Evidence map›Paper›PMID 31828982›Full record

ArticleJournal of cachexia, sarcopenia and muscle2020

Identification of microRNAs in skeletal muscle associated with lung cancer cachexia.

Wouter R P H van de Worp, Annemie M W J Schols, Anne-Marie C Dingemans, Céline M H Op den Kamp, Juliette H R J Degens, Marco C J M Kelders, Susan Coort, Henry C Woodruff, Gueorqui Kratassiouk, Annick Harel-Bellan and 3 more

Open access · goldAbstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 9% 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

39 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Observational
  5. Review
  6. Epigenetics of Skeletal Muscle Atrophy.International journal of molecular sciences · 2024
    Review
  7. Review
  8. Article
  9. Review
  10. Small non-coding RNA profiling in patients with gastrointestinal cancer.Journal of cachexia, sarcopenia and muscle · 2023
    Article
  11. Article
  12. A novel orthotopic mouse model replicates human lung cancer cachexia.Journal of cachexia, sarcopenia and muscle · 2023
    Article
  13. Article
  14. Article
  15. Muscle and Bone Defects in Metastatic Disease.Current osteoporosis reports · 2022
    Review
  16. Review
  17. Review
  18. Review
  19. Review
  20. 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

13 authors at 4 institutions in 2 countries.

Wouter R P H van de WorpDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Annemie M W J ScholsDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Anne-Marie C DingemansDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Céline M H Op den KampDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Juliette H R J DegensDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Marco C J M KeldersDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Susan CoortDepartment of Bioinformatics-BiGCaT, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Henry C WoodruffDepartment of Precision Medicine, GROW, Maastricht University Medical Center+, Maastricht, The Netherlands.
Gueorqui KratassioukPlateforme ARN interférence, Service de Biologie Intégrative et de Génétique Moléculaire (SBIGeM), I2BC, CEA, CNRS, University of Paris-Saclay, Paris, France.
Annick Harel-BellanLaboratory of Epigenetics and Cancer, Institut de Hautes Études Scientifiques, University of Paris-Saclay, Paris, France.
Jan TheysDepartment of Precision Medicine, GROW, Maastricht University Medical Center+, Maastricht, The Netherlands.
Ardy van HelvoortDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.
Ramon C J LangenDepartment of Respiratory Medicine, NUTRIM, Maastricht University Medical Center+, Maastricht, The Netherlands.ORCID 0000-0002-8622-3058
Maastricht University Medical Centre · NLCentre National de la Recherche Scientifique · FRMaastricht University · NLUniversité Paris-Saclay · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCachexia, highly prevalent in patients with non-small cell lung cancer (NSCLC), impairs quality of life and is associated with reduced tolerance and responsiveness to cancer therapy and decreased survival. MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in post-transcriptional gene regulation. Changes in intramuscular levels of miRNAs have been implicated in muscle wasting conditions. Here, we aimed to identify miRNAs that are differentially expressed in skeletal muscle of cachectic lung cancer patients to increase our understanding of cachexia and to allow us to probe their potential as therapeutic targets.

methodsA total of 754 unique miRNAs were profiled and analysed in vastus lateralis muscle biopsies of newly diagnosed treatment-naïve NSCLC patients with cachexia (n = 8) and age-matched and sex-matched healthy controls (n = 8). miRNA expression analysis was performed using a TaqMan MicroRNA Array. In silico network analysis was performed on all significant differentially expressed miRNAs. Differential expression of the top-ranked miRNAs was confirmed using reverse transcription-quantitative real-time PCR in an extended group (n = 48) consisting of NSCLC patients with (n = 15) and without cachexia (n = 11) and healthy controls (n = 22). Finally, these miRNAs were subjected to univariate and multivariate Cox proportional hazard analysis using overall survival and treatment-induced toxicity data obtained during the follow-up of this group of patients.

resultsWe identified 28 significant differentially expressed miRNAs, of which five miRNAs were up-regulated and 23 were down-regulated. In silico miRNA-target prediction analysis showed 158 functional gene targets, and pathway analysis identified 22 pathways related to the degenerative or regenerative processes of muscle tissue. Subsequently, the expression of six top-ranked miRNAs was measured in muscle biopsies of the entire patient group. Five miRNAs were detectable with reverse transcription-quantitative real-time PCR analysis, and their altered expression (expressed as fold change, FC) was confirmed in muscle of cachectic NSCLC patients compared with healthy control subjects: miR-424-5p (FC = 4.5), miR-424-3p (FC = 12), miR-450a-5p (FC = 8.6), miR-144-5p (FC = 0.59), and miR-451a (FC = 0.57). In non-cachectic NSCLC patients, only miR-424-3p was significantly increased (FC = 5.6) compared with control. Although the statistical support was not sufficient to imply these miRNAs as individual predictors of overall survival or treatment-induced toxicity, when combined in multivariate analysis, miR-450-5p and miR-451a resulted in a significant stratification between short-term and long-term survival.

conclusionsWe identified differentially expressed miRNAs putatively involved in lung cancer cachexia. These findings call for further studies to investigate the causality of these miRNAs in muscle atrophy and the mechanisms underlying their differential expression in lung cancer cachexia.

Indexed as

CachexiaFemaleHumansLung NeoplasmsMaleMicroRNAsMiddle AgedMuscle, SkeletalMicroRNAsAtromiRsCancer cachexiamiRNAsNSCLCSkeletal muscle

Identifiers

PMID31828982
PMCPMC7113505
OpenAlexW2995813106

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LicenceCC BY
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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.