Open access · goldAbstract read
In one paragraph

Article in Journal of hepatocellular carcinoma, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 92% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 2 institutions in 1 country.

Yihong SunDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, 410006, People's Republic of China.ORCID 0000-0002-0884-3235
Xia TangDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, 410006, People's Republic of China.ORCID 0000-0003-3939-605X
Bo YeDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, 410006, People's Republic of China.ORCID 0000-0002-1267-8586
Keyue DingDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, 410006, People's Republic of China.ORCID 0000-0002-3496-6415
Chongqing Medical University · CNHenan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimMetabolic reprogramming has recently attracted extensive attention for understanding cancer development. We aimed to demonstrate a genomic and transcriptomic landscape of metabolic reprogramming underlying liver cancer cell lines.

methodsWe investigated metabolic aberrant at both the transcriptome and genome levels using transcriptome and whole-exome sequencing data from 12 human liver cancer cell lines (hLCCLs) and one normal liver cell line.

resultsThree subgroups of hLCCLs characterized from transcriptome sequencing data exhibit significantly different aberrations in various metabolic processes, including amino acid, lipid, energy, and carbohydrate metabolism. Furthermore, whole-exome sequencing revealed distinct mutational signatures among different subgroups of hLCCLs and identified a total of 19 known driver genes implicated in metabolism.

conclusionOur findings highlighted differential metabolic mechanisms in the development of liver cancer and provided a resource for further investigating its metabolic mechanisms.

Indexed as

human liver cancer cell lines (hLCCLs)sequencingtranscriptometumor metabolismwhole-exome

Identifiers

PMID34350138
PMCPMC8327295
OpenAlexW3183702008

What OpenQuestion holds

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LicenceCC BY-NC
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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.