Evidence map›Paper›PMID 39247624›Full record

ReviewFrontiers in cell and developmental biology2024

miR-137: a potential therapeutic target for lung cancer.

Shuanshuan Liu, Yanyun Ruan, Xu Chen, Bao He, Qi Chen

Abstract readReview
In one paragraph

Review in Frontiers in cell and developmental biology, 2024. 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. Review
  2. MicroRNAs as biomarkers and molecular mediators of cognitive dysfunction in schizophrenia.Journal of neural transmission (Vienna, Austria : 1996) · 2026
    Review
  3. Liquid biopsy in lung cancer.Breathe (Sheffield, England) · 2025
    Review
  4. Review
  5. 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.

Shuanshuan LiuPrecision Medicine Center, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China.
Yanyun RuanPrecision Medicine Center, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China.
Xu ChenDepartment of Pathology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Bao HeDepartment of Neurosurgery, The First People's hospital of Kunshan, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, Jiangsu, China.
Qi ChenPrecision Medicine Center, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is a prevalent malignancy and the leading cause of cancer-related deaths, posing a significant threat to human health. Despite advancements in treatment, the prognosis for lung cancer patients remains poor due to late diagnosis, cancer recurrence, and drug resistance. Epigenetic research, particularly in microRNAs, has introduced a new avenue for cancer prevention and treatment. MicroRNAs, including miR-137, play a vital role in tumor development by regulating various cellular processes. MiR-137 has garnered attention for its tumor-suppressive properties, with studies showing its potential in inhibiting cancer progression. In lung cancer, miR-137 is of particular interest, with numerous reports exploring its role and mechanisms. A comprehensive review is necessary to consolidate current evidence. This review highlights recent studies on miR-137 in lung cancer, covering cell proliferation, migration, apoptosis, drug resistance, and therapy, emphasizing its potential as a biomarker and therapeutic target for lung cancer treatment and prognosis.

Indexed as

biomarkerlung cancerMicroRNAsmiR-137therapeutic target

Identifiers

PMID39247624
PMCPMC11377224

What OpenQuestion holds

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LicenceCC BY
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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.