To reveal biomarkers related to macrophage and lactic acid metabolism in renal fibrosis and explore their mechanisms
IntroductionLactate can influence the fibrotic process by regulating cellular metabolism, inflammatory responses, and cell proliferation, which may be closely related to macrophage function in diseases. Therefore, this research sought to identify biomarkers linked to lactate metabolism and macrophag...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1609903/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | IntroductionLactate can influence the fibrotic process by regulating cellular metabolism, inflammatory responses, and cell proliferation, which may be closely related to macrophage function in diseases. Therefore, this research sought to identify biomarkers linked to lactate metabolism and macrophages in renal fibrosis (RF).MethodsFirstly, key modular genes associated with macrophage score and lactate metabolism score were identified by combining single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis. Then, candidate genes were obtained by overlapping them with differentially expressed genes between RF and control groups. Subsequently, candidate genes were incorporated into machine learning algorithms to identify key feature genes associated with RF. Expression analysis was then completed to determine biomarkers for this study. Furthermore, the relationship between biomarkers and RF was elucidated by a series of bioinformatics methods, including enrichment analysis, immunosignature analysis, and molecular regulatory analysis. Finally, we validated these key biomarkers in animal experiments.ResultsThe ssGSEA results showed significantly higher macrophage score and lower lactate metabolism score in the RF samples compared to control samples. Next, AGR3, CD74, and SYT11 were identified as biomarkers for this study because they had consistent expression trends in GSE76882 and GSE135327 datasets and were significantly different between RF and control samples. Moreover, receiver operating characteristic curves showed their excellent accuracy in predicting the occurrence of RF. Subsequent enrichment analysis revealed that three biomarkers were collectively enriched to 50 signaling pathways, including “Toll-like receptor signaling pathway”, “oxidative phosphorylation”, and “P53 signaling pathway”. Notably, CD74 showed a significant positive correlation with macrophages. In lncRNA-miRNA-mRNA network, multiple relationship pairs could be found, e.g., hsa-miR-548x-3p and hsa-miR-548aj-3p were regulators of AGR3, as well as multiple lncRNAs (PCAT6, POLR2J4, SMIM25) could co-regulate CD74 through hsa-miR-4731-5p. Animal experiments also confirmed that the expression of key biomarkers were significantly elevated in the RF rat/mice model. Moreover, the localization and expression of these biomarkers were related to infiltrating inflammatory cells in the kidney tissue.ConclusionIn this study, we found that AGR3, CD74, and SYT11 were biomarkers associated with lactate metabolism and macrophages in RF, providing valuable insights for further RF research. |
|---|---|
| ISSN: | 1664-3224 |