Evidence map›Paper›PMID 35280822›Full record

ArticleFrontiers in oncology2022

Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma.

Xianmo Wang, Huawei Yi, Jiancheng Tu, Wen Fan, Jiahao Wu, Li Wang, Xiang Li, Jinrong Yan, Huali Huang, Rong Huang

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

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 53% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

10 authors at 4 institutions in 1 country.

Xianmo WangClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Huawei YiClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Jiancheng TuClinical Laboratory, The Second Clinical College of Wuhan University, Wuhan, China.
Wen FanClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Jiahao WuClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Li WangClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Xiang LiClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Jinrong YanClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Huali HuangDepartment of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
Rong HuangClinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
First People's Hospital of Jingzhou · CNYangtze University · CNFirst People's Hospital of Nanning · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Hepatitis B (HBV)-infected hepatocellular carcinoma is one of the most common cancers, and it has high incidence and mortality rates worldwide. The incidence of hepatocellular carcinoma has been increasing in recent years, and existing treatment modalities do not significantly improve prognosis. Therefore, it is important to find a biomarker that can accurately predict prognosis. Methods: This study was analyzed using the The Cancer Genome Atlas (TCGA) database and validated by the International Cancer Genome Consortium (ICGC) database. The STRING database was used to construct a gene co-expression network and visualize its functional clustering using Cytoscape. A prognostic signature model was constructed to observe high and low risk with prognosis, and independent prognostic factors for HBV-infected hepatocellular carcinoma were identified by Cox regression analysis. The independent prognostic factors were then analyzed for expression and survival, and their pathway enrichment was analyzed using gene set enrichment analysis (GSEA). Results: 805 differentially expressed genes (DEGs) were obtained by differential analysis. Protein-protein interaction (PPI) showed that DEGs were mostly clustered in functional modules, such as cellular matrix response, cell differentiation, and tissue development. Prognostic characterization models showed that the high-risk group was associated with poor prognosis, while Cox regression analysis identified ASF1B as the only independent prognostic factor. As verified by expression and prognosis, ASF1B was highly expressed in HBV-infected hepatocellular carcinoma and led to a poor prognosis. GSEA showed that high ASF1B expression was involved in cell cycle-related signaling pathways. Conclusion: Bioinformatic analysis identified ASF1B as an independent prognostic factor in HBV-infected hepatocellular carcinoma, and its high expression led to a poor prognosis. Furthermore, it may promote hepatocellular carcinoma progression by affecting cell cycle-related signaling pathways.

Indexed as

ASF1BASF1B overexpressionHBVlung cancerprognosis

Identifiers

PMID35280822
PMCPMC8907517
OpenAlexW4213457382

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.