Evidence map›Paper›PMID 36124029›Full record

ArticleDisease markers2022

Profiles of Acetylation Regulation Genes Contribute to Malignant Progression and Have a Clinical Prognostic Impact on Liver Cancer.

Wei Zhu, Xiaofen Zhang, Mengli Yu, Yu Zhang, Shaowei Li, Chaohui Yu

Open access · hybridAbstract read
In one paragraph

Article in Disease markers, 2022. 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 35% 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, 7 citations in OpenAlex.

  1. Article
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  4. Review
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 2 institutions in 1 country.

Wei ZhuDepartment of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China.ORCID https://orcid.org/0000-0001-8923-3713
Xiaofen ZhangDepartment of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China.ORCID https://orcid.org/0000-0001-9957-1775
Mengli YuDepartment of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China.ORCID https://orcid.org/0000-0002-1368-1242
Yu ZhangDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, Zhejiang 317000, China.ORCID https://orcid.org/0000-0002-5522-3178
Shaowei LiDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, Zhejiang 317000, China.ORCID https://orcid.org/0000-0002-3276-1037
Chaohui YuDepartment of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China.ORCID https://orcid.org/0000-0003-4842-3646
First Affiliated Hospital Zhejiang University · CNWenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Several studies have demonstrated that acetylation was involved in the process of liver cancer. This study aimed to establish an effective predictive prognostic model using acetylation regulation genes in liver cancer. Methods: Two datasets were downloaded from the Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. Differentially expressed acetylation regulation genes were identified in the TCGA-LIHC dataset, and then, Gene Ontology (GO) functional annotation analysis was used to investigate the molecular mechanism. After grouping the patients into clusters based on consensus clustering, we explored the correlation between clusters and clinical characteristics. A risk model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis to calculate the risk score. Patients were divided into high-risk and low-risk groups according to the risk score using the acetylation regulation genes. Data downloaded from LIRI-JP were used for external validation. Univariate and multivariate Cox regressions were performed to identify independent risk factors. A prognostic nomogram was constructed according to the TCGA-LIHC dataset. The effect of HDAC11 expression on the proliferation and migration of liver cancer was detected by the CCK-8 method and cell scratch test, respectively. Results: Eleven of 29 acetylation regulation genes were identified as upregulated differentially expressed genes. Go enrichment analysis showed that they were involved in "protein and histone deacylation and deacetylation." Patients were categorized into two clusters according to the expression of 29 acetylation regulation genes. Compared with cluster 2, cluster 1 correlated with shorter overall survival (OS) and higher expression. Stage, T stage, grade, gender, age, and follow-up state were significantly different between two clusters. Pathways involved in DNA repair were significantly enriched in cluster 1. The risk score was calculated by HDAC1, HDAC2, HDAC4, HDAC11, HAT1, and SIRT6. Patients in the high-risk group had a worse prognosis in both datasets. Risk score was not only an independent prognostic marker but could also predict the clinicopathological features of liver cancer. A nomogram containing risk score, T stage, and M stage was built to predict overall survival. After transfection with HDAC11 overexpression plasmid, the proliferation ability of HepG2 cells increased, while the migration ability had no change. Conclusions: Our findings suggested that acetylation regulation genes contribute to malignant progression and have a clinical prognostic impact on liver cancer.

Indexed as

Liver NeoplasmsSirtuinsAcetylationBiomarkers, TumorHistonesHumansPrognosisBiomarkers, TumorHistonesSIRT6 protein, humanSirtuins

Identifiers

PMID36124029
PMCPMC9482539
OpenAlexW4295110343

What OpenQuestion holds

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LicenceCC BY
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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.