Bioinformatics analysis and identification of cuproptosis characteristic genes for acute pancreatitis by machine learning

doi:10.16138/j.1673-6087.2024.04.02

Abstract

Abstract:

Objective To discover the characteristic genes of cuproptosis in acute pancreatitis (AP). Methods The expression of cuproptosis-related genes (CRG) in the GSE1943 dataset was extracted and performed differential analysis and immune cell correlation analysis. The different subtypes were classified according to the expression of CRG, and metabolic pathway enrichment was performed using gene set variation analysis. Four machine learning algorithms, including generalized linear models, random forest, support vector machine and extreme gradient boosting were used to screen disease characteristic genes. Results A total of 13 CRG were differentially expressed in AP (P<0.05), and CRG were not only correlated to each other in different degrees, but also had correlation with multiple immune cells (P<0.05). There were four immune-related pathways among the two subtypes obtained by cluster analysis, in which the T-cell receptor signaling pathway was noteworthy. Further analysis revealed significant difference between the two subtypes of multiple T cells (P<0.05). Each machine learning algorithm screened out five characteristic genes, and dihydrolipoamide dehydrogenase (DLD) was obtained as the next target of research. Conclusions CRG-based machine learning and bioinformatics analyses could be used to explore CRG in AP to discover potential biomarkers.

Key words: Cuproptosis, Acute pancreatitis, Machine learning, Bioinformatics analysis

CLC Number:

R576

WANG Zhuoxin, HUANG Xinyang, JIN Yixun, WANG Lifu. Bioinformatics analysis and identification of cuproptosis characteristic genes for acute pancreatitis by machine learning[J]. Journal of Internal Medicine Concepts & Practice, 2024, 19(04): 224-230.

Figures/Tables 5

References 36

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