Evidence map›Paper›PMID 35508298›Full record

ArticleAging2022

miRNAs-mediated overexpression of Periostin is correlated with poor prognosis and immune infiltration in lung squamous cell carcinoma.

Xu Bai, Hui Chen, Brian G Oliver

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2022. 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
0.3field-weighted citation impact, top 41% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  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 at 2 institutions in 1 country.

Xu BaiSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.
Hui ChenSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.
Brian G OliverSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.
University of Technology Sydney · AUWoolcock Institute of Medical Research · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is one of the most common malignancies with a high mortality rate worldwide. POSTN has been shown to be strongly correlated with the poor prognosis of lung cancer patients. However, the function and mechanism of action of POSTN in lung cancer remain unclear. Here, we carried out a pan-cancer analysis to assess the clinical prognostic value of POSTN based on the TCGA, TIMER, Oncomine, Kaplan-Meier, and UALCAN databases. We found that upregulated POSTN can be a promising biomarker to predict the prognosis of patients with lung cancer. High levels of POSTN correlated with immune cell infiltration in lung cancer, especially lung squamous cell carcinoma (LUSC), which was further confirmed based on the results from the TISIDB database. Moreover, the expression analysis, correlation analysis, and survival analysis revealed that POSTN-targeted miRNAs, downregulation of has-miR-144-3p and has-miR-30e-3p, were significantly linked to poor prognosis in patients with LUSC. Taken together, we identified that POSTN can act as a novel biomarker for determining the prognosis related to immune infiltration in patients with LUSC and deserves further research.

Indexed as

Carcinoma, Non-Small-Cell LungCarcinoma, Squamous CellLung NeoplasmsMicroRNAsBiomarkers, TumorGene Expression Regulation, NeoplasticHumansLungPrognosisBiomarkers, TumorMicroRNAsimmune infiltrationLUSCmicRNAPOSTNprognosis

Identifiers

PMID35508298
PMCPMC9134939
OpenAlexW4229042860

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.