Evidence map›Paper›PMID 38737272›Full record

ArticleHeliyon2024

Construction of a TP53 mutation-associated ceRNA network as prognostic biomarkers in hepatocellular carcinoma.

Dong Wang, Wenxiang Shi, Chenjie Qiu

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Dong WangDepartment of General Surgery, Changzhou Hospital of Traditional Chinese Medicine, Changzhou 213000, China.
Wenxiang ShiDepartment of Pediatric Cardiology, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
Chenjie QiuDepartment of General Surgery, Changzhou Hospital of Traditional Chinese Medicine, Changzhou 213000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hepatocellular carcinoma (HCC) continues to endanger human health worldwide. Regulatory networks of competing endogenous RNAs (ceRNAs) play important roles in HCC. TP53 is the second most often altered gene in HCC and has a significant role in regulating target genes such as miRNAs and lncRNAs. Methods: Data from patients with TP53 mutation were collected through the cBioPortal database and differential analysis was performed to screen RNAs related to TP53 mutation. The lncRNA-miRNA-mRNA relationship was predicted by the miRcode, miRDB, and TargetScan databases. The ceRNA networks were screened and visualized by Cytoscape. Core ceRNA networks were generated by differential analysis, coexpression analysis, prognostic analysis and subcellular localization. Finally, methylation, mutation, PPI, GSEA, immunity and drug sensitivity analyses of MEX3A were performed to determine the role of MEX3A in HCC. Results: We identified 1508 DEmRNAs, 85 DEmiRNAs and 931 DElncRNAs and obtained a ceRNA network including 28 lncRNAs, 4 miRNAs and 31 mRNAs. Twenty hub DERNAs in the TP53-altered-related ceRNA network were screened out by Cytoscape and the core ceRNA network (LINC00491/TCL6-hsa-miR-139-5p-MEX3A) was obtained by multiple analyses. In addition, we discovered that the methylation level of MEX3A was decreased and the mutation frequency was raised in HCC. Furthermore, elevated MEX3A expression was associated with alterations in the HCC immunological microenvironment. Conclusion: We successfully constructed a reciprocal ceRNA network, which could provide new ideas for exploring HCC mechanisms and therapeutic approaches.

Indexed as

Competing endogenous RNAHepatocellular carcinomaLINC00491MEX3AmiR-139-5pTCL6TP53

Identifiers

PMID38737272
PMCPMC11088256

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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.