Construction and evaluation of prognostic model of genes related to cell burial in idiopathic pulmonary fibrosis (IPF)

doi:10.21203/rs.3.rs-2702947/v1

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Research Article

Construction and evaluation of prognostic model of genes related to cell burial in idiopathic pulmonary fibrosis (IPF)

https://doi.org/10.21203/rs.3.rs-2702947/v1

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Background

Idiopathic pulmonary fibrosis (IPF) is a complex lung disease. Efferocytosis was related to IPF initiation and progression. The study aimed to mine efferocytosis-related genes (ECRGs) and establish corresponding prognostic signature in IPF.

Methods

Differentially expressed ECRGs (DEECRGs) were obtained by overlapping differentially expressed genes (DEGs) between IPF and normal samples and ECRGs. Univariate COX and the least absolute shrinkage and selection operator (LASSO) regression were applied to construct a risk model. The model was evaluated by Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves. Multivariate Cox model was performed, nomogram was further constructed. Moreover, gene set variation analysis (GSVA) and immune infiltration of two risk groups were explored. Last, the study evaluated the predictive power of EC-related model genes in both GSE70866 training dataset and GSE10667 validation dataset.

Results

A risk model was constructed with 5 ECRGs (CXCR4, ODC1, AXL, DOCK5 and MERTK). K-M analysis showed IPF patients in high risk group performed noteworthy poorer survival than those in low risk group. ROC curves indicated good performance of the risk model. GSVA illustrated that biological processes of diacyl bacterial lipopeptide and amino acid betaine biosynthetic process, and KEGG pathways of clycosaminoglycan biosynthesis chondroitin sulfate and butanoate metabolism signaling pathway were significantly different in two risk groups. Immune infiltration analysis showed that there were significant differential immune cells(Mast cells, naive B cells, actiated NK cells, M0 Macrophages, resting Dendritic cell and resting Mast cell)in two risk groups.

Conclusions

A risk model consisting of 5 ECRGs (CXCR4, ODC1, AXL, DOCK5 and MERTK) was successfully constructed, which could provide a new idea for the prognosis of IPF.

Idiopathic pulmonary fibrosis

immune infiltration

efferocytosis related-genes

prognosis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive and irreversible respiratory disease with unknown pathogenesis, which can lead to the formation of lung scar (called fibrosis in medical terminology). Its pathological features includes repeated epithelial cell injury, inflammatory cell infiltration, fibroblast activation and a large number of extracellular matrix (ECM) deposition. These harmful pathological effects eventually lead to progressive loss of lung function, respiratory failure caused by disease progression is the main cause of death of IPF patients [1, 2]. The prevalence of IPF is about 0.33–4.51 per 10000 people, which is still a rare disease. The prognosis of IPF patients is poor. Due to the lack of effective treatment drugs, the estimated median survival time after diagnosis is 3–5 years, and the 5-year survival rate is only 20% [3, 4]. The incidence of IPF and its associated mortality are significantly positively correlated with the growth of age, which has become one of the difficult problems in the field of medical respiration. Therefore, accurate assessment of the prognosis of IPF patients at the early stage of the disease helps to strengthen disease management and improve patient outcomes [5, 6]. The advent of pirfenidone and nidanib has been proved to delay the IPF process. Lung transplantation is a feasible treatment option for IPF patients, but it is only applicable to a few IPF patients [7]. We still have a long way to go in understanding the exact pathophysiology and treatment of IPF. Therefore, it is important and necessary to explore new prognostic markers and models to improve the prognosis, improve the survival rate of patients, and provide appropriate treatment for patients.

Efferocytosis is the basic process of identifying and removing apoptotic cells by professional (such as macrophages, dendritic cells (DC), etc.) and non-professional phagocytes (such as fibroblasts, endothelial cells and some epithelial cells, etc.), which is essential for maintaining lung homeostasis and controlling inflammation [8]. The significance of these phagocytes to play the role of efferocytosis is that they can be quickly and safely cleared before cell death, membrane rupture and content release to surrounding tissues. Pulmonary macrophage group is the main outflow cell in the tissue, responsible for controlling the immune response, and avoiding unrestricted inflammation and autoimmunity by expressing a large number of receptors that recognize multiple "eat me" signals on apoptotic cells. The effect of efferocytosis is anti-inflammatory and tolerant in nature, which is crucial for terminating the inflammatory reaction of lung and promoting the repair of injury [9]. There has been a lot of research progress on the pathogenesis of IPF at home and abroad, mainly focusing on the "inflammatory response theory" and "damage repair theory". There are similar efferocytosis damage in IPF, which strengthens the link between fibrosis and defective efferocytosis [10]. Nevertheless, it is still unknown which genes related to efferocytosis are crucial to the development of IPF. Therefore, the correlation between IPF and the genes related to efferocytosis remains to be understood. Further research is needed to determine new biomarkers for the treatment of IPF according to the potential genes related to efferocytosis involved in IPF.

With the rapid development of technology, massive data generated by second-generation sequencing are widely used in biomedical research. Bioinformatics is a powerful tool for processing such data and obtaining useful information. In this study, we tried to retrieve the gene expression data of IPF patients in GEO, extract the differential genes related to efferocytosis, and construct the gene risk model related to efferocytosis using Univariate COX and LASSO regression analysis, providing a way to further study the molecular mechanism and prognosis prediction of IPF.

Data Acquisition

IPF-related datasets were downloaded from the Gene Expression Omnibus (GEO) database. The GSE70866 training dataset included 132 cohorts, including 20 normal and 112 IPF bronchoalveolar lavage fluid, among them, the GSE28221 validation risk model dataset contained 45 survival information whole blood samples. The GSE10667 validation model genes dataset included 23 samples, including 15 normal tissues and 8 IPF tissues. In addition, a total of 74 efferocytosis related genes (ECRGs) were obtained from the documents [11, 12].

Differentially expressed ECRGs (DEECRGs) identification and functional enrichment analysis

The differentially expression genes (DEGs) in IPF and normal samples from GSE70866 dataset was screened by “limma” package (p_value < 0.05) [13]. Volcano Plot of DEGs was made by R package “ggpubr”(version0.4.0) [14]. The intersection of DEGs with ECRGs was shown by Venn diagram to capture DEECRGs. The package “clusterProfiler” package [15] was used to perform enrichment analysis of DEECRGs by gene GO and KEGG enrichment analyses (p.adj < 0.05).

Construction of a risk model

The 112 IPF patients with survival information in GSE70866 dataset (training set), in addition, the IPF-related gene signatures were constructed and verified in GSE28211 validation set (with 45 IPF survival information). The univariate Cox analysis and LASSO regression analysis were applied to obtain IPF-related model genes in the GSE70866 dataset. Based on Riskscore = \(\sum _{i=1}^{n}\beta\)_i ∗xi (i = the number of prognostic genes,βand x respectively represented the coefficient obtained by LASSO and the amount of gene expression) [16], we calculated the risk value of each patient. According to the optimal threshold of risk value of IPF patients, the patients were classified into two risk groups. The accuracy of the model was evaluated by ROC and K-M curves.

GSVA (gene set variation analysis)

Based on C2: KEGG gene setsand C5:Gene Ontology gene sets (BP, CC and MF) from MSigDB datebase, GSVA was carried out in low-high risk groups. Then differential biological functions and signaling pathways of two risk groups were analyzed by “GSVA”(version1.42.0) and “limma”(version3.50.1) packages [17, 13].

Immune microenvironment

First, the infiltration proportion of 22 kinds of immune cells were calculated by CIBERSORT algorithm (p value < 0.05) and LM22 gene set of immune cells in all samples (112 samples) of GSE70866 dataset [18]. Second, differential immune cells were analyzed between the two risk groups.

Construction of prognostic model

The clinicopathological factors (age, gender and Risk score) of 112 IPF samples in GSE70866 dataset were utilized to construct multivariate Cox model, and the nomogram of prognostic model was constructed using R package "RMS"(version6.2.0) [19]. The correction curve of the nomogram were drawn based on the above prediction model.

The expression of model genes

The expression box diagram of the model genes were respectively drawn in the GSE70866 dataset (training set) and GSE10667 dataset (validation set) by R package “beeswarm”(version0.4.0, PMID:34843276) [20].

Analysis of DEGs between normal and IPF patients

First, a sum of 3764 DEGs were obtained between normal and IPF bronchoalveolar lavage fluid samples (221 down-regulated and 3543 up-regulated genes). The results were shown in a volcano Plot and heatmap (Fig. 1A-1B). Next, the intersection of DEGs and ECRGs was taken to identify 18 DEECRGs, the result was presented as a Venn diagram (Fig. 1C).

Enrichment analysis of DEECRGs

Functional enrichment analysis played a key role to discover biological functions and signaling pathways of genes. The results with GO of BP (top5), MF (top5) and CC (top2) enrichment analyses were shown in Fig. 2A-2D. Among them, in the BP entris, biological processes of "negative regulation of immune system process", "protein kinase B signal transduction", "negative regulation of cell activation", "negative regulation of neuronal death" and "negative regulation of leukocyte activation" were associated with 18 DEECRGs. Besides, MF of "virus receptor activity", "foreign protein binding", "cytokine receptor binding", "chemokine receptor binding", "cytokine activity", and the two types of CC of "cell body membrane" and "plasma membrane lateral" were also related to 18 DEECRGs. Meanwhile, pathway of "cytokine-cytokine receptor interaction", "chemokine signal pathway", "intestinal immune network produced by IgA", "interaction of viral proteins with cytokines and cytokine receptors", "arginine and proline metabolism" were relevant to 18 DEECRGs (Fig. 2E-2F). Therefore, we hypothesized these genes could influence IPF development through these pathways and functions.

Construction and evaluation of a risk model

To screen genes associated with IPF prognosis in GSE70866 dataset (training set), firstly, 11 genes were mined by univariate Cox regression (Fig. 3A, p < 0.05). Then, five genes ( CXCR4, ODC1, AXL, DOCK5 and MERTK ) were further screened by LASSO algorithm (Fig. 3B-3C). Meanwhile, the risk values of the patients were calculated by risk score model, and the IPF patients were classified into high-low risk groups with an optimal threshold (Fig. 3D). Survival status showed that the mortality risk of the IPF patient increased gradually as the risk score increased (Fig. 3E), and K-M survival analysis manifested IPF patients with higher risk reprented noteworthy poorer survival than those in lower risk (Fig. 3F). According to the results of heatmap, in IPF patients with higher risk scores, CXCR4, ODC1, DOCK5 and MERTK were highly expressed, while the AXL was lowly expressed (Fig. 3G). Finally, the survival of IPF patients were performed by the ROC curves, the results illustrated the AUC values for OS were 0.809 (1 year), 0.802 (2 year), and 0.788 (3 year) in the training set, indicating good performance of prognostic model(Fig. 3H).

To further confirm predictive applicability of risk model, the above analyses were also performed in the external validation set (GSE28221 dataset). The comparable trend was verified in the external validation set (Fig. 3I-M).Above results revealed that the EC-related gene signature was a valid survival predictor for IPF patients.

Gene Set variation Analysis (GSVA) in two risk groups

To analyze differential signaling pathways and biological functions of two risk groups, GSVA was deployed. The BP pathways of diacyl bacterial lipopeptide, regulation of growth rate, L serine catabolic process, tnfsf11 mediated signaling pathway and induction of bacterial agglutination were markedly up-regulated in high risk group, while regulation of endosome to plasma membrane protein transport, amino acid betaine biosynthetic process, ubiquitin dependent glycoprotein erad pathway and positive regulation of connective tissue replacement were observably down-regulated in low risk group (Fig. 4A). In addition, KEGG pathways showed that clycosaminoglycan biosynthesis chondroitin sulfate, phenylanine metabolism, ecm receptor interaction, bladder cancer and glycosaminoglycan biosynthesis heparan sulfate were activated in high risk group, whereas propanoate metabolism, butanoate metabolism, biosynthesis of unsaturated fatty acids, beta alanine metabolism and limone and pinene degradation were suppressed in low risk group (Fig. 4B).

Immune cell infiltration analysis in two risk groups

In order to understand the differential immune cell types in two risk groups, CIBERSORT algorithm was uitilized, among them, the abundance of M0 Macrophages and Monocytes accounted for the largest proportion (Fig. 5A). In high risk group, activated mast cells was higher, while in low risk group, naive B cells, resting dendritic cell, actiated NK cells, M0 macrophages and resting mast cell were found more abundant, as shown in Fig. 5B.

Construction and verification analysis of nomogram model

The multivariate Cox analyses was utilized to mine the prognostic value of the clinicopathological features and the risk score. The results indicated that Gender, risk score and age could be used as a multivariate Cox model, as shown in Fig. 6A. Then those risk factors were used to generate the nomogram to predict survival probabilities of 1/2/3 years of IPF patients. (Fig. 6B), and calibration curve indicated that decent prediction effect for IPF patients (Fig. 6C). Finally, the model genes of CXCR4, ODC1, DOCK5 and MERTK were up-regulated in GSE70866 dataset (training set), while the AXL was down-regulated (Fig. 6D). Meanwhile, in GSE10667 (validation dataset), the genes erpression of CXCR4, DOCK5, MERTK and AXL were consistented in GSE70866 dataset, however, the ODC1 was down-regulated (Fig. 6E).

Idiopathic pulmonary fibrosis (IPF) is a serious and complex lung disease characterized by repeated skin cell injury, fibroblast activation and a large amount of ECM deposition ultimately lead to lung function progressive loss, patients may even die from respiratory failure [21]. The prognosis of IPF is extremely poor [22]. Therefore, it is of great significance to clarify the pathogenesis of IPF, explore new treatment strategies and establish a prognosis model. In the process of efferocytosis, phagocytic cells will swallow apoptotic cells and form a large vacuole containing dead cells, which is called "efferocytosis body" [23]. At the early stage of apoptosis, the redistribution of phosphatidylserine (PS) to the outer leaves of the plasma membrane is a key signal for phagocytes to recognize apoptotic cells [24]. Recently, several receptors have been identified to recognize PS and thus mediate efferocytosis. The lack of these receptors will lead to the deterioration of inflammation and autoimmune diseases [25]. Efferocytosis can trigger signal transduction pathways downstream of the cell, such as anti-inflammatory, anti-protease and growth promotion effects. However, the damage of the mechanism of efferocytosis will lead to autoimmune diseases and tissue damage. Diseases related to the impairment of efferocytosis function include cystic fibrosis, bronchiectasis, chronic obstructive pulmonary disease, asthma, idiopathic pulmonary fibrosis, rheumatoid arthritis, lupus erythematosus, glomerulonephritis and atherosclerosis [26].

However, no bioinformatics analysis has been carried out on the genes related to IPF efferocytosis. In this study, we first screened DEGs between IPF samples and normal samples in GSE70866 data set, including 3543 differentially up-regulated genes and 221 differentially down-regulated genes. Among the top 10 up-regulated genes, some of them have been confirmed to be consistent with the results of this analysis, and some of them have not been studied, indicating that these genes may be new research targets for the pathogenesis of IPF. For example, SPP1 is up-regulated in the lower lung of IPF (late IPF) [27], CYTL1 has the function of promoting fibrosis and angiogenesis [28, 29], MMP7 promotes fibrosis [30, 31], and CAMP can inhibit IPF fibrosis [32]. After crossing DEGs with ECRGs, 18 IPF DEECRGs were obtained.

Then GO and KEGG enrichment analysis was carried out on these 18 DEECRGs to study the potential biological functions. GO and KEGG pathway analysis showed that these DEECRGs were mainly concentrated in “protein kinase B signal transduction”, “cytokine receptor binding”, and “interaction of viral proteins with cytokines and cytokine receptors”.

In order to further evaluate the prognostic significance of these genes, we used univariate Cox regression and Lasso regression to screen five genes related to the survival and prognosis of IPF patients from 18 potential genes related to efferocytosis, namely CXCR4, ODC1, AXL, DOCK5, MERTK. The overexpression of CXCR4 is related to the early death of IPF patients. The overexpression of CXCR4 is related to the early death of IPF patients. They recruit circulating fibroblasts (CFs) to the lungs and release extracellular matrix protein, collagen I and transforming growth factor β 1. Promote the proliferation of fibroblasts and their differentiation into myofibroblasts, thus promoting pulmonary fibrosis [33, 34]. In addition, CXCR4 can be down-regulated by small molecular antagonists (AMD3100, AMD070, BL−8040), among which AMD3100 has recently been proved to reduce pulmonary fibrosis after exposure to bleomycin (BLM) and paraquat [33, 35]. Ornithine decarboxylase (ODC1) metabolizes l-ornithine to polyamines, is a rate-limiting enzyme involved in polyamine metabolism, and is involved in cell differentiation, proliferation and migration [36]. In 2016, Lo H. C, Hardpower DM and others believed that ODC1 could cause pulmonary vasoconstriction, and then lead to capillary inflammation and leakage, leading to systemic inflammation and alveolar injury, and ODC1 could be up-regulated during bacterial infection [37, 38]. However, a document published in Nature in September 2022 said that ODC1 could inhibit the production of proinflammatory cytokines, reduce inflammatory cell death, and make it a potential therapeutic target for inflammatory diseases [39]. As the pathogenesis of IPF varies, so far, it is not known whether ODC1 plays a positive or negative role in IPF. However, the above evidence has shown that ODC1, as one of the ECRGs, may be related to inflammatory reaction and can be used as a new research target for the pathogenesis of IPF. AXL is a member of the TAM family. It is noteworthy that the Univariate COX regression results in this study show that only the HR of AXL is less than 1, which means that the high expression of AXL may delay the IPF process and reduce mortality. In 2014, Fujimori T and other scholars believed that AXL usually showed high expression on healthy airway macrophages, and inflammatory stimulation would induce high expression of AXL, while AXL led macrophages to phagocytosis of apoptotic cells (efferocytosis) under inflammatory conditions, which was the key step of inflammation regression [40]. However, there are 2021 literatures that believe that AXL is a smoke reaction molecule, which may interact with activated MARCKS to form a fibrosis molecular complex to drive the invasion of lung fibroblasts, thus aggravating the disease [41]. Not only that, AXL also shows high expression in various human malignant tumors, and AXL targeted drugs have also played a good therapeutic effect in cancer [42]. Therefore, whether AXL plays a positive role in delaying the IPF process needs further research and verification. There are few studies on Dock5 in respiratory diseases. It is known that Dock5 is highly up-regulated in the proliferative phase of wound repair and can affect the function of extracellular matrix (ECM) deposition [43]. Because the pathogenesis of IPF is related to ECM deposition, we speculate that the high expression of Dock5 may aggravate IPF, which can be used as a new target for further research. MerTK is a receptor of the complex of Gas6 or protein S and phosphatidylserine, which is often highly expressed on macrophages [44]. Blocking Gas6 can inhibit the marker of fibroblast activation [45]. The results of in vitro experiments also show that MERTK is highly expressed in IPF lung tissue [46]. In clinical studies, MERTK inhibitors may significantly reduce the activation of pro-fibrotic macrophages and fibroblasts in IPF lung [47]. Therefore, MERTK can promote IPF.

Lasso regression was used to construct a risk model based on five genes, and the patients in the training set and the validation set were divided into high risk group and low risk group with the median risk score as the critical value. K-M survival curve and ROC curve show certain prediction feasibility.

In order to analyze the different signal pathways and biological functions of the two risk groups, we used GSVA to explore the potential mechanism of how DEGs affect the prognosis of IPF. The full name of GSVA is Gene set variation analysis, which is a non-parametric and unsupervised algorithm. GSVA quantifies the results of gene enrichment, which can be more convenient for subsequent statistical analysis. After that, based on the grouping information, the difference analysis of GSVA results can find gene sets with significant differences between samples, which is more biological and more interpretable. We found that some pathways, such as tnfsf11 signal pathway and diall bacterial lipopeptide, were significantly enriched in IPF patients with high risk scores. Research has shown that TNFSF11 is also known as the nuclear factor- κ B ligand (RANKL) can reduce cell death, inhibit cell burial, and promote the proliferation and repair of lung epithelium. Therefore, we speculate that this pathway may be related to IPF caused by excessive proliferation and repair of lung epithelium[48].

In order to accurately evaluate the composition of immune cells in the microenvironment of diseased tissues, the immunoinfiltration analysis was carried out, and the abundance of M0 macrophages and monocytes accounted for the largest proportion. In addition, the infiltration degree and expression level of immune cells in the two groups were also different. In the high-risk group, activated mast cells (MCs) are higher, and MCs activate TGF-β Induce the differentiation of fibroblasts into myofibrin cells [49, 50], while in the low-risk group, immature B cells, resting dendritic cells, activated NK cells, M0 macrophages, and resting mast cells are abundant. Among them, mature B cells can promote the inflammation and fibrosis changes in IPF patients [51], and NK cell dysfunction may develop serious fibrotic lung disease [52]. As a professional phagocyte, M0 macrophages have high abundance in the low-risk group of IPF, indicating that the damage of efferocytosis aggravates IPF.

At the end of this study, our multivariate Cox analysis included age and sex as variables to predict the survival probability of IPF patients. As we all know, IPF can be seen all over the world, and it is more common in men than in women. Old age is one of the most important risk factors for IPF. Its incidence rate and prevalence rate increase significantly with age. Two thirds of IPF patients are more than 60 years old at the time of onset, and the average age at the time of diagnosis is 66 years old. Among people over 65 years old, the estimated prevalence rate may be as high as 400 cases per 100000 people [53, 54]. Toren et al found that longevity gene can resist IPF gene through animal experiments, and confirmed that age is an important risk factor for IPF [55].

In the construction of a prognostic model for predicting the survival of IPF patients, the expression trend of five model genes in the training set and the external validation set GSE10667 was compared. The results showed that four of the five genes had the same trend. In addition, the model genes that make up the risk score can also be used as the basis and direction of the next research.

Due to the poor prognosis, high variability and unpredictability of IPF progression, effective risk classification and treatment strategies are needed to develop personalized and targeted treatment. Compared with earlier research, our research is based on bioinformatics technology, which links the pathogenesis of IPF with the role of efferocytosis, which is innovative in IPF-related research so far and provides a good theoretical basis for IPF research.

However, our research also has several limitations. First, our results are based on bioinformatics analysis, so we need further experimental verification. Second, the data used for the study was retrieved from the public database, because there are not enough IPF patients in our clinical practice. Therefore, more detailed investigation is needed in the future.

In conclusion, a risk model consisting of 5 ECRGs (CXCR4, ODC1, AXL, DOCK5 and MERTK) was successfully constructed, which could provide a new idea for the prognosis of IPF.

IPF

idiopathic pulmonary fibrosis

ECRGs

efferocytosis-related genes

DEECRGs

Differentially expressed efferocytosis-related genes

DEGs

differentially expressed genes

ROC

receiver operating characteristic

LASSO

least absolute shrinkage and selection operator

GSVA

gene set variation analysis

K- M

L- Kaplan-Meier

KEGG

Kyoto Encyclopedia of Genes and Genomes

ECM

extracellular matrix

dendritic cells

GEO

Gene Expression Omnibus

Gene Ontology

biological process

molecular function

cellular component

phosphatidylserine

Availability of data and materials

The datasets generated during the current study are available in the Gene expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/, accession codes: GSE28221, GSE70866, GSE10667) repository.

Competing interests

The authors declare that they have no competing interests.

Funding

This review was supported by the National Natural Science Foundation of China (No. 82004285).

Authors' contributions

YFS and YYZ performed data analysis. YFS and YYZ performed data collection, prepared the frst manuscript draft, validated data collection, refned the research idea, performed data analysis and edited manuscripts. FW developed the research idea.XL are the guarantors of the manuscript. All authors read and approved the fnal manuscript.

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Contributor Information

Yuefeng Sun,Email:605396940.qq.com

Yueyang Zhang,Email:1962183952.qq.com

Fan Wu,Email:[email protected]

Xue Liu,Email:[email protected]

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No competing interests reported.

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Construction and evaluation of prognostic model of genes related to cell burial in idiopathic pulmonary fibrosis (IPF)

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

Data Acquisition

Differentially expressed ECRGs (DEECRGs) identification and functional enrichment analysis

Construction of a risk model

GSVA (gene set variation analysis)

Immune microenvironment

Construction of prognostic model

The expression of model genes

Results

Analysis of DEGs between normal and IPF patients

Enrichment analysis of DEECRGs

Construction and evaluation of a risk model

Gene Set variation Analysis (GSVA) in two risk groups

Immune cell infiltration analysis in two risk groups

Construction and verification analysis of nomogram model

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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