Evidence map›Paper›PMID 35116696›Full record

ArticleTranslational cancer research2021

A hypoxia-linked gene signature for prognosis prediction and evaluating the immune microenvironment in patients with hepatocellular carcinoma.

Jukun Wang, Yu Li, Chao Zhang, Xin Chen, Linzhong Zhu, Tao Luo

Open access · diamondAbstract read
In one paragraph

Article in Translational cancer research, 2021. 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
0.6field-weighted citation impact, top 31% 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

4 citing papers in PubMed, 4 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

6 authors at 1 institution in 1 country.

Jukun WangDepartment of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China.
Yu LiDepartment of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China.
Chao ZhangDepartment of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China.
Xin ChenDepartment of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China.
Linzhong ZhuDepartment of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China.
Tao LuoDepartment of General Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China.
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious research indicates that hypoxia critically affects the initiation and progression of hepatocellular carcinoma (HCC). Nevertheless, the molecular mechanisms responsible for HCC development are poorly understood. Herein, we purposed to build a prognostic model using hypoxia-linked genes to predict patient prognosis and investigate the relationship of hypoxia with immune status in the tumor microenvironment (TME).

methodsThe training cohort included transcriptome along with clinical data abstracted from The Cancer Genome Atlas (TCGA). The validation cohort was abstracted from Gene Expression Omnibus (GEO). Univariate along with multivariate Cox regression were adopted to create the prediction model. We divided all patients into low- and high-risk groups using median risk scores. The estimation power of the prediction model was determined with bioinformatic tools.

resultsSix hypoxia-linked genes,

conclusionsA prediction model for predicting HCC prognosis using six hypoxia-linked genes was designed in this study, facilitating the diagnosis and treatment of HCC.

Indexed as

Hepatocellular carcinoma (HCC)hypoxiaprognosissurvivalThe Cancer Genome Atlas (TCGA)

Identifiers

PMID35116696
PMCPMC8798548
OpenAlexW3197851533

What OpenQuestion holds

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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.