Proteomic profile of <scp>CSF</scp> obtained at the time of diagnosis determines amyotrophic lateral sclerosis progression and survival: <scp>CXCL7</scp> levels in disease prognosis and survival

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease primarily affecting motor neurons. Neurofilament light chain (NfL) is the most established prognostic biomarker; however, its diagnostic resolution is limited, particularly within intermediate concentration ranges, and it does not capture the molecular heterogeneity of ALS. This study aimed to identify complementary cerebrospinal fluid (CSF) biomarkers and pathway-specific signatures through a non-targeted multiomic approach. We performed SWATH-MS-based proteomics and LC-MS/MS lipidomics on CSF from ALS patients stratified by survival (ALS-SS and ALS-LS) and healthy controls. Weighted protein co-expression network analysis (WPCNA) was applied to identify biologically coherent protein modules associated with disease phenotype and progression. Top biomarker candidates were further evaluated using immunoassays in an independent cohort. Post-mortem ALS spinal cord tissues were analyzed to explore the pathophysiological relevance of identified proteins. CSF proteomic profiles robustly distinguished ALS patients from controls and stratified patient subgroups by survival, revealing a molecular signature characterized by inflammation, downregulation of detoxification mechanisms, and synaptic dysregulation in aggressive disease forms. In contrast, lipidomic profiles showed limited discriminatory power. WPCNA identified modular proteomic signatures capturing ALS heterogeneity, and machine learning models based on these profiles yielded optimal biomarker panels for diagnosis and prognosis. CXCL7 emerged as a promising complementary biomarker, and shed light in disease physiopathology. Immunoassay validation supported the diagnostic and prognostic potential of CXCL7 and its association with survival time. Histopathological analysis further confirmed CXCL7 localization in anterior horn motor neurons, despite no detectable changes in whole spinal cord lysates at late disease stages. Comprehensive CSF proteomic profiling, combined with network-based analysis, enhances our understanding of ALS molecular heterogeneity and provides a framework for precision biomarker discovery. CXCL7 complements NfL as a diagnostic and prognostic biomarker, supporting improved patient stratification and advancing the development of personalized therapeutic strategies in ALS.

Authors

Institutions

Publication Details

Journal
Brain Pathology
Published
2026-03-03
DOI
https://doi.org/10.1111/bpa.70089
Primary Topic
Amyotrophic Lateral Sclerosis Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Proteomic profile of CSF obtained at the time of diagnosis determines amyotrophic lateral sclerosis progression and survival: CXCL7 levels in disease prognosis and survival

A. Di Marco, Pol Andrés‐Benito, M.J. Colomina, Isabel León Moreno et al.
Brain Pathology
Amyotrophic Lateral Sclerosis Research
article

Proteomic profile of CSF obtained at the time of diagnosis determines amyotrophic lateral sclerosis progression and survival: CXCL7 levels in disease prognosis and survival

A. Di Marco, Pol Andrés‐Benito, M.J. Colomina, Isabel León Moreno, R.O. Domínguez, Sergio Roca-Pereira, Mònica Povedano, Èlia Obis, Yaiza López-Sampere, Gabriel Santpere, Joaquín Fernández-Irigoyen, Enrique Santamaría, Alejandro Caravaca-Puchades, Antonio Martínez Yélamos, Mario Peña-Fonteboa, Pol Mengod-Soler
article en

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease primarily affecting motor neurons. Neurofilament light chain (NfL) is the most established prognostic biomarker; however, its diagnostic resolution is limited, particularly within intermediate concentration ranges, and it does not capture the molecular heterogeneity of ALS. This study aimed to identify complementary cerebrospinal fluid (CSF) biomarkers and pathway-specific signatures through a non-targeted multiomic approach. We performed SWATH-MS-based proteomics and LC-MS/MS lipidomics on CSF from ALS patients stratified by survival (ALS-SS and ALS-LS) and healthy controls. Weighted protein co-expression network analysis (WPCNA) was applied to identify biologically coherent protein modules associated with disease phenotype and progression. Top biomarker candidates were further evaluated using immunoassays in an independent cohort. Post-mortem ALS spinal cord tissues were analyzed to explore the pathophysiological relevance of identified proteins. CSF proteomic profiles robustly distinguished ALS patients from controls and stratified patient subgroups by survival, revealing a molecular signature characterized by inflammation, downregulation of detoxification mechanisms, and synaptic dysregulation in aggressive disease forms. In contrast, lipidomic profiles showed limited discriminatory power. WPCNA identified modular proteomic signatures capturing ALS heterogeneity, and machine learning models based on these profiles yielded optimal biomarker panels for diagnosis and prognosis. CXCL7 emerged as a promising complementary biomarker, and shed light in disease physiopathology. Immunoassay validation supported the diagnostic and prognostic potential of CXCL7 and its association with survival time. Histopathological analysis further confirmed CXCL7 localization in anterior horn motor neurons, despite no detectable changes in whole spinal cord lysates at late disease stages. Comprehensive CSF proteomic profiling, combined with network-based analysis, enhances our understanding of ALS molecular heterogeneity and provides a framework for precision biomarker discovery. CXCL7 complements NfL as a diagnostic and prognostic biomarker, supporting improved patient stratification and advancing the development of personalized therapeutic strategies in ALS.

Brain Pathology
Universitat de Lleida (ES), Barcelona Biomedical Research Park (ES), Bellvitge University Hospital (ES), Hospital Universitari i Politècnic La Fe (ES), Biomedical Research Networking Center on Neurodegenerative Diseases (ES), Institut d'Investigació Biomédica de Bellvitge (ES), Hospital Universitari de Santa Maria (ES), Ajuntament de L’Hospitalet (ES), Centro de Investigación Biomédica en Red (ES), Navarrabiomed (ES), Hospital Del Mar (ES), Instituto de Investigación Biomédica de Lleida (ES)
Reduced inequalities
Openalex Percentile: Top 40%
Amyotrophic Lateral Sclerosis Research
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.