Integrative Multi-omics and machine learning analysis of sphingolipid-associated molecular stratification identifies diagnostic and prognostic signatures in idiopathic pulmonary fibrosis
- 1Department of Pulmonary and Critical Care Medicine, School of Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
Abstract
Background: Emerging evidence suggests that sphingolipid metabolism is involved in respiratory diseases, including idiopathic pulmonary fibrosis (IPF). This study aimed to evaluate the diagnostic and prognostic value of sphingolipid-related genes in IPF and to identify potential sphingolipid-associated biomarkers.
Methods: Sphingolipid-related genes were obtained from the GeneCards database. Non-negative matrix factorization (NMF) was performed to identify molecular clusters and differentially expressed genes (DEGs). High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to determine fibroblast-associated genes. The intersecting genes were used to construct diagnostic and prognostic models using multiple machine learning algorithms. Hub genes were identified from the overlap between the diagnostic and prognostic signatures. In silico gene knockout was conducted using scTenifoldKnk. Functional validation was performed using Cell Counting Kit-8 (CCK-8) assay, wound healing assay, quantitative real-time polymerase chain reaction (RT-qPCR), and western blotting (WB).
Results: Three molecular clusters with significant prognostic differences were identified. hdWGCNA revealed 60 key fibroblast-associated genes. Diagnostic and prognostic models constructed from these genes showed promising diagnostic and prognostic utility across independent cohorts, while the prognostic model exhibited some degree of cohort-dependent variability. CCDC80 was identified as a hub gene associated with sphingolipid-defined molecular heterogeneity and fibroblast-related transcriptional programs. scTenifoldKnk analysis indicated that simulated CCDC80 knockout affected fibrosis-related signaling pathways. In vitro experiments demonstrated that CCDC80 knockdown attenuated fibroblast proliferation, migration, and fibrotic activation.
Conclusion: This study identified sphingolipid-related molecular signatures with potential diagnostic and prognostic value in IPF and highlighted CCDC80 as a candidate profibrotic regulator. Further validation in larger and clinically standardized cohorts is required before clinical translation.
Clinical trial number: Not applicable.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13023-026-04563-4.
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BibTeX
@article{liao2026integrative,
title = {{Integrative Multi-omics and machine learning analysis of sphingolipid-associated molecular stratification identifies diagnostic and prognostic signatures in idiopathic pulmonary fibrosis}},
author = {Yi Liao and Lingjing Yang and Xiaoshu Liu and Liqing Yang},
journal = {Orphanet Journal of Rare Diseases},
year = {2026},
month = aug,
doi = {10.1186/s13023-026-04563-4},
url = {https://doi.org/10.1186/s13023-026-04563-4},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrative Multi-omics and machine learning analysis of sphingolipid-associated molecular stratification identifies diagnostic and prognostic signatures in idiopathic pulmonary fibrosis
AU - Yi Liao
AU - Lingjing Yang
AU - Xiaoshu Liu
AU - Liqing Yang
PY - 2026
DA - 2026/08/25
JO - Orphanet Journal of Rare Diseases
DO - 10.1186/s13023-026-04563-4
UR - https://doi.org/10.1186/s13023-026-04563-4
AB - Background: Emerging evidence suggests that sphingolipid metabolism is involved in respiratory diseases, including idiopathic pulmonary fibrosis (IPF). This study aimed to evaluate the diagnostic and prognostic value of sphingolipid-related genes in IPF and to identify potential sphingolipid-associated biomarkers. Methods: Sphingolipid-related genes were obtained from the GeneCards database. Non-negative matrix factorization (NMF) was performed to identify molecular clusters and differentially expressed genes (DEGs). High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to determine fibroblast-associated genes. The intersecting genes were used to construct diagnostic and prognostic models using multiple machine learning algorithms. Hub genes were identified from the overlap between the diagnostic and prognostic signatures. In silico gene knockout was conducted using scTenifoldKnk. Functional validation was performed using Cell Counting Kit-8 (CCK-8) assay, wound healing assay, quantitative real-time polymerase chain reaction (RT-qPCR), and western blotting (WB). Results: Three molecular clusters with significant prognostic differences were identified. hdWGCNA revealed 60 key fibroblast-associated genes. Diagnostic and prognostic models constructed from these genes showed promising diagnostic and prognostic utility across independent cohorts, while the prognostic model exhibited some degree of cohort-dependent variability. CCDC80 was identified as a hub gene associated with sphingolipid-defined molecular heterogeneity and fibroblast-related transcriptional programs. scTenifoldKnk analysis indicated that simulated CCDC80 knockout affected fibrosis-related signaling pathways. In vitro experiments demonstrated that CCDC80 knockdown attenuated fibroblast proliferation, migration, and fibrotic activation. Conclusion: This study identified sphingolipid-related molecular signatures with potential diagnostic and prognostic value in IPF and highlighted CCDC80 as a candidate profibrotic regulator. Further validation in larger and clinically standardized cohorts is required before clinical translation. Clinical trial number: Not applicable. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13023-026-04563-4.
SN - 1750-1172
ER -