Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis

Severe acute pancreatitis (SAP) is a life-threatening gastrointestinal emergency. The study aimed to identify biomarkers and investigate molecular mechanisms of SAP. The GSE194331 dataset from GEO database was analyzed using bioinformatics. Differentially expressed genes (DEGs) associated with SAP w...

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Bibliographic Details
Published in:Molecular biotechnology 2024-05, Vol.66 (5), p.1246-1265
Main Authors: Sun, Yang, Xie, Jingjun, Zhu, Jun, Yuan, Yadong
Format: Article
Language:English
Subjects:
Algorithms
Biochemistry
Bioinformatics
Biological Techniques
Biomarkers
Biotechnology
Blood
Cell Biology
Chemistry
Chemistry and Materials Science
Gene set enrichment analysis
Glutathione
Glutathione transferase
Human Genetics
Immune system
Infiltration
Learning algorithms
Machine learning
Molecular modelling
Original Paper
Pancreatic cancer
Pancreatitis
Peptidyltransferase
Peripheral blood
Protein Science
Proteins
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Summary:Severe acute pancreatitis (SAP) is a life-threatening gastrointestinal emergency. The study aimed to identify biomarkers and investigate molecular mechanisms of SAP. The GSE194331 dataset from GEO database was analyzed using bioinformatics. Differentially expressed genes (DEGs) associated with SAP were identified, and a protein–protein interaction network (PPI) was constructed. Machine learning algorithms were used to determine potential biomarkers. Gene set enrichment analysis (GSEA) explored molecular mechanisms. Immune cell infiltration were analyzed, and correlation between biomarker expression and immune cell infiltration was calculated. A competing endogenous RNA network (ceRNA) was constructed, and biomarker expression levels were quantified in clinical samples using RT-PCR. 1101 DEGs were found, with two modules most relevant to SAP. Potential biomarkers in peripheral blood samples were identified as glutathione S-transferase 1 (MGST1) and glutamyl peptidyltransferase (QPCT). GSEA revealed their association with immunoglobulin regulation, with QPCT potentially linked to pancreatic cancer development. Correlation between biomarkers and immune cell infiltration was demonstrated. A ceRNA network consisting of 39 nodes and 41 edges was constructed. Elevated expression levels of MGST1 and QPCT were verified in clinical samples. In conclusion, peripheral blood MGST1 and QPCT show promise as SAP biomarkers for diagnosis, providing targets for therapeutic intervention and contributing to SAP understanding.
ISSN:1073-6085
1559-0305
DOI:10.1007/s12033-023-01026-0