Identifying the Oxidative Stress-Related Diagnostic Biomarkers in Dilated Cardiomyopathy by Bioinformatics Analysis

Identifying the Oxidative Stress-Related Diagnostic Biomarkers in Dilated Cardiomyopathy by Bioinformatics Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 7 August 2025 V1 Latest version Share on Identifying the Oxidative Stress-Related Diagnostic Biomarkers in Dilated Cardiomyopathy by Bioinformatics Analysis Authors : Ruifeng Cao , Junchen Ji , and Yaling Wang 0009-0002-1309-6382 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175458263.38117056/v1 183 views 101 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Dilated cardiomyopathy (DCM) is a primary cardiomyopathy of unknown etiology that is common in children and older adults. Nevertheless, the absence of noticeable symptoms and the lack of suitable biomarkers pose obstacles to the timely detection and management of DCM. Results: By comparing samples from dilated cardiomyopathy and controls, 629 differentially expressed genes (DEGs) were identified. Combined with WGCAN results, a total of 13 hub genes were identified by intersecting DEGs and oxidative stress (OS)-related modular genes.These hub genes included MVP, WISP1, LCP1, FTL, FCN1, PAPSS1, KRT14, SULT1C2, RARRES1, DIRAS3, AMPD3, F2RL1 and COL6A3. The analysis of the ROC curve showed a good predictive ability for DCM of these hub genes. Furthermore, enrichment analysis revealed that these hub genes have primarily participated in the process of transmembrane transport and nucleotide metabolism. The analysis of immune cell infiltration found significantly increased infiltration of activated B cells, natural killer cells, CD56dim natural killer cells, macrophages, and monocytes in the DCM group compared to the control group. Additionally, we validated the hub genes in an induced DCM mouse model and demonstrated the upregulation of MVP, WISP1, FTL, FCN1, and KRT14 in the DCM mice. Conclusion: The hub genes MVP, WISP1, FTL, FCN1, AMPD3, KRT14, and RARRES1 have the potential to serve as diagnostic biomarkers for DCM, offering novel insights into the clinical diagnosis. Identifying the Oxidative Stress-Related Diagnostic Biomarkers in Dilated Cardiomyopathy by Bioinformatics Analysis Ruifeng Cao 1 , Junchen Ji 2 , Yaling Wang* * Correspondence: [email protected] * ,1,2 Department of Cardiology, the Second Hospital of Hebei Medical University Abstract Background: Dilated cardiomyopathy (DCM) is a primary cardiomyopathy of unknown etiology that is common in children and older adults. Nevertheless, the absence of noticeable symptoms and the lack of suitable biomarkers pose obstacles to the timely detection and management of DCM. Results: By comparing samples from dilated cardiomyopathy and controls, 629 differentially expressed genes (DEGs) were identified. Combined with WGCAN results, a total of 13 hub genes were identified by intersecting DEGs and oxidative stress (OS)-related modular genes.These hub genes included MVP, WISP1, LCP1, FTL, FCN1, PAPSS1, KRT14, SULT1C2, RARRES1, DIRAS3, AMPD3, F2RL1 and COL6A3. The analysis of the ROC curve showed a good predictive ability for DCM of these hub genes. Furthermore, enrichment analysis revealed that these hub genes have primarily participated in the process of transmembrane transport and nucleotide metabolism. The analysis of immune cell infiltration found significantly increased infiltration of activated B cells, natural killer cells, CD56dim natural killer cells, macrophages, and monocytes in the DCM group compared to the control group. Additionally, we validated the hub genes in an induced DCM mouse model and demonstrated the upregulation of MVP, WISP1, FTL, FCN1, and KRT14 in the DCM mice. Conclusion: The hub genes MVP, WISP1, FTL, FCN1, AMPD3, KRT14, and RARRES1 have the potential to serve as diagnostic biomarkers for DCM, offering novel insights into the clinical diagnosis. Keywords: Dilated cardiomyopathy; Oxidative stress; Immune cell infiltration Introduction Dilated cardiomyopathy (DCM) is a primary myocardial disease of undetermined cause, which is characterized by the enlargement of one or both ventricles, with or without congestive heart failure[1]. DCM has many causes and all of them affect the ventricular function to a varying degree. Clinical manifestations include progressive cardiac enlargement, decreased ventricular systolic function, heart failure, ventricular or supraventricular arrhythmias, conduction system abnormalities, thromboembolism, and sudden death[2]. The majority of cases of DCM are usually first diagnosed between the ages of 20-60 years; however, DCM can be seen in children and the elderly. Many patients are clinically asymptomatic and may have a long latency period, preventing early detection and missing the best time for diagnosis and treatment[3]. Therefore, exploring biomarkers that would make an early diagnosis possible is crucial for improving the prognosis of DCM patients. Oxidative stress (OS), characterized by the overproduction of reactive oxygen species (ROS), is a critical factor in the development of DCM. It leads to cardiomyocyte dysfunction, lipid peroxidation, and DNA mutations, contributing to cardiac remodeling and functional deterioration. This damage activates a variety of signaling pathways, including inflammatory responses and cell death pathways, thereby promoting the development of myocardial dysfunction and heart failure[4,5]. In patients with DCM, the level of OS is inversely correlated with exercise capacity, underscoring its impact on patient outcomes[6]. Molecularly, the interaction between peroxisome proliferator-activated receptor α (PPAR-α) and its coactivator, PGC-1α, provides antioxidant effects within the myocardium, protecting it from oxidative damage and slowing the progression of heart failure[7]. Furthermore, the disruption of reactive oxygen radical homeostasis due to Lamin A/C deficiency plays a significant role in the development of LMNA-DCM, and targeting impaired sirtuin 1 (SIRT1) activity and OS could be a promising therapeutic strategy[8]. Research has shown that suppressing latent transforming growth factor beta binding protein 2 (LTBP2) with siRNA can counteract oxidative stress damage, fibrosis, and myocardial remodeling in DCM through the NF-κB signaling pathway[9]. Additionally, the use of amoxicillin has been found to exacerbate DCM by inducing apoptosis, involving the AMPK/mTOR pathway and the downregulation of autophagy, leading to the accumulation of ROS and mitochondrial damage in cardiomyocytes[10]. These findings highlight the pivotal role of OS in cardiac remodeling and functional deterioration, offering new insights and potential therapeutic targets for the management of DCM. The infiltration of immune cells is another key feature of DCM, closely associated with the myocardial inflammatory response[11]. Studies have shown that the infiltration of immune cells such as T cells and macrophages in DCM is related to myocardial injury and fibrosis. The activation and recruitment of these immune cells may be partially due to the increased expression of inflammatory factors caused by oxidative stress[12]. Oxidative stress and immune response interact in DCM, forming a vicious cycle. OS not only directly damages cardiomyocytes but may also promote the expression of inflammatory factors through the activation of transcription factors such as NF-kB, thereby attracting and activating immune cells[13]. In turn, the activation of these immune cells can produce more ROS, further exacerbating oxidative stress[14]. Treatment strategies targeting oxidative stress and immune response may offer new therapeutic avenues for DCM. For example, the use of antioxidants can alleviate OS[15], while immunomodulatory therapy may help reduce the infiltration of immune cells and inflammatory responses. Immunoadsorption therapy has shown effects in reducing oxidative stress and improving cardiac performance in some studies[16]. The study of OS in DCM is therefore essential, as therapeutic approaches targeting this pathway could significantly improve treatment outcomes and enhance the quality of life for patients with DCM. In current years, bioinformatic methods have been extensively adopted to analyze high-throughput and microarray data to identify DEGs and perform various analyses. It has been validated as an excellent way to identify underlying mechanisms in a variety of human diseases. Based on a comprehensive genomic analysis of public datasets, we aimed to investigate the putative important genes, critical modules, pathways, and infiltrating immune cells implicated in the pathogenesis of DCM. Materials and Methods 2.1 Data Sources and Preprocessing All data used in this study are freely available to the public and were obtained from the GEO (Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/) database. The corresponding expression profile data and clinical data were downloaded using the R package “GEOquery” (version 2.62.2). We acquired two datasets related to mRNA expression matrices from patients with DCM and healthy controls, GSE120895 and GSE9800. GSE120895 is based on the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array platform and includes myocardial biopsy tissues from 47 DCM patients and 8 healthy controls. GSE9800 is based on the GPL887 Agilent-012097 Human 1A Microarray (V2) G4110B (Feature Number version) platform and includes a total of 30 myocardial tissues from patients in different disease states. In this study, we selected the left ventricular myocardial samples from 12 DCM patients and 6 healthy controls. We merged GSE120895 and GSE9800 to create a new dataset that includes 59 DCM patients and 14 healthy control samples. The batch effects between the datasets were corrected for non-biological technical biases using the ComBat method from the R package “sva” (version 3.42.0)[17] to enable the merging of the data. Principal Component Analysis (PCA) was employed to check the extent of correction. 2.2 Differentially Expressed Genes Associated with DCM Differentially expressed genes (DEGs) between control (n = 14) and DCM (n = 59) samples were identified using “limma (version 3.50.0)”[18] package in R with the thresholds of |log2Fold Change| > 0.25 and an adjusted p < 0.05, which were included in the following research. Subsequently, the heatmap was generated using the R package “pheatmap” with Euclidean distance and complete linkage clustering method. 2.3 Gene Set Variation Analysis (GSVA) GSVA (Gene set variation analysis) is an unsupervised and non-parametric gene set enrichment method that permits the use of gene expression profiles to assess associations between biological pathways and gene features. To investigate the difference of the biological function between control and DCM groups, GSVA was performed with “c2.cp.kegg.v7.5.1.symbols” using R package “GSVA (version 1.42.0)”. R package “pheatmap (version 1.0.12)” was applied to visualize the results. And we downloaded 50 hallmark gene sets from the MSigDB database (http://software.broadinstitute.org/gsea/msigdb) to serve as reference gene sets. We applied the ssGSEA function in the GSVA package to calculate GSVA scores for each gene set in different samples. Then, we used the Limma package to compare GSVA score differences between control and DCM groups. 2.4 Weighted Gene Co‑expression Network Analysis (WGCNA) and Identification of Significant Modules Co-expression networks were constructed using the WGCNA algorithm implemented in R WGCNA package (version 1.70-3)[19]. To assess the similarity of gene expression profiles, the Pearson correlation coefficient was calculated. Using a power function, the correlation coefficients between genes were weighted to create a scale-free network. Using the R package ‘PickSoftThreshold’, we established a weighted adjacency matrix by raising the co-expression similarity to a power β = 6. A gene module is a cluster of densely interconnected genes in terms of co-expression. WGCNA uses hierarchical clustering to identify gene modules and color to indicate modules. Dynamic tree cut method was used to identify different modules, during module selection, the adjacency matrix (a measure of topology similarity) was converted to a topology overlay matrix (TOM) and modules were detected by cluster analysis. To detect associations of modules to OS, the associations of the module eigengene (ME, the first principal component of the module and represents the overall expression level of the module) to the OS were calculated by Pearson’s correlation analysis. Modules showing significant association to OS were obtained. The co-expression module structure was visualized by heatmap plots of topological overlap in the gene network. Relationships among modules were summarized by a hierarchical clustering dendrogram of the eigengenes and by a heatmap plot of the corresponding eigengene network. The OS-related differentially expressed genes (OS-related DEGs) were obtained from the intersection of DEGs and genes from the OS-related module. 2.5 GO and KEGG Pathway Enrichment Analysis Gene Ontology (GO)[20] enrichment analysis includes Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG)[21] is a bioinformatics resource for mining significantly altered metabolic pathways enriched in the gene list. The R package “clusterProfiler (version 4.2.2)”[22] was applied to perform GO and KEGG enrichment analysis (p value < 0.05) on the OS-related DEGs. 2.6 GeneMANIA GeneMANIA (http://genemania.org) is an online tool for predicting gene functions and constructing gene interaction networks, including protein-protein, protein-DNA interactions, pathways, physiological and biochemical reactions, co-expression, and co-localization[23]. Based on the input genes, it can identify genes that are strongly related to them. We utilized GeneMANIA, based on previously analyzed hub genes, to construct corresponding protein-protein interaction (PPI) networks to understand the interactions among hub genes. In the analysis, all parameters were set to the default values provided by the website. 2.7 The Receiver Operating Characteristic (ROC) Curve The receiver operating characteristic (ROC) curve, which is defined as a plot of test sensitivity as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of evaluating the performance of diagnostic tests. The most common metric is the area-under-the-curve (AUC), obtained from the receiver operating characteristic plot of sensitivity against 1-specificity. We used the R package “pROC”[24] to create Receiver Operating Characteristic (ROC) curves to determine the area under the curve (AUC) for screening signature genes and evaluating their diagnostic values. As such, it is measured on a scale from 0.5 (a “coin flip”) to 1 (perfect discrimination). In general, an AUC of 0.5 indicates no discrimination, 0.6–0.8 is acceptable, 0.8–0.9 is excellent, and over 0.9 is outstanding. 2.8 Immune Infiltration Analysis Single-sample Gene Set Enrichment Analysis (ssGSEA)[25], an extension of Gene Set Enrichment Analysis (GSEA), calculates separate enrichment scores for each pairing of a sample and gene set. Each ssGSEA enrichment score is representative of the extent to which genes in a particular gene set are coherently up-regulated or down-regulated in the sample. Single-sample GSEA (ssGSEA) is a variation of the GSEA algorithm that instead of calculating enrichment scores for groups of samples (i.e Control vs Disease) and sets of genes (i.e pathways), it provides a score for each sample and gene set pair. Based on the 28 types of immune cells downloaded from the TISIDB (Tumor and Immune System Interactions Database) (http://cis.hku.hk/TISIDB/index.php)[26], including Activated CD8 T cell, Central memory CD8 T cell, Effector memory CD8 T cell, Activated CD4 T cell, Central memory CD4 T cell, Effector memory CD4 T cell, T follicular helper cell, Gamma delta T cell, Type 1 T helper cell, Type 17 T helper cell, Type 2 T helper cell, Regulatory T cell, Activated B cell, Immature B cell, Memory B cell, Natural killer cell, CD56bright natural killer cell, CD56dim natural killer cell, Myeloid derived suppressor cell, Natural killer T cell, Activated dendritic cell, Plasmacytoid dendritic cell, Immature dendritic cell, Macrophage, Eosinophil, Mast cell, Monocyte and Neutrophil, every immunocyte’s relative enrichment score was quantified from each sample’s gene expression profile. Variations in immune cell infiltration levels among DCM and control groups were visualized using the R package ggplot2 (version 3.3.6)[27]. 2.9 Animal model and echocardiography C57BL/6 mice (male, 8 weeks old, body weight 20-22 g, License No.: SYXK 2020-002) were purchased from SPF Biotechnology Co., Ltd. (Beijing, China). The mice were housed under conditions of 22 ± 2°C and a relative humidity of 55% ± 5%, with a 12-hour light/12-hour dark cycle. The mice were housed at a density of five per cage and provided with unlimited access to food and water. All animal handling procedures were conducted in accordance with the guidelines issued by the Ministry of Science and Technology of China for the Care and Use of Laboratory Animals. The animal experiments were approved by the Animal Ethics Committee of Hebei Medical University(2023-AE250). Mice were randomly divided into two groups, with eight mice in each group. The mice in the model group received intraperitoneal injections of doxorubicin (DOX) at a dosage of 2.5 mg/kg every 48 hours for a duration of two weeks, amounting to a cumulative dose of 15 mg/kg. The control group received injections of an equivalent volume of saline. Body weight changes of the mice before and after DOX injection were recorded. Throughout the drug treatment period, the mice had unrestricted access to food and water. Each mouse was examined by echocardiography using the Vevo 2100 high-resolution imaging system after anesthesia with 2% isoflurane. Left ventricular ejection fraction (EF), fractional shortening (FS), cardiac output (CO), left ventricular internal diameter at end-diastole (LVID), and left ventricular internal diameter at end-systole (LVID) were measured as indicators of left ventricular function. 2.10 Histopathology After sample collection, the mouse hearts were perfused with 1× phosphate-buffered saline (PBS) and fixed with 4% paraformaldehyde for 24 hours. They were then processed for paraffin embedding, sectioned into 5 μm slices, and deparaffinized and hydrated using a gradient of ethanol. Following this, the sections were subjected to H&E staining, Masson staining, and WGA staining. The stained samples were observed and imaged using an optical microscope. 2.11 Quantitative real-time PCR (qRT-PCR) Total RNAs of the left ventricular tissues were extracted using TRIzol Reagent (Life Technologies). The cDNAs were synthesized using the M-MLV First Strand Kit (Life Technologies), and quantitative real-time PCR was performed using SYBR Green qPCR SuperMixUDG (#Q221-01, Vazyme Biotech Co., Ltd, China, Nanjing). The primer sequences of the HUB genes used for qRT-PCR are listed in supplementary Table 1. For quantification, all RNA expression was normalized to the amount of GAPDH. 2.12 Statistical Analysis Statistical analyses were performed conducted in R software v4.1.2. To infer the correlation between two parameters, we used the Spearman correlation test. Wilcoxon tests were adopted to compare the differences between two groups, while Kruskal–Wallis tests were conducted to compare the differences between three or more groups. In molecular biology experiments, the data is expressed as mean ± standard error (SEM). Statistical analysis was performed using GraphPad Prism software (version 8.0), and the exact number of biological replicates is indicated in the legend. The two-tailed independent sample t- test was used to compare the mean between the two groups. Two-sided p -values < 0.05 were considered statistically significant. Results 3.1 Weighted Gene Co-expression Network Construction and Module Identification WGCNA was applied to investigate gene sets that were related to OS. The scale independence and mean connectivity analysis showed that when the weighted value equals 6 (Figure 1A), the average degree of connectivity was close to 0, and scale independence was greater than 0.85. Fifteen co-expressed modules were identified and uncorrelated genes were assigned to a gray module which was ignored in the following study (Figure 1B). To study the relationships among modules and determine their correlation, we conducted correlation analysis on the module eigengenes (MEs). A dendrogram and a heatmap plot were used to depict the eigengene network (Figure 1C). The heatmap plot of topological overlap in the gene network is depicted (Figure 1D). To understand the physiologic significance of the modules, we correlated the 15 MEs with OS and searched for the most significant associations. According to the heatmap of module-trait correlation (Figure 1E), genes clustered in red module (n = 407) had the strongest positive correlation with OS (r = 0.729, p < 0.05). Thus, we would mainly consider red module in the following because this module may indicate OS more accurately. Figure 1F shows the scatterplots of gene significance (GS) for the trait of OS versus module membership (MM) in red module. MM and GS for OS (Figure 1F) exhibited very significant positive correlations (cor = 0.67， p < 0.05), implying that the most important elements of red module also tended to be highly correlated with the OS trait. Figure 1 Construction of WGCNA co-expression network. (A) Soft threshold β = 6 and scale-free topological fit index (R2). (B) Network analysis of gene expression in DCM identifies distinct modules of co-expression data. (C) Relationships among modules. Top: Hierarchical clustering of module eigengenes that summarize the modules found in the clustering analysis. Branches of the dendrogram (the meta-modules) group together eigengenes that are positively correlated. Bottom: Heatmap plot of the adjacencies in the eigengene network. Each row and column in the heatmap corresponds to one module eigengene (labeled by color). In the heatmap, red represents high adjacency, while blue color represents low adjacency. Squares of red color along the diagonal are the meta-modules. (D) Heatmap plot of topological overlap in the gene network. In the heatmap, each row and column corresponds to a gene, light color denotes low topological overlap, and progressively darker red denotes higher topological overlap. Darker squares along the diagonal correspond to modules. The gene dendrogram and module assignment are shown along the left and top. (E) Relationships of consensus module eigengenes and OS. Each row in the table corresponds to a consensus module and each column to a sample or trait. Correlations between module eigengenes and traits are reported in the table, with corresponding P-values in parentheses. The table is color coded by correlation according to the color legend. (F) Correlation between module membership (MM) and gene significance (GS) for OS of all genes in the red module. ‘Cor’ represents the absolute correlation coefficient between GS and MM. 3.2 DEGs Identification From the comparison of the DCM samples and controls, a total of 629 differentially expressed genes (DEGs) were identified to be statistically significant between the two groups (adjusted p 0.25). In DCM samples, 319 genes were upregulated and 310 genes were downregulated. All DEGs were visualized using a volcano plot (Figure 2A). Furthermore, the top 5 upregulated (CACNB1, TRIM35, ALPP, PIP5K1C, SLC22A12) concurrent with the top 5 downregulated DEGs (SCGB1D2, LSAMP, ASB1, RALGPS2, STARD13) were shown using a heatmap (Figure 2B). Revealed by Wilcoxon tests, these top 10 genes showed significant differences in expression levels between two groups ( p < 0.05, Figure 2C). A total of 13 OS-related DEGs was obtained from the intersection of DEGs and OS-related module genes, which were considered as hub genes. \received DD MMMM YYYY \acceptedDD MMMM YYYY Figure 2 DEGs identification. (A) A volcano plot depicting the distribution of DEGs between DCM and control samples. The red dots, purple dots, and gray dots represent gene expression levels corresponding to upregulated, downregulated, and insignificant expression, respectively. (B) A heatmap depicting the top 5 upregulated and top 5 downregulated DEGs. (C) The variations of top 10-gene expression levels between DCM and control groups were revealed by Wilcoxon tests. Asterisks represented p-value (**** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05). 3.3 GSVA To further explore the functional annotation between DCM and control samples, we performed GSVA analyses to evaluate the relative expression difference of the pathways in the two groups. GSVA analysis enriched a lot of differentially expressed pathways, which was visualized by the heatmap. In comparison to the control groups, the expression of pathways associated with KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_BIOSYNTHESIS and KEGG_PROTEIN_EXPORT were significantly lower in the DCM, whereas the expression of KEGG_MATURITY_ONSET_DIABETES_OF_THE_YOUNG and KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION associated pathways were significantly higher (Figure 3). Figure 3 Significantly enriched pathways, Heatmap illustrating the result of GSVA analysis. 3.4 Enrichment Analyses of GO Oxidative stress significantly contributes to the progression of dilated cardiomyopathy (DCM) by disrupting intracellular pathways, including secretory granules and vesicle lumen[28]. This leads to excessive or damaged release of cytokines and growth factors, promoting inflammation and myocardial remodeling[29]. Additionally, oxidative stress impairs calcium regulation and vesicle transport, causing cardiomyocyte dysfunction and cardiac dilation[30]. It also interferes with protein folding and autophagy, exacerbating waste product accumulation and cellular damage, ultimately leading to cardiomyocyte death and cardiac deterioration in DCM[31]. Overall, oxidative stress promotes cardiomyocyte death and cardiac decline by affecting these vesicular pathways, further contributing to the progression of DCM. We take the intersection of the gene modules related to oxidative stress obtained from WGCNA with DEGs. To investigate the biological functions of the OS-related DEGs, we performed GO terms enrichment analyses. The GO results showed that these genes were strongly enriched in secretory granule lumen (GO:0034774), cytoplasmic vesicle lumen (GO:0060205), vesicle lumen (GO:0031983) (CC) (Figure 4A-B). \received DD MMMM YYYY \acceptedDD MMMM YYYY Figure 4 Functional enrichment based on OS -related DEGs. (A) GO enrichment result. (B) CC enrichment result. 3.5 Trait Gene Interaction Analysis We used the GeneMANIA database to create a PPI network for the signature genes, and 12 genes were found in the PPI network (Figure 5A). To further investigate the function of the signature genes, GO and KEGG analysis were performed on 33 genes which included 12 hub genes and 21 related genes. The GO results show that these genes are strongly enriched in nucleoside metabolic process (GO:0009116), positive regulation of interleukin-8 production (GO:0032757), chloride transmembrane transport (GO:1902476), cellular iron ion homeostasis (GO:0006879) (BP), collagen-containing extracellular matrix (GO:0062023), ficolin-1-rich granule lumen (GO:1904813), ficolin-1-rich granule (GO:0101002) (CC), dicarboxylic acid transmembrane transporter activity (GO:0005310), adenylyltransferase activity (GO:0070566), solute: anion antiporter activity (GO:0140323), sulfur compound transmembrane transporter activity (GO:1901682) (MF) (Figure 5B). The main enriched pathways, according to KEGG analysis, were Purine metabolism (hsa00230), Sulfur metabolism (hsa00920), Nucleotide metabolism (hsa01232), Chemical carcinogenesis - DNA adducts (hsa05204), Selenocompound metabolism (hsa00450) (Figure 5C). Figure 5 Interaction analysis of hub genes (A) Characterized gene co-expression network. (B) GO analysis of co-expressed genes. (C) Co–expressed gene KEGG analysis. 3.6 Validation of the Gene Set The enrichment of the gene set secretory granule lumen was validated by detecting the expression level of key genes. The results in Figure 6 indicate that the expression level of most key genes is elevated in the DCM group (supplementary Figure 1). 3.7 Validation of the Hub Genes To further validate the diagnostic value of hub genes, we used receiver operating characteristic (ROC) analysis. MVP (AUC = 0.812), WISP1 (AUC = 0.791), LCP1 (AUC = 0.783), FTL (AUC = 0.771), FCN1 (AUC = 0.764), PAPSS1 (AUC = 0.764), KRT14 (AUC = 0.76), SULT1C2 (AUC = 0.757), RARRES1 (AUC = 0.719), DIRAS3 (AUC = 0.715), AMPD3 (AUC = 0.707), F2RL1 (AUC = 0.65), COL6A3 (AUC = 0.643) were found to have similar area under the ROC curve (AUC) values (supplementary Figure 2A-L), showing that the identified hub genes demonstrated an acceptable discrimination capability as potential biomarkers for DCM. 3.8 Immune Cells Infiltration The immune cell infiltration might play an essential part in the pathogenesis of DCM. Thus, we investigated the associations between DCM/control samples and infiltrated immune cells. Among 28 types of immune cells, the infiltrated degrees of 5 types were significantly different between the two groups ( p < 0.05). For 5 types of immune cells (Activated B cell, Natural killer cell, CD56dim natural killer cell, Macrophage, Monocyte), a significantly higher infiltrated degree was observed in the DCM group than the control group (Figure 6A). As depicted in Figure 6B, the overall infiltration levels of immune cells varied greatly between DCM and control groups. Furthermore, the significant correlations between each hub gene and the corresponding immune cells were also detected. It should be noted that LCP1 was significantly associated with Activated B cell (R = 0.753, p < 0.001) (Figure 6C), MVP was significantly associated with CD56dim natural killer cell (R = 0.683, p < 0.001, Figure 6D). Subsequently, correlations of each infiltrated immune cell were estimated. The majority of the immune cells were positively correlated with each other (Figure 6E). Figure 6 Distinction of immune infiltrations between DCM and control samples. (A) The estimated proportions of infiltrating immune cells in DCM and control groups. (B) The heatmap showed changes in immune infiltration levels between DCM and control groups. (C) Correlations between the hub gene LCP1 and Activated B cell. (D) Correlations between the hub gene MVP and CD56dim natural killer cell. (E) Correlation between immune cells.Asterisks represented p value：**** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05. 3.9 Validation of Hub Genes in a Doxorubicin-Induced Animal Model Based on the results of ROC curve analysis we selected nine hub genes for verification in a mouse model. First, we established a model of DCM by intraperitoneal injection of doxorubicin at a dose of 2.5 mg/kg, three times a week for two weeks, with a cumulative dose of 15 mg/kg. The control group received intraperitoneal injections of an equivalent volume of normal saline. Following the DOX challenge, the survival rate of the mice significantly decreased (Figure 7B), accompanied by a reduction in body weight and heart weight, while the cardiac index increased (Figure 7A, 7C, 7D). Echocardiography revealed that compared to the control group, the DOX-treated mice showed reduced EF and FS, increased ventricular internal diameter, and decreased cardiac output (Figure 7I-M). Additionally, mice in the DOX group exhibited pathological changes such as enlarged cardiomyocytes, disorganized arrangement, inflammatory cell infiltration, and collagen fiber deposition (Figure 7F-H). To validate the differential expression of hub genes in dilated cardiomyopathy, we detected the expression of MVP, WISP1, LCP1, FTL, FCN1, PAPSS1, KRT14, RARRES1, and AMPD3 in the left ventricular tissues of the two groups of mice using qRT-PCR. The results showed that compared to the control group, the expression of MVP, WISP1, FTL, FCN1, and KRT14 in the left ventricular tissues of the DCM group was significantly increased, while the expression of RARRES1 and AMPD3 was significantly decreased, with all differences being statistically significant (Figure 7N-T). These findings may serve as new biomarkers for the diagnosis of dilated cardiomyopathy. We found an increase in the expression of LCP1 and PAPSS1 in the DCM group, but this increase was not statistically significant, and their roles in dilated cardiomyopathy need further confirmation (Figure 7U and Figure 7V). \received DD MMMM YYYY \acceptedDD MMMM YYYY Figure 7 Hub gene validation in mice. (A) Body weight of mice before and after dosing (n=10). (B) Percentage of mice surviving. (C) Heart weight (n = 8). (D) Heart weight-to-body weight ratio(HW/BW) (n = 8). (E) Representative example of M-mode echocardiography of mouse hearts in sham group and DCM group (n = 8). (F-H) Representative images of H&E staining, Masson staining, and WGA staining of heart sections of mice in the sham group and DCM group (n = 3). (I-J) Quantification of left ventricular ejection fraction (EF%) and fraction shortening (FS%). (K-L) The values of left ventricular internal dimension at systole(LVID;s) and left ventricular internal dimension at diastole(LVID;d). (M) cardiac output (CO). (N-V) The expression of MVP mRNA, WISP1 mRNA, FCN1 mRNA, AMPD3 mRNA, RARRES1 mRNA, FTL mRNA, KRT14 mRNA, PAPSS1 mRNA and LCP1 mRNA in the left ventricle of mice. Asterisks represented p value：**** p < 0.0001, *** p < 0.001, ** p < 0.01, * p 0.05. Discussion DCM is a heterogeneous cardiomyopathy characterized by ventricular enlargement and reduced myocardial contractile function. Due to the lack of early diagnostic indicators, patients with DCM often lose the best opportunity for treatment, resulting in a poor prognosis. Oxidative stress is a critical factor in the development of DCM. It significantly influences the progression of heart damage and remodeling by triggering the expression of numerous genes[32]. Therefore, analyzing and identifying the oxidative stress-related biomarkers has significant implications for improving prognosis for DCM patients[33]. In this study, 13 oxidative stress-related DEGs were identified by the intersection of WGCNA module genes and DEGs, which laid a foundation for subsequent mechanism studies. Further validation in a mouse DCM model confirmed the potential value of MVP, WISP1, FCN1, AMPD3, RARRES1, FTL, KRT14, these hub genes as biomarkers. Based on the expression of PCR and pathway enrichment, MVP, WISP1, FTL, and FCN1 genes were selected as the focus of in-depth study. MVP(Major Vault Protein) is the main component of cellular ribonucleoprotein particles located in the cytoplasm [34]. MVP is known to regulate several cellular processes including nucleocytoplasmic transport, signal transduction, cellular differentiation, cell survival, and immune responses[35]. MVP in macrophages specifically can promote SR-A-mediated TNF-α synthesis and apoptosis [36], and it can also prevent metabolic diseases via NF-κB signaling [37]. In this study, MVP performance showed its value as a potential biomarker. The ROC curve analysis showed that MVP had an AUC value of 0.812, indicating that MVP had a good ability to distinguish between patients with DCM and healthy controls, suggesting that MVP may play an important role in the early diagnosis of DCM. Further analysis showed that MVP expression was closely related to the infiltration of specific immune cells, especially with CD56dim natural killer cells (correlation coefficient R=0.683, p the immune response and cell interactions in DCM.This is consistent with previous studies[38-40]. In the PCR results of animal models, the expression level of MVP in the left ventricular tissue of DCM mice was significantly higher than that in the control group. This result further confirmed the importance of MVP in the pathogenesis of DCM. Therefore, MVP not only shows good discriminative ability in distinguishing DCM patients from healthy populations, but also its correlation with immune cells provides a new perspective on understanding the pathogenesis of DCM. WISP1(Wnt Inducible Signaling Pathway Protein 1) matricellular protein, a target gene of the WNT signaling, has been shown to modulate immune cell behavior and ECM remodeling in various diseases[41]. The matricellular protein WISP1 is key components of the ECM milieu, playing crucial roles in regulating immune responses[42]. In this study, the expression of WISP1 highlighted its potential value as a biomarker. ROC curve analysis showed that the AUC value of WISP1 was 0.791, indicating that the gene had a good discrimination ability and could effectively distinguish patients with DCM from healthy controls. This result emphasized the possible key role of WISP1 in the diagnosis of DCM. Furthermore, through real-time quantitative PCR analysis of the mouse model, we observed that the expression level of WISP1 in the left ventricular tissue of DCM mice was significantly higher than that in the control group. This finding further supports the importance of WISP1 in the development of heart disease, suggesting its potential role in cardiac remodeling and apoptosis processes. Previous studies have shown WISP1 is a key molecule in various disease development, which is involved in oxidative stress, apoptosis and autophagy[43-45]. These findings make WISP1 a promising biomarker for DCM diagnosis and prognosis, and further studies are warranted to explore its specific mechanisms and clinical application potential. FTL(Ferritin Light Chain) as the light chain of iron storage protein has been widely regarded as one of the regulators of iron metabolism for a long time[46]. In this study, the performance of FTL showed its value as a potential biomarker. The ROC curve analysis revealed that the AUC value of FTL was 0.771, indicating its strong ability to differentiate between patients with DCM and healthy controls. This finding underscores the importance of FTL in DCM diagnosis and suggests its potential for significant clinical application. Further experiments showed that, through real-time quantitative PCR, the expression level of FTL in the left ventricular tissue of DCM mice was significantly higher than in the control group. The increase of FTL may reflect the response of cardiomyocytes to iron overload and its associated oxidative damage[47-49]. This finding suggests that the upregulation of FTL may be closely related to the development of DCM, especially in terms of iron metabolism imbalance and oxidative stress response. FCN1(Ficolin-1), a protein involved in the immune response, plays a crucial role by binding to pathogens and activating the complement system[50-52]. In this study, FCN1 was significantly expressed, indicating its potential as a biomarker. The ROC curve analysis indicates that the AUC value of FCN1 is 0.764, indicating its strong ability to differentiate between patients withDCM and healthy controls. This finding highlights the potential of FCN1 in the early diagnosis of DCM and suggests its critical role in disease progression. Additionally, real-time quantitative PCR analysis in animal models revealed that the expression level of FCN1 in the left ventricular tissue of DCM mice was significantly higher than in the control group. This finding suggests that the upregulation of FCN1 may be associated with the enhanced inflammatory response in DCM, and further speculate its immunomodulatory role in cardiopathy[53]. Future studies can further explore the functional mechanism of FCN1 and its application potential in DCM treatment, in order to provide new strategies to improve the prognosis of this disease. In this study, our analysis of the signaling pathways of key genes MVP and WISP1 showed that these two genes were closely associated with several significantly downregulated pathways, including HALLMARK_OXIDATIVE_PHOSPHORYLATION (p<0.0001), HALLMARK_ FATTY_ACID_METABOLISM (p<0.01), and HALLMARK_BILE_ACID_METABOLISM (p <0.05). The downregulation of these pathways suggests that the dysregulation of energy metabolism and lipid metabolism may be complementary in the pathogenesis of DCM, supporting the metabolic abnormality theory of DCM. Furthermore, in the GO pathways associated with oxidative stress, the gene expression levels of secretory granule lumen also showed upregulation of key genes. In the disease group, the expression of MVP, FTL, and FCN1 was significantly elevated, suggesting that these genes may play a crucial role in the body’s response to oxidative stress. This high expression likely reflects the adaptive response of myocardial cells during the progression of DCM, although persistent high levels of oxidative stress can still lead to cell damage. According to Zhou Jun’s research[54], the BMP signaling pathway and the interaction between cytokines and their receptors play a crucial role in the development of DCM. The results of DEGs also indicate that cell apoptosis, hypoxia, heme metabolism, and epithelial-mesenchymal transformation pathways are closely linked to DCM. Prior studies suggest that cell apoptosis and metabolic disorders may be significantly associated with the pathological progression of DCM[55,56] In general, these enrichment analysis results provide important clues to understand the potential biological mechanisms of DCM. These results emphasize the key role of oxidative phosphorylation, fatty acid metabolism, bile acid metabolism and other pathways in heart disease, and also suggest the importance of apoptosis and metabolic disorders in the occurrence of DCM. This provides a potential direction for future intervention studies targeting these pathways, which may help improve the prognosis and treatment outcomes of DCM. Based on the performance of hub genes MVP, WISP1, FTL and FCN1 identified in this study and the analysis of related signaling pathways, it can be speculated that the potential biological mechanism of DCM involves the interweaving of multiple factors such as oxidative stress, metabolic disorder and immune response. Oxidative stress plays a crucial role in the development of DCM. This study shows that MVP, FTL, and FCN1 are significantly upregulated in GO pathways associated with oxidative stress, suggesting they may be adaptive responses of myocardial cells to oxidative stress. The increase in MVP is likely related to myocardial protection mechanisms, enhancing the heart’s resistance to oxidative damage. The upregulation of FTL may reflect the cell’s response to iron overload and associated oxidative damage, as it regulates iron metabolism, thereby reducing myocardial injury to some extent. Metabolic disorders are another key link in DCM. Our signal pathway analysis showed that MVP and WISP1 were significantly associated with HALLMARK_OXIDATIVE_ PHOSPHORYLATION, HALLMARK_FATTY_ACID_METABOLISM, and HALLMARK_ BILE_ACID_METABOLISM pathways, respectively. The downregulation of these pathways suggests that the disorder of energy metabolism and fatty acid metabolism may play an important role in the pathogenesis of DCM. As the cardiac energy supply decreases, the further deterioration of myocardial function accelerates the progression of DCM. Immune response is also an important aspect of DCM. The upregulation of FCN1 suggests its role in the inflammatory response of the heart, which may affect the pathological process of the heart by regulating the infiltration and activation of immune cells. The enhanced infiltration of related immune cells (such as CD56dim natural killer cells) may further aggravate myocardial injury. Based on the above analysis, the potential biological mechanisms of DCM encompass multiple roles, including oxidative stress, metabolic disorders, and immune responses. MVP, WISP1, FTL, and FCN1 not only play crucial roles in the pathogenesis of DCM but also have the potential to serve as biomarkers for future targeted therapies and early diagnosis. Further research should focus on the specific functions of these genes and their signaling pathways to develop more effective intervention strategies, thereby improving the prognosis and quality of life for DCM patients. We not only integrated and validated multiple datasets at the dataset level but also conducted expression validation through molecular biology experiments, making the experimental results more reliable. We must acknowledge that this study has its limitations. The validation of hub genes and their functions has only been tested in mouse models and not in human clinical trials. In the future, we will further investigate the functions of these hub genes. Figure 8 Flowchart of this study. WGCNA, weighted gene co-expression network analysis; GO, gene ontology; GSVA, gene set variation analysis; ROC, receiver operating characteristic. Conclusion In summary, through extensive bioinformatics strategies, we identified 13 key genes associated with oxidative stress. Furthermore, we established a doxorubicin-induced DCM animal model and explored the roles of these genes in the disease. We found that MVP, WISP1, FCN1, FTL, and KRT14 were significantly upregulated in DCM, while AMPD3 and RARRES1 were notably downregulated. These genes, as indicators of oxidative stress, are potential diagnostic biomarkers for DCM. However, it must be emphasized that these findings require confirmation through more extensive research, including longitudinal studies and clinical trials, to validate their diagnostic and prognostic significance, and to assess the impact of targeted interventions in clinical practice. List of abbreviations DCM: Dilated cardiomyopathy OS: Oxidative stress ROS : reactive oxygen species GSVA: Gene set variation analysis DEGs: differentially expressed genes WGCNA: Weighted Gene Co‑expression Network Analysis GO: Gene Ontology BP: Biological Process MF: Molecular Function CC: Cellular Component PPI: The protein–protein interaction ROC: receiver operating characteristic ssGSEA: Single-sample Gene Set Enrichment Analysis RBP: RNA binding protein DOX: Doxorubicin Declarations Ethics approval and consent to participate: Approval letter No.2023-AE250. Consent for publication: Not applicable. Availability of data and materials: Two DCM datasets (GSE120895, GSE9800) from the Gene Expression Omnibus (GEO) database were merged into an integrated dataset, and batch effects were removed. Author Contributions: Methodology, R.C.; software, R.C.; validation, R.C. and J.J.; formal analysis, R.C.; data curation, J.J., and R.C.; molecular biology experiment, R.C.; writing—original draft preparation, R.C.; writing—review and editing, R.C., Y.W.; visualization, Y.W.; su-pervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Natural Science Foundation of Hebei Province (H2021206141). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data is provided within the manuscript. Competing interests: The authors declare no conflict of interest. Acknowledgments: Not applicable. 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Frontiers in Pharmacology. 2024;15:1367848. doi:10.3389/fphar.2024.1367848 Supplementary Material File (image8.tiff) Download 404.19 KB Information & Authors Information Version history V1 Version 1 07 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords diseases gene regulation Authors Affiliations Ruifeng Cao The Second Hospital of Hebei Medical University View all articles by this author Junchen Ji The Second Hospital of Hebei Medical University View all articles by this author Yaling Wang 0009-0002-1309-6382 [email protected] The Second Hospital of Hebei Medical University View all articles by this author Metrics & Citations Metrics Article Usage 183 views 101 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ruifeng Cao, Junchen Ji, Yaling Wang. Identifying the Oxidative Stress-Related Diagnostic Biomarkers in Dilated Cardiomyopathy by Bioinformatics Analysis. Authorea . 07 August 2025. 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