Abstract

Background

   Chronic Thromboembolic Pulmonary Hypertension (CTEPH) is a form of
   pulmonary hypertension with a high mortality rate. A new type of
   iron-mediated cell death is Ferroptosis, which is characterized by the
   accumulation of lethal iron ions and lipid peroxidation leading to
   mitochondrial atrophy and increased mitochondrial membrane density.
   Now, there is a lack of Ferroptosis-related biomarkers (FRBs)
   associated with pathogenic process of CTEPH.

Methods

   The differentially expressed genes (DEGs) of CTEPH were obtained by
   GEO2R. Genes related to Ferroptosis were obtained from FerrDb database.
   The intersection of Ferroptosis and DEGs results in FRBs. Gene Ontology
   (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were
   performed in Database for Annotation, Visualization and Integrated
   Discovery (DAVID) database. The optimal potential biomarkers for CTEPH
   were analyzed by least absolute shrinkage and selection operator
   (LASSO) and support vector machine-recursive feature elimination
   (SVM-RFE) machine learning. The four hub genes were verified from the
   Gene Expression Omnibus (GEO) dataset [31]GSE188938. Immune
   infiltration was analyzed by CIBERSORT. SPSS software was used to
   analyze the Spearman rank correlation between FRBs identified and
   infiltration-related immune cells, and p < 0.05 was considered as
   statistically significant.

Results

   In this study, potential genetic biomarkers associated with Ferroptosis
   in CTEPH were investigated and explored their role in immune
   infiltration. In total, we identified 17 differentially expressed
   Ferroptosis-associated genes by GEOquery package. The key FRBs
   including ARRDC3, HMOX1, BRD4, and YWHAE were screened using Lasso and
   SVM-RFE machine learning methods.Through gene set [32]GSE188938
   verification, only upregulation of gene ARRDC3 showed statistical
   difference. In addition, immune infiltration analysis using the
   CIBERSORT algorithm revealed the infiltration of Eosinophils and
   Neutrophils in CTEPH samples was less than that in the control group.
   And correlation analysis revealed that ARRDC3 was positively correlated
   with T cells follicular helper (r = 0.554, p = 0.017) and negatively
   correlated with Neutrophils (r = -0.47, p = 0.049).

Conclusions

   In conclusion, ARRDC3 upregulation with different immune cell
   infiltration were involved in the development of CTEPH. ARRDC3 might a
   potential Ferroptosis-related biomarker for CTEPH treatment. This study
   provided a new insight into pathogenesis CTEPH.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12872-023-03511-5.

   Keywords: CTEPH, Ferroptosis, Biomarker, Immune cell infiltration,
   Machine learning

Introduction

   Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare
   progressive pulmonary vascular disease that usually results from acute
   pulmonary embolism. CTEPH is characterized by persistent thromboembolic
   pulmonary artery obstruction and the presence of variable small vessel
   lesions [[33]1]. In recent years, studies have shown that between 3 and
   30 people per 1 million develop CTEPH [[34]2]. The early symptoms of
   CTEPH are usually not obvious. The mortality of patients with advanced
   CTEPH is high, and the recent 5-year survival rate is less than 40%
   [[35]3]. The pathogenesis of CTEPH is believed to be caused by multiple
   factors, including hypercoagulability, fibrinolysis disease,
   inflammation, pulmonary microvascular hyperplasia [[36]4]. CTEPH is
   difficult to diagnose in early stages, and biomarkers to predict its
   severity and clinical outcome are still lacking. As a result, it is
   crucial to explore more valuable genetic biomarkers for the potential
   etiology and therapeutic targets of CTEPH.

   Ferroptosis is characterized by mitochondrial atrophy and increased
   mitochondrial membrane density, the accumulation of iron and lipid
   reactive oxygen species and the involvement of a unique set of genes.
   Divalent iron ions in the unstable iron pool of the cell generate
   hydroxyl radicals (lipid ROS) through the Fenton reaction, causing
   peroxidation of polyunsaturated fatty acids. This process is
   accompanied by overloading or inactivation of the lipid peroxide
   scavenging system, and eventually the accumulation of toxic lipid
   metabolites leads to cell death [[37]5]. The pulmonary circulation has
   the physiological basis for Ferroptosis because it is rich in blood
   oxygen supply, polyunsaturated fatty acids and iron ions. Although
   Ferroptosis was first formally proposed in tumors, more and more
   studies have shown that Ferroptosis is involved in the development of
   chronic obstructive pulmonary disease(COPD), acute lung injury,
   pulmonary fibrosis and other lung diseases [[38]6]. Ferroptosis is
   involved in the damage of pulmonary vessels through oxidative stress
   and lipid peroxidation [[39]7]. Recently, a growing number of studies
   have focused on discovering biomarkers of Ferroptosis at multiple
   levels, including morphology, biochemistry, protein, and genes [[40]8].
   However, it remains a great challenge to explore the reliable gene
   biomarkers and specific regulatory details associated with Ferroptosis
   in CTEPH.

   Bioinformatics analysis is a new cross analysis combining molecular
   biology and information technology. The aim is to efficiently and
   extensively obtain differential gene expression of diseases by using
   gene chips, and provide signaling pathways for exploring the specific
   mechanisms of diseases or prediction for drug therapy [[41]9–[42]12].
   Immune cells play an important role in the pathological process of
   CTEPH by participating in the release of inflammatory factors,
   cytotoxic effects, thrombosis and so on [[43]13]. In the present study,
   we applied the CIBERSORT tool to the assessment of immune infiltration
   in the pathological process of CTEPH. Besides, we screened and verified
   Ferroptosis-related genes as CTEPH biomarkers, analyzed their role in
   the pulmonary vascular immune microenvironment by bioinformatics
   analysis. And, the pathway analysis was carried out to provide specific
   pathway guidance for the subsequent experimental research.

Materials and methods

Data download

   We used the “GEOquery” package of R software (version 4.1.3) to
   download the CTEPH expression profile datasets [44]GSE130391 from the
   Gene Expression Omnibus (GEO) ([45]https://www.ncbi.nlm.nih.gov/geo/)
   database (Supplementary Material [46]1) [[47]14]. Details of the
   [48]GSE130391 dataset: Samples surgically extracted from CTEPH
   pulmonary arteries were compared to post-transplant pulmonary arteries
   from IPAH and failed donor pulmonary arteries from controls. The goal
   was to determine if altered gene expression in CTEPH associated with
   development of chronic thrombi. The CTEPH group (n = 14) and the
   Control group (n = 4) were selected for subsequent analysis.

Differential expression analysis

   We first extracted 676 differentially expressed genes (DEGs) between
   CTEPH and Control from [49]GSE130391 database using GEO2R
   (Supplementary Material [50]2). DEGs with p < 0.05 and |log2FC|> 1 were
   considered significant. Genes related to Ferroptosis were obtained from
   FerrDb database [[51]15]. The intersection genes of Ferroptosis and
   DEGs were considered to be the most important genes. Venn diagram was
   drawn by Venn diagram intersection
   (bioinformatics.psb.ugent.be/webtools/Venn/).

Functional enrichment

   GO and KEGG analysis of 17 genes were performed in Database for
   Annotation, Visualization and Integrated Discovery (DAVID) [[52]16].
   Metascape ([53]http://metas.cape.org/) was used to analyse Reactome
   pathway enrichment and Immunologic Signatures enrichment [[54]17].The
   KEGG formal permission had been obtained from Kanehisa laboratories,
   which copyrighted KEGG pathway database [[55]18–[56]20]. p < 0.05 was
   considered significant enrichment. Gene set enrichment analysis (GSEA)
   was performed on the gene expression matrix through Xiangtao Tool
   [[57]21]. p < 0.05 was considered significant enrichment.

Identification of optimal potential biomarkers for CTEPH

   Glmnet package of R was applied for the LASSO algorithm. And the
   average misjudgment rate was compared by its tenfold cross validation
   in addition. SVM-RFE is a machine learning method based on support
   vector machine (SVM), which deletes the feature vectors generated by
   SVM to find the best variables. “e1071” package was used to identify
   the diagnostic value of these biomarkers in CTEPH [[58]22]. The area
   under the curve (AUC) was used to evaluate the diagnostic ability of
   the best genetic biomarkers by SVM-RFE. The intersection genes of Lasso
   and SVM-RFE were considered as the best diagnostic gene biomarker for
   CTEPH. The code for Lasso and SVM-RFE machine learning algorithms has
   been shown in Supplementary Material [59]1.

Verification of optimal potential biomarkers for CTEPH

   The CTEPH-related dataset [60]GSE188938 was obtained from the GEO
   database. R software was used to obtain gene expression
   profiles(Control, n = 5;CTEPH, n = 7). In the study of [61]GSE188938,
   Professor Xu sought to identify key genes and pathways that contribute
   to the diagnosis and treatment of CTEPH. It provided the research
   direction for further understanding the molecular mechanism of CTEPH
   [[62]23]. SPSS software was used for difference analysis between
   Control group and CTEPH group.

Immune infiltration analysis

   We uploaded [63]GSE130391 gene expression matrix data to CIBERSORT
   ([64]https://cibersort.stanford.edu/), and obtained 22 kinds of immune
   cell infiltration information [[65]24]. p < 0.05 of the samples were
   identified significantly.“corrplot” package and “ggplot2” package were
   used to draw a correlation heatmap and boxplot. The proportion of 22
   types of infiltrating immune cell types in each tissue from the
   [66]GSE130391 dataset were showed in Supplementary Material [67]5.

Correlation analysis between diagnostic biomarkers and infiltration-related
immune cells

   SPSS software was used to analyze the Spearman rank correlation between
   FRBs identified and the level of related immune cells. P < 0.05 was
   considered statistically significant [[68]25]. We also uesed "ggplot2"
   package in R software to diplay the correlation.

Statistical analysis

   The data were analyzed by one-way Analysis of Variance(ANOVA) to
   determine differences between the Control group and the CTEPH group.
   For all statistical tests, p < 0.05 was considered as statistically
   significant.

Results

Identification of Ferroptosis related genes and DEGs in CTEPH

   There were 676 differentially expressed genes (DEGs) between CTEPH and
   Control from [69]GSE130391 database. PCA diagram showed the expression
   of these genes (Fig. [70]1A). 259 genes related to Ferroptosis were
   obtained from FerrDb database (Supplementary Material [71]3). There
   were 17 intersection genes of Ferroptosis and DEGs (Fig. [72]1B, Table
   [73]1). The clustering heatmap showed the expression pattern of 17
   Ferroptosis related biomarkers (FRBs) among samples (Fig. [74]1C). The
   correlation between these 17 genes were shown in Fig. [75]1D.

Fig. 1.

   [76]Fig. 1
   [77]Open in a new tab

   Identification of Ferroptosis  related genes and DEGs in CTEPH. A PCA
   diagram of the DEGs expression with p < 0.05 and |log2FC|> 1. Red
   represents genes up-regulation. Blue represents genes down-regulation.
   B Venn diagram of Ferroptosis and DEGs. C The clustering heatmap of 17
   Ferroptosis related intersection genes. D The correlation between 17
   intersection genes. ^*p < 0.05, ^**p < 0.01

Table 1.

   17 Ferroptosis-related biomarkers of Ferroptosis and DEGs
   Gene p- Value Expressing trend Gene ID The link of gene information
   HMOX1 0.0000983 down 3162 [78]https://www.ncbi.nlm.nih.gov/gene/3162
   EPAS1 0.0034 down 2034 [79]https://www.ncbi.nlm.nih.gov/gene/2034
   YWHAE 3.25E-10 down 7532 [80]https://www.ncbi.nlm.nih.gov/gene/7531
   JUN 0.0012 down 3725 [81]https://www.ncbi.nlm.nih.gov/gene/3725
   ULK2 0.00000456 down 9706 [82]https://www.ncbi.nlm.nih.gov/gene/9706
   BRD4 1.32E-10 down 23,476 [83]https://www.ncbi.nlm.nih.gov/gene/23476
   NCF2 0.00372 down 4688 [84]https://www.ncbi.nlm.nih.gov/gene/4688
   ZFP69B 0.0000634 down 65,243
   [85]https://www.ncbi.nlm.nih.gov/gene/65243
   PTGS2 0.00509 down 5743 [86]https://www.ncbi.nlm.nih.gov/gene/5743
   RB1 0.000413 up 5925 [87]https://www.ncbi.nlm.nih.gov/gene/5925
   ATM 0.00000589 up 472 [88]https://www.ncbi.nlm.nih.gov/gene/472
   BNIP3 0.000212 up 664 [89]https://www.ncbi.nlm.nih.gov/gene/664
   TFRC 3.52E-10 up 7037 [90]https://www.ncbi.nlm.nih.gov/gene/7037
   ALOX5 0.03 up 240 [91]https://www.ncbi.nlm.nih.gov/gene/240
   ARRDC3 0.00000483 up 57,561 [92]https://www.ncbi.nlm.nih.gov/gene/57561
   SLC2A3 0.0000263 up 6515 [93]https://www.ncbi.nlm.nih.gov/gene/6515
   HILPDA 0.000173 up 29,923 [94]https://www.ncbi.nlm.nih.gov/gene/29923
   [95]Open in a new tab

Functional enrichment of 17 Ferroptosis-related DEGs

   GO enrichment analysis indicated that the above 17 Ferroptosis related
   biomarkers were significantly related to regulation of inflammatory
   response, Cell growth cycle, cell death, iron ion homeostasis,
   mitophagy and so on (Fig. [96]2A). KEGG analysis showed 17 FRBs
   significantly enriched in Leishmaniasis, Pathways in cancer, Cell
   cycle, etc. (Fig. [97]2B). Reactome pathway analysis indicated that
   Cellular responses to stress and Signaling by Rho GTPases were enriched
   (Fig. [98]2C). Surprisingly, the 17 FRBs were also obviously enriched
   in many immune-related signatures (Fig. [99]2D). It was showed the top
   five GSEA enrichment gene sets involved in inflammation, CD5 cells,
   natural killer cells and B lymphocyte in Fig. [100]2E. Collectively,
   These evidences indicated that 17 intersection genes mentioned may play
   an important role in the pathogenesis of CTEPH by participating in the
   regulation of immune cells and cytokines.

Fig. 2.

   [101]Fig. 2
   [102]Open in a new tab

   Functional enrichment and GSEA enrichment analysis of 17
   Ferroptosis-related genes. A GO enrichment analysis of 17 intersection
   genes. Biology Process (BP), Molecular Function (MF), Cellular Componet
   (CC). B KEGG enrichment analysis.The KEGG formal permission had been
   obtained from Kanehisa laboratories, which copyrighted KEGG pathway
   database. Y-axis indicates GO or KEGG entries and X-axis indicates the
   number of genes enriched under the entry, the redder the color, the
   smaller the value of p value, which means more significant
   enrichment.The bubble diagram X-axis indicates the ratio of genes, the
   larger the circle, the more genes are present in each pathway. C
   Reactome pathway enrichment analysis. D Immunologic Signatures
   enrichment analysis. E GSEA enrichment analysis. The top part is the
   line chart of the top 5 gene Enrichment scores. The horizontal axis is
   each gene that corresponds to this gene set, and the vertical axis is
   the corresponding Running Enrichment Score. The peak is the Enrichemnt
   score of the gene set, and the genes before the peak are the core genes
   under the gene set. The middle part is hit, and the genes under this
   gene set are marked with lines. The bottom part is the distribution of
   rank values for all genes

Screening and verification of potential biomarkers for CTEPH

   We screened out 5 genes from 17 FRBs as the most suitable potential
   biomarkers for CTEPH by using LASSO algorithm (Fig. [103]3A-B). 13
   potential biomarkers were selected from DEGs by SVM-RFE algorithm.
   AUC = 0.788 indicated that SVM-RFE had a good gene selection effect
   (Fig. [104]3C). The intersection potential biomarkers obtained by the
   two algorithms were considered the most accurate potential biomarkers,
   including ARRDC3, HMOX1, BRD4 and YWHAE (Fig. [105]3D).

Fig. 3.

   [106]Fig. 3
   [107]Open in a new tab

   Screening and verification of potential biomarkers for CTEPH. A LASSO
   logistic regression algorithm, with penalty parameter tuning conducted
   by 10- fold cross-validation. Five different colored lines represent
   five important diagnostic markers that LASSO has identified. B The
   misclassification error in the jackknife rates analysis. The number of
   genes (n = 5) corresponding to the lowest point of the curve is the
   most suitable number for CTEPH Ferroptosis-related potential biomarkers
   using Lasso algorithm. C The diagnostic efficiency of SVM-RFE. The
   closer the AUC is to 1, the better the result of gene selection. D The
   intersection potential biomarkers of the Lasso and SVM-RFE. LASSO,
   least absolute shrinkage and selection operator; SVM-RFE, support
   vector machine recursive feature elimination

Verification of four hub genes from the GEO dataset [108]GSE188938

   In the GEO dataset [109]GSE188938, ARRDC3 was significantly upregulated
   in CTEPH group compared with control group (p = 0.0051, Fig. [110]4A).
   However, we found that the gene expression of HMOX1, BRD4 and YWHAE
   were not statistically significant (Fig. [111]4B-D).These results
   indicated that ARRDC3 may be the most important hub gene of CTEPH and a
   potential therapeutic target. The expression of ARRDC3, HMOX1, BRD4 and
   YWHAE in [112]GSE188938 were showed in Supplementary Material [113]4.

Fig. 4.

   [114]Fig. 4
   [115]Open in a new tab

   Verification of four hub genes from the GEO dataset [116]GSE188938.
   p < 0.05 indicates that there is a statistical difference between the
   CTEPH group and the Control group. Red represents the Control group.
   Blue represents the CTEPH group

Immune infiltration analysis

   The boxplot of the immune cell infiltration difference showed a
   reduction in Eosinophils and Neutrophils at CTEPH compared with Control
   samples (Fig. [117]5A). In correlation heatmap, Pearson correlation
   analysis of the 22 types of immune cells reveals that Eosinophils is
   negatively correlated with Neutrophils (Fig. [118]5B). As a result,
   Eosinophils and Neutrophils may be involved in the pathological
   regulation of CTEPH.

Fig. 5.

   [119]Fig. 5
   [120]Open in a new tab

   Immune infiltration analysis. A The boxplot of the immune cell. Red
   represents the Control group. Blue represents the CTEPH group. Red
   represents the Control group. Blue represents the CTEPH group. B
   Correlation heatmap of Immune infiltration analysis.Red means positive
   correlation. Blue represents the negative correlation

Correlation analysis between ARRDC3 and infiltration-related immune cells

   Correlation analysis revealed that ARRDC3 was positively correlated
   with T cells follicular helper (r = 0.554, p = 0.017, Fig. [121]6A-B).
   ARRDC3 was negatively correlated with Neutrophils (r = -0.47,
   p = 0.049, Fig. [122]6C, Supplementary Material [123]5).

Fig. 6.

   Fig. 6
   [124]Open in a new tab

   Correlation analysis between ARRDC3 and infiltration-related immune
   cells. A The correlation between ARRDC3 and Infiltrating Immune Cells.
   B Scatter diagram indicates the correlation between ARRDC3 expression
   and T cells follicular helper. The expression of ARRDC3 is positively
   correlated with T cells follicular helper (r = 0.554, p = 0.017). C
   Scatter diagram indicates the correlation between ARRDC3 expression and
   Neutrophils. The expression of ARRDC3 is negatively correlated with
   Neutrophils (r = -0.47, p = 0.049)

Discussion

   Despite the advancements in CTEPH treatment during the past decades,
   pulmonary endarterectomy (PEA) treatment has not been completely
   satisfactory and there are still some patients with recurrent attacks.
   With the development of sequencing technology, it is increasingly
   expected to uncover the biological effects of genes related to CTEPH.
   Ferroptosis is believed to involve in the development of vascular
   inflammation and thrombosis. However, few studies have focused on the
   genes and underlying regulatory details associated with iron deficiency
   and the immune infiltration landscape, which have profound implications
   for patients with CTEPH. In this study, a total of 17 differentially
   expressed genes related to Ferroptosis in CTEPH patients were
   identified in the metadata cohort. Among them, four genes including
   ARRDC3, HMOX1, BRD4, and YWHAE are considered as key genes for
   Ferroptosis in CTEPH development by the integration of LASSO and
   SVM-RFE machine learning methods. Only the upregulation of ARRDC3 was
   found to have a statistical difference through gene set [125]GSE188938
   verification. In addition, analysis of immune infiltration using the
   CIBERSORT algorithm revealed less infiltration of Eosinophils and
   Neutrophils in CTEPH samples than in controls. ARRDC3 was found to be
   positively correlated with T cells follicular helper and negatively
   correlated with Neutrophils according to the correlation analysis.

   Firstly, We downloaded the CTEPH expression profile dataset
   [126]GSE130391 from the GEO database and identified a total of 676 DEGs
   on CTEPH. There were 17 intersection genes of Ferroptosis and DEGs,
   indicating that Ferroptosis is likely to be involved in the
   pathogenesis of CTEPH through these 17 genes. GO enrichment analysis
   revealed that 17 intersection genes were mainly associated with
   regulation of inflammatory response, regulation of cell cycle and cell
   death. In the results of KEGG enrichment analysis, there were
   significantly enriched in pathways of cancer, cell cycle, chemical
   carcinogenesis-reactive oxygen species. Evidences have confirmed that
   immune response participated in CTEPH development [[127]26] and chronic
   inflammatory diseases including inflammatory bowel disease,
   osteomyelitis and anticardiolipin antibody syndrome and chronic venous
   ulcers would increase the risk of CTEPH [[128]27]. CTEPH pulmonary
   vascular remodeling could be attributed partly to the abnormal gene
   regulation of cell cycle and cell death via apoptosis and Ferroptosis
   in pulmonary artery endothelial cells and smooth muscle cells [[129]5,
   [130]28]. These studies mentioned above were consistent with our GO and
   KEGG results. In the present study, Immunologic Signatures enrichment
   analysis also indicated that Ferroptosis-related genes were involved in
   CTEPH pathogenesis by regulating autophagy, immune cells, cytokines and
   multiple kinases. In addition, the main gene sets enriched in GSEA
   analysis were NFLAMMATORY RESPONSE, CD5 TARGETS, NATURAL KILLER
   DIFFERENTIATION, CELL CYCLE MITOTIC, HADDAD B LYMPHOCYTE PROGENITOR.
   All above results suggested that Ferroptosis and immune inflammatory
   response might play important roles in CTEPH progression.

   Next, four genes including ARRDC3, HMOX1, BRD4, and YWHAE were
   identified as key hub genes in CTEPH combined with Ferroptosis by LASSO
   and SVM-RFE algorithms which are mainly used to filter the most
   characteristic variables and construct the best classification models
   [[131]26]. ARRDC3 is associated with inflammation, cellular invasion,
   etc., which promotes H. pylori-associated gastritis by regulating
   protease-activated receptor 1 and inhibits invasive metastasis of
   breast cancer cells by regulating G protein-coupled receptor lysosomes
   [[132]29]. HMOX1 could regulate the proliferation and apoptosis of
   human cardiomyocytes and play an anti-oxidative stress role in
   apoptosis induced by high glucose [[133]30, [134]31]. BRD4 inhibition
   prevented diabetic cardiomyopathy induced by high-fat diet through
   activating mediated mitochondrial autophagy [[135]32]. YWHAE promoted
   proliferation and metastasis of breast cancer cells, and regulated
   proliferation and cell cycle protein D1 through RAF/MAPK and Hippo
   pathways [[136]33]. The above studies indicated that ARRDC3, HMOX1,
   BRD4, and YWHAE play a role in promoting inflammation, cell
   proliferation and metastasis, and abnormal cell apoptosis. The
   pathogenesis of CTEPH is closely related to chronic inflammation,
   abnormal cell proliferation and abnormal cell cycle regulation
   [[137]34]. In this study, our results indicated that ARRDC3, HMOX1,
   BRD4, and YWHAE were involved in CTEPH progress possibly through
   regulating inflammatory, vascular cell proliferation, smooth muscle
   cell invasion and apoptosis. Through gene set [138]GSE188938
   verification, only ARRDC3 upregulation showed statistical difference
   suggesting ARRDC3 might be the most important hub gene of CTEPH and a
   potential therapeutic target for CTEPH.

   Additionally, we screened the proportion of 22 immune cells in CTEPH
   using CIBERSORT analysis, and found that there were significant
   differences in the cells infiltration of Eosinophils and Neutrophils
   compared with the control sample. We also analyzed the correlation
   between ARRDC3 and immune cells infiltration. ARRDC3 was positively
   correlated with T cells follicular helper and negatively correlated
   with Neutrophils. Eosinophils and Neutrophils were believed to be
   involved in pulmonary artery atherosclerosis and thrombotic lesions
   leaded to CTEPH [[139]35, [140]36]. Ferroptosis-inducing agents induced
   Ferroptosis-like cell death of Eosinophils [[141]37]. Neutrophil
   extracellular traps mediated m6A modification and regulated
   sepsis-associated acute lung injury by activating Ferroptosis in
   alveolar epithelial cells [[142]38]. These studies were similar with
   our results that both Eosinophils and Neutrophils were involved in
   Ferroptosis processes of CTEPH. Our study found that
   Ferroptosis-related hub gene ARRDC3 was related to immune cell
   infiltration including T cells follicular helper and Neutrophils, and
   further experiments are necessary to verify its exact effect and
   underlying regulatory mechanism.

   The results of bioinformatics analysis provided a new clue and
   viewpoint for our further research on CTEPH. Undeniably, there were
   some limitations in the current study, such as the sample size of the
   selected database, heterogeneity of the clinical parameters of CTEPH
   patients and lack of grading of disease severity. To minimize sample
   heterogeneity, it is necessary to include more samples and improve
   clinical data. Further experimental verification are warranted to
   confirm the effects and functional correlation of the identified genes
   and pathways, and provide more accurate characterization of immune cell
   populations in CTEPH in vivo and vitro studies.

   In conclusion, ARRDC3 upregulation with different immune cell
   infiltration were involved in the development of CTEPH and ARRDC3 might
   a potential and promising Ferroptosis-related biomarker for CTEPH
   treatment. Our study might provide new insights into the prevention,
   monitoring and potential therapeutic intervention for CTEPH.

Supplementary Information

   [143]12872_2023_3511_MOESM1_ESM.xlsx^ (5MB, xlsx)

   Additional file 1: Supplementary Material 1. Data of GSE130391.
   [144]12872_2023_3511_MOESM2_ESM.xlsx^ (33.7KB, xlsx)

   Additional file 2: Supplementary Material 2. 676 DEGs between CTEPH and
   Control group.
   [145]12872_2023_3511_MOESM3_ESM.xlsx^ (13.2KB, xlsx)

   Additional file 3: Supplementary Material 3. 259 Ferroptosis-related
   genes.
   [146]12872_2023_3511_MOESM4_ESM.xlsx^ (9.2KB, xlsx)

   Additional file 4: Supplementary Material 4. The expression of ARRDC3,
   HMOX1, BRD4 and YWHAE in GSE188938.
   [147]12872_2023_3511_MOESM5_ESM.xlsx^ (15.3KB, xlsx)

   Additional file 5: Supplementary Material 5. The expression of 22
   immune cells in the sample and Correlation analysis.

Acknowledgements