Evidence map›Paper›PMID 32903581›Full record

ArticleFrontiers in oncology2020

Identification of Energy Metabolism Genes for the Prediction of Survival in Hepatocellular Carcinoma.

Qinjunjie Chen, Fengwei Li, Yuzhen Gao, Gaoran Xu, Leilei Liang, Jingchao Xu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing 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

48 citing papers in PubMed, 115 citations in OpenAlex.

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  20. Identification of driving genes of familial adenomatous polyposis by differential gene expression analysis and weighted gene co-expression network analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024
    Article
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

6 authors at 4 institutions in 1 country.

Qinjunjie ChenDepartment of General Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Fengwei LiDepartment of Hepatic Surgery, The Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.
Yuzhen GaoDepartment of Molecular Diagnosis, Clinical Medical College, Yangzhou University, Jiangsu, China.
Gaoran XuDepartment of General Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Leilei LiangDepartment of Surgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jingchao XuDepartment of General Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Dalian Medical University · CNSecond Military Medical University · CNShanghai Jiao Tong University · CNYangzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) samples were clustered into three energy metabolism-related molecular subtypes (C1, C2, and C3) with different prognosis using the gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). HCC energy metabolism-related molecular subtype analysis was conducted based on the 594 energy metabolism genes. Differential expression analysis yielded 576 differentially expressed genes (DEGs) among the three subtypes, which were closely related to HCC progression. Six genes were finally selected from the 576 DEGs through LASSO-Cox regression and used in constructing a six-gene signature-associated prognostic risk model, which was validated using the TCGA internal and three GEO external validation cohorts. The risk model showed that high

Indexed as

bioinformaticsenergy metabolismhepatocellular carcinomaprognosisrisk model

Identifiers

PMID32903581
PMCPMC7438573
OpenAlexW3048518217

What OpenQuestion holds

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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.