Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning
[2]
- Univ. of California, Irvine, CA (United States); The Scripps Research Inst., La Jolla, CA (United States)
- Univ. of California, Irvine, CA (United States)
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- VA Long Beach Healthcare System, Long Beach, CA (United States)
- Univ. of Southern California, Los Angeles, CA (United States)
- Univ. of California, Los Angeles, CA (United States)
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.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 2481126
- Report Number(s):
- PNNL-SA--187034
- Journal Information:
- PLOS Digital Health, Journal Name: PLOS Digital Health Journal Issue: 2 Vol. 3; ISSN 2767-3170
- Publisher:
- Public Library of Science (PLOS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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