Evidence map›Paper›PMID 37508548›Full record

ArticleCells2023

A Diagnostic Gene-Expression Signature in Fibroblasts of Amyotrophic Lateral Sclerosis.

Giovanna Morello, Valentina La Cognata, Maria Guarnaccia, Vincenzo La Bella, Francesca Luisa Conforti, Sebastiano Cavallaro

Open access · goldAbstract read
In one paragraph

Article in Cells, 2023. 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
1.2field-weighted citation impact, top 20% 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, 6 citations in OpenAlex.

  1. MK4 Repositioning for IAHSP: OvercomingACS chemical neuroscience · 2026
    Article
  2. Article
  3. Experimental Cell Models for Investigating Neurodegenerative Diseases.International journal of molecular sciences · 2024
    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 at 3 institutions in 1 country.

Giovanna MorelloInstitute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy.
Valentina La CognataInstitute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy.
Maria GuarnacciaInstitute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy.
Vincenzo La BellaALS Clinical Research Center and Neurochemistry Laboratory, BiND, University of Palermo, 90133 Palermo, Italy.ORCID 0000-0003-2045-1864
Francesca Luisa ConfortiMedical Genetics Laboratory, Department of Pharmacy and Health and Nutritional Sciences, University of Calabria, 87036 Rende, Italy.ORCID 0000-0001-8364-1783
Sebastiano CavallaroInstitute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy.ORCID 0000-0001-7590-1792
Institute for Biomedical Research and Innovation · ITUniversity of Calabria · ITUniversity of Palermo · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease with limited treatment options. Diagnosis can be difficult due to the heterogeneity and non-specific nature of the initial symptoms, resulting in delays that compromise prompt access to effective therapeutic strategies. Transcriptome profiling of patient-derived peripheral cells represents a valuable benchmark in overcoming such challenges, providing the opportunity to identify molecular diagnostic signatures. In this study, we characterized transcriptome changes in skin fibroblasts of sporadic ALS patients (sALS) and controls and evaluated their utility as a molecular classifier for ALS diagnosis. Our analysis identified 277 differentially expressed transcripts predominantly involved in transcriptional regulation, synaptic transmission, and the inflammatory response. A support vector machine classifier based on this 277-gene signature was developed to discriminate patients with sALS from controls, showing significant predictive power in both the discovery dataset and in six independent publicly available gene expression datasets obtained from different sALS tissue/cell samples. Taken together, our findings support the utility of transcriptional signatures in peripheral cells as valuable biomarkers for the diagnosis of ALS.

Indexed as

Amyotrophic Lateral SclerosisNeurodegenerative DiseasesFibroblastsGene Expression ProfilingHumansTranscriptomeamyotrophic lateral sclerosisclass predictiondisease diagnosismachine learningmolecular signaturenetworktranscriptomics

Identifiers

PMID37508548
PMCPMC10378077
OpenAlexW4384828408

What OpenQuestion 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 OpenQuestion graph.