Evidence map›Paper›PMID 41173979›Full record

ArticleNature communications2025

Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survival.

Yue Zhao, Masha G Savelieff, Xiayan Li, Kai Guo, Kai Wang, Minghua Li, Bo Li, Gayatri Iyer, Stacey A Sakowski, Lili Zhao and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

20 authors.

Yue ZhaoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-6029-7123
Masha G SavelieffDepartment of Biomedical Sciences, University of North Dakota, Grand Forks, ND, USA.ORCID http://orcid.org/0000-0001-5575-2494
Xiayan LiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Kai GuoDepartment of Neurology, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-4651-781X
Kai WangDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Minghua LiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-7373-6535
Bo LiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-5843-0925
Gayatri IyerDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Stacey A SakowskiDepartment of Neurology, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-5064-9022
Lili ZhaoDepartment of Preventive Medicine (Biostatistics Division), Northwestern University, Chicago, IL, USA.ORCID http://orcid.org/0000-0002-6366-8206
Samuel J TeenerNeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0009-0003-9072-8184
Kelly M BakulskiDepartment of Epidemiology, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-9605-6337
John F DouDepartment of Epidemiology, University of Michigan, Ann Arbor, MI, USA.
Bryan J TraynorNeuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0003-0527-2446
Alla KarnovskyDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Stuart A BattermanDepartment of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA.
Junguk HurDepartment of Biomedical Sciences, University of North Dakota, Grand Forks, ND, USA.ORCID http://orcid.org/0000-0002-0736-2149
Stephen A GoutmanDepartment of Neurology, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-8780-6637
Maureen A Sartor *Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. sartorma@umich.edu.ORCID http://orcid.org/0000-0001-6155-5702
Eva L Feldman *Department of Neurology, University of Michigan, Ann Arbor, MI, USA. efeldman@umich.edu.ORCID http://orcid.org/0000-0002-9162-2694

Funding

Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI AMY J SCHULZ · 2011 to 2026
$21.3M
Mapping the ALS Exposome to Gain New Insights into Disease Risk and PathogenesisR01ES030049 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BATTERMAN, STUART A, FELDMAN, EVA LUCILLE · 2020 to 2024
$3.5M
Developing novel strategies for personalized treatment and prevention of ALS: Leveraging the global exposome, genome, epigenome, metabolome, and inflammasome with data science in a case/control cohortR01NS127188 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BATTERMAN, STUART A, FELDMAN, EVA LUCILLE · 2021 to 2024
$3.4M
Creating a foundation for personalized age- and sex-based immune-targeted therapies from an ALS longitudinal cohort by identifying peripheral and central immune signaturesR01NS120926 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GOUTMAN, STEPHEN, MURDOCK, BENJAMIN JOSEPH · 2021 to 2025
$3.0M
Amyotrophic Lateral Sclerosis Association (ALS Association) 20-IIA-532NIEHS NIH HHS P30 ES017885U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS) P30ES017885U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS) R01ES030049U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) R01NS120926U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) R01NS127188
6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disease with a median survival of only 2 to 4 years from diagnosis. Improved tools are needed to shorten diagnostic delays and improve prognostication to benefit clinical care. Herein, we profiled whole blood gene expression by RNA sequencing in a large cohort of ALS participants (n = 422) versus controls (n = 272). Several machine learning classifiers trained on our detailed gene expression dataset accurately predicted case-control status, including in a fully independent external test cohort, achieving an area under the receiver operating characteristic curve of 0.894 with the best performing model. Integrating gene expression features with clinical variables improved our ability to discriminate ALS cases into shorter, intermediate, and longer survival in an external dataset. Finally, we identified ALS-relevant pathways in our blood transcriptomics dataset as well as "core genes" that overlapped with gene expression changes occurring in the primary disease tissue, facilitating a drug perturbation analysis that identified several candidates. Overall, our results highlight the potential diagnostic and prognostic applications of whole blood gene expression data, with important implications for improving ALS clinical care.

Indexed as

Amyotrophic Lateral SclerosisTranscriptomeAdultAgedCase-Control StudiesFemaleGene Expression ProfilingHumansMachine LearningMaleMiddle AgedPrognosisROC CurveSequence Analysis, RNA

Identifiers

PMID41173979
PMCPMC12579231

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.