Evidence map›Paper›PMID 37626987›Full record

ArticleBiology2023

Cuproptosis- and m6A-Related lncRNAs for Prognosis of Hepatocellular Carcinoma.

Yuezhi Zhu, Jen Kit Tan, Jo Aan Goon

Open access · goldAbstract read
In one paragraph

Article in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

10 citing papers in PubMed, 9 citations in OpenAlex.

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  10. Analysis of mJournal of Cancer · 2024
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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

3 authors at 1 institution in 1 country.

Yuezhi ZhuDepartment of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.
Jen Kit TanDepartment of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.ORCID 0000-0002-0824-0581
Jo Aan GoonDepartment of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.ORCID 0000-0003-1757-3780
National University of Malaysia · MY

Funding

Heze Huiren Chinese Medicine Hospital FF-2022-261Universiti Kebangsaan Malaysia DPK-2022-007
6 · The paper itself

Abstract

Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis and m6A were obtained through correlation analysis. To construct a risk assessment model, univariate Cox regression analysis and LASSO Cox regression were employed. Analyze the regulatory effect of relevant risk assessment models on tumor mutation load (TMB) and immune microenvironment. A total of five lncRNAs (AC007405.3, AL031985.3, TMCC1-AS1, MIR210HG, TMEM220-AS1) with independent overall survival-related risk models were obtained by LASSO survival regression. TP53 and CTNNB1 were the three genes found to have the most mutations in high-risk group patients. The high-risk group with low TMB had the worst survival, whereas the low-risk group with high TMB had the best survival. KEGG pathway analysis revealed that the high-risk group was enriched with cell cycle, oocyte meiosis, cell senescence, and glycolysis/glucose production pathways. We constructed a reliable cuproptosis- and m6A-related lncRNA model for the prognosis of HCC. The model may provide new insights into managing HCC patients, but further research is needed to validate it.

Indexed as

cuproptosishepatocellular carcinomaLncRNAsN6-methyladenosineprognosisRNA methylation

Identifiers

PMID37626987
PMCPMC10451969
OpenAlexW4385650260

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.