Your browser doesn't support javascript.
loading
Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning.
Listopad, Stanislav; Magnan, Christophe; Day, Le Z; Asghar, Aliya; Stolz, Andrew; Tayek, John A; Liu, Zhang-Xu; Jacobs, Jon M; Morgan, Timothy R; Norden-Krichmar, Trina M.
Afiliación
  • Listopad S; Department of Computer Science, University of California, Irvine, California, United States of America.
  • Magnan C; Department of Computer Science, University of California, Irvine, California, United States of America.
  • Day LZ; Biological Sciences Division and Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America.
  • Asghar A; Medical and Research Services, VA Long Beach Healthcare System, Long Beach, California, United States of America.
  • Stolz A; Division of Gastrointestinal & Liver Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.
  • Tayek JA; Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Department of Internal Medicine, David Geffen School of Medicine, University of California Los Angeles, Torrance, California, United States of America.
  • Liu ZX; Division of Gastrointestinal & Liver Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.
  • Jacobs JM; Biological Sciences Division and Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America.
  • Morgan TR; Medical and Research Services, VA Long Beach Healthcare System, Long Beach, California, United States of America.
  • Norden-Krichmar TM; Department of Computer Science, University of California, Irvine, California, United States of America.
PLOS Digit Health ; 3(2): e0000447, 2024 Feb.
Article en En | MEDLINE | ID: mdl-38335183
ABSTRACT
Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Risk_factors_studies Idioma: En Revista: PLOS Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Risk_factors_studies Idioma: En Revista: PLOS Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos