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PXD038601-1

PXD038601 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleDiagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning
DescriptionBackground: Biopsy-based diagnosis is essential for maintaining renal allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods. Methods: Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from renal transplant patients, comprising five biopsies with the diagnosis of T-cell-mediated rejection (TCMR), five biopsies for polyomavirus BK nephropathy (BKPyVN), and normal kidney control tissue (STA). Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. Results: The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. Conclusions: This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-derived diagnostic model. It costs less than 10 dollars per test.
HostingRepositoryPRIDE
AnnounceDate2023-02-01
AnnouncementXMLSubmission_2023-02-01_04:28:00.899.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterFeiFANG
SpeciesList scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationListacetylated residue; iodoacetamide derivatized residue
InstrumentLTQ Orbitrap
Dataset History
RevisionDatetimeStatusChangeLog Entry
02022-12-07 03:25:16ID requested
12023-02-01 04:28:01announced
22023-11-14 08:59:35announced2023-11-14: Updated project metadata.
Publication List
Keyword List
submitter keyword: machine learning, kidney transplantation, mass spectrometry, FFPE, diagnosis,biomarker, quantitative proteomics
Contact List
KunhongXiao
contact affiliationCenter for Proteomics & Artificial Intelligence Center for Clinical Mass Spectrometry Allegheny Health Network Cancer Institute 100 S Jackson Ave., Pittsburgh, PA 15202
contact emailkevin.kh.xiao@gmail.com
lab head
FeiFANG
contact affiliationMichigan State University
contact emailfiona.dicp@outlook.com
dataset submitter
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