ArticlePloS one2018
Whole blood transcriptome analysis in amyotrophic lateral sclerosis: A biomarker study.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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.
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.
Who cites it
35 citing papers in PubMed, 63 citations in OpenAlex.
- JP1 peptide modulates oxidative stress and autophagy via Keap1-Nrf2-ARE in ALS model mice.BMC medicine · 2026Article
- Classification of ALS molecular subtypes: a literature review on machine learning applications and their clinical value.BMC medicine · 2026Review
- Article
- Nonlinear combinatorial analysis of blood transcriptomes identifies PRKAR1A as a regulator of TDP-43 pathophysiology in amyotrophic lateral sclerosis.Biology methods & protocols · 2026Article
- Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survival.Nature communications · 2025Article
- Convergent and Divergent Mitochondrial Pathways as Causal Drivers and Therapeutic Targets in Neurological Disorders.Current issues in molecular biology · 2025Article
- Emerging Ferroptosis Involvement in Amyotrophic Lateral Sclerosis Pathogenesis: Neuroprotective Activity of Polyphenols.Molecules (Basel, Switzerland) · 2025Review
- Article
- Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction.Heliyon · 2024Article
- Whole blood transcriptome profile identifies motor neurone disease RNA biomarker signatures.Experimental biology and medicine (Maywood, N.J.) · 2024Article
- Abnormal Brain Protein Abundance and Cross-tissue mRNA Expression in Amyotrophic Lateral Sclerosis.Molecular neurobiology · 2024Article
- Establishing mRNA and microRNA interactions driving disease heterogeneity in amyotrophic lateral sclerosis patient survival.Brain communications · 2024Article
- Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies.Frontiers in immunology · 2024Article
- Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data.Acta neuropathologica communications · 2023Article
- RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery.Neuropathology and applied neurobiology · 2023Article
- Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis.Brain : a journal of neurology · 2023Article
- Article
- Blood RNA transcripts reveal similar and differential alterations in fundamental cellular processes in Alzheimer's disease and other neurodegenerative diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction.medRxiv : the preprint server for health sciences · 2023Article
- Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease.Biomolecules · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The biological pathways involved in amyotrophic lateral sclerosis (ALS) remain elusive and diagnostic decision-making can be challenging. Gene expression studies are valuable in overcoming such challenges since they can shed light on differentially regulated pathways and may ultimately identify valuable biomarkers. This two-stage transcriptome-wide study, including 397 ALS patients and 645 control subjects, identified 2,943 differentially expressed transcripts predominantly involved in RNA binding and intracellular transport. When batch effects between the two stages were overcome, three different models (support vector machines, nearest shrunken centroids, and LASSO) discriminated ALS patients from control subjects in the validation stage with high accuracy. The models' accuracy reduced considerably when discriminating ALS from diseases that mimic ALS clinically (N = 75), nor could it predict survival. We here show that whole blood transcriptome profiles are able to reveal biological processes involved in ALS. Also, this study shows that using these profiles to differentiate between ALS and mimic syndromes will be challenging, even when taking batch effects in transcriptome data into account.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.