Evidence map›Paper›PMID 35126595›Full record

ArticleJournal of research in medical sciences : the official journal of Isfahan University of Medical Sciences2021

Expression of miRNA-25 in young and old lung adenocarcinoma.

Laura Boldrini, Mirella Giordano, Franca Melfi, Marco Lucchi, Gabriella Fontanini

Open access · goldAbstract read
In one paragraph

Article in Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences, 2021. 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 51% 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, 3 citations in OpenAlex.

  1. Article
  2. Review
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 at 1 institution in 1 country.

Laura BoldriniDepartment of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy.
Mirella GiordanoDepartment of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy.
Franca MelfiDepartment of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy.
Marco LucchiDepartment of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy.
Gabriella FontaniniDepartment of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy.
University of Pisa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn appropriate personalized molecular testing ensures the most efficacious treatment in lung cancer. It is still controversial whether younger lung adenocarcinoma (LUAD) patients have different molecular features compared with their older counterparts. MicroRNAs have been involved in lung cancer and their altered expression has been suggested as a potential biomarker in the pathogenesis, diagnosis, prognosis, and therapy of LUAD. MATERIALS AND

methodsTo analyze putative differences in miR-25 expression between young (with age ≤50 years) and old adenocarcinoma patients, we quantified miR-25 levels with NanoString technology in 88 LUAD specimens. We further investigated a cohort of 309 LUAD patients from the cancer genome atlas (TCGA) database to test our hypothesis.

resultsmiR-25 expression was upregulated in young LUAD patients in comparison to the older ones (

conclusionsThe interaction of miR-25 and PTEN in young LUAD may define a subgroup of patients, highlighting the concept of molecular testing in different age subtypes.

Indexed as

Lung adenocarcinomaMicroRNAsmiR-25phosphatase and tensin homologyoung

Identifiers

PMID35126595
PMCPMC8772505
OpenAlexW4206251180

What OpenQuestion holds

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LicenceCC BY-NC-SA
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Registered trials

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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.