PXD067174-1

PXD067174 is an original dataset announced via ProteomeXchange.

Title	Development and Validation of a Proteomics and Machine Learning-Based Predictive Model for Secondary Infection in Patients with HBV-Related Liver Failure: A Prospective Multicenter Study
Description	Abstract Background and Aims: Patients with HBV-related cirrhosis are highly susceptible to secondary infections (SI) during hospitalization, which substantially increases mortality risk. Reliable tools for early prediction remain limited. This study aimed to develop and validate a multi-parameter predictive model based on plasma proteomic profiling to assess the risk of SI. Methods: In a prospective, multicenter cohort study, 114 patients were enrolled in the discovery cohort and 60 in the validation cohort. Differentially expressed proteins were identified using untargeted plasma proteomics. Feature selection was performed using the minimum redundancy maximum relevance (mRMR) algorithm, and logistic regression was applied to construct the predictive model. Model performance was subsequently validated in an independent cohort using targeted proteomics. Results: Proteomic analysis indicated that dysregulation of inflammatory and coagulation pathways contributes to SI development. Ten infection-associated proteins were selected, and a final predictive model incorporating LYZ, CALM1, SERPIND1, DPT, total bilirubin (Tbil), and aspartate aminotransferase (AST) was established. The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.980 in the discovery cohort and 0.873 in the validation cohort, significantly outperforming conventional inflammatory markers such as C-reactive protein (CRP; 0.674 and 0.702), white blood cell count (WBC; 0.645 and 0.694), and neutrophil percentage (NE%; 0.642 and 0.793). The model also demonstrated strong prognostic performance for predicting 28-day mortality, with an AUROC of 0.957, exceeding that of the Chronic Liver Failure Consortium Acute-on-Chronic Liver Failure (CLIF-C ACLF) score (0.545) and the Model for End-stage Liver Disease (MELD) score (0.824). Conclusions: The proteomics-based predictive model accurately identifies patients with HBV-related liver failure at high risk of SI, offering promising clinical applicability and generalizability.
HostingRepository	iProX
AnnounceDate	2025-08-08
AnnouncementXML	Submission_2025-12-28_19:46:49.882.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Feixiang Xiong
SpeciesList	scientific name: Homo sapiens; NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Astral

Revision	Datetime	Status	ChangeLog Entry
0	2025-08-10 21:09:04	ID requested
⏵ 1	2025-12-28 19:46:50	announced

Dataset with its publication pending

submitter keyword: secondary infections, prognostic biomarkers, prediction model, proteomics

Yixin Hou
contact affiliation	Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University
contact email	xuexin162@163.com
lab head
Feixiang Xiong
contact affiliation	Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University
contact email	xiangakb@163.com
dataset submitter

iProX dataset URI