التفاصيل البيبلوغرافية
| العنوان: |
Identification of genes related to tubulointerstitial injury in type 2 diabetic nephropathy based on bioinformatics and machine learning. |
| المؤلفون: |
Jia-ming, SU, Jing, PENG, Hai-min, CHEN, Ying, ZHOU, Yang, SHI, Zhao-xi, DONG, Ya-xuan, WEN, Zi-xuan, LIN, Hong-fang, LIU |
| المصدر: |
Journal of Hainan Medical University; Oct2022, Vol. 28 Issue 20, p36-44, 9p |
| مصطلحات موضوعية: |
GENE ontology, MACHINE learning, CELL adhesion molecules, STAPHYLOCOCCUS aureus infections, TH2 cells, GENE expression, DIABETIC nephropathies |
| مستخلص: |
Objective: To explore the genes related to renal tubulointerstitial injury in DN and to elucidate their underlying mechanism by using bioinformatics multi-chip joint analysis and machine learning technology, so as to provide new ideas for the diagnosis and treatment of DN. Methods: Four gene expression datasets of DN tubulointerstitial tissues were retrieved from the GEO database. GSE30122, GSE47185 and GSE99340 were used as the combined microarray datasets, and GSE104954 was used as the independent verification datasets. The differentially expressed genes (DEGs) were identified by R language, and Gene Ontology (GO) enrichment, KEGG pathway enrichment, Gene Set Enrichment Analysis (GSEA) and Immune Cell Infiltration Analysis were performed. Furthermore, LASSO regression, SVMRFE and RF machine learning algorithm were used to screen core genes, while external validation and Receiver Operating Curve (ROC) analysis as well as the model of prediction nomogram were performed. Finally, the influence of the clinical characteristics of DN patients was explored by Nephroseq. Results: A total of 107 DEGs were obtained, enrichment analysis revealed that the tubulointerstitial injury in DN was mainly involved in adaptive immune response, lymphocyte mediated immunity, regulation of immune effector process and immune-inflammatory pathways such as staphylococcus aureus infection, complement and coagulation cascades, phagosomes, and Th1 and Th2 cell differentiation. In addition, cell adhesion molecule, cytokine-cytokine receptor interaction and ECM-receptor interaction pathways were also significantly enriched. Memory resting CD4 T cells, γδ T cells, resting mast cells and neutrophil cells were up-regulated, while CD8 T cells were down-regulated. Machine learning identified MARCKSL1, CX3CR1, FSTL1, AGR2, GADD45B as core genes with good diagnostic and predictive efficacy. Conclusion: The key pathological mechanism of tubulointerstitial injury in DN is immune disorder, inflammatory reaction, cytokine action and extracellular matrix deposition. Moreover, MARCKSL1, CX3CR1, FSTL1 may be the potential biomarkers for the diagnosis and prediction of DN. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Hainan Medical University is the property of Journal of Hainan Medical College Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder