PXD062229

PXD062229 is an original dataset announced via ProteomeXchange.

Title	Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome
Description	Background: Sepsis therapy is still limited to the treatment of the underlying infection and supportive measures. Therapeutic options that address the molecular changes of sepsis have not yet been identified. With the aim of a future individualized therapy, we used unsupervised machine learning (ML) to identify clinical phenotypes in a prospective multicenter cohort of patients with sepsis and characterized them using plasma proteomics. Methods: Routine clinical data and blood samples were collected from 384 sepsis patients. Clinical phenotypes were identified based on clinical routine measurements using the k-means algorithm. In addition, plasma samples from 276 patients were analyzed using liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). The obtained data were analyzed and interpreted in the context of the phenotypes and supervised ML classifiers were developed to prospectively allocate patients to the clusters and to identify the most important features for discrimination of the phenotypes. Results: Three clinical phenotypes were identified. Cluster C was characterized by the highest disease severity and multi-organ failure with leading liver failure and a mortality rate of 92%. Cluster B, with a mortality rate of 45 %, also showed relevant organ failure, with renal damage being particularly prominent in comparison to cluster A. The plasma proteome reflected the clinical features of the phenotypes and revealed the excessive consumption of complement and coagulation factors in severe sepsis. Supervised ML and feature importance analysis underlined these findings and highlighted specific clinical measures and proteins. Conclusions: ML identified clinical phenotypes showed different degrees of sepsis severity and could be translated to the plasma proteome. Plasma proteomics provided novel insights into the molecular processes of sepsis and allowed the characterization of the phenotypes. This approach represents a blueprint to identify molecular features of sepsis subgroups and may pave the way for future targeted sepsis therapy.
HostingRepository	PRIDE
AnnounceDate	2025-08-29
AnnouncementXML	Submission_2025-08-29_04:40:18.080.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Thilo Bracht
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Fusion Lumos; Orbitrap Exploris 240

Revision	Datetime	Status	ChangeLog Entry
0	2025-03-25 16:18:13	ID requested
⏵ 1	2025-08-29 04:40:18	announced

10.1007/S15010-025-02628-3;

submitter keyword: Machine Learning, Precision Medicine, Subclasses, Plasma, Hierarchical Clustering,Sepsis, Clinical Routine Data

Thilo Bracht
contact affiliation	Clinical Proteomics, Medizinisches Proteom-Center, Ruhr-University Bochum; Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital, Knappschaftskrankenhaus Bochum
contact email	thilo.bracht@rub.de
lab head
Thilo Bracht
contact affiliation	Clinical Proteomics
contact email	thilo.bracht@rub.de
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD062229
     1. Label: PRIDE project
     2. Name: Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome