Evidence map›Paper›PMID 35224155›Full record

ArticleGenes & diseases2022

Development of a tRNA-derived small RNA diagnostic and prognostic signature in liver cancer.

Yi Zuo, Shaoqiu Chen, Lingling Yan, Ling Hu, Scott Bowler, Emory Zitello, Gang Huang, Youping Deng

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

Article in Genes & diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

22 citing papers in PubMed, 29 citations in OpenAlex.

  1. Review
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  6. The Roles of tRNA-Derived Fragments in Cancer: Updates and Perspectives.International journal of molecular sciences · 2025
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  20. mtTB: A Web-Based R/Shiny App for Pulmonary Tuberculosis Screening.Frontiers in cellular and infection microbiology · 2022
    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

8 authors at 3 institutions in 2 countries.

Yi ZuoTianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan 430064, PR China.
Shaoqiu ChenDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA.
Lingling YanTianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan 430064, PR China.
Ling HuTianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan 430064, PR China.
Scott BowlerDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA.
Emory ZitelloDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA.
Gang HuangShanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, PR China.
Youping DengDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA.
University of Hawaiʻi at Mānoa · USWuhan University of Science and Technology · CNShanghai University of Medicine and Health Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver cancer presents divergent clinical behaviors. There remain opportunities for molecular markers to improve liver cancer diagnosis and prognosis, especially since tRNA-derived small RNAs (tsRNA) have rarely been studied. In this study, a random forests (RF) diagnostic model was built based upon tsRNA profiling of paired tumor and adjacent normal samples and validated by independent validation (IV). A LASSO model was used to developed a seven-tsRNA-based risk score signature for liver cancer prognosis. Model performance was evaluated by a receiver operating characteristic curve (ROC curve) and Precision-Recall curve (PR curve). The five-tsRNA-based RF diagnosis model had area under the receiver operating characteristic curve (AUROC) 88% and area under the precision-recall curve (AUPR) 87% in the discovery cohort and 87% and 86% in IV-AUROC and IV-AUPR, respectively. The seven-tsRNA-based prognostic model predicts the overall survival of liver cancer patients (Hazard Ratio 2.02, 95% CI 1.36-3.00,

Indexed as

DiagnosisLiver cancerPrognosisRandom foreststRNA-derived small RNAs

Identifiers

PMID35224155
PMCPMC8843861
OpenAlexW3122820446

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.