# Integrated Bioinformatics IdentifiesFREM1 as a Diagnostic Gene Signature for Heart Failure

Authors: Chenyang Jiang; Weidong Jiang
Journal: Applied Bionics and Biomechanics
 (2022)

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## Abstract

Objective. This study is aimed at integrating bioinformatics and machine learning to determine novel diagnostic gene signals in the progression of heart failure disease. Methods. The heart failure microarray datasets and RNA-seq datasets have been downloaded from the public database. Differentially expressed genes (DE genes) are screened out, and then, we analyze their biological functions and pathways. Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. Then, external independent RNA-seq datasets evaluate the diagnostic value of gene signals. Finally, the convolution tool CIBERSORT estimated the composition pattern of immune cell subtypes in heart failure and carried out a correlation analysis combined with gene signals. Results. Under the set threshold, we obtained 47 DE genes with the most significant differences. Enrichment analysis shows that most of them are related to hypertrophy, matrix structural constituent, protein binding, inflammatory immune pathway, cardiovascular disease, and inflammatory disease. Three machine learning methods assisted in determining the potential characteristic signals Fras1-related extracellular matrix 1 (FREM1) and meiosis-specific nuclear structural 1 (MNS1). Validation of external datasets confirms that FREM1 is a diagnostic gene signal for heart failure. Immune cell subtypes of tissue specimens found T cell CD8, mast cell resting, T cell CD4 memory resting, T cell regulation (Tregs), monocytes, macrophages M2, T cell CD4 naive, macrophages M0, and neutrophils are associated with HF. Conclusion. The gene signal FREM1 may be a potential molecular target in the development of HF and is related to the difference in immune infiltration of HF tissue.


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## Body

## 1. Introduction

Heart failure (HF) is a clinical syndrome with high morbidity and high mortality due to the development of heart disease to a serious stage, leading to dysfunction of cardiac mechanical activity [1]. There is a large number of patients with heart failure in the world, and 64.3 million people have heart failure with obvious symptoms [2]. The classifications of HF are mostly based on the ratio of left ventricular ejection fraction: HF patients with reduced ejection fraction (HFrEF), HF patients with preserved ejection fraction (HFpEF), and HF patients with critical ejection fraction (HFmEF) [3]. Most cardiovascular diseases eventually lead to heart failure, and one of the most common causes of heart failure is cardiomyopathy [4]. Coronary artery disease, secondary cardiovascular damage caused by multiple organ damage [5], and continuous left ventricular pressure overload state [6] can lead to the occurrence of HF. In addition, heart failure itself is a common result of genetic susceptibility and environmental impact. Clinically, there are limitations to the diagnostic methods for HF, most of which are based on BNP/NT-proBNP dynamic monitoring and left ventricular ejection fraction (LVEF) diagnostic methods. However, echocardiographic diagnosis depends on the technical level and experience of the attending physician. The level of BNP/NT-proBNP may still increase in some diseases that are not accompanied by HF, such as liver and kidney failure [7]. Studies have shown that genes such as SERCA2a have become one of the most likely genes to treat heart failure [8–10]. Therefore, the search for new diagnostic models and the identification of specific gene signals for HF have become the targets of our exploration.In recent years, there has been a rapid development of microarray expression data and next-generation sequencing data. Discovering potential signature gene signals based on bioinformatics is a more novel and reliable method. We select the microarray expression datasets in the gene expression database Gene Expression Omnibus (GEO) to explore the differentially expressed genes (DE genes) between HF and normal samples and explore potential biological functions through biological enrichment analysis. At the same time, three high-efficiency machine learning methods are used to screen and diagnose gene signals of differential genes, including the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) algorithm. They are validated in RNA-seq external datasets. Finally, we use the deconvolution tool CIBERSORT to study the potential results of HF tissue immune infiltration and comprehensively analyze the correlation between diagnostic gene signals and immune cells.


## 2. Materials and Methods

### 2.1. HF Microarray Data and RNA-Seq Data

We screened the required heart failure datasets from the GEO database on the public platform. The selection criteria were as follows: (1) the dataset excludes cancer samples; (2) the dataset excludes complications such as diabetes, chronic kidney disease, and chronic obstructive pulmonary disease. Based on the above standards, we obtained the GSE57338, GSE5406, and GSE71613 human heart tissue sample datasets. We downloaded the microarray dataset GSE57338 [11] containing 313 HF and normal left ventricular tissue samples as the operation dataset. Among them, HF includes 177 samples, and normal includes 136 samples. In addition, we use the RNA-seq dataset containing HF and normal myocardial tissue sample information as an external validation dataset. GSE116250 [12] contains 14 normal samples and 50 HF tissue samples. GSE71613 [13] contains 4 normal samples and 4 HF tissue sample. The GEOquery [14] package is used to download these data and use the average value of multiple probes as the gene expression data. RNA-seq expression data can use the org.Hs.eg.db software package for gene ID conversion.


### 2.2. Identification of DE Genes

After normalizing GSE57338 expression data, check whether log2 processing is required. To establish DE genes between HF and normal, we used limma [15] packets for processing. Use log2foldchangeFC ≥1, adjust P value < 0.05 as the cutoff value to measure the required differential gene situation. The Benjamini and Hochberg method was used for calibration. The volcano map and heat map show the results.


### 2.3. Functional Enrichment Analysis of DE Genes

We use the clusterProfiler software package for Gene Ontology (GO) analysis, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) to explore and analyze the biological functions of DE genes. The cutoff criterion for GO and DO is set to adjustP value < 0.05. In GSEA analysis, “org.Hs.eg.db” can convert Entrez ID, “c2.cp.kegg.v7.2.symbols.gmt” can be used as a reference, and “ggplot2” package is used for plotting, the cut-off value of GSEA set to NES >1.0, adjust P value < 0.05.


### 2.4. Machine Learning Algorithm Model Construction

In biomedicine, machine learning strategies are used to screen potential biomarkers. We use machine learning methods to build diagnostic models and screen gene signals. The RF method is a promising method for dataset prediction. Evaluate the crucial dimension of the target variable, sort the importance of different predictor variables according to the difference in predictive ability, and obtain the screening results [16, 17]. The purpose of LASSO regression is to obtain the variable result with the smallest prediction error and its corresponding regression coefficient. By constraining the regression coefficient (λ), the optimal result is obtained. The Glmnet package is used to obtain the best lambda value [18]. SVM model is another powerful tool for identification, prediction, or classification that has only recently become popular in biomedicine. We can use them to select the best variables for the advantages that cannot be classified by linear decision data, and RFE can sort different features [19, 20]. The e1071 package is used to implement the SVM-RFE algorithm. Obtaining the intersection gene as a diagnostic gene signal through three machine learning methods has extremely high accuracy.


### 2.5. Diagnostic Gene Signal Verification

To further test the diagnostic efficacy of gene signal screening by machine learning, we use external datasets GSE116250 and GSE71613 as verification datasets to verify them. The corresponding gene expression and receiver-operating curve (ROC) will be displayed. The cutoff value is set toP value < 0.05. The areaundertheROCAUCvalue>0.9 indicates that the genetic diagnosis effect is better.


### 2.6. Immune Penetration Assessment

Adopting immunotherapy for diseases has become a relatively novel clinical treatment strategy. We also explored the infiltration of immune cells in heart failure samples and analyzed the correlation between diagnostic gene signals and immune cells to find possible pathophysiological processes. The deconvolution tool CIBERSORT is used to calculate quantitative immune cell components in tissue gene expression [21, 22]. Analyzing 313 samples in the GSE57338 expression dataset to obtain 22 kinds of immune cell infiltration conditions, we set the signature matrix to 1000 permutations by default to get the results. We compared the expression levels of 22 immune cells between HF and normal and the correlation between immune cells. Finally, the relationship between diagnostic gene signals and immune cell subtypes is obtained by Spearman’s rank correlation analysis. The ggplot2 package is used to draw graphics.


### 2.7. Statistical Analysis

We used Student’st test for normally distributed variables and the Mann–Whitney U test for abnormally distributed variables. The resulting data were processed and analyzed using R software (version 3.6.3).


## 2.1. HF Microarray Data and RNA-Seq Data

We screened the required heart failure datasets from the GEO database on the public platform. The selection criteria were as follows: (1) the dataset excludes cancer samples; (2) the dataset excludes complications such as diabetes, chronic kidney disease, and chronic obstructive pulmonary disease. Based on the above standards, we obtained the GSE57338, GSE5406, and GSE71613 human heart tissue sample datasets. We downloaded the microarray dataset GSE57338 [11] containing 313 HF and normal left ventricular tissue samples as the operation dataset. Among them, HF includes 177 samples, and normal includes 136 samples. In addition, we use the RNA-seq dataset containing HF and normal myocardial tissue sample information as an external validation dataset. GSE116250 [12] contains 14 normal samples and 50 HF tissue samples. GSE71613 [13] contains 4 normal samples and 4 HF tissue sample. The GEOquery [14] package is used to download these data and use the average value of multiple probes as the gene expression data. RNA-seq expression data can use the org.Hs.eg.db software package for gene ID conversion.


## 2.2. Identification of DE Genes

After normalizing GSE57338 expression data, check whether log2 processing is required. To establish DE genes between HF and normal, we used limma [15] packets for processing. Use log2foldchangeFC ≥1, adjust P value < 0.05 as the cutoff value to measure the required differential gene situation. The Benjamini and Hochberg method was used for calibration. The volcano map and heat map show the results.


## 2.3. Functional Enrichment Analysis of DE Genes

We use the clusterProfiler software package for Gene Ontology (GO) analysis, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) to explore and analyze the biological functions of DE genes. The cutoff criterion for GO and DO is set to adjustP value < 0.05. In GSEA analysis, “org.Hs.eg.db” can convert Entrez ID, “c2.cp.kegg.v7.2.symbols.gmt” can be used as a reference, and “ggplot2” package is used for plotting, the cut-off value of GSEA set to NES >1.0, adjust P value < 0.05.


## 2.4. Machine Learning Algorithm Model Construction

In biomedicine, machine learning strategies are used to screen potential biomarkers. We use machine learning methods to build diagnostic models and screen gene signals. The RF method is a promising method for dataset prediction. Evaluate the crucial dimension of the target variable, sort the importance of different predictor variables according to the difference in predictive ability, and obtain the screening results [16, 17]. The purpose of LASSO regression is to obtain the variable result with the smallest prediction error and its corresponding regression coefficient. By constraining the regression coefficient (λ), the optimal result is obtained. The Glmnet package is used to obtain the best lambda value [18]. SVM model is another powerful tool for identification, prediction, or classification that has only recently become popular in biomedicine. We can use them to select the best variables for the advantages that cannot be classified by linear decision data, and RFE can sort different features [19, 20]. The e1071 package is used to implement the SVM-RFE algorithm. Obtaining the intersection gene as a diagnostic gene signal through three machine learning methods has extremely high accuracy.


## 2.5. Diagnostic Gene Signal Verification

To further test the diagnostic efficacy of gene signal screening by machine learning, we use external datasets GSE116250 and GSE71613 as verification datasets to verify them. The corresponding gene expression and receiver-operating curve (ROC) will be displayed. The cutoff value is set toP value < 0.05. The areaundertheROCAUCvalue>0.9 indicates that the genetic diagnosis effect is better.


## 2.6. Immune Penetration Assessment

Adopting immunotherapy for diseases has become a relatively novel clinical treatment strategy. We also explored the infiltration of immune cells in heart failure samples and analyzed the correlation between diagnostic gene signals and immune cells to find possible pathophysiological processes. The deconvolution tool CIBERSORT is used to calculate quantitative immune cell components in tissue gene expression [21, 22]. Analyzing 313 samples in the GSE57338 expression dataset to obtain 22 kinds of immune cell infiltration conditions, we set the signature matrix to 1000 permutations by default to get the results. We compared the expression levels of 22 immune cells between HF and normal and the correlation between immune cells. Finally, the relationship between diagnostic gene signals and immune cell subtypes is obtained by Spearman’s rank correlation analysis. The ggplot2 package is used to draw graphics.


## 2.7. Statistical Analysis

We used Student’st test for normally distributed variables and the Mann–Whitney U test for abnormally distributed variables. The resulting data were processed and analyzed using R software (version 3.6.3).


## 3. Results

### 3.1. DE Genes Result in Analysis

GSE57338 contains a microarray dataset of 177 HF and 136 normal left ventricular tissue samples. The difference analysis revealed 47 DE genes, of which 24 had an upregulated expression level, and 23 had a downregulated expression level. The heat map (Figure1(a)) and volcano map (Figure 1(b)) show the results.Figure 1
The heat map (a) and volcano map (b) show the different genes of HF and normal. The blue in the heat map represents the HF sample, and the red represents the normal sample. The red dots in the volcano map indicate downregulated genes, blue dots indicate upregulated genes, and green dots indicate genes with no significant difference.
(a)(b)


### 3.2. Functional Enrichment Analysis of DE Genes

Gene Ontology (GO) enrichment found that biological processes (BP) are mainly concentrated in the processes of cardiac muscle hypertrophy, striated muscle hypertrophy, muscle hypertrophy, cell-matrix adhesion, and muscle adaptation. Cell component (CC) is concentrated in the collagen-containing extracellular matrix, interstitial matrix, vacuum lumen, blood microparticle, primary lysosome, etc. The molecular function (MF) part focuses on functions such as extracellular matrix structural constituent conferring compression resistance, extracellular matrix structural constituent, Wnt-protein binding, glycosaminoglycan binding, and collagen binding (Figure2(a)). In the Disease Ontology (DO) enrichment analysis, it is found that the differences in DE genes are mainly concentrated in some cardiovascular systems and inflammatory diseases. Mainly include male reproductive organ cancer, dilated cardiomyopathy, psoriatic arthritis, prostate cancer, membranous glomerulonephritis, atrial heart septal defect, asthma, intrinsic cardiomyopathy, atherosclerosis, and arteriosclerotic cardiovascular disease (Figure 2(b)). GSEA-enriched pathways found that the three pathways of Th1 and Th2 cell differentiation, MAPK signaling pathway, and B cell receptor signaling pathway are related to inflammation and immunity and showed significant differences in HF disease and normal samples (Figure 2(c)).Figure 2
Multiple enrichment analysis results. (a) Gene Ontology (GO) enrichment. The red bar represents the number of pathways in the BP part, the green bar represents the number of pathways in the CC part, and the blue bar represents the number of pathways in the MF part; (b) Disease Ontology (DO) enrichment. The yellow bars represent the number of pathways in the DO part; (c) GSEA-enriched pathways. Yellow lines represent B cell receptor signaling pathway, red lines represent MAPK signaling pathway, and blue lines represent Th1-Th2 cell differentiation.
(a)(b)(c)


### 3.3. Machine Learning Determines Diagnostic Genetic Signals for HF

Determine the diagnostic gene signal of heart failure through three algorithms in machine learning. The LASSO adopted 5X crossvalidation and determined lambda.min as 0.0128181 and finally selected 14 diagnostic gene signals from 47 DE genes (Figure3(a)). The feature selection algorithm SVM-RFE selected the 8 best diagnostic gene signals after 5X crossvalidation (Figure 3(b)). The RF algorithm sets the best mtry node value as 9, and the top nine genes ranked by MeanDecreaseGini are considered the best diagnostic gene signals (Figures 3(c)–3(e)). Integrating three machine learning algorithms determined that Fras1-related extracellular matrix 1 (FREM1) and meiosis-specific nuclear structural 1 (MNS1) are diagnostic gene signals for heart failure (Figure 3(f)).Figure 3
Machine learning algorithms determine genetic signals. (a) LASSO algorithm. LASSO selects 14 gene signals; (b) SVM-RFE algorithm. SVM-RFE selects 8 gene signals; (c) RF algorithm selects the best mtry; (d) top nine genes in RF algorithm; (e) ntree in RF algorithm; (f) Venn diagram of the integration of three algorithms.
(a)(b)(c)(d)(e)(f)


### 3.4. Verification of Diagnostic Gene Signals

We verified the two heart failure gene signalsFREM1 and MNS1 in the external RNA-seq datasets GSE116250 and GSE71613. The expression of the FREM1 gene in the two datasets was higher than the normal control in the external dataset, but the MNS1 gene was not statistically significant in GSE71613 (Figure 4(a)). The AUC found that FREM1 was 0.953 (95% CI: 0.904-1.000) and 1.000 (95% CI: 1.000-1.000) in the two datasets. The AUC value of MNS1 was less than 0.9 in both datasets (Figure 4(b)). The external RNA-seq dataset verification indicates that the diagnostic gene signal FREM1 has a high diagnostic level and is more suitable as a potential biomarker.Figure 4
External RNA-seq dataset verification. (a) Gene expression levels ofFREM1 and MNS1. The red dots represent the HF samples, and the blue dots represent the normal samples; (b) ROC curve of FREM1 and MNS1. The AUC values and 95% CI of FREM1 and MNS1 are marked in the figure.
(a)(b)


### 3.5. Analysis of Tissue Immune Cell Infiltration Subtypes

Tissue immune cell infiltration analysis revealed 22 immune cell subtypes in the gene collection of 313 samples. We found differences in nine types of immune cells. T cells CD8 (P=0.0028) and mast cell resting (P<0.001) were significantly increased in the HF disease group, and the cell subtype with low expression in HF tissues was T cells CD4 memory resting (P=0.017), T cells regulatory (Tregs) (P=0.047), monocytes (P<0.001), and macrophages M2 (P<0.001). T cells CD4 naïve (P=0.027), macrophages M0 (P<0.001), and neutrophils (P<0.001) also have certain differences (Figure 5(a)). In terms of the correlation between immune cell subtypes, T cells regulatory (Tregs) and B cells naïve have the most significant positive correlation (r=0.67), and T cells CD4 memory resting and T cells CD8 have the most significant negative correlation (r=−0.75) (Figure 5(b)).Figure 5
Tissue immune cell infiltration. (a) Immune cell difference between HF and normal samples (“∗∗∗” means P<0.001, “∗∗” means P<0.01, “∗” means P<0.05). The red dots represent each HF sample, while the blue dots represent each normal sample; (b) immune cell subtype correlation. The specific correlation values of 22 immune cells are displayed, in which the darker color of each correlation square represents higher correlation, and the lighter color represents low correlation. Red represents the degree of positive correlation, while blue represents the degree of negative correlation.
(a)(b)


### 3.6. The Relationship between FREM1 and the Immune Cell Subtype

The correlation analysis between the heart failure characteristic genes and immune cell subtypes showed thatFREM1 has the most significant positive correlation with mast cell resting (r=0.353, P<0.001), and the most significant negative correlation with neutrophils (r=−0.270, P<0.001). In addition, FREM1 is also associated with macrophage subtypes and T cell subpopulations (Figure 6).Figure 6
Correlation betweenFREM1 and immune cell subtypes. The size of each dot in the figure represents the degree of correlation. Larger dots represent a high degree of correlation. The specific P value is shown in yellow and red; the smaller the P value, the more obvious the yellow point. All immune cell names with P<0.05 are marked in red.


## 3.1. DE Genes Result in Analysis

GSE57338 contains a microarray dataset of 177 HF and 136 normal left ventricular tissue samples. The difference analysis revealed 47 DE genes, of which 24 had an upregulated expression level, and 23 had a downregulated expression level. The heat map (Figure1(a)) and volcano map (Figure 1(b)) show the results.Figure 1
The heat map (a) and volcano map (b) show the different genes of HF and normal. The blue in the heat map represents the HF sample, and the red represents the normal sample. The red dots in the volcano map indicate downregulated genes, blue dots indicate upregulated genes, and green dots indicate genes with no significant difference.
(a)(b)


## 3.2. Functional Enrichment Analysis of DE Genes

Gene Ontology (GO) enrichment found that biological processes (BP) are mainly concentrated in the processes of cardiac muscle hypertrophy, striated muscle hypertrophy, muscle hypertrophy, cell-matrix adhesion, and muscle adaptation. Cell component (CC) is concentrated in the collagen-containing extracellular matrix, interstitial matrix, vacuum lumen, blood microparticle, primary lysosome, etc. The molecular function (MF) part focuses on functions such as extracellular matrix structural constituent conferring compression resistance, extracellular matrix structural constituent, Wnt-protein binding, glycosaminoglycan binding, and collagen binding (Figure2(a)). In the Disease Ontology (DO) enrichment analysis, it is found that the differences in DE genes are mainly concentrated in some cardiovascular systems and inflammatory diseases. Mainly include male reproductive organ cancer, dilated cardiomyopathy, psoriatic arthritis, prostate cancer, membranous glomerulonephritis, atrial heart septal defect, asthma, intrinsic cardiomyopathy, atherosclerosis, and arteriosclerotic cardiovascular disease (Figure 2(b)). GSEA-enriched pathways found that the three pathways of Th1 and Th2 cell differentiation, MAPK signaling pathway, and B cell receptor signaling pathway are related to inflammation and immunity and showed significant differences in HF disease and normal samples (Figure 2(c)).Figure 2
Multiple enrichment analysis results. (a) Gene Ontology (GO) enrichment. The red bar represents the number of pathways in the BP part, the green bar represents the number of pathways in the CC part, and the blue bar represents the number of pathways in the MF part; (b) Disease Ontology (DO) enrichment. The yellow bars represent the number of pathways in the DO part; (c) GSEA-enriched pathways. Yellow lines represent B cell receptor signaling pathway, red lines represent MAPK signaling pathway, and blue lines represent Th1-Th2 cell differentiation.
(a)(b)(c)


## 3.3. Machine Learning Determines Diagnostic Genetic Signals for HF

Determine the diagnostic gene signal of heart failure through three algorithms in machine learning. The LASSO adopted 5X crossvalidation and determined lambda.min as 0.0128181 and finally selected 14 diagnostic gene signals from 47 DE genes (Figure3(a)). The feature selection algorithm SVM-RFE selected the 8 best diagnostic gene signals after 5X crossvalidation (Figure 3(b)). The RF algorithm sets the best mtry node value as 9, and the top nine genes ranked by MeanDecreaseGini are considered the best diagnostic gene signals (Figures 3(c)–3(e)). Integrating three machine learning algorithms determined that Fras1-related extracellular matrix 1 (FREM1) and meiosis-specific nuclear structural 1 (MNS1) are diagnostic gene signals for heart failure (Figure 3(f)).Figure 3
Machine learning algorithms determine genetic signals. (a) LASSO algorithm. LASSO selects 14 gene signals; (b) SVM-RFE algorithm. SVM-RFE selects 8 gene signals; (c) RF algorithm selects the best mtry; (d) top nine genes in RF algorithm; (e) ntree in RF algorithm; (f) Venn diagram of the integration of three algorithms.
(a)(b)(c)(d)(e)(f)


## 3.4. Verification of Diagnostic Gene Signals

We verified the two heart failure gene signalsFREM1 and MNS1 in the external RNA-seq datasets GSE116250 and GSE71613. The expression of the FREM1 gene in the two datasets was higher than the normal control in the external dataset, but the MNS1 gene was not statistically significant in GSE71613 (Figure 4(a)). The AUC found that FREM1 was 0.953 (95% CI: 0.904-1.000) and 1.000 (95% CI: 1.000-1.000) in the two datasets. The AUC value of MNS1 was less than 0.9 in both datasets (Figure 4(b)). The external RNA-seq dataset verification indicates that the diagnostic gene signal FREM1 has a high diagnostic level and is more suitable as a potential biomarker.Figure 4
External RNA-seq dataset verification. (a) Gene expression levels ofFREM1 and MNS1. The red dots represent the HF samples, and the blue dots represent the normal samples; (b) ROC curve of FREM1 and MNS1. The AUC values and 95% CI of FREM1 and MNS1 are marked in the figure.
(a)(b)


## 3.5. Analysis of Tissue Immune Cell Infiltration Subtypes

Tissue immune cell infiltration analysis revealed 22 immune cell subtypes in the gene collection of 313 samples. We found differences in nine types of immune cells. T cells CD8 (P=0.0028) and mast cell resting (P<0.001) were significantly increased in the HF disease group, and the cell subtype with low expression in HF tissues was T cells CD4 memory resting (P=0.017), T cells regulatory (Tregs) (P=0.047), monocytes (P<0.001), and macrophages M2 (P<0.001). T cells CD4 naïve (P=0.027), macrophages M0 (P<0.001), and neutrophils (P<0.001) also have certain differences (Figure 5(a)). In terms of the correlation between immune cell subtypes, T cells regulatory (Tregs) and B cells naïve have the most significant positive correlation (r=0.67), and T cells CD4 memory resting and T cells CD8 have the most significant negative correlation (r=−0.75) (Figure 5(b)).Figure 5
Tissue immune cell infiltration. (a) Immune cell difference between HF and normal samples (“∗∗∗” means P<0.001, “∗∗” means P<0.01, “∗” means P<0.05). The red dots represent each HF sample, while the blue dots represent each normal sample; (b) immune cell subtype correlation. The specific correlation values of 22 immune cells are displayed, in which the darker color of each correlation square represents higher correlation, and the lighter color represents low correlation. Red represents the degree of positive correlation, while blue represents the degree of negative correlation.
(a)(b)


## 3.6. The Relationship between FREM1 and the Immune Cell Subtype

The correlation analysis between the heart failure characteristic genes and immune cell subtypes showed thatFREM1 has the most significant positive correlation with mast cell resting (r=0.353, P<0.001), and the most significant negative correlation with neutrophils (r=−0.270, P<0.001). In addition, FREM1 is also associated with macrophage subtypes and T cell subpopulations (Figure 6).Figure 6
Correlation betweenFREM1 and immune cell subtypes. The size of each dot in the figure represents the degree of correlation. Larger dots represent a high degree of correlation. The specific P value is shown in yellow and red; the smaller the P value, the more obvious the yellow point. All immune cell names with P<0.05 are marked in red.


## 4. Discussion

With the rapid population growth and huge population base, the total number of heart failure patients in an aging society is increasing. This imposes a huge clinical, social, and economic burden. According to statistics from developed countries around the world, the prevalence of heart failure among people aged 70 years and over is increasing and is generally estimated to be 1-2% of the total population [23, 24]. The main purpose of heart failure treatment is to improve ventricular function, symptoms, and signs, and the long-term goal is to reduce morbidity and mortality [25]. Several criteria for the clinical diagnosis of heart failure have been proposed in the past, and most studies have focused on BNP levels and echocardiographic exploration. However, current research tends to combine a variety of diagnostic methods to achieve better diagnostic results. The era of big data provides new technologies and methods for the pathogenesis and biomarker research of heart failure. Immunity and inflammation have been considered common pathobiological features affecting heart failure. Therefore, we try to determine the characteristic genes of heart failure and provide new ideas for immunotherapy in heart failure.We analyzed the microarray dataset of 313 left ventricular tissue samples obtained from GEO and identified the 47 most distinct DE genes as candidate biomarkers for HF. The GO enrichment analysis shows that most of them are related to hypertrophy, matrix structural constituent, protein binding, etc. The DO enrichment analysis found that DE genes are mainly concentrated in some cardiovascular systems and inflammatory diseases. GSEA enrichment revealed pathways related to inflammatory immune pathways in HF samples, such as Th1-Th2 cell differentiation, MAPK signaling pathway, and B cell receptor signaling pathway. These results were confirmed in previous studies, and we considered chronic heart failure to be a systemic T cell colony activation process. In HF progression, changes in the spatiotemporal distribution of T cell subsets (Th1-Th2) have a considerable impact on ventricular remodeling. Th2 cells may represent a therapeutic target in chronic HF, helping to reduce tissue inflammation. In addition, the MAPK signaling pathway is linked to heart failure through angiotensin-II (Ang-II). We believe that inhibiting the MAPK signaling pathway can block the secretion of Ang-II, thereby reducing the severity of heart failure. García-Rivas et al. [26] found that B cells play an essential role in the progression of HF through mechanisms that are dependent and independent of antibody production. This also provides new insights into the role of B cell response pathways in heart failure.To further confirm the HF diagnostic characteristic signals, we used three machine learning methods for DE genes to assist in determining the potential characteristic signals. After a comprehensive analysis of machine learning algorithms, it was determined thatFREM1 and MNS1 are potential heart failure diagnostic gene signals. Subsequently, we verified FREM1 and MNS1 on two external RNA-seq datasets and found that FREM1 has a better verification effect. The AUC found that FREM1 was 0.953 (95% CI: 0.904-1.000) and 1.000 (95% CI: 1.000-1.000) in the two datasets. Fras1-related extracellular matrix 1 (FREM1) is a TILRR transcript variant. Studies have found that TILRR can stimulate the innate immune response, and its expression in monocytes and hardened plaques increases significantly after myocardial infarction. It can cause abnormal activation of inflammatory genes, leading to faster progression of cardiovascular disease [27, 28]. In addition, studies have also found that TILRR can regulate Ras-dependent nuclear factor amplification and immune inflammation [29]. This is associated with inflammatory activation in the pathogenesis of HF, suggesting that it may be closely related to HF therapeutic targets. Inflammation activation is related to the body’s regulatory effect and is a critical part of the body’s innate immune response. For a long time, the degree of inflammation has been closely related to the prognosis of heart failure. The current research progress on targeting proinflammatory cells is a valuable therapy for the treatment of heart failure, which is worthy of further study.We further explored the distribution of immune cells in HF tissues. T cells CD8 (P=0.0028), mast cell resting (P<0.001) were significantly increased in the HF disease group, and the cell subtype with low expression in HF tissues was T cells CD4 memory resting (P=0.017), T cells regulatory (Tregs) (P=0.047), monocytes (P<0.001), and macrophages M2 (P<0.001). T cells CD4 naïve (P=0.027), macrophages M0 (P<0.001), and neutrophils (P<0.001) also have certain differences. Corresponding results have also appeared in previous studies. Li et al. [30] found that the main inflammatory cell type CD4+ T cells existed in patients with heart failure, and the expression of mast cells, macrophages, and neutrophils was significantly different from that of normal people. Tissue damage and fibrosis after heart failure are accompanied by complex immune cell responses. Immune cell function is the central link in the pathological process of myocardial injury in heart failure. Activation of CD4+ T cells inhibits ventricular remodeling, and inhibition of mast cell degranulation reduces cardiac dysfunction. Liu et al.’s research found that neutrophils exert harmful functions in experimental models of heart failure caused by pressure overload [31]. This finding is consistent with the basic mechanism of action of neutrophils, which can participate in the occurrence and development of various cardiovascular diseases by releasing degranulation and recruiting microvesicles. Finally, we explored the correlation between FREM1 and immune cells. FREM1 has the most significant positive correlation with mast cell resting (r=0.353, P<0.001) and the most significant negative correlation with neutrophils (r=−0.270, P<0.001). In addition, FREM1 is also associated with macrophage subtypes and T cell subpopulations. We believe that FREM1 may be involved in the pathophysiological process of HF with mast cells, neutrophils, macrophage subpopulations, and T cell subpopulations. These results suggest that FREM1 seems to play a key role in heart failure by regulating immune infiltration.There are still some limitations to this study. The main one is that due to the impact of COVID-19, we will subsequently collect human tissue samples to complete wet experiments for verification.


## 5. Conclusions

In summary, we believe thatFREM1 may be a potential diagnostic gene signal for HF. FREM1 may become an important target for molecular targeted therapy in patients with heart failure.



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