A prognostic model of idiopathic pulmonary fibrosis was constructed based on macrophage-related genes and mitochondria-related genes

doi:10.21203/rs.3.rs-3616585/v1

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Research Article

A prognostic model of idiopathic pulmonary fibrosis was constructed based on macrophage-related genes and mitochondria-related genes

https://doi.org/10.21203/rs.3.rs-3616585/v1

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published 12 Apr, 2024

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Background

Studies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the mechanism of macrophages and mitochondria in IPF is not fully understood.

Methods

To construct a prognostic model for IPF based on Macrophage-associated genes (MaRGs) and Mitochondria-associated genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the crossover genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve biomarkers. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of biomarkers. Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups.

Results

There were 4791 DEGs between IPF and Control groups. Furthermore, 26 crossover genes were achieved by the intersection processing. Three biomarkers including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the biomarkers was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that biomarkers were associated with classical pathways including ‘Apoptosis Signaling’, ‘PI3K/AKT Signaling’, and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2.

Conclusion

We achieved 3 biomarkers including ALDH2, MCL1, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF.

Mitochondria

Macrophage

Idiopathic pulmonary fibrosis

Biomarkers

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, and highly fatal interstitial lung disease characterized by progressive dyspnea and irreversible decline in lung function [1], which seriously threatens patients' quality of life and survival time [2]. IPF is variable, unpredictable and significantly heterogeneous, and the rate of progression in a single patient in the short term is difficult to predict [3]. Repeated damage and dysrepair of alveolar epithelial cells cause pulmonary fibrosis, including uncontrolled proliferation of lung fibroblasts and excessive deposition of extracellular matrix proteins in the interstitial space, leading to irreversible changes in lung parenchyma and ultimately IPF [4]. The exact mechanism of pulmonary fibrosis involves many theories, such as excessive epithelial damage repair, cellular senescence and mitochondrial dysfunction, insufficient autophagy, epithelial-mesenchymal transformation (EMT), activation of macrophage subpopulation, and telomere shortening [5, 6]. At present, the only anti-fibrosis drugs approved by the FDA are pirfenidone and Nidanib, both of which have obvious drug toxicity, and the treatment of IPF remains to be explored [7, 8]. Biomarkers involve many aspects such as immunity and genetics, and can be used to evaluate physiological and pathological processes and responses to drug intervention from an objective perspective. Exploring the pathogenesis of IPF from the level of biomarkers is helpful for the diagnosis, treatment and prognosis evaluation of the disease [9, 10].

Mitochondria are membrane-closed organelles with independent circular genomes in eukaryotic cells [11]. Mitochondria can not only provide energy for cells by producing ATP through oxidative phosphorylation, but also play an important role in many processes such as apoptosis, signal transduction and substance metabolism [12]. Mitochondria undergo fission and fusion, destroying the original physiological functions and leading to cellular senescence [13]. Mitochondrial autophagy is an important way to maintain functional homeostasis and is closely related to pulmonary fibrosis [14]. Animal experiments have shown that p53 and p21 activate age-related pathways and promote fibrosis [15]. The pathological results of the lung tissues of IPF patients also showed that the expression of telomere-associated DDR sites (TAFs), p16 and other aging markers was increased [16]. Studies have shown that the expression of mitochondrial DNA is negatively correlated with the survival of IPF patients, and therefore, mitochondrial DNA may be a biomarker for predicting acute exacerbation and progression of IPF [17]. In summary, mitochondria are closely related to the occurrence and development of IPF.

Macrophages are an important part of innate immunity. They exist in different tissues as alveolar macrophages, microglia and Kupfer cells. They play an important role in maintaining the balance of the environment in tissues and regulating immunity. Depending on their environment, they can be polarized into different subtypes such as M1 macrophages (also known as classical activated macrophages) and M2 macrophages (also known as alternative activated macrophages) [18]. Both M1 and M2 macrophages are involved in the pathogenesis of pulmonary fibrosis. M1 plays a major pro-inflammatory role in the early inflammatory stage. Subsequently, M2 increases, sustained damage repair and reduction of inflammatory response through various signaling pathways such as TGF-β1/Smad2/3, leading to pulmonary fibrosis [19]. Studies have shown that IPF is related to apoptosis resistance of M2 macrophages [20, 21]. Mitochondrial autophagy, fission, fusion and other dysfunction exist in pulmonary fibrosis [22]. The absence of miR-33 in alveolar macrophages can stabilize the structure and increase the number of mitochondria, thus improving pulmonary fibrosis. Akt1-mediated mitochondrial autophagy can promote the apoptotic resistance of alveolar macrophages and further aggravate pulmonary fibrosis [14]. Further study of the relationship between mitochondria, alveolar macrophages and IPF may enrich the pathogenesis of IPF and contribute to diagnosis and treatment.

Therefore, in this study, genes related to mitochondrial function and macrophages were first obtained, and bioinformatics methods such as univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct prognostic risk models and search for biomarkers of IPF to better predict the disease progression and prognosis of patients.

2.1 Data sources

The datasets of IPF were achieved by the Gene Expression Omnibus (GEO) online database. The GSE28042 dataset contained 75 IPF samples and 19 Control samples, and these samples were utilized for training set. Moreover, the GSE27957 dataset had 45 IPF samples with clinical information, and it was utilized as validation set. All the samples in GSE28042 and GSE27957 datasets were peripheral blood mononuclear cell (PBMC) samples. In addition, 1136 Mitochondria-related genes (MitoRGs) were achieved through the MitoCarta3.0 online database (http://www.broadinstitute.org/mitocarta). Besides, we acquired 3201 Macrophage-related genes (MaRGs) by the GeneCards online database (https://www.genecards.org/) (Score > 2.5).

2.2 Screening of crossover genes

In our study, DEGs between IPF and Control groups were acquired by the limma (v 3.50.1) package [23](p.adjust < 0.05). A heat map and a volcano map of DEGs were plotted by pheatmap (v 1.0.12) and ggplot2 (v 3.3.5) [24]packages, respectively. Furthermore, 1136 MitoRGs, 3201 MaRGs and DEGs were overlapped to screen out the crossover genes. For further exploring the biological functions and signaling pathways involved in the crossover genes, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses (p.adjust < 0.05) were employed by the clusterProfiler (v 4.2.2) package [25]. In addition, for further understanding the potential interactions of the crossover genes, a Protein Protein Interaction (PPI) network was established using the STRING online (https://string-db.org/) database (minimum required interaction score = 0.4).

2.3 Construction and verification of risk model

According to above crossover genes, the univariate Cox analysis was performed to identify the candidate genes that related to prognosis of IPF (p < 0.05). Furthermore, the LASSO algorithm was implemented to achieve biomarkers. According to the expression of above biomarkers, the risk model was created, and the samples in the GSE28042 and GSE27957 datasets were classified into the high- and low-risk groups using the optimum cut-off value of the risk score (risk score = \(\sum _{1}^{\text{n}}\text{c}\text{o}\text{e}\text{f}\left(\text{g}\text{e}\text{n}\text{e}\text{i}\right)\text{*}expression\left(\text{g}\text{e}\text{n}\text{e}\text{i}\right)\).) respectively. In addition, the Kaplan-Meier (K-M) survival curves and the ROC curves (1-, 2- and 3-year) were plotted, respectively.

2.4 Independent prognostic analysis

In order to construct a prognostic model for clinical survival prediction of IPF patients, riskScore and clinical characteristics (Age and Gender) were combined, then univariate Cox and multivariate Cox analyses were conducted to achieve the independent prognostic factors. Furthermore, a nomogram for forecasting survival rates of the IPF patients (1-, 2- and 3-year) was created. Moreover, the calibration curve was employed to verify the validity of the above nomogram.

2.5 Functional enrichment analysis

In our study, the Gene Set Enrichment Analysis (GSEA) was conducted on all genes (High-risk vs. Low-risk groups) in the GSE28042 dataset (p.adj < 0.05 and |NES| > 1). Furthermore, to further understand the molecular mechanism of biomarkers, the classical signaling pathway analysis was performed by the ingenuity pathway analysis (IPA) to explore the signaling pathways that biomarkers were mainly involved in (p.value < 0.05). Subsequently, we selected the largest |z-score| signaling pathway for further displaying the signal pathway transduction process.

2.6 The immune infiltration analysis

For evaluating the degree of the immune cell infiltration, the CIBERSORT algorithm was performed. Meanwhile, immune cells that did not exist in 75% samples were excluded. Subsequently, the Wilcoxon test was implemented to analyze differential immune infiltrating cells between the two risk subgroups. The IPF samples were classified into high- and low-expression groups based on the median expression of biomarkers. The differences in the differential immune cells between the two expression subgroups of biomarkers were analyzed. Moreover, the relationships between biomarker and differential immune cells were computed (|cor| > 0.3, p < 0.05).

2.7 Regulatory network and the drug prediction

To create the regulatory network, the miRNAs and long noncoding RNA (lncRNA) genes were forecasted using the miRNet online database (www.mirnet.ca). In addition, for exploring potential drugs for the treatment of IPF, the potential drugs for biomarkers were acquired based on the DGIdb database (www.dgidb.org), and a biomarker-drug network was created. Besides, all the networks were visualized using the cytoscape software [26].

2.8 RT-qPCR

The blood samples were gained from the 5 IPF patients in Affiliated Hospital of Shandong University of Traditiona. And the blood samples obtained from 5 healthy individuals were utilized as Control samples. The blood samples were acquired from the samples to perform RT-qPCR. This study was approved by Ethics Committee of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine. All patients had signed an informed consent form. The expression of the four biomarkers was further validated via RT-qPCR. Total RNA of 20 samples were extracted using TRIzol (Ambion, Austin, USA) according to the manufacturer’s guidance. Reverse transcription of total RNA to cDNA was carried out by using SureScript-First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China) based on the manufacturer’s instructions. RT-qPCR was performed utilizing the 2xUniversal Blue SYBR Green qPCR Master Mix (Servicebio, Wuhan, China). The primer sequences for PCR were shown in Table 1. GAPDH was as an internal reference gene. The 2^−ΔΔCt method was utilized to calculate the expression of biomarkers [27].

Table 1

Sequence of primers used in the RT-qPCR experiments.
Gene name	Primer sequences
ALDH2-F	GCATGGACGCATCACACAG
ALDH2-R	TTGCCATTGTCCAGGGTCTC
MCL1-F	TTGCCATTGTCCAGGGTCTC
MCL1-R	AGGTTGCTAGGGTGCAACTC
BCL2A1-F	AGTGCTACAAAATGTTGCGTTC
BCL2A1-R	GGCAATTTGCTGTCGTAGAAGTT
GAPDH-F	CGAAGGTGGAGTCAACGGATTT
GAPDH-R	ATGGGTGGAATCATATTGGAAC

3.1 A total of 26 crossover genes were acquired

In the GSE28042 dataset, there were 4791 DEGs between IPF and Control groups (Fig. 1A, Supplementary Table 1). The expression heat map of the IPF-associated top 10 up- and down-regulated DEGs was shown in Fig. 1B. Subsequently, 26 crossover genes were achieved by the cross-processing (Fig. 1C). According to the functional enrichment analysis, 26 crossover genes were mainly associated with ‘signal transduction in absence of ligand’, ‘outer membrane’, ‘protein transmembrane transporter activity’ etc. GO items, and KEGG pathways such as ‘Apoptosis’, ‘p53 signaling pathway’, ‘Apoptosis-multiple species’ and so on (Fig. 1D-E, Supplementary Table 2–3). Besides, the PPI network including 26 crossover genes was established, there were interactions between 21 crossover genes, and there was a relatively strong interaction between CYCS and BCL2L1 (Fig. 1F).

3.2 Three biomarker was acquired

A total of 3 prognostic candidate genes including ALDH2, MCL1, and BCL2A1 were identified by the univariate Cox analysis (Fig. 2A). Furthermore, there were 3 biomarkers (ALDH2, MCL1, and BCL2A1) sifted out by the LASSO algorithm (Fig. 2B). In the GSE28042 dataset, IPF samples were classified into high- and low-risk groups based on the optimal cut-off value of risk scores that equal to 21.8207, with the increase of risk score, the number of the dead patients also increased. Moreover, we found that the expression of ALDH2, MCL1, and BCL2A1 were more lower in the low-risk group (Fig. 2C-D). In addition, it was found that there was a distinct difference in survival between these two subgroups (p < 0.0001), moreover, the survival rate of the low-risk group was more higher (Fig. 2E). The area under curve (AUC) values (1-, 2-, and 3-year) were all above 0.65, it demonstrated that the model had a favorable prediction accuracy and favorable model performance (Fig. 2F). Besides, we verified the risk model in the GSE27957 dataset, moreover, we found that the results were consistent with the GSE28042 dataset (Fig. 3A-D).

3.3 A nomogram was created

Independent prognostic analysis was employed to establish a nomogram. First of all, the riskScore, Gender, and Age were screened out by the univariate Cox analysis (p < 0.05) (Fig. 4A). Furthermore, the Age, riskScore, and Gender were achieved as independent prognostic factors (Fig. 4B). Accordingly, a nomogram for survival forecasting of IPF patients (1-, 2- and 3-year) was created based on those above independent prognostic factors (Fig. 4C). The calibration curve indicated that the nomogram had a high prediction accuracy (Fig. 4D).

3.4 The GSEA and IPA based on the risk model

We conducted the GSEA, and we found that some pathways such as ‘Osteoclast differentiation’, ‘TNF signaling pathway’, ‘Cell adhesion molecules’ and ‘Purine metabolism’ were enriched in those two risk subgroups (Fig. 5A-B, Supplementary Table 4). In addition, the IPA results showed that biomarkers were associated with 30 classical pathways including ‘Apoptosis Signaling’, ‘PI3K/AKT Signaling’, ‘IL-7 Signaling Pathway’, and so on, and it showed that the influence of biomarkers on signaling pathways was mostly promoting (Fig. 5C, Supplementary Table 5). The ‘IL-7 Signaling Pathway’ with the highest |z-score| (2.683) was selected to display, and we could find that ‘IL-7 Signaling Pathway’ regulated the growth, proliferation, and survival of immune cells by activating multiple signaling cascades such as JAK-STAT, PI3K-Akt and mTOR (Fig. 5D).

3.5 The immune infiltration analysis between two risk subgroups

There were 2 differential immune cells including Monocytes and CD8 T cells between these two risk subgroups (Fig. 6A). Furthermore, two differential immune cells were all significantly different in the two expression subgroups of ALDH2 (Fig. 6B). The Monocytes had a positively association with ALDH2, and the correlation coefficient was 0.54. Meanwhile, there was a negatively relationship between ALDH2 and CD8 T cells (Cor = -0.36) (Fig. 6C-D).

3.6 The lncRNA-miRNA-mRNA network and the biomarker-drug network were created

The lncRNA-miRNA-mRNA regulatory network was created, including 3 biomarkers, 10 miRNAs, and 64 lncRNAs. Further, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2, and MCL1 could be regulated by hsa-mir-15a-5p and XIST (Fig. 7A, Supplementary Table 6). Besides, the biomarkers-drug network was created, including “OBATOCLAX MESYLATE”, “SIROLIMUS”, “OMEPRAZOLE”, etc. (Fig. 7B, Supplementary Table 7).

At present, the only anti-fibrosis drugs approved by the FDA are pirfenidone and Nidanib, both of which have obvious drug toxicity, and the treatment of IPF remains to be explored [7]. The median survival time after diagnosis of IPF is short, the prognosis is poor, and the mortality is high [2]. Mitochondria of IPF patients have different types of dysfunction. Macrophages regulate pulmonary fibrosis through various pathways such as NF-kB and PI3K-Akt-mTOR. Moreover, studies have shown that mitochondrial autophagy and apoptosis resistance of M2 macrophages are causally and causally involved in the process of IPF. Mitochondria-related biomarkers may be able to predict the prognosis of IPF, and it is important for the treatment and prognosis prediction of IPF to construct a prognostic model by combining the study with macrophages.

Using univariate cox regression to screen 26 shared genes, three genes, ALDH2, MCL1, and BCL2A1, were found to be significantly associated with survival. The risk ratio of all genes significantly associated with survival was greater than 1, indicating that they were all risk factors promoting disease development. ALDH2 gene (aldehyde dehydrogenase 2), located on human chromosome 12, is a key mitochondrial enzyme in the metabolism of acetaldehyde and is closely related to oxidative stress [28, 29]. ALDH2 gene enrichment in Alcoholic liver disease, Pantothenate and CoA biosynthesis pathways. The Alcoholic liver disease pathway is closely related to oxidative stress [30], and the Pantothenate and CoA biosynthesis pathways are closely related to lipid, protein and other compounds and energy metabolism [31]. High expression of ALDH2 can reduce the expression of fibrotic genes and excessive deposition of extracellular matrix in fibroblasts [32]. ALDH2 deficiency can cause mitochondrial biogenesis disorder of cardiomyocytes [33]. The upregulation of ALDH2 can inhibit myocardial cell damage induced by high glucose and alleviate myocardial fibrosis in rats. The mechanism of action may be related to oxidative stress and inflammation involved in MMP14 and TIMP4 [34]. In addition, upregulation of ALDH2 can regulate autophagy to protect renal tubular epithelial cells through the Beclin-1 pathway [35], thereby ameliorating acute kidney injury and neuronal damage caused by hypoxia [36]. Both the MCL-1 gene and BCL2A1 gene are derived from the BCL-2 gene family. MCL-1 gene is one of the anti-apoptotic proteins, which can be targeted to induce mitochondrial autophagy and enriched in the Apoptosis Signaling pathway [37]. MCL-1 is overexpressed in breast cancer, lung cancer and other cancers, and is associated with resistance to chemotherapy drugs [38]. BCL1A2 is enriched in Apoptosis Signaling and NF-kappa B signaling pathway. Studies have shown that BCL1A2 can inhibit TNF-α-induced endothelial cell damage and reduce cell death through the NF-κB pathway [39]. In addition, BCL1A2 is involved in advanced metastasis of ovarian cancer, breast cancer, acute and chronic leukemia and other blood diseases and cancers [40]. In addition, TNF released by M1 macrophages changes the phenotype of macrophages and fibroblasts, delays tissue repair, and also produces TGF-β1 and platelet-derived growth factor (PDGF), promoting pulmonary fibrosis [41]. Therefore, it is speculated that the profibrotic effect of ALDH2 gene is related to oxidative stress and energy metabolism. In addition, this study found for the first time that MCL1 and BCL1A2 genes may play an important role in the occurrence and development of IPF, and both genes are enriched in the Apoptosis Signaling pathway. We speculate that the mechanism of MCL-1 and BCL1A2 participating in promoting pulmonary fibrosis is related to apoptosis resistance. But the specific functional mechanism needs to be further studied.

We found that monocytes and CD8 T cells were different between the high and low risk groups of IPF. There were significant differences in ALDH2 gene expression between the two types of immune cells, but no significant differences in MCL1 and BCL2A1 expression between the two groups. Monocytes are derived from hematopoietic stem cells in bone marrow and can differentiate into macrophages and myeloid dendritic cells. A retrospective analysis in 2021 indicated that monocyte count was positively correlated with the number of acute exacerbations and mortality in patients with IPF, and the blood routine was simple and convenient, so monocytes could be a promising serum biomarker for the prognosis of IPF [42]. CD8 T is a key component of the adaptive immune system that monitors the body and clears infections. Bleomycin was injected into both IL-21 receptor deficient mice and wild mice, but collagen deposition and α-smooth muscle actin expression were significantly reduced in lung tissue of IL-21 receptor deficient mice. IL-21 is a key cytokine for differentiation of CD8 T into Tc2 cells, demonstrating that CD8 T is involved in pulmonary fibrosis in an IL-21-dependent manner [43]. We analyzed the differences in immune cells between ALDH2 expression subgroups and found that ALDH2 was positively correlated with monocytes and negatively correlated with CD8 T cells. Studies have shown that people with ALDH2 genotype are more likely to have damage to the number and function of peripheral blood monocytes after receiving alcohol [44], which is different from our analysis results, and may be related to the existence of monocytes in peripheral blood or differentiation into macrophages. In summary, the occurrence and development of IPF may be closely related to monocytes and CD8 T cells.

We further made potential drug prediction of biomarkers, and found that Obatoclax is a hydrophobic small molecule inhibitor of the BCL-2 family, which mainly acts on tumors, respiratory diseases, and immune system diseases [45]. The MCL-1 gene and BCL2A1 gene that can promote fibrosis are derived from the BCL-2 family. Obatoclax may play an anti-fibrosis role by regulating cell apoptosis resistance. Docetaxel is a plant-derived drug that exerts anti-tumor effects by inducing cell cycle arrest and is closely related to CD8 T cells [46]. Docetaxel may be associated with IPF via CD8 T cells and the ALDH2 gene. There are also Sirolimus, Aspirin, Prochlorazine,Disulfiram, Romidepsin and Omeprazole 6 drugs. Sirolimus is a specific mTOR inhibitor used to suppress the function of the human immune system [47]. Aspirin is mainly used as antipyretic and analgesic drugs, non-steroidal anti-inflammatory drugs, anti-platelet aggregation drugs [48]. Prochlorperazine is a phenothiazine drug with a piperazine side chain, which has antipsychotic effects [49]. Disulfiram acts primarily by irreversibly inhibiting intracytoplasmic and intracmitochondrial acetaldehyde dehydrogenase in the treatment of alcohol-dependent and cooperative alcoholics [50]. Romidepsin is a histone deacetylase (HDAC) inhibitor with antitumor activity for cutaneous T-cell lymphoma [51]. Omeprazole is a first-generation proton pump inhibitor, which can inhibit gastric acid secretion and protect the stomach mucosa [52]. The above-mentioned drugs have been widely used in other fields, but the anti-fibrotic effect still needs further study.

IPF is an irreversible chronic lung disease with poor prognosis, poor quality of life and short survival time. Its course and survival rate are difficult to predict. In this study, clinical and biological characteristics of patients were analyzed and comprehensively evaluated. The intersection of IPF differentially expressed genes, macrophage related genes and mitochondria related genes was combined with database analysis to obtain ALDH2, MCL1, and BCL2A1 genes, and it was speculated that the mechanism of their participation in IPF was related to oxidative stress, energy metabolism and cell apoptosis resistance. Moreover, a prognostic model of IPF was constructed to identify two related immune cells, monocyte and CD8T, and to predict potential effective drugs. However, the specific experimental mechanism needs to be further studied, and we will continue to pay attention to the research progress of 13 genes ALDH2, MCL1 and BCL2A in the future. In conclusion, this study fills the gap in prognostic biomarkers of IPF, can better predict the disease progression and prognosis of patients with IPF, and promote the development and progress of clinical research.

IPF

Idiopathic Pulmonary Fibrosis

EMT

epithelial-mesenchymal transformation

TAFs

telomere-associated DDR sites

GEO

Gene Expression Omnibus

PBMC

peripheral blood mononuclear cell

MitoRGs

Mitochondria-related genes

MaRGs

Macrophage-related genes

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

PPI

Protein Protein Interaction

LASSO

least absolute shrinkage and selection operator

K-M

Kaplan-Meier

GSEA

Gene Set Enrichment Analysis

IPA

ingenuity pathway analysis

lncRNA

long noncoding RNA

AUC

area under curve

PDGF

platelet-derived growth factor

HDAC

histone deacetylase

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The GSE28042 dataset and the GSE27957 dataset analysed during the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds). 1136 Mitochondria-related genes (MitoRGs) analysed during the current study are available in the MitoCarta3.0 online database (http://www.broadinstitute.org/mitocarta). 3201 Macrophage-related genes (MaRGs) analysed during the current study are available in the GeneCards online database (https://www.genecards.org/).

Competing interests

The authors declare that they have no competing interests.

Funding

This research was funded by the National Natural Science Foundation of China [grant numbers 82104799].

Authors' contributions

Yu Bao,Shiyuan Yang and Xue Zhu were conceived and experimentally validated. Yezhen Wang andKe Li conducted data collection and data analysis. Hailan Zhao, Xue Liu and Wei Zhang have completed the data analysis. All authors participated in the writing of the paper and have final approval for the submitted and published version.

Acknowledgements

Thank you very much for the support and help of the National Natural Science Foundation. Thanks to Xue Zhu for his assistance in the experiment and Wei Zhang for his valuable advice on the discussion.

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No competing interests reported.

SupplementaryTable1.xls
Supplementary Table 1.xls： Supplementary Table 1. Differentially expressed genes (DEGs)between Idiopathic Pulmonary Fibrosis (IPF) and Control groups.
SupplementaryTable2.xls
Supplementary Table 2.xls： Supplementary Table 2. The 307 Gene Ontology (GO) Biological Process (BP) terms, 14 GO Cellular Components (CC) terms, and 12 GO Molecular Function (MF) terms were enriched for 26 crossover genes.
SupplementaryTable3.xls
Supplementary Table 3.xls： Supplementary Table 3. The 50 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched for 26 crossover genes.
SupplementaryTable4.xls
Supplementary Table 4.xls： Supplementary Table 4. The 41 Gene Set Enrichment Analysis (GSEA) pathways in the GSE28042 dataset.
SupplementaryTable5.xls
Supplementary Table 5.xls： Supplementary Table 5.The 30 classical pathways of the column plot of the 30 classical pathways in the GSE28042 dataset.
SupplementaryTable6.xls
Supplementary Table 6.xls： Supplementary Table 6. The 268 pairs of regulatory relationships in the competing endogenous RNAs (ceRNA) regulatory network.
SupplementaryTable7.xls
Supplementary Table 7.xls： Supplementary Table 7. The 52 pairs relationships in the predictive network of prognostic genes and drugs.

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published 12 Apr, 2024

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You are reading this latest preprint version

A prognostic model of idiopathic pulmonary fibrosis was constructed based on macrophage-related genes and mitochondria-related genes

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Journal Publication

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Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1 Data sources

2.2 Screening of crossover genes

2.3 Construction and verification of risk model

2.4 Independent prognostic analysis

2.5 Functional enrichment analysis

2.6 The immune infiltration analysis

2.7 Regulatory network and the drug prediction

2.8 RT-qPCR

3. Results

3.1 A total of 26 crossover genes were acquired

3.2 Three biomarker was acquired

3.3 A nomogram was created

3.4 The GSEA and IPA based on the risk model

3.5 The immune infiltration analysis between two risk subgroups

3.6 The lncRNA-miRNA-mRNA network and the biomarker-drug network were created

4. Discussion

5. Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1