Evidence mapPaperPMID 41870821Full record

ReviewDiscover oncology2026

Exercise-associated microRNA programs as candidate modulators and biomarkers in glioblastoma: a narrative review.

Tianlun Zheng, Bin Jing, Heng Li

Abstract readReview
In one paragraph

Review in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Tianlun ZhengDepartment of Basic Courses, Chengxian College of Southeast University, Nanjing, 210088, Jiangsu, China.
Bin JingMoscow State University of Sport and Tourism, Moscow, Russia. jingbin1551388@gmail.com.
Heng LiFaculty of Education and Liberal Arts, INTI International University, Nilai, 71800, Negeri Sembilan, Malaysia. hengli76@proton.me.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioblastoma Multiforme (GBM) remains one of the most challenging malignancies to treat due to its aggressive proliferation, resistance to apoptosis, extensive angiogenesis, and capacity to evade conventional therapies. MicroRNAs (miRNAs) act as critical post-transcriptional regulators of gene expression, modulating these pathogenic hallmarks. OncomiRs such as miR-10b, miR-221/222, and miR-21 promote tumorigenesis by enhancing cell survival, invasion, and drug resistance. Conversely, tumor-suppressor miRNAs (TS-miRNAs)—including miR-128, miR-34a, and miR-7—inhibit these malignant traits and are frequently downregulated in GBM. Emerging evidence suggests that physical exercise, a non-pharmacological intervention with systemic anti-cancer effects, can modulate the expression of miRNAs linked to tumor suppression and immune regulation. In other cancer models, exercise-induced miRNA alterations have been shown to disrupt signaling pathways governing proliferation, apoptosis, and stem cell maintenance, suggesting a potential therapeutic benefit in GBM. This study explores the hypothesis that exercise can favorably influence GBM biology by regulating oncogenic and tumor-suppressor miRNAs. By altering the tumor microenvironment and modifying circulating and tumor-intrinsic miRNA profiles, exercise may counteract key mechanisms of GBM progression and treatment resistance. Elucidating these miRNA-mediated effects could uncover novel biomarkers and provide a compelling rationale for integrating exercise into multimodal GBM treatment strategies.

Indexed as

ExerciseGlioblastoma multiformMicroRNATreatment

Identifiers

PMID41870821
PMCPMC13133296

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.