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Updated: Jun 29, 2025
Isolation of Adipose Derived Regenerative Cells for the Treatment of Erectile Dysfunction Following Radical Prostatectomy
Published on: December 28, 2021
Identification and validation of new fatty acid metabolism-related mechanisms and biomarkers for erectile dysfunction
Yanfeng He1,2,3, Changyi Liu1,2, Zhongjie Zheng4
1Department of Urology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China.
Background:
Erectile dysfunction (ED) is a common condition affecting middle-aged and elderly men.
Aim:
The study sought to investigate differentially expressed fatty acid metabolism-related genes and the molecular mechanisms of ED.
Methods:
The expression profiles of GSE2457 and GSE31247 were downloaded from the Gene Expression Omnibus database and merged. Differentially expressed genes (DEGs) between ED and normal samples were obtained using the R package limma. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of DEGs were conducted using the R package clusterProfiler. Fatty acid metabolism-related DEGs (FAMDEGs) were further identified and analyzed. Machine learning algorithms, including Lasso (least absolute shrinkage and selection operator), support vector machine, and random forest algorithms, were utilized to identify hub FAMDEGs with the ability to predict ED occurrence. Coexpression analysis and gene set enrichment analysis of hub FAMDEGs were performed.
Outcome:
Fatty acid metabolism-related functions (such as fatty acid metabolism and degradation) may play a vital role in ED.
Results:
In total, 5 hub FAMDEGs (Aldh2, Eci2, Acat1, Acadl, and Hadha) were identified and found to be differentially expressed between ED and normal samples. Gene set enrichment analysis identified key pathways associated with these genes. The area under the curve values of the 5 hub FAMDEGs for predicting ED occurrence were all >0.8.
Clinical Translation:
Our results suggest that these 5 key FAMDEGs may serve as biomarkers for the diagnosis and treatment of ED.
Strengths And Limitations:
The strengths of our study include the use of multiple datasets and machine learning algorithms to identify key FAMDEGs. However, limitations include the lack of validation in animal models and human tissues, as well as research on the mechanisms of these FAMDEGs.
Conclusion:
Five hub FAMDEGs were identified as potential biomarkers for ED progression. Our work may prove that fatty acid metabolism-related genes are worth further investigation in ED.
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