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. 2024 Nov 19;22:1042. doi: 10.1186/s12967-024-05759-7

Identification of fibrosis-associated biomarkers in heart failure and human cancers

Can Hou 2,#, Junyu Huo 2,#, Si Yan 2, Fei Sun 1,✉, Xiaoyu Yang 2,✉
PMCID: PMC11575019  PMID: 39563337

Abstract

Background

Heart failure (HF) and cancer share common risk factors and pathophysiological mechanisms, including fibrosis. Identifying biomarkers and therapeutic targets for both conditions is crucial.

Materials and methods

RNA sequencing data from HF patients were analyzed to identify 12 genes associated with myocardial fibrosis. Validation was performed using public datasets, and functional enrichment analyses were conducted. Gene expression patterns and prognostic value in various cancers were assessed.

Results

Fibromodulin (FMOD), Periostin (POSTN), Latent Transforming Growth Factor Beta Binding Protein 2 (LTBP2), Collagen Type I Alpha 1 Chain (COL1A1), Collagen Type VIII Alpha 1 Chain (COL8A1), Asporin (ASPN), and Hemoglobin Subunit Beta (HBB) showed significant dysregulation in heart failure tissues and were implicated in multiple cancer types. Pan-cancer analysis revealed associations between these genes and prognosis. Correlations with cancer-associated fibroblasts were also observed.

Conclusion

FMOD, POSTN, LTBP2, COL1A1, COL8A1, ASPN, and HBB are potential biomarkers for HF and cancer with fibrotic microenvironments. Targeting fibrosis may offer novel therapeutic approaches. Further validation and mechanistic studies are needed. This study contributes to understanding HF and cancer at the molecular level and suggests personalized treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-024-05759-7.

Keywords: Heart failure, Cancer-associated fibroblasts, Bioinformatics analysis, Cardiovascular diseases

Introduction

Heart failure (HF) is a global health issue that continues to be a significant contributor to mortality, morbidity, and diminished quality of life. HF is characterized by cardiac remodeling, typically resulting from conditions such as hypertension, myocardial infarction (MI), myocarditis, or heart valve diseases. Alcoholic cardiomyopathy, a consequence of chronic alcohol consumption leading to chamber dilatation, myofibrillar disorganization, interstitial fibrosis, and diminished myocardial contractility, presents as an additional cause of HF [1]. Moreover, Lipopolysaccharide (LPS) from Gram-negative bacteria and the apelinergic system play crucial roles in HF [2, 3]. It is a condition influenced by inflammation and fibrosis, which has profound implications for overall well-being [4]. Cardiac overload and injury are the leading causes of heart failure in conventional wisdom [5]. Angiotensin-converting-enzyme inhibitors and β blockers are crucial therapies for heart failure, aiming to alleviate symptoms, decrease hospitalizations, and improve survival rates. Despite significant progress in heart failure treatment over the years, the prevalence and hospitalization rates continue to rise. Therefore, there is a pressing need for early detection, accurate prognosis, and the development of new therapeutic approaches to effectively manage heart failure progression.

In response to injury, fibrosis is marked by the activation of fibroblasts and immune cells, leading to the gradual accumulation of extracellular matrix (ECM) and inflammation [6]. Cardiac fibrosis can initiate further pathological changes that ultimately can lead to HF [7]. Cardiac fibroblasts are basic cell type that composes the heart and pathological fibrosis will change the ECM and the myocardial structure of the heart, promoting cardiac dysfunction, arrhythmias, and affecting the clinical course and outcome of patients with HF [8]. Over the years, experiments about myocardial fibroblasts have been brought to the forefront and specific targeting of fibrosis could be a new therapeutic horizon for HF [4]. Hence, elucidating the molecular pathway of fibrosis in heart failure would facilitate the exploration of targeted medications to enhance the efficacy of HF treatment.

Cancer, is the second leading cause of death worldwide [9]. Both heart failure and cancer share the common risk factors and pathophysiology that lead to the development and progression of the diseases [10]. Studies have demonstrated that patients with cancer frequently experience concurrent heart conditions, while conversely, a prior history of cancer is linked to an elevated risk of cancer recurrence and mortality [11]. Hasin showed that compared to non-HF controls, patients with HF had a 60% higher risk of developing malignancies [12]. Recent clinical and experimental studies suggest a remarkable overlap in features between cancer and organ fibrosis, highlighting their interconnectedness [6].In tumor microenvironment, quiescent fibroblasts activate into cancer-associated fibroblasts (CAFs), while into myofibroblasts (myoFbs) in the setting of cardiac diseases. According to current research, the dynamic transition between cancer and organ fibrosis is governed by shared triggers and signaling pathways, including the TGF-β dependent cascade, metabolic reprogramming, mechanotransduction, secretory properties, and epigenetic regulation. These findings have significant implications for potential interventions aimed at combating fibrosis in the future [13]. Hence, gaining a deeper understanding of the fundamental mechanisms driving fibroblast hyperactivity could pave the way for innovative therapeutic interventions aimed at mitigating myocardial or tumor stiffness and ultimately enhancing patient prognosis.

In this study, we identified 12 key genes associated with both heart failure (HF) and myocardial fibrosis. Among these genes, seven were found to be closely linked to cardiac fibrosis: Fibromodulin (FMOD), Periostin (POSTN), Latent Transforming Growth Factor Beta Binding Protein 2 (LTBP2), Collagen Type I Alpha 1 Chain (COL1A1), Collagen Type VIII Alpha 1 Chain (COL8A1), Asporin (ASPN), and Hemoglobin Subunit Beta (HBB). Interestingly, we observed that these genes are also implicated in various types of cancer.

Materials and methods

Acquisition and Processing of RNA sequencing datasets

The RNA-seq transcriptome data obtained from patients with heart failure (HF) were retrieved from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/geo). We utilized the datasets GSE48166 and GSE135055, comprising myocardium samples from 15 normal individuals and 15 heart failure patients for each dataset. Additionally, GSE48166 included 9 normal individuals and 15 heart failure patients with viral myocarditis-induced heart failure. For validation, we utilized GSE133054, which included 8 myocardium samples from normal individuals and 7 from HF patients. Myocardial fibrosis-related genes were obtained from the GeneCards database (https://www.genecards.org/). xCell was used to investigate different immune cell types in GSE48166 and GSE135055 [14]. In our study, prognostic correlations between selected gene expressions and various cancers were examined using the sangerbox web server (http://sangerbox.com/Tool). The analysis of tumor immune cell infiltration levels and differential gene expression between tumor and normal tissues was conducted using the TIMER2.0 database (http://timer.cistrome.org/) [15]. The workflow implemented in this study is depicted in Fig. 1.

Fig. 1.

Fig. 1

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Flowchart depicting the multistep screening strategy used to analyze the bioinformatics data

Analysis of Variance

Classification of GSE48166 Dataset Samples into HF groups and control groups. To identify differentially expressed genes (DEGs), a differential analysis was performed using the R package “limma”. Volcano plots were generated to visualize the results, where DEGs were considered significant if they met the criteria of |log2FC| > 1 and P < 0.05. Overlapping DEGs between the significant DEGs, key weighted gene co-expression network analysis (WGCNA) modules, and myocardial fibrosis-related genes were identified using Venn diagrams. This process aimed to obtain a set of DEGs associated with myocardial fibrosis (MfDEGs).

Weighted gene co-expression network construction

The construction of a weighted gene co-expression network analysis (WGCNA) was performed using the WGCNA package in R. This methodology aimed to establish a co-expression network that encompasses all genes. To determine the strength of co-expression similarity, the soft thresholding power (β) was calculated using the R function “pickSoftThreshold”. The resulting power value was then utilized to raise the co-expression similarity and calculate the adjacency. Subsequently, the adjacency was transformed into a topological overlap matrix (TOM) to measure the connectivity between genes within the network. Hierarchical clustering with average linkage was applied to group genes exhibiting similar expression patterns into modules, denoted by different colors and branches on the cluster tree. By computing module relationships and correlations between gene modules and phenotypes, modules associated with clinical traits were identified. Gene significance (GS) and module membership (MM) were computed to assess the relationship between modules and clinical traits. To explore core genes and potential roles, particular attention was given to the highly correlated module (R2 > 0.7, P < 0.05).

Functional and Pathway Enrichment Analysis

Functional Implications of Gene Expression Explored through Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analyses were used by oebiotech database (https://cloud.oebiotech.com/task/). Significance of enriched pathways was determined based on a false discovery rate threshold of less than 0.05.

PPI Network Analysis and Hub MfDEGs Identification

Analysis of Functional Protein Association Networks Using the Search Tool for the Retrieval of Interacting Genes (STRING) [16]. Visualization of the Protein-Protein Interaction (PPI) Network using Cytoscape Software [17]. Identification of Hub Genes in the Protein-Protein Interaction (PPI) Network using cytoHubba Plugin and MCODE in Cytoscape [18].

Predicting the Network of Validated Hub Genes, transcription factors (TFs), and miRNAs

Prediction of upstream regulators for validated hub genes: transcription factors (TFs) and miRNAs. Utilizing miRNet, a tool that integrates data from 11 different miRNA databases (www.mirnet.ca/) [19]. Visualization of the Network of Validated Hub Genes, Transcription Factors (TFs), and miRNAs using Cytoscape. The Maximal Clique Centrality (MCC) method within the cytoHubba plugin was utilized to identify the top 10 most significantly associated miRNAs with Heart Failure [18]. Subsequently, an analysis of the tumors related to these top 10 miRNAs was conducted using the OncomiR database (https://oncomir.org/).

Expression and survival analyses

Evaluation of Expression Levels and Prognostic Value of Common Hub Genes in Various Tumor Tissues using Kaplan-Meier Survival Analysis. Correlations between Expression of Validated Common Hub Genes and Overall Survival (OS) as well as Disease-Free Survival (RFS) in Cancer Patients Analyzed. Univariate Cox Regression Analysis conducted to Determine Hazard Ratio (HR), with HR > 1 indicating High Expression of Validated Common Hub Genes as a High-Risk Factor. Statistical Significance Assessed through Log-Rank Test (P < 0.05).

Analysis of Cancer-Associated fibroblasts (CAFs)

The correlation between expression levels of validated genes and immune cell infiltration was investigated across 33 cancer types. This analysis was conducted utilizing the TIMER2.0 database. Additionally, the association between CAFs and the expression of hub genes was assessed using Spearman rank correlation analysis. Statistical significance was defined as a threshold of P < 0.05.

Statistical analysis

R Project (V 4.3.0) was used to statistically analyze. P < 0.05 was considered statistically significant.

Results

Analysis of Variance

A total of 442 differentially DEGs were identified in GSE48166. Among them, 90 genes were found to be downregulated while 352 genes were upregulated when compared to normal control samples (Fig. 2A and B). To gain insights into the biological functions associated with these DEGs, GO analysis and KEGG pathway enrichment analysis were performed. The GO analysis revealed enrichment in various terms, including extracellular space, collagen-containing extracellular matrix, extracellular matrix organization, blood microparticle, and keratan sulfate catabolic process (Fig. 2C). Furthermore, the KEGG pathway analysis demonstrated enrichment in pathways related to protein digestion and absorption, African trypanosomiasis, malaria, neuroactive ligand-receptor interaction, and signaling pathways regulating pluripotency of stem cells (Fig. 2D).

Fig. 2.

Fig. 2

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Differential expression analysis and functional enrichment of genes in GSE48166 dataset. (A) Heatmap displaying the clustered analysis of DEGs. (B) Volcano plot showing upregulated (red) and downregulated (blue) DEGs in heart failure patients compared to normal controls. (C) GO enrichment terms. (D) KEGG pathways

Identification and analysis of Key HF Module using WGCNA

Using the gene expression data from GSE135055, we employed the R package “WGCNA” to construct a gene co-expression network. Average linkage and Pearson correlation coefficients were calculated, followed by cluster analysis on all samples in the dataset. A scale-free network was successfully constructed with a soft threshold power (β) set to 13 (Fig. 3A). Hierarchical clustering was performed utilizing a dynamic hybrid cut method, resulting in a clustering tree where each leaf represented a gene and each branch represented a module that grouped genes with similar expression patterns (Fig. 3B). By merging functionally equivalent modules, a total of 22 modules were identified (Fig. 3C). Among these modules, the black, blue, green, purple, and tan modules exhibited significant associations with the disease (R2 > 0.7, P < 0.05). Subsequently, a comparison was made between this set of genes and previously identified DEGs and myocardial fibrosis-related genes, leading to the identification of 12 intersecting genes (Fig. 3D): COL1A1, Natriuretic Peptide A (NPPA), FMOD, COL8A1, ASPN, POSTN, HBB, Proline/arginine-rich end leucine-rich repeat protein (PRELP), Ubiquitin C-Terminal Hydrolase L1 (UCHL1), Natriuretic Peptide B (NPPB), LTBP2, and Apolipoprotein A1 (APOA1). These 12 genes were considered as hub genes for further investigations.

Fig. 3.

Fig. 3

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Gene co-expression network analysis and identification of hub genes in GSE135055 dataset. (A) Selection of a soft threshold power (β) for constructing a scale-free gene co-expression network. (B) Dendrogram representing cluster analysis results, grouping genes with similar expression patterns into modules. (C) Identification of 22 modules by merging functionally equivalent gene clusters. (D) Intersection analysis between gene modules and previously identified DEGs and myocardial fibrosis-related genes, resulting in the identification of 12 hub genes

Identification of hub MfDEGs and construction of Network

To identify hub MfDEGs, we inputted the 12 common DEGs into the STRING database, resulting in an 18-node and 28-edge network (Fig. 4A). The MCC algorithm of the CytoHubba plugin was utilized to identify 10 candidate hub genes from the protein-protein interaction (PPI) network. These hub genes included FMOD, POSTN, PRELP, LTBP2, COL1A1, COL8A1, ASPN, HBB, NPPA, and APOA1 (Fig. 4B). Furthermore, significant gene clusters, referred to as modules, were identified using the MCODE plugin in Cytoscape. The following filter criteria were applied: degree cut-off = 2, node score cut-off = 0.2, k-core = 2, and max depth = 100. As a result, a module consisting of 8 nodes and 26 edges was identified as significant. The genes within this module included FMOD, POSTN, PRELP, LTBP2, COL1A1, COL8A1, ASPN, and HBB (Fig. 4C and D). Combining these results, we obtained a list of 8 hub MfDEGs, including FMOD, POSTN, PRELP, LTBP2, COL1A1, COL8A1, ASPN, and HBB.

Fig. 4.

Fig. 4

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Identification of hub myocardial fibrosis-related differentially expressed genes (MfDEGs) and network construction. (A) PPI network generated using the STRING database. (B) Top 10 candidate hub genes identified from the PPI network using the MCC algorithm of the CytoHubba plugin. (C) Significant gene module identified using the MCODE plugin in Cytoscape. (D) Visualization of the genes within the significant module

Validation of Hub Genes

Validation of FMOD, POSTN, PRELP, LTBP2, COL1A1, COL8A1, ASPN, and HBB Expression in Heart Failure Tissue Using the GSE133054 Dataset. Figure 5 displays the verified expression levels of these genes in human heart samples, comparing heart failure samples to normal control samples.

Fig. 5.

Fig. 5

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Validation of hub gene expression in heart failure tissue using the GSE133054 dataset. Expression levels of FMOD, POSTN, PRELP, LTBP2, COL1A1, COL8A1, ASPN, and HBB in human heart samples were verified by comparing heart failure samples to normal control samples. *P < 0.05, **P < 0.01, ***P < 0.001

Validated hub genes, transcription factors (TFs), and miRNAs Regulatory Network

In order to explore the regulatory network of validated hub genes, we utilized the miRNet database to predict the transcription factors (TFs) and miRNAs associated with these genes. The analysis identified a total of 24 TFs, including CDX1, CEBPB, CEBPG, CIITA, ETS1, GATA1, KLF1, MKL1, MYB, MYBL2, NFE2L2, NFIC, NFKB1, NFYA, NFYB, NFYC, POU2F2, RELA, SP1, SP3, STAT6, TFAP2A, TWIST2, and YY1. Additionally, a total of 293 miRNAs were identified in relation to the validated hub genes. These findings led to the construction of a regulatory network comprising TFs and miRNAs, as illustrated in Fig. 6. The top 10 miRNAs most significantly associated with HF include hsa-let-7b-5p, hsa-mir-27a-3p, hsa-mir-129-2-3p, hsa-mir-182-5p, hsa-mir-16-5p, hsa-mir-155-5p, hsa-mir-29a-3p, hsa-mir-1-3p, hsa-mir-124-3p, and hsa-let-7i-5p. Tumorigenesis in 16 cancer types is notably associated with hsa-mir-182-5p. Likewise, tumorigenesis in 14, 13, 12, and 11 cancer types is significantly correlated with hsa-mir-27a-3p, hsa-mir-16-5p, hsa-mir-155-5p, and both hsa-let-7b-5p and hsa-let-7i-5p respectively (Supplementary Fig. 1). Studies reveal that hsa-mir-182-5p intensifies myocardial injury during myocardial infarction by promoting apoptosis [20]. Further investigation has demonstrated an increase in hsa-mir-182-5p expression in CHF patients, with a positive correlation observed between serum hsa-mir-182-5p in CHF patients and BNP, and an inverse correlation with LVEF [21]. It has been discovered that inhibition of hsa-mir-182-5p significantly reduces the size of the infarct and decreases the serum CK-MB level in I/R rats, accompanied by a marked reduction in ROS level [22]. Research has also uncovered a correlation between hsa-mir-182-5p and various types of cancers, including cholangiocarcinoma, breast cancer, prostate cancer, nasopharyngeal carcinoma, clear cell renal cell carcinoma, non-small cell lung cancer, colon cancer, glioma, head and neck cancer, bladder cancer, hepatocellular carcinoma, renal cancer, and colorectal cancer [23–36]. These findings suggest that certain miRNAs not only play a pivotal role in heart failure but also serve different functions in various types of tumors.

Fig. 6.

Fig. 6

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Regulatory network of transcription factors (TFs) and miRNAs associated with validated hub genes. The red squares represent hub genes, the blue dots represent TFs, and the yellow dots represent miRNAs

Expression of validated genes in various Cancer types

To gain insights into the potential roles of FMOD, POSTN, LTBP2, COL1A1, COL8A1, ASPN, and HBB in tumorigenesis, we examined their expression patterns across various cancer types. This analysis aimed to uncover any associations between these genes and different malignancies. Utilizing comprehensive data from the sangerbox database, we performed differential expression analysis to uncover distinct expression profiles associated with these genes in different malignancies. Significant upregulation of ASPN was observed in 16 tumor types, including glioblastoma multiforme (GBM), glioma (GBMLGG), brain lower grade glioma (LGG), breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), esophageal carcinoma (ESCA), stomach and esophageal carcinoma (STES), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), wilms tumor (WT), pancreatic adenocarcinoma (PAAD), testicular germ cell tumors (TGCT), acute lymphoblastic leukemia (ALL), acute myeloid leukemia (LAML), and CHOL (Fig. 7A). Conversely, ASPN showed significant downregulation in 12 tumor types, such as uterine corpus endometrial carcinoma (UCEC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), kidney renal papillary cell carcinoma (KIRP), (pan-kidney cohort) KIPAN, colon adenocarcinoma (COAD), liver hepatocellular carcinoma (LIHC), skin cutaneous melanoma (SKCM), thyroid carcinoma (THCA), ovarian serous cystadenocarcinoma (OV), uterine carcinosarcoma (UCS), adrenocortical carcinoma (ACC), and kidney chromophobe (KICH) (Fig. 7A).

Fig. 7.

Fig. 7

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Expression profiles of validated hub genes across diverse cancer types. (A-G) Expression level of ASPN, COL1A1, COL8A1, FMOD, HBB, LTBP2 and POSTN in the combination of GTEx database and TCGA. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Furthermore, significant upregulation of COL1A1 was identified in 25 tumor types, including GBM, GBMLGG, LGG, BRCA, LUAD, ESCA, STES, KIPAN, COAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COADREAD), STAD, HNSC, kidney renal clear cell carcinoma (KIRC), lung squamous cell carcinoma (LUSC), LIHC, WT, SKCM, THCA, rectum adenocarcinoma (READ), PAAD, TGCT, ALL, LAML, pheochromocytoma and Paraganglioma (PCPG), and CHOL(Fig. 7B). Conversely, COL1A1 exhibited significant downregulation in 4 tumor types, including UCEC, CESC, PRAD, and ACC (Fig. 7B).

Similarly, significant upregulation of COL8A1 was found in 17 tumor types, namely GBM, GBMLGG, BRCA, STES, KIPAN, COAD, COADREAD, STAD, HNSC, KIRC, LIHC, THCA, READ, PAAD, TGCT, LAML, and CHOL (Fig. 7C). Conversely, COL8A1 demonstrated significant downregulation in 10 tumor types, comprising LUAD, KIRP, PRAD, LUSC, WT, SKCM, bladder urothelial carcinoma (BLCA), ALL, ACC, and KICH (Fig. 7C). FMOD expression was significantly upregulated in nine types of tumors, including GBM, GBMLGG, LGG, PRAD, HNSC, WT, PAAD, TGCT, and CHOL. Conversely, we observed a significant downregulation of FMOD in 22 types of tumors such as UCEC, BRCA, CESC, LUAD, ESCA, KIRP, KIPAN, COAD, COADREAD, STAD, KIRC, LUSC, LIHC, SKCM, BLCA, THCA, READ, OV, UCS, ALL, ACC and KICH (Fig. 7D). We observed a significant upregulation of HBB in two types of tumors, including GBM and PAAD. At the same time, a significant downregulation of HBB was observed in 28 types of tumors, these include LGG, UCEC, BRCA, CESC, LUAD, ESCA, STES, KIRP, KIPAN, COAD, COADREAD, PRAD, STAD, HNSC, LUSC, LIHC, WT, SKCM, BLCA, THCA, READ, OV, TGCT, UCS, ALL, LAML, KICH, and CHOL (Fig. 7E). Moreover, significant upregulation of LTBP2 was observed in 15 tumor types, including GBM, GBMLGG, LGG, ESCA, STES, COAD, COADREAD, STAD, HNSC, KIRC, LIHC, READ, PAAD, KICH, and CHOL. Conversely, LTBP2 exhibited significant downregulation in 13 tumor types, such as UCEC, BRCA, CESC, LUAD, KIRP, KIPAN, PRAD, LUSC, WT, SKCM, THCA, OV, and UCS (Fig. 7F). Additionally, significant upregulation of POSTN was identified in 20 tumor types, namely GBM, GBMLGG, BRCA, LUAD, ESCA, STES, KIPAN, COAD, COADREAD, PRAD, STAD, HNSC, KIRC, LUSC, LIHC, WT, OV, PAAD, TGCT, and CHOL. Conversely, POSTN showed significant downregulation in 7 tumor types, including UCEC, CESC, KIRP, SKCM, ALL, LAML, and KICH (Fig. 7G). These findings highlight pronounced differential expression levels of these genes across diverse tumor types, suggesting their potential involvement in tumor development and progression.

Prognostic potential of validated genes in Pan-cancer

Survival analysis is commonly used to assess the prognosis of diseases and investigate the impact of prognostic factors on disease outcomes. In our study, correlation analysis between the expression of validated genes and overall survival (OS) revealed significant associations in various cancer types. The analysis indicated that ASPN is associated with a poor prognosis when highly expressed in 14 tumor types: GBMLGG, LGG, TARGET-LAML, STES, KIRP, KIPAN, COAD, COADREAD, STAD, BLCA, THCA, PAAD, LAML, and KICH. Conversely, ASPN is linked to a favorable prognosis when its expression is low in two tumor types: TARGET-NB and TARGET-ALL (Fig. 8A). Regarding COL1A1, it was found to be associated with a poor prognosis due to high expression in 15 tumor types: GBMLGG, LGG, LUAD, KIRP, KIPAN, STAD, KIRC, SKCM, BLCA, SKCM-M, MESO, UVM, PAAD, ACC, and KICH (Fig. 8B). Similarly, COL8A1 was also found to be associated with a poor prognosis due to high expression in 10 tumor types: GBMLGG, LGG, KIRP, KIPAN, STAD, GBM, BLCA, PAAD, ACC, and KICH (Fig. 8C). Furthermore, FMOD exhibited a poor prognosis with high expression in 10 tumor types: GBMLGG, LGG, KIRP, KIPAN, STAD, GBM, KIRC, BLCA, THCA, and ACC. Conversely, FMOD exhibited a favorable prognosis with low expression in three tumor types: SARC, HNSC, and MESO (Fig. 8D). In the case of HBB, high expression was associated with a poor prognosis in three tumor types: GBMLGG, STAD, and THYM. Conversely, low expression of HBB was associated with a favorable prognosis in two tumor types: PRAD and KIRC (Fig. 8E). LTBP2 demonstrated a poor prognosis with high expression in 11 tumor types: GBMLGG, LGG, STES, KIRP, STAD, GBM, LUSC, BLCA, PAAD, TARGET-ALL, and ACC (Fig. 8F). Lastly, POSTN exhibited a poor prognosis with high expression in 13 tumor types: GBMLGG, LGG, CESC, LUAD, KIRP, KIPAN, STAD, GBM, LIHC, BLCA, THCA, MESO, and PAAD. Conversely, POSTN exhibited a favorable prognosis with low expression in one tumor type: UVM (Fig. 8G). Collectively, these findings suggest that FMOD, POSTN, LTBP2, COL1A1, COL8A1, ASPN, and HBB expression can influence the prognosis of various tumors, indicating their potential as pan-cancer prognostic indicators.

Fig. 8.

Fig. 8

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Association between expression levels of validated hub genes and patient overall survival. (A-G) Forest plot of the risk ratio of ASPN, COL1A1, COL8A1, FMOD, HBB, LTBP2 and POSTN in human pan-cancer

Analysis of different Immune cell types in myocardium samples and various cancers

There is increasing evidence implying a substantial connection between HF and the immune microenvironment. The distributions of varying immune cells are depicted in Fig. 9A and B. FMOD, POSTN, LTBP2, COL8A1, ASPN, and HBB demonstrated a positive correlation with Fibroblasts cells within the GSE48166 dataset (Fig. 9C). However, FMOD, POSTN, LTBP2, COL1A1, COL8A1, and ASPN were positively correlated with Fibroblasts cells in the GSE135055 dataset (Fig. 9D). The association between the validated genes and the infiltrating immune cells in various cancers was examined using the TIMER2.0 database (Supplementary Fig. 2). Collectively, these findings imply that the expression of FMOD, POSTN, LTBP2, COL1A1, COL8A1, ASPN, and HBB can impact the infiltrating immune cells in myocardium samples and different tumors, thereby suggesting their potential as indicators for heart failure and cancers.

Fig. 9.

Fig. 9

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Immune Infiltration Landscape in myocardium samples. (A-B) Stacked bar charts showing the proportions of different immune cell types in myocardium samples from datasets GSE48166 and GSE135055. (C-D) Correlation analysis between validated genes and immune cells. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Correlation between validated genes and Cancer-Associated fibroblasts (CAFs)

CAFs are known to play a crucial role in tumor progression and prognostic associations. In this study, we utilized the xCell algorithm implemented in TIMER2.0 to analyze the correlation between validated genes and CAFs. The analysis revealed significant correlations between gene expression and CAFs abundance in various tumor tissues. Specifically, ASPN expression showed significant correlations with CAFs abundance in 39 different cancer types. COL1A1 expression exhibited significant correlations with CAFs abundance in 33 different cancer types. Similarly, COL8A1 expression significantly correlated with CAFs in 26 different cancer types. FMOD expression demonstrated significant correlations with CAFs in 33 different cancer types. Furthermore, HBB expression displayed significant correlations with CAFs in 13 different cancer types. Additionally, LTBP2 expression showed significant correlations with CAFs in 30 different cancer types, while POSTN expression exhibited significant correlations with CAFs in 29 different cancer types. These findings emphasize the important roles of FMOD, POSTN, LTBP2, COL1A1, COL8A1, ASPN, and HBB in tumor immune infiltration through their significant correlations with CAFs across various cancer types (Fig. 10).

Fig. 10.

Fig. 10

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Correlation between validated genes and CAFs

Discussion

Heart failure and cancer both impact global health and impose a significant economic burden on society. Currently, the curative treatments of them are still lacking and new therapeutic strategies to limit the progression of HF and cancer are remain challenging. Recent years, emerging data suggest that identical risk factors may lead to cardiovascular disease in the one individual, but may cause cancer in another, or even both diseases in the same individual. Indeed, there exists an overlap between some risk factors traditionally classified as HF risk factors and their association with cancer development. Studies have demonstrated that certain risk factors, including smoking, obesity, high blood pressure, and diabetes, can escalate the probability of both cardiovascular diseases and certain types of cancer [37]. Abnormal fibroblasts have been observed in both myocardial injury and the stroma of various solid tumors. Fibrosis is a main hinder for cardiac remodeling and influence of tumor immune cell infiltration, modulation of CAFs activity has been recognized as a potential strategy to enhance the effectiveness of chemotherapy, what’s more, it is plausible that chemotherapeutic agents targeting CAFs might also affect myocardial fibroblasts [38]. Thus, further research is needed to better understand the specific mechanisms and interactions between CAFs and myocardial fibroblasts and novel molecular targets for fibrosis may offer a promising new avenue to address HF and cancer.

In this study, we firstly identified 442 DEGs in HF patients and normal individuals, then, a key module was constructed by WGCNA and a total of 12 intersection genes were obtained. Furtherly, 7 key genes, namely FMOD, POSTN, LTBP2, COL1A1, COL8A1, ASPN and HBB were verified using bioinformatic analysis. We finally investigated the expression, prognosis, and immune cell infiltration of these 7 genes in various cancer types through pan-cancer analysis, confirming that these genes are potential therapeutic targets for HF patients at an increased risk of developing cancer.

FMOD is a small leucine-rich proteoglycan that regulates collagen fibrillogenesis and is expressed in the cardiac extracellular matrix [39]. By exploring the role of FMOD in HF patients and mice, Kine.et found FMOD is upregulated in clinical and experimental HF and affects cardiac remodeling [40]. Identically, FMOD is regarded as a potential biomarker for various cancers, including prostate cancer, colon cancer, lung cancer, breast cancer and oral squamous cancer [41–44].

POSTN, is a secreted extracellular matrix protein that was originally identified in mesenchymal-lineage cells. Recent studies indicated that POSTN, as a component of the ECM produced by fibroblasts, plays a critical role in the formation and remodeling of cancer microenvironments and cancer associated fibroblasts [45]. Also, POSTN can promote defect closure by facilitating the activation, differentiation, and contraction of fibroblasts [46]. Since POSTN is a key matricellular protein, its expression is closely related to fibroblasts activation, involving CAFs and myoFbs. So far, the pro-oncogenic effect of POSTN has been widely verified in colon cancer, gastric cancer, esophageal cancer, the direct mechanism in heart failure needs further exploration [47]. Novel diagnostic and therapeutic strategies targeting periostin or related signaling pathway will give a new horizon for cancer and heart failure disposition.

LTBP2 is an isoform of latent transforming growth factor β binding proteins (LTBPs), appears to associate with fibrillin. LTBP-2 associates with ECM and is expressed abundantly in elastic tissues, including aorta and lung [48]. LTBP2 acts important effects on tumourigenesis via regulation of tumour necrosis factor beta (TGF-β) activity, elastogenesis and maintenance of extracellular matrix structure [49]. Also, it’s highly expressed in myocardial tissues from heart failure patients and in neonatal rat cardiomyocytes its expression was enhanced, which can serve as a promising marker for the diagnosis of heart failure with decreased ejection fraction. Multiple studies have indicated LTBP2 overexpression is corelated with the genesis and progression of heart failure, gastric cancer, head and neck squamous cell cancer, prostate cancer and so on, which is correspondence to our analysis [50–53].

COL1A1 and COL8A1 are both members of the collagen family. Collagen is a structural protein in the ECM and is mainly composed of ribbon fibers. In many tissues, collagen acts as a major building block and plays substantial roles in cell proliferation, migration, and differentiation [54]. Abnormal collagen expression was known to be linked to the development, invasion and chemoresistance of malignant tumors [55]. COL1A1 and COL8A1 upregulation was found to promote cardiac pathological remodeling and cardiac fibrosis, causing heart failure [56, 57].What’s more, a plenty of investigations have demonstrated that these two genes are closely linked to cancers, they possess oncogenic function and may be therapeutic targets in malignancies [58, 59].

ASPN, also known as periodontal ligament associated protein-1 (PLAP-1) is a member of class I small leucine-rich proteoglycans (SLRP) class I family, which directly binds to type I collagen and can play a crucial part in collagen fibrillogenesis [60]. In various tumors, ASPN, expressed primarily by stromal fibroblasts, is positively associated with pancreatic, colorectal, gastric, and prostate cancers [43, 61–63]. While in breast cancer, also plays dual roles with both pro-and anti‐tumor effects [64]. ASPN is supposed to promote cancer cell invasion, proliferation, and migration by mediating the epithelial-to-mesenchymal transition (EMT) process via various signaling pathways, such as TGFβ, Wnt/β-catenin, notch, EGFR, HER2, and CD44-mediated Rac1 [65]. What’s more, studies revealed that ASPN plays a positive role in cardiac remodeling by protecting excessive fibrosis and cardiomyocyte cell death, thus limiting the decline of cardiac function during pressure overload. Therapies targeting ASPN can prevent cardiac fibrosis and prevent adverse cardiac re remodeling, which is benefit for handling heart failure [66].

HBB is a β subunit of hemoglobin, its causative mutation leads to the formation of various genotypic variants of the disease and results in a cascade of sickling and unsickling erythrocytes, leading to hemolysis and/or vaso-occlusion. Ultimately, various manifestations and complications appear, including hyposthenuria, acute chest syndrome, renal papillary necrosis, painful crisis, pulmonary hypertension, lower limb ulcerations, osteonecrosis, cancer, and chronic hemolysis [67]. What’s more, several bioinformatics revealed HBB is a potential biomarker of heart failure [68, 69].

In conclusion, this study identifies FMOD, POSTN, LTBP2, COL1A1, COL8A1, ASPN, and HBB as potential biomarkers for heart failure and cancer with fibrotic microenvironments. Although our research has provided some evidence for the significant role of fibrosis in heart failure and cancer, it is important to note that the data used in our study was derived solely from bioinformatics analysis. Therefore, further in vivo and in vitro experiments is necessary to validate these findings. These findings highlight the importance of targeting fibrosis in the development of novel therapies for heart failure and cancer. Future studies should focus on understanding the underlying mechanisms and evaluating the therapeutic efficacy of interventions targeting these genes. Overall, these findings contribute to our understanding of the molecular processes involved in heart failure and cancer and offer potential avenues for personalized treatment approaches.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (2MB, docx)

Acknowledgements

We would like to express our sincere gratitude to all the staff members of the Department of Cardiology at the First People’s Hospital of Changzhou for their valuable contributions and support throughout this research. Their dedication and assistance have been instrumental in the successful completion of this study.

Author contributions

As the guarantor, Fei Sun and Xiaoyu Yang conceived the study. Can Hou initially drafted the manuscript. Junyu Huo enrolled participants and collected data. Si Yan helped with data calculation.

Funding

This work was Funding from Changzhou Key Medical Discipline (CZXK202202) Changzhou Sci&Tech Program (CJ20235085) and Changzhou "Longcheng Talent Program"（No.CQ20220127).

Data availability

All data and materials have been made available.

Declarations

Ethics approval and consent to participate

Not applicable.

Human subjects/informed consent statement

No human studies were carried out by the authors for this article.

Animal studies

No animal studies were carried out by the authors for this article.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Can Hou and Junyu Huo contributed equally to this work. Corresponding Author: Fei Sun, Email: sunfei@njmu.edu.cn; Xiaoyu Yang, Email: jsczyxy@163.com

Contributor Information

Fei Sun, Email: sunfei@njmu.edu.cn.

Xiaoyu Yang, Email: jsczyxy@163.com.

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