Trends in cancer, Journal Year: 2024, Volume and Issue: 10(9), P. 781 - 791
Published: July 19, 2024
Language: Английский
medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: March 22, 2024
Abstract Sepsis is one of the leading causes mortality in world. Currently, heterogeneity sepsis makes it challenging to determine molecular mechanisms that define syndrome. Here, we leverage population scale proteomics analyze a well-defined cohort 1364 blood samples taken at time-of-admission emergency department from patients suspected sepsis. We identified panels proteins using explainable artificial intelligence predict clinical outcomes and applied these reduce high-dimensional data low-dimensional interpretable latent space (ILS). Using ILS, constructed an adaptive digital twin model accurately predicted organ dysfunction, mortality, early-mortality-risk only available time-of-admission. In addition being highly effective for investigating sepsis, this approach supports flexible incorporation new can generalize other diseases aid translational research development precision medicine.
Language: Английский
Citations
2
Analytical Chemistry, Journal Year: 2024, Volume and Issue: 96(17), P. 6618 - 6627
Published: April 16, 2024
Tumor-derived extracellular vesicles (EVs) carry tumor-specific proteins and RNAs, thus becoming prevalent targets for early cancer diagnosis. However, low expression of EV cargos insufficient diagnostic power individual biomarkers hindered EVs application in clinical practice. Herein, we propose a multiplex Codetection platform RNAs (Co-PAR) EVs. Co-PAR adopted pair antibody-DNA probes to recognize the same target protein, which turn formed double-stranded DNA. Thus, protein could be quantified by detecting DNA via qPCR. Meanwhile, qRT-PCR simultaneously RNAs. with regular qPCR instrument, enabled codetection sensitivity 102 EVs/μL (targeting CD63) 1 EV/μL snRNA U6). We analyzed coexpressions three markers (CD63, GPC-1, HER2) RNA (snRNA U6, GPC-1 mRNA, miR-10b) on from pancreatic cell lines 30 human plasma samples using Co-PAR. The accuracy 6-biomarker combination reached 92.9%, was at least 6.2% higher than that 3-biomarker combinations 13.5% 6 single biomarkers. Co-PAR, as multiparameter detection EVs, has great potential disease
Language: Английский
Citations
2
Journal of Proteome Research, Journal Year: 2024, Volume and Issue: 23(12), P. 5296 - 5311
Published: Nov. 8, 2024
The Human Proteome Project (HPP), the flagship initiative of Organization (HUPO), has pursued two goals: (1) to credibly identify at least one isoform every protein-coding gene and (2) make proteomics an integral part multiomics studies human health disease. past year seen major transitions for HPP. neXtProt was retired as official HPP knowledge base, UniProtKB became reference proteome Ensembl-GENCODE provides protein target list. A function evidence FE1–5 scoring system been developed functional annotation proteins, parallel PE1–5 UniProtKB/neXtProt scheme expression. This report includes updates from (version 2023–09) release 2024_04, with expression detected (PE1) 18138 19411 GENCODE genes (93%). number non-PE1 proteins ("missing proteins") is now 1273. transition a net reduction 367 (19,411 instead 19,778 PE1–4 last in neXtProt). We include reports Biology Disease-driven HPP, Protein Atlas, Grand Challenge Project. expect new Functional Evidence energize throughout global community, including π-HuB China.
Language: Английский
Citations
2
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: July 2, 2024
Abstract Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients retrospectively categorized into nonmetastatic, metastatic, recurrence groups, differential expression was assessed. Machine learning gene set enrichment analysis (GSEA) methods applied discover biomarkers predict prognosis. Four proteins (MUC16, CAIX, 5’-NT, CD8A) significantly elevated metastatic compared control group. Additionally, GSEA indicated that response interleukin-4 hypoxia-related pathways enriched patients. Random forest classification decision-tree modeling showed MUC16 could be marker for ELISA validation confirmed levels Notably, high independently associated with progression T3 higher GC. These findings suggest potential as clinically relevant identifying at risk metastasis.
Language: Английский
Citations
1
Trends in cancer, Journal Year: 2024, Volume and Issue: 10(9), P. 781 - 791
Published: July 19, 2024
Language: Английский
Citations
1