Evidence map›Paper›PMID 38680213›Full record

ArticleJournal of hepatocellular carcinoma2024

A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation.

Jiaxin Zhao, Cheng Wang, Liang Zhao, Huiying Zhou, Rui Wu, Tao Zhang, Jiawei Ding, Junjie Zhou, Huilin Zheng, Lei Zhang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of hepatocellular carcinoma, 2024. 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
–field-weighted citation impact
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. 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

13 authors.

Jiaxin ZhaoDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.ORCID 0009-0005-6703-4779
Cheng WangDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.ORCID 0000-0002-1319-1438
Liang ZhaoDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.
Huiying ZhouDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.
Rui WuDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.
Tao ZhangDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.ORCID 0009-0004-3878-9307
Jiawei DingDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.
Junjie ZhouDepartment of Radiology, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.
Huilin ZhengZhejiang Provincial Collaborative Innovation Center of Agricultural Biological Resource Biochemical Manufacturing, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang Province, People's Republic of China.
Lei ZhangZhejiang Provincial Collaborative Innovation Center of Agricultural Biological Resource Biochemical Manufacturing, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang Province, People's Republic of China.
Tianci KongZhejiang Provincial Collaborative Innovation Center of Agricultural Biological Resource Biochemical Manufacturing, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang Province, People's Republic of China.ORCID 0009-0002-7035-8705
Jie ZhouDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China.ORCID 0000-0001-9150-1713
Zhenhua HuDepartment of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, People's Republic of China.ORCID 0000-0002-8636-0757

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Nonsense-mediated RNA decay (NMD), a surveillance pathway for selective degradation of aberrant mRNAs, is associated with cancer progression. Its potential as a predictor for aggressive hepatocellular carcinoma (HCC) is unclear. Here, we present an innovative NMD risk model for predicting HCC prognosis. Methods: The Cancer Genome Atlas (TCGA) data of 374 liver HCC (LIHC) and 50 normal liver samples were extracted. A risk model based on NMD-related genes was developed through least absolute shrinkage and selection operator Cox (LASSO-Cox) regression of the LIHC-TCGA data. Prognostic validation was done using GSE54236, GSE116174, and GSE76427 data. Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the model. We also constructed nomograms for survival prediction. Tumor immune infiltration was evaluated using the CIBERSORT algorithm, and the tumor cell phenotype was assessed. Finally, mouse experiments verified UPF3B knockdown effects on HCC tumor characteristics. Results: We developed a risk model based on four NMD-related genes ( Conclusion: Our NMD-related gene-based risk model can help identify therapeutic targets and biomarkers for HCC. Additionally, it assists clinicians in predicting the prognosis of HCC patients.

Indexed as

biomarkerhepatocellular carcinomaLASSOnonsense-mediated RNA decaysurvivaltumor immunity

Identifiers

PMID38680213
PMCPMC11055534

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.