Evidence map›Paper›PMID 34513699›Full record

ArticleFrontiers in oncology2021

Construction and Characterization of Long Non-Coding RNA-Associated Networks to Reveal Potential Prognostic Biomarkers in Human Lung Adenocarcinoma.

Wenting Zhou, Chen Bai, Chaojun Long, Li Hu, Yanfei Zheng

Abstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. RNA mMolecular oncology · 2023
    Review
  3. Article
  4. Reply to Schramm, L. Comment on "Li et al.Diagnostics (Basel, Switzerland) · 2022
    Article
  5. Diagnostics (Basel, Switzerland) · 2021
    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

5 authors.

Wenting ZhouSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Chen BaiSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Chaojun LongSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Li HuSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Yanfei ZhengSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) is one type of the malignant tumors with high morbidity and mortality. The molecular mechanism of LUAD is still unclear. Studies demonstrate that lncRNAs play crucial roles in LUAD tumorigenesis and can be used as prognosis biomarkers. Thus, in this study, to identify more robust biomarkers of LUAD, we firstly constructed LUAD-related lncRNA-TF network and performed topological analyses for the network. Results showed that the network was a scale-free network, and some hub genes with high clinical values were identified, such as lncRNA RP11-173A16 and TF ZBTB37. Module analysis on the network revealed one close lncRNA module, which had good prognosis performance in LUAD. Furthermore, through integrating ceRNAs strategy and TF regulatory information, we identified some lncRNA-TF positive feedback loops. Prognostic analysis revealed that ELK4- and BDP1-related feedback loops were significant. Secondly, we constructed the lncRNA-m6A regulator network by merging all the high correlated lncRNA-m6A regulator pairs. Based on the network analysis results, some key m6A-related lncRNAs were identified, such as MIR497HG, FENDRR, and RP1-199J3. We also investigated the relationships between these lncRNAs and immune cell infiltration. Results showed that these m6A-related lncRNAs were high correlated with tumor immunity. All these results provide a new perspective for the diagnostic biomarker and therapeutic target identification of LUAD.

Indexed as

lncRNAlung adenocarcinomam6Anetwork analysisprognosis biomarkers

Identifiers

PMID34513699
PMCPMC8430225

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