Evidence map›Paper›PMID 38304137›Full record

ReviewChonnam medical journal2024

MiR-4492, a New Potential MicroRNA for Cancer Diagnosis and Treatment: A Mini Review.

Aida Alizamir, Mohammad Amin Amini, Ashkan Karbasi, Mehdi Beyrami

Open access · diamondAbstract readReview
In one paragraph

Review in Chonnam medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

4 authors at 1 institution in 1 country.

Aida AlizamirDepartment of Pathology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Mohammad Amin AminiDepartment of Clinical Biochemistry, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Ashkan KarbasiDepartment of Clinical Biochemistry, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Mehdi BeyramiDepartment of Clinical Biochemistry, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Hamedan University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is no doubt that the incidence of cancer sufferers is rising in the world, and it is estimated that in the next several decades, the number of people suffering from malignancies or the cancer rate will double. Diagnostic and therapeutic targeting of noncoding RNAs (ncRNAs), especially microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), represent an excellent approach for cancer diagnosis and treatment, as well as many other diseases. One of the latest miRNAs is miR-4492, upregulating some genes in tumor tissues including ROMO1, HLA-G, NKIRAS2, FOXK1, and UBE2C. It represents an attractant example of a miRNA acting at multiple levels to affect the same malignancy hallmark. Based on the studies, miR-4492 plays a key role in several cancers such as, breast cancer, bladder cancer, osteosarcoma, glioblastoma multiforme, hepatocellular carcinoma, colorectal cancer, and ovarian cancer. Putting it all together, identifying the precise mechanisms of miR-4492 in the pathogenesis of cancer, could pave the way to find better diagnostic and therapeutic strategies for cancer sufferers. For this reason, it might be a novel potential diagnostic biomarker and therapeutic target for neoplasms.

Indexed as

Long Noncoding RNAMicroRNAsmiR-4492Neoplasms

Identifiers

PMID38304137
PMCPMC10828084
OpenAlexW4391363396

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.