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

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.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-10-31
DOI
https://doi.org/10.1038/s41467-025-64622-5
Citations
1
Primary Topic
Amyotrophic Lateral Sclerosis Research
Type
article
Field-Weighted Citation Impact
1.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Stacey A. Sakowski, Alla Karnovsky, Kai Guo, Masha G. Savelieff et al.
1 citations
Nature Communications
Amyotrophic Lateral Sclerosis Research
1.39
article

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

Stacey A. Sakowski, Alla Karnovsky, Kai Guo, Masha G. Savelieff, Maureen A. Sartor, Bryan J. Traynor, John Dou, Kai Wang, Stuart Batterman, Eva L. Feldman, Samuel J. Teener, Gayatri Iyer, Kelly M. Bakulski, Junguk Hur, Lili Zhao, Stephen A. Goutman, Minghua Li, Bo Li, Yue Zhao, Xiayan Li
article en
1 citations

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.

Nature CommunicationsVol. 16(1)
Northwestern University (US), National Institutes of Health (US), Johns Hopkins University (US), University of North Dakota (US), University of Michigan (US), University Medical Center (US), Northwestern University (PH), Michigan Medicine (US), National Institute on Aging (US), Northwestern Medicine (US)
Sinai Medical Staff Foundation, National Institutes of Health, Centers for Disease Control and Prevention, National Institute of Neurological Disorders and Stroke, National Institute of Environmental Health Sciences, Agency for Toxic Substances and Disease Registry, National Center for Advancing Translational Sciences
Openalex Percentile: Top 14%
Amyotrophic Lateral Sclerosis Research
1.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.