Abstract
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.
Keywords: erectile dysfunction, fatty acid metabolism, machine learning algorithms, hub genes, biomarkers
Graphical Abstract
Graphical Abstract.
Introduction
Sexual problems within a marriage are one of the most important problems in life, and approximately half of divorces are caused by sexual problems.1 Studies have shown that men have a 54% probability of experiencing sexual problems in their lifetime.2,3 Among sexual dysfunctions, erectile dysfunction (ED) is the most prevalent.4 ED is a prevalent medical condition that affects millions of men worldwide. It is characterized by the consistent inability to achieve or maintain an erection firm enough for satisfactory sexual intercourse.5 ED can have a significant impact on the quality of life, self-esteem, and interpersonal relationships of those affected. Despite its prevalence and impact, there is still much to discover about the molecular mechanisms behind ED.6 Genomic studies have shown that genetic variations may contribute to the susceptibility and severity of ED.7-9 Identifying specific genes and their functional implications in ED could provide valuable insights into the disease’s pathogenesis and potential therapeutic targets. The main problem experienced by ED patients is the inability to perform sexually due to penile laxity and poor erection. These symptoms may be a sign of an underlying disease such as diabetes or heart disease. Diseases that cause ED include diabetes, heart disease, metabolic syndrome, hypertension, ageing, obesity, and excessive alcohol consumption.10 Understanding the genetic basis of ED through genomic research holds great promise for elucidating its pathogenesis and developing more effective therapies. By unraveling the intricate genetic and molecular mechanisms underlying ED, we can advance toward personalized medicine approaches that improve the diagnosis, prevention, and treatment of this prevalent condition.
Biomarkers of fatty acid intake have been widely used in epidemiological studies to predict disease risk.11 The ingestion of fatty acids can affect glucose metabolism by altering cellular enzyme activity, cell membrane function, gene expression, and insulin signaling,12 thereby increasing the risk of diabetic complications, including ED. ED is one of the major complications of type 2 diabetes.13 The dysregulation of fatty acid metabolism is a key event leading to insulin resistance and type 2 diabetes.14 These data suggest that dysfunctional fatty acid metabolism may play a key role in the pathogenesis of ED. However, there are few studies on fatty acid metabolism in ED at present.
Herein, we identified differentially expressed genes (DEGs) in ED patients compared with normal control subjects. Enrichment analysis of the DEGs was conducted to reveal the pathways associated with ED. Fatty acid metabolism–related DEGs (FAMDEGs) were further identified and analyzed. Machine learning algorithms, including least absolute shrinkage and selection operator (Lasso), support vector machine (SVM), and random forest algorithms, were utilized to screen for hub FAMDEGs to predict the occurrence of ED. Coexpression analysis and gene set enrichment analysis (GSEA) of the hub genes were performed. Our work may provide new and deeper insights into the study of ED, including its early detection and treatment.
Methods
Dataset collection
The messenger RNA expression profiles and clinical information of rat from GSE2457 and GSE31247 were acquired from the Gene Expression Omnibus datasets. Only ED and normal samples were selected for this study. GSE2457 was based on the platform GPL341 with 5 ED samples and 5 normal samples. GSE31247 was based on the platform GPL7289 with 3 ED samples and 3 normal samples. The R software (version 4.1.1; R Foundation for Statistical Computing) packages limma and sva were used to merge the expression profiles of GSE2457 and GSE31247 and remove batch effects. Fatty acid metabolism–related genes were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, including fatty acid biosynthesis (Rattus norvegicus) (rno00061), fatty acid elongation (R. norvegicus) (rno00062), and fatty acid degradation (R. norvegicus) (rno00071) pathways (a total of 75 genes).
Data normalization and differential expression analysis
The R package preprocessCore was used to normalize the data before differential expression analysis. After normalization, DEGs were screened with a threshold of P value <.05 using the limma package.
Functional enrichment analysis
The R package clusterProfiler is a versatile tool used for biological data analysis and interpretation. It is specifically designed to perform GSEA on high-throughput genomic data, such as RNA sequencing or microarray data. The package offers a wide range of functions to analyze and visualize enriched gene sets, identify overrepresented pathways or Gene Ontology terms, and conduct various statistical tests. Functional enrichment analysis of DEGs or FAMDEGs was conducted using the R package clusterProfiler, including Gene Ontology (GO) terms and the KEGG pathways. GSEA was performed using the R package clusterProfiler to explore the function of hub FAMDEGs.
Protein–protein interaction analysis
FAMDEGs were input into the STRING (https://string-db.org/) and GeneMANIA (http://genemania.org/) databases to build protein–protein interaction networks.
Machine learning algorithms
Three machine learning algorithms, including Lasso, SVM, and random forest, were conducted via the R packages glmnet, kernlab, and randomForest. We selected genes that were identified by at least 2 of the algorithms as hub FAMDEGs.
Statistical analysis
All statistical analyses in this study were performed by R software. Asterisks indicate statistically significant differences (*P < .05, **P < .01, ***P < .001, ****P < .0001).
Results
Identification of DEGs in ED
We first merged (Figure 1A, B) and homogenized (Figure 1C, D) the expression data from GSE2457 and GSE31247 and performed differential expression analysis. We identified 572 upregulated DEGs and 347 downregulated DEGs (Figure 1E, F).
Figure 1.
Identification of differentially expressed genes (DEGs). (A, B) The combined data from GSE2457 and GSE31247. The principal component analysis plots before (A) and after (B) merging are shown. (C, D) Data homogeneity. The boxplots show the data before (C) and after (D) batch effect removal. (E) The volcano plot of difference analysis. (F) Heatmap of the top upregulated and downregulated genes.
Functional enrichment analysis
Functional enrichment of the DEGs was performed to identify possible biological functions. GO analysis showed that DEGs were related mainly to the biological process terms fatty acid metabolic process, epithelial cell proliferation, response to hypoxia, and response to decreased oxygen levels (Figure 2A); the cellular component terms extracellular matrix and external encapsulating structure (Figure 2B); and the molecular function terms amide binding, sulfur compound binding, and peptide binding (Figure 2C). KEGG analysis identified the PPAR signalling pathway, protein digestion and absorption, insulin secretion, fatty acid degradation, aldosterone synthesis and secretion, and antigen processing and presentation as enriched (Figure 2D). These results suggested that fatty acid metabolism–related factors (such as those involved in fatty acid metabolism and degradation) may play a vital role in the occurrence of ED.
Figure 2.
Differentially expressed gene (DEG) enrichment analysis. (A-C) Gene Ontology (GO) enrichment analysis of overlapping DEGs, including biological process (BP) (A), cellular component (CC) (B), and molecular function (MF) (C) terms. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of overlapping DEGs. The top 20 terms are displayed.
Identification and exploration of FAMDEGs
Considering the close relationship between fatty acid metabolism and ED, we further screened the FAMDEGs present in the differentially expressed genes. We identified 8 upregulated FAMDEGs (Eci2, Aldh2, Acaa2, Acadl, Acacb, Cpt2, Acadvl, and Hadha) and 4 downregulated FAMDEGs (Fasn, Acat1, Ppt1, and Acadsb) (Figure 3A, B). Functional enrichment of these FAMDEGs was also performed to identify possible biological functions. GO analysis showed that the FAMDEGs were related mainly to lipid catabolic processes, mitochondrial matrix, and amide binding (Figure 3C). KEGG analysis indicated that fatty acid degradation and fatty acid metabolism were enriched (Figure 3D). Figure 3E shows the association of the top 5 KEGG results with the indicated FAMDEGs. A protein–protein interaction network of the FAMDEGs was also constructed using the STRING (Supplementary Figure 1A) and GENEMANIA (Supplementary Figure 1B) databases, revealing the associations among the FAMDEGs.
Figure 3.
Venn diagrams and enrichment analysis. (A, B) Venn diagrams displaying the intersection of upregulated (A) or downregulated (B) differentially expressed genes (DEGs) with fatty acid–related genes. (C) Gene Ontology (GO) enrichment analysis of overlapping fatty acid metabolism–related differentially expressed genes (FAMDEGs), including biological process (BP), cellular component (CC), and molecular function (MF). (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of overlapping FAMDEGs. The top 20 terms are displayed. (E) The top 5 KEGG terms and their correlations with the indicated FAMDEGs are displayed.
We also visualized the FAMDEGs in a volcano plot and heatmap based on the results of difference analysis (Figure 4A, B). As shown in Figure 4C, Eci2, Aldh2, Acaa2, Acadl, Acacb, Cpt2, Acadvl, and Hadha were upregulated in ED, while Fasn, Acat1, Ppt1, and Acadsb were downregulated.
Figure 4.
Differential expression analysis of fatty acid metabolism–related differentially expressed genes (FAMDEGs). (A) The volcano plot of FAMDEGs. (B) The heatmaps of the FAMDEGs are displayed. (C) The differential expression of FAMDEGs in ED and normal samples.
Machine learning algorithm
Three machine learning algorithms, Lasso, SVM, and random forest, were applied via the R packages glmnet, kernlab, and randomForest to screen hub FAMDEGs for predicting the occurrence of ED. Six genes were identified by Lasso, namely Eci2, Aldh2, Acadl, Hadha, Fasn, and Acat1 (Figure 5A). Two genes were screened by SVM, namely Eci2 and Aldh2 (Figure 5B), and the top 10 genes selected via random forest were Eci2, Acat1, Acadl, Acaa2, Acadvl, Acacb, Ppt1, Hadha, Acadsb, and Aldh2 (Figure 5C). We further selected the genes that were selected by at least 2 algorithms as hub FAMDEGs: Aldh2, Eci2, Acat1, Acadl, and Hadha (Figure 5D).
Figure 5.
Machine learning algorithms and correlation and receiver-operating characteristic analysis of hub fatty acid metabolism–related differentially expressed genes (FAMDEGs). (A-C) Three machine learning algorithms, least absolute shrinkage and selection operator (Lasso) (A), support vector machine (SVM) (B), and random forest (C), were applied via the R packages glmnet, kernlab, and randomForest to screen hub FAMDEGs. (D) Venn diagrams displaying the intersection of the hub FAMDEGs. (E) Correlation of 5 hub genes. (F) Receiver-operating characteristic curves of the hub FAMDEGs.
We also explored the correlations among the FAMDEGs (Figure 5E). For example, Acadl was positively correlated with Eci2 expression and negatively correlated with Acat1 expression. By evaluating the predictive capacity of the FAMDEGs, we found that all 5 FAMDEGs had high efficiency in predicting ED occurrence (Figure 5F) and that Eci2 exhibited the highest area under the curve (close to 1).
Exploring the functions of the hub FAMDEGs
We further explored the correlations among the 5 hub FAMDEGs. The correlations between the hub genes and all genes were analyzed. The heatmaps of the top 50 genes with positive correlations are displayed in Figure 6. Based on the results of this correlation analysis, single-gene Reactome-based GSEA was performed. The top 20 results of the 5 hub FAMDEGs are presented in Figure 7. For example, we predicted that Eci2 was closely associated with fatty acid metabolism, neutrophil degranulation, the innate immune system, and lipid metabolism.
Figure 6.
Coexpression analysis of the hub fatty acid metabolism–related differentially expressed genes (FAMDEGs). The top 50 genes most positively associated with the indicated hub FAMDEGs are shown in the heatmap.
Figure 7.
Gene set enrichment analysis (GSEA) of hub genes. The top 20 GSEA results for the indicated hub fatty acid metabolism–related differentially expressed genes (FAMDEGs) are shown.
Discussion
ED is caused by a variety of pathological conditions, including vascular risk factors or diseases, neurological abnormalities, and hormonal disorders.15-17 Diabetes mellitus is one of the most common causes of ED, and approximately 50-75% of male diabetes patients have ED.18,19 Symptoms in diabetes mellitus patients with ED tend to be more severe and occur earlier than in nondiabetic ED patients.20 Currently, phosphodiesterase type 5 inhibitor is the clinical first-line treatment for ED, but it is not as effective as expected.21 Therefore, we mined core biomarkers for ED and investigated the relationship between ED and fatty acid metabolism, paving the way for future studies to elucidate the underlying mechanisms.
In this work, we identified 572 upregulated DEGs and 347 downregulated DEGs in ED. Functional enrichment of these DEGs was performed to identify possible biological functions. GO and KEGG analyses showed that the DEGs were related mainly to fatty acid metabolic processes, PPAR signaling pathway, protein digestion and absorption, insulin secretion, fatty acid degradation, aldosterone synthesis and secretion, and antigen processing and presentation. These results suggested that fatty acid metabolism–related functions (such as fatty acid metabolism and degradation) may play a vital role in ED.
It was reported that high levels of saturated fatty acid affect the integrity, fluidity, and structure of cell membranes, leading to a decrease in the number of insulin receptors and an increase in insulin resistance severity.22 Metabolic homeostasis of fatty acids is required for the regulation of key pathways involved in the stimulation of erection.23 Understanding the cellular mechanisms of fatty acid dysregulation offers the prospect of more targeted and more effective therapeutic interventions for the treatment and prevention of ED.
To explore the roles of fatty acid–related genes in ED progression, we selected FAMDEGs for further research. Three machine learning algorithms, Lasso, SVM, and random forest, were used to screen hub FAMDEGs for predicting the occurrence of ED. We finally identified 5 FAMDEGs, Aldh2, Eci2, Acat1, Acadl, and Hadha, for further analysis. Due to limited research on ED, none of the 5 genes we screened have been studied in ED. This also demonstrates the novelty of our conclusion. However, these genes are involved in the progression of many other diseases. For example, ALDH2-mediated aldehyde metabolism promotes tumor immune evasion by regulating the NOD/VISTA axis.24 ECI2 was also a disease promoting factor in colorectal cancer,25 ovarian cancer,26 and gestational diabetes mellitus.27 We believe that with further research in the future, the roles of these genes in ED will be revealed. ACAT1 was cloned by functional complementation of a Chinese hamster ovary cell mutant lacking ACAT enzyme activity. ACAT1 is located mainly at the endoplasmic reticulum and is ubiquitously expressed in all human tissues examined.28 ACAT1 was also a therapeutic target in many diseases, such as Alzheimer’s disease,29 colorectal cancer,30 and uterine cancer.31 Unlike other genes, ACADL mainly plays a role in inhibiting disease progression. For example, ACADL functions as a tumor suppressor in liver cancer by inhibiting matrix metalloproteinase 14.32 HADHA was reported to alleviate hepatic steatosis and oxidative stress in nonalcoholic fatty liver disease via inactivation of the MKK3/MAPK pathway.33
We next explored the predictive functions of the FAMDEGs and found that all 5 FAMDEGs have high efficiency in predicting ED occurrence and that Eci2 exhibited the highest area under the curve (close to 1). Our research still has some limitations, the most important of which is the lack of validation in animal models and human tissues, as well as research on the mechanisms of these FAMDEGs. We believe that future analyses of larger sample sizes of animal models will be needed to validate the predictive efficacy of these genes.
Further, GSEA was performed to better understand the functions of these hub genes. For example, we predicted that Eci2 was closely associated with fatty acid metabolism, neutrophil degranulation, the innate immune system, and lipid metabolism. These results provide a reliable basis for future in-depth studies of these genes.
In conclusion, our study identified 5 hub FAMDEGs and their potential pathways involved in ED. Our work provides a new direction for future research to investigate the mechanisms underlying ED.
Supplementary Material
Acknowledgments
The authors acknowledge the Gene Expression Omnibus, STRING, and GeneMANIA databases for free use.
Contributor Information
Yanfeng He, Department of Urology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Department of Urology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Andrology and Sexual Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
Changyi Liu, Department of Urology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Department of Urology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
Zhongjie Zheng, Department of Urology, Peking University Third Hospital, Peking University, Beijing 100191, China.
Rui Gao, Department of Urology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Department of Urology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
Haocheng Lin, Department of Urology, Peking University Third Hospital, Peking University, Beijing 100191, China.
Huiliang Zhou, Department of Andrology and Sexual Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
Author contributions
Y.H. (Writing-review & editing, Writing-original draft), C.L. (Data curation), Z.Z. (Formal analysis, Software, Visualization), R.G. (Project administration), H.L. and H.Z. (Conceptualization, Supervision). All authors contributed to the article and approved the submitted version. Yanfeng He and Changyi Liu contributed equally to this work.
Funding
This work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (No. 2020Y9115) and the National Natural Science Foundation of China (No. 82371633).
Conflicts of interest
The authors have no relevant financial or nonfinancial interests to disclose.
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