Evidence map›Paper›PMID 36080280›Full record

SynthesisMolecules (Basel, Switzerland)2022

Gene Ontology (GO)-Driven Inference of Candidate Proteomic Markers Associated with Muscle Atrophy Conditions.

Angelique Stalmach, Ines Boehm, Marco Fernandes, Alison Rutter, Richard J E Skipworth, Holger Husi

Abstract readMeta-Analysis
In one paragraph

Synthesis in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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

6 authors.

Angelique StalmachCentre for Health Science, Division of Biomedical Sciences, Institute of Health Research and Innovation, School of Health, Social Care and Life Sciences, University of the Highlands and Islands, Inverness IV2 3JH, UK.
Ines BoehmEdinburgh Cancer Research UK Tissue Group, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK.ORCID 0000-0002-9343-0944
Marco FernandesDepartment of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.ORCID 0000-0002-4768-0934
Alison RutterCentre for Health Science, Division of Biomedical Sciences, Institute of Health Research and Innovation, School of Health, Social Care and Life Sciences, University of the Highlands and Islands, Inverness IV2 3JH, UK.
Richard J E SkipworthClinical Surgery, University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK.ORCID 0000-0003-1428-4078
Holger HusiCentre for Health Science, Division of Biomedical Sciences, Institute of Health Research and Innovation, School of Health, Social Care and Life Sciences, University of the Highlands and Islands, Inverness IV2 3JH, UK.ORCID 0000-0001-9049-4304

Funding

Highlands & Islands Enterprise N/A
6 · The paper itself

Abstract

Skeletal muscle homeostasis is essential for the maintenance of a healthy and active lifestyle. Imbalance in muscle homeostasis has significant consequences such as atrophy, loss of muscle mass, and progressive loss of functions. Aging-related muscle wasting, sarcopenia, and atrophy as a consequence of disease, such as cachexia, reduce the quality of life, increase morbidity and result in an overall poor prognosis. Investigating the muscle proteome related to muscle atrophy diseases has a great potential for diagnostic medicine to identify (i) potential protein biomarkers, and (ii) biological processes and functions common or unique to muscle wasting, cachexia, sarcopenia, and aging alone. We conducted a meta-analysis using gene ontology (GO) analysis of 24 human proteomic studies using tissue samples (skeletal muscle and adipose biopsies) and/or biofluids (serum, plasma, urine). Whilst there were few similarities in protein directionality across studies, biological processes common to conditions were identified. Here we demonstrate that the GO analysis of published human proteomics data can identify processes not revealed by single studies. We recommend the integration of proteomics data from tissue samples and biofluids to yield a comprehensive overview of the human skeletal muscle proteome. This will facilitate the identification of biomarkers and potential pathways of muscle-wasting conditions for use in clinics.

Indexed as

CachexiaSarcopeniaBiomarkersGene OntologyHumansMuscle, SkeletalMuscular AtrophyProteomeProteomicsQuality of LifeBiomarkersProteomebiomarkercancer cachexiamuscle wastingproteomicssarcopenia

Identifiers

PMID36080280
PMCPMC9457532

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.