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Identification and validation of targets of swertiamarin on idiopathic pulmonary fibrosis through bioinformatics and molecular docking-based approach



Swertiamarin is the main hepatoprotective component of Swertiapatens and has anti-inflammatory and antioxidation effects. Our previous study showed that it was a potent inhibitor of idiopathic pulmonary fibrosis (IPF) and can regulate the expressions of α-smooth muscle actin (α-SMA) and epithelial cadherin (E-cadherin), two markers of the TGF-β/Smad (transforming growth factor beta/suppressor of mothers against decapentaplegic family) signaling pathway. But its targets still need to be investigated. The main purpose of this study is to identify the targets of swertiamarin.


GEO2R was used to analyze the differentially expressed genes (DEGs) of GSE10667, GSE110147, and GSE71351 datasets from the Gene Expression Omnibus (GEO) database. The DEGs were then enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for their biological functions and annotated terms. The protein-protein interaction (PPI) network was constructed to identify hub genes. The identified hub genes were predicted for their bindings to swertiamarin by molecular docking (MD) and validated by experiments.


76 upregulated and 27 downregulated DEGs were screened out. The DEGs were enriched in the biological function of cellular component (CC) and 7 cancer-related signaling pathways. Three hub genes, i.e., LOX (lysyl oxidase), COL5A2 (collagen type V alpha 2 chain), and CTGF (connective tissue growth factor) were selected, virtually tested for the interactions with swertiamarin by MD, and validated by in vitro experiments.


LOX, COL5A2, and CTGF were identified as the targets of swertiamarin on IPF.

Peer Review reports


IPF is a chronic progressive disorder and shares many common clinical, pathological, and immune characteristics with pulmonary fibrosis [1], one of the post-sequelae of COVID-19 [2]. IPF can significantly lower patients’ life quality and life expectancy [3] because of the irreversible decline in lung functions. Some environmental factors, genetic susceptibilities [4], and oxidative stress [5] were reported to increase the risk of getting IPF. Oxidative stress is believed to play an important role in the initiation of inflammation and the damage to DNA [6], which first occurs in the development of IPF. Various signaling pathways, e.g., hedgehog [7], TGF-β/Smad [8], and Wnt/β-catenin (Wingless and Int-1/beta catenin) [9], etc., are involved in the initiation and development of the IPF. Marker proteins of these signaling pathways, for instance, TGFBRI (transforming-beta type I receptor) [10] and CTGF [11], etc., were reported to be the targets of anti-IPF drugs. Our previous study of screening novel anti-IPF drugs with machine learning from traditional Chinese medicines found that swertiamarin, a secoiridoid glycoside with high anti-oxidation and anti-inflammatory effects [12], has a strong effect on arresting the development of IPF [13], and identified that α-SMA and E-cadherin, two marker proteins of TGF-β/Smad signaling pathway, were significantly downregulated and upregulated, respectively, by swertiamarin. However, the targets still need to be investigated. For this new anti-IPF lead, it is tough and time-and-cost-consuming work to identify its targets with wet-lab experiments. Fortunately, bioinformatics provides us with powerful tools to exploit genomic, transcriptomic, and proteomic data [14] to gain insights into the anti-IPF mechanisms and to identify targets for further validation. Molecular docking (MD) is another effective in silico approach to accurately predict the stability of a ligand-receptor complex and understand the activity of the ligand. MD showed remarkable advantages [15] over traditional experiment paradigms in avoiding large amounts of intensive experiments. In this study, bioinformatics was first used to analyze the microarray datasets from the GEO database to obtain hub genes. MD was then applied to decide whether these hub genes were the targets of swertiamarin by predicting the interactions between swertiamarin and the corresponding proteins of these genes. Finally, the screened targets were experimentally validated.

Materials and methods

Microarray data

GSE10667 series [16] on GPL4133 platform (Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F), GSE110147 series [17] on GPL6244 platform (Affymetrix Human Gene 1.0 ST Array transcript), and GSE71351 series [18] on GPL10558 platform (Illumina HumanHT-12 V4.0 expression bead chip) were downloaded from the GEO database ( The probs in each series were replaced by official gene symbols according to the platform files. For the GSE10667 dataset, the samples with the title of control and UIP (usual interstitial pneumonia) were set as control and IPF groups, respectively. The samples of the GSE110147 dataset that has the title of normal control and idiopathic pulmonary fibrosis patient were selected as control and IPF groups, respectively. In the GSE71351 dataset, the samples of normal lung fibroblasts were set as the control group and those of rapidly/slowly lung fibroblasts were set as the IPF group. The detailed information on these groups is shown in Table 1. The above three datasets are freely accessed on the GEO database and do not include any other experiment data of the authors.

Table 1 Statistics of the three microarray databases

Identification of differentially expressed genes

To avoid the batch effect, GSE10667, GSE110147, and GSE71351 datasets were separately analyzed for DEGs with GEO2R ( between control and IPF groups. The genes with |logFC|≥0.5 and P ≤ 0.05 were identified as DEGs. The Venn Diagram ( was used to select the overlapping genes that simultaneously exist in the DEGs of GSE10667, GSE110147, and GSE71351 datasets for GO and KEGG enrichment analysis.

GO and KEGG function enrichment analyses

The overlapping DEGs including upregulated and downregulated genes were used for GO and KEGG [19] enrichment analyses with David [20] ( For the GO analysis, the result was filtered with P ≤ 0.05 and Count ≥ 10 and the resultant genes were investigated for their classified physiological functions i.e., cellular component (CC), and their annotated terms. For KEGG analysis, the genes with P ≤ 0.05 and Count ≥ 4 were considered as significantly enriched in the corresponding biological pathways.

PPI network and hub genes identification

The DEGs including upregulated and downregulated genes were used to construct PPI networks with STRING [21]. The PPI pairs were extracted with a combined score of 0.4. Nodes with higher degrees of connectivity are considered more important to the stability of the PPI network. The PPI networks were plotted by Cytoscape 3.9.1.

The hub genes were calculated with the CytoHubba [22] plugin of Cytoscape 3.9.1 [23] and the algorithm of Closeness was used to rank the nodes from which the top 3 hub genes were selected for validation.

Molecular docking

To decide whether the selected hub genes were the potential targets of swertiamarin, MD was used to predict the interactions between swertiamarin and the proteins of selected genes. 3D crystal structure of human LOX (PDB ID 5ZE3) [24] with a resolution of 2.40Å was downloaded from the PDB database ( The 3D structures of CTGF ( and COL5A2 ( were obtained from AlphaFold. The docking pocket of LOX was decided with its catalytic domain [24]. The docking pocket of CTGF was defined as its heparin-binding region ( The VWFC domain of COL5A2 protein ( was used as its docking pocket. Autodock vina 1.2.3 [25] was used to perform the MD of swertiamarin into LOX, CTGF, and COL5A2. The structural files of LOX, CTGF, COL5A2 in PDB format, and swertiamarin in SD format were converted into PDBQT format with OpenBabel 2.4.1 [26] for docking, and other docking parameters were set as default.

Western blot testing

The western blot experiments were carried out with three experiment groups, i.e., the control, the IPF model, and the test groups (detailed information is shown in Fig. 5). The A549 cells from the above three groups were lysed with RIPA (radioimmunoprecipitation assay) lysis buffer and vortexed at 4℃. After the cells were collected. Total protein was loaded and separated by electrophoresis on 10% SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis) and transferred onto polyvinylidene difluoride membranes (Millipore, USA). The membranes were blocked with 5% defatted milk for 1.5 h at 20℃ and then incubated with primary antibodies at 4˚C overnight. After being washed with 10% TBST (mixture of tris-buffered saline and polysorbate 20), the membranes were incubated with HRP-conjugated (horseradish peroxidase-conjugated) secondary antibodies of anti-mouse IgG (immunoglobulin G) (1:5000) and anti-rabbit IgG (1:5000) for 2 h at 20℃. The bound antibodies were visualized using an enhanced chemiluminescence kit (Millipore, USA).

Statistical analysis

The GEO analyses were carried out by the GEO2R. The statistical analysis of expressions of the hub genes in the western blot experiments was performed by Pearson’s correlation analysis in the Python environment. The confidence interval was set as 0.95 for DEGs and western blot analysis.


Identification of DEGs

Three gene expression profiles (GSE10667, GSE110147, and GSE71351) were selected and analyzed. The GSE10667, GSE110147, and GSE71351 datasets contained 15 normal samples and 23 IPF (usual interstitial pneumonia) samples, 11 normal samples and 22 IPF (rapidly/slowly lung fibroblast) samples, and 4 normal samples and 8 IPF samples, respectively (Table 1). There are 3144, 4594, and 938 genes that were upregulated in GSE10667, GSE110147, and GSE71351 datasets (Fig. 1), respectively. And a total of 76 overlapping genes were found upregulated (Fig. 2A). There are 848, 4452, and 1069 genes that were downregulated in the GSE10667, GSE110147, and GSE71351 datasets (Fig. 1), respectively, from which 27 overlapping genes were observed (Fig. 2B). The DEGs were mainly related to the biological functions of the plasma membrane, extracellular region, extracellular exosome, integral component of membrane, and extracellular space (Fig. 2C).

Fig. 1
figure 1

The distributions of gene expressions of GSE10667 (A), GSE110147 (B), and GSE71351 (C) datasets. The genes with log|FC|≥0.5 and P ≤ 0.05 were considered as DEGs.

Fig. 2
figure 2

The DEGs and the biological functions of GO-enriched genes. A, the intersecting upregulated genes. There are 76 overlapping upregulated genes (supplement 1). B, the intersecting downregulated genes. There are 27 overlapping downregulated genes (supplement 2); C, the biological functions of DEGs enriched with GO analysis. The GO-enriched genes were selected with Count ≥ 18 and P ≤ 0.05


GO and KEGG enrichment analysis

The GO enrichment aims to analyze the main functions, e.g., cellular component, biological process, molecular function, etc., of DEGs. The results (Table 2) showed that the DEGs were enriched in cellular components including the plasma membrane, integral component of membrane, extracellular region, extracellular space, extracellular exosome, and Golgi apparatus, which were consistent with the biological process of IPF. The results of KEGG analysis indicated that these DEGs were mainly enriched in 7 pathways including Hsa05202, Hsa04020, Hsa04115, Hsa04068, Hsa04978, Hsa05218, and Hsa05205, which are relative to cancer and tissue development.

Table 2 Significantly enriched GO terms and KEGG pathways of DEGs

PPI network and hub genes

The protein interactions among the DEGs (supplement 3) were constructed with STRING and filtered by confidence over 0.4. The results showed that a total of 50 nodes and 130 edges were involved in the PPI network (Fig. 3). There are 12 nodes i.e., LOX, COL5A2, COL3A1 (collagen type III alpha 1), FNIP1 (folliculin interacting protein 1), FBN2 (fibrillin 2), ADAMTS5 (ADAM metallopeptidase with thrombospondin type 1 motif 5), SEMA5A (semaphorin 5 A), FBN1 (fibrillin 1), ATP6V1A (ATPase H + transporting V1 subunit A), ITGAV (integrin subunit alpha V), CHEK2 (checkpoint kinase 2), and MDM2 (MDM2 proto-oncogene) and 122 edges have combined scores over 0.85. The result of hub genes calculation (Table 3) showed that CTGF and COL5A2 were the two most outstanding genes with scores of 20, followed by LOX (score = 19), COL3A1 (score = 16), FBN1 (score = 14), HGF (hepatocyte growth factor, score = 12), FBN2 (score = 12), HBEGF (heparin-binding EGF like growth factor, score = 10), FGF7 (fibroblast growth factor 7, score = 10) and ITGAV (score = 10). Finally, the top 3 genes (CTGF, COL5A2, and LOX) were selected for the validation.

Fig. 3
figure 3

The PPI network of DEGs.

Table 3 The top ten hub genes ranked with scores

Molecular docking

The docking score for swertiamarin and LOX was − 9.344 kcal/mol and the interactions were shown in Fig. 4A. Besides 5 carbon-hydrogen bonds, 6 conventional hydrogen bonds (between swertiamarin and residues of Ser609, Thr546, Ser544, Ser486, and Asn487 of LOX with bond lengths of 3.11, 2.80, 2.52, 3.38, 3.22 and 3.23Å, respectively) and 3 alkyl bonds (between swertiamarin and residues of Pro548, Val720, and Arg612 of LOX with bond lengths of 4.10, 4.83 and 3.68Å, respectively) were found in the swertiamarin-LOX complex, which indicated that swertiamarin has strong interactions with LOX and the swertiamarin-LOX complex was very stable.

Fig. 4
figure 4

The 3D interactions between swertiamarin and LOX (A), CTGF (B), and COL5A2 (C), respectively. Green lines, conventional hydrogen bonds. Pink lines, alkyl bonds. Light blue lines, conventional hydrogen bonds. The numbers beside the bonds represent the bond lengths. The interactions were plotted by the Discovery Studio 2019 client

The docking score for swertiamarin and CTGF was − 8.125 kcal/mol. The swertiamarin-CTGF interactions (Fig. 4B) gave out 5 conventional hydrogen bonds (between swertiamarin and Glu269, Ser271, Met215, and Lys211 with bond lengths of 2.95, 3.13, 3.57, 2.94, and 3.18Å, respectively) and 6 alkyl bonds (between swertiamarin and Met215, Leu236, Len270, Ile217, and Lys21 with bond lengths of 4.86, 4.66, 5.42, 4.21, 4.83 and 3.81Å, respectively). The vinyl of the swertiamarin formed 3 alkyl bonds with residues of Leu270, Ile217, and Leu236, which suggested that this vinyl is important to the stability of the complex. The residue of Ser271 provided three strong conventional hydrogen bonds (bond lengths < 3Å) with swertiamarin, which suggested that the Ser271 and the hydroxyls from the glucosyl of swertiamarin are the key groups keeping the stability of the swertiamarin-CTGF complex.

The docking score for swertiamarin and COL5A2 was − 12.681 kcal/mol, which indicated that the swertiamarin-COL5A2 complex is more stable than the swertiamarin-LOX and swertiamarin-CTGF complexes. The swertiamarin-COL5A2 interactions (Fig. 4C) showed the same result. There were more and stronger conventional hydrogen bonds were formed: six of the nine conventional hydrogen bonds are shorter than 3Å and three conventional hydrogen bonds are close to 3Å (3.26Å between swertiamarin and Cys1336, 3.07Å between swertiamarin and Ser1338, and 3.06Å between swertiamarin and Ser1342). Therefore, glucosyl is very important to the anti-IPF capability of swertiamarin.

Western blot

Experiment results showed that TGF-β1 and swertiamarin have no toxicity to the A540 cells (supplement 4). Figure 5 showed that, after being treated with swertiamarin for 24 h, the expressions of LOX and COL5A2 were significantly downregulated, and the expression of CTGF was observed to be slightly downregulated.

Fig. 5
figure 5

The expressions (A) and the statistics results (B) of COL5A2, LOX, and CTGF in the western blot analysis. *Represents the P < 0.05. The A549 cells (control group) were pretreated with 10ng/ml of TGF-β1 to build the in vitro IPF model, and then the cells were treated with 1.5µmol/l of swertiamarin (test group) for 24 h. The samples for determining the expressions of COL5A2, LOX, CTGF, and GAPDH (glyceraldehyde 3-phosphate dehydrogenase) were derived from the same batch of experiments. The gels were processed in parallel


Target identification is of central importance to the understanding of the anti-IPF mechanism of swertiamarin. However, solving this problem using web-lab experiments usually means expensive and slow processes, whereas computation-aided approaches provide efficient complements. Here we used the bioinformatics and MD-based approach to screen the anti-IPF targets of swertiamarin with the public database. In this study, GSE10667, GSE110147, and GSE71351 datasets were used to analyze the DEGs. It is important to clarify here that the GSE10667 dataset contains samples with interstitial pneumonia and it made the results more reliable but also more difficult to obtain the overlapping DEGs with high log|FC| values because of considering the gene expressions in the early stage of IPF. Even with lower log|FC| values, the hub genes were still successfully screened out. The KEGG analysis suggested that the selected DEGs were related to tissue and cancer development. This result was consistent with the fact that IPF shares several pathogenetic similarities, e.g., DNA methylation [27], with lung cancer [28] and that patients with IPF are at high risk of getting lung cancer [29].

Three hub genes (LOX, COL5A2, and CTGF) were screened out through the above analysis. The LOX is a cuproenzyme and is also known as protein-lysine 6-oxidase encoded by the human LOX gene [30]. LOX catalyzes the conversion of lysine into highly reactive aldehydes that form the cross-linking collagen and elastin in ECM proteins [31], contributing to the ECM’s stiffness. The stiffness of the ECM would increase fibroblast proliferation and contraction [32]. Aberrant expression and activity of LOX were associated with IPF [33] and led to the development of the IPF microenvironment. Therefore, LOX is a key participant in ECM remodeling [34]. In this study, the PPI analysis of LOX showed that the LOX has strong interactions with TGF-β1, TGFBR1, TGFBR2 (TGF-β receptor 2), and SMAD2/3 (suppressor of mothers against decapentaplegic family member 2 and 3) (Supplement 5), which are the main marker proteins of TGF-β/Smad signaling pathway. Therefore, LOX is the potential target of IPF [35] and inhibition of its activity would alleviate the IPF [33], which was identified by the MD model. The interactions between LOX and swertiamarin indicated a stable ligand-receptor complex (Fig. 4A), which is consistent with the western blot results showing the downregulation of the LOX expression.

CTGF, also known as CCN2 (cellular communication network factor 2), is a matricellular protein of the CCN family of ECM-associated heparin-binding proteins [36]. CTGF was reported to be associated with wound healing (the key initial process of IPF) and all fibrotic pathology [37]. In IPF tissues, CTGF expression is upregulated by TGF-βs [38], SMAD2 [39], and other physiological and pathological factors. The upregulation of CTGF expression would further exacerbate the ECM accumulation and aggravate the development of IPF [37]. The CTGF-involved PPI analysis (Supplement 6) showed that CTGF has interactions with TGF-β1, TGF-β3, and TGFBR2 (one of the key targets of exogenous factors that promote the expression of TGF-βs). The MD model also suggested that the swertiamarin-CTGF complex is highly stable and CTGF should be the target of swertiamarin on IPF. Western blot results showed that swertiamarin slightly but not significantly downregulated the expression of CTGF. Therefore, we concluded that swertiamarin can only inhibit the activity of CTGF rather than downregulate the expression of CTGF.

COL5A2 is a protein encoded by the COL5A2 gene [40] and is responsible for the formation of other collagen fibrils in tissues of the body. COL5A2 was reported to be involved in the development of pathological scarring [41]. COL5A2 is modulated by TGF-βs [42] and is highly related to human systemic sclerosis [43] and IPF [44]. In this study, the COL5A2 protein has a strong interaction with ITGB1 (integrin beta-1, a cell surface receptor in human and function as collagen receptors and modulate the migration across basement membranes in human [45]) and ADAMTS14 (ADAM metallopeptidase with thrombospondin type 1 motif 14, an enzyme cleaves the amino-propeptide of fibrillar collagens) (Supplement 7). The DEG analysis showed that the COL5A2 gene was significantly upregulated in the IPF patients. The MD model suggested that swertiamarin could modulate the activity of COL5A2 and the western blot showed that the expression of COL5A2 was downregulated by swertiamarin.


Our bioinformatics analysis identified 103 DEGs that co-exist in GSE10667, GSE110147, and GSE71351 datasets. The GO and KEGG functional analyses showed that these DEGs were mainly related to the cell process that related to cancer and tissue development. The MD models and experimental results proved that these three genes/proteins are the targets of swertiamarin. The experiment data didn’t show significant downregulation of CTGF. It is very important to further experimentally investigate the effects of swertiamarin on the activities of LOX, CTGF, and COL5A2, and especially to validate its effect on the expression of CTGF.

Data availability






Idiopathic pulmonary fibrosis


Gene Expression Omnibus


Usual interstitial pneumonia


Differentially expressed genes


Cellular component


Gene Ontology


Kyoto Encyclopedia of Genes and Genomes


Protein-protein interaction network


Transforming growth factor beta


Transforming-beta type I receptor


Lysyl oxidase


Collagen type V alpha 2 chain


Connective tissue growth factor


Cellular communication network factor 2


suppressor of mothers against decapentaplegic family member 2


Suppressor of mothers against decapentaplegic family member 3


Glyceraldehyde 3-phosphate dehydrogenase.


ADAM metallopeptidase with thrombospondin type 1 motif 14


α-smooth muscle actin


Epithelial cadherin


Extracellular matrix


Wingless and Int-1


Molecular docking


Mixture of tris-buffered saline and polysorbate 20


Horseradish peroxidase


Radioimmunoprecipitation assay


  1. Patrucco F, Solidoro P, Gavelli F, Apostolo D, Bellan M. Idiopathic pulmonary fibrosis and post-COVID-19 lung fibrosis: links and risks. Microorganisms. 2023.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bazdyrev E, Rusina P, Panova M, Novikov F, Grishagin I, Nebolsin V. Lung fibrosis after COVID-19: treatment prospects. Pharmaceuticals. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Richeldi L, Collard HR, Jones MG. Idiopathic pulmonary fibrosis. Lancet. 2017.

    Article  PubMed  Google Scholar 

  4. Margaritopoulos GA, Romagnoli M, Poletti V, Siafakas NM, Wells AU, Antoniou KM. Recent advances in the pathogenesis and clinical evaluation of pulmonary fibrosis. Eur Respir Rev. 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Estornut C, Milara J, Bayarri MA, Belhadj N, Cortijo J. Targeting oxidative stress as a therapeutic approach for idiopathic pulmonary fibrosis. Front Pharmacol. 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhu J, Liu L, Ma X, Cao X, Chen Y, Qu X, Ji M, Liu H, Liu C, Qin X, Xiang Y. The role of DNA damage and repair in idiopathic pulmonary fibrosis. Antioxidants. 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Effendi WI, Nagano T. The hedgehog signaling pathway in idiopathic pulmonary fibrosis: resurrection time. Int J Mol Sci. 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Li XZ, Wang XL, Wang YJ, Liang QK, Li Y, Chen YW, Ming HX. Total flavonoids of Oxytropis falcata Bunge have a positive effect on idiopathic pulmonary fibrosis by inhibiting the TGF-beta 1/smad signaling pathway. J Ethnopharmacol. 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chen L, Hou J, Fu X, Chen X, Wu J, Han X. tPA promotes the proliferation of lung fibroblasts and activates the Wnt/beta-catenin signaling pathway in idiopathic pulmonary fibrosis. Cell Cycle. 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gao S, Li X, Jiang Q, Liang Q, Zhang F, Li S, Zhang R, Luan J, Zhu J, Gu X, Xiao T, Huang H, Chen S, Ning W, Yang G, Yang C, Zhou H. PKM2 promotes pulmonary fibrosis by stabilizing TGF-beta 1 receptor I and enhancing TGF-beta 1 signaling. Sci Adv. 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Anagihara T, Tsubouchi K, Gholiof M, Chong SG, Lipson KE, Zhou Q, Scallan C, Upagupta C, Tikkanen J, Keshavjee S, Ask K, Kolb MRJ. Connective-tissue growth factor contributes to TGF-beta 1-induced lung fibrosis. Am J Resp Cell Mol. 2022.

    Article  Google Scholar 

  12. Lad HM, Bhatnagar D. Amelioration of oxidative and inflammatory changes by Swertia chirayita leaves in experimental arthritis. Inflammopharmacology. 2016.

    Article  PubMed  Google Scholar 

  13. Chang J, Zou SQ, Xu SB, Xiao YW, Zhu D. Screening of inhibitors against idiopathic pulmonary fibrosis: few-shot machine learning and molecule docking based drug repurposing. Curr Comput-Aid Drug. 2023.

    Article  Google Scholar 

  14. Jiang ZR, Zhou YH. Using bioinformatics for drug target identification from the genome. Am J Pharmacogenomics. 2005.

    Article  PubMed  Google Scholar 

  15. Chen G, Seukep AJ, Guo M. Recent advances in molecular docking for the research and discovery of potential marine drugs. Mar Drugs. 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rosas IO, Richards Thomas J, Konishi K, Zhang Y, Gibson K, Lokshin AE, Lindell KO. MMP1 and MMP7 as potential peripheral blood biomarkers in idiopathic pulmonary fibrosis. Plos Med. 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cecchini MJ, Hosein K, Howlett CJ, Joseph M, Mura M. Comprehensive gene expression profiling identifies distinct and overlapping transcriptional profiles in non-specific interstitial pneumonia and idiopathic pulmonary fibrosis. Resp Res. 2018.

    Article  Google Scholar 

  18. Lee JU, Cheong HS, Shim EY, Bae DJ, Chang HS, Uh ST, Kim H, Park JS, Lee B, Shin HD. Gene profile of fibroblasts identify relation of CCL8 with idiopathic pulmonary fibrosis. Resp Res. 2017.

    Article  Google Scholar 

  19. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of genes and genomes. Nucleic Acids Res. 2000.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009.

    Article  PubMed  Google Scholar 

  21. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ, Von Mering. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. CytoHubba: identifying hub objects and sub-networks from complex interactome. Bmc Syst Biol. 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ono K, Demchak B, Ideker T. Cytoscape tools for the web age: D3.js and Cytoscape.js exporters. F1000Res. 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhang X, Wang Q, Wu J, Wang J, Shi YG, Liu M. Crystal structure of human lysyl oxidase-like 2 (hLOXL2) in a precursor state. PNAS. 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  26. O’Boyle NM, Banck Mi, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: an open chemical toolbox. J Cheminformatics. 2011.

    Article  Google Scholar 

  27. Duan J, Zhong B, Fan Z, Zhang H, Xu M, Zhang X, Sanders YY. DNA methylation in pulmonary fibrosis and lung cancer. Expert Rev Resp Med. 2022.

    Article  Google Scholar 

  28. Naoi H, Suzuki Y, Mori K, Aono Y, Kono M, Hasegawa H, Yokomura K, Inoue Y, Hozumi H. Impact of antifibrotic therapy on lung cancer development in idiopathic pulmonary fibrosis. Thorax. 2022.

    Article  PubMed  Google Scholar 

  29. Fisher DA, Murphy MC, Montesi SB, Hariri LP, Hallowell RW, Keane FK, Lanuti M, Mooradian MJ, Fintelmann FJ. Diagnosis and treatment of lung cancer in the setting of interstitial lung disease. Radiol Clin N Am. 2022.

    Article  PubMed  Google Scholar 

  30. Hämäläinen ER, Jones TA, Sheer D, Taskinen K, Pihlajaniemi T, Kivirikko KI. Moleclar cloning of human lysyl oxidase and assignment of the gene to chromosome 5q23. 3-31.2. Genomics. 1991.

    Article  PubMed  Google Scholar 

  31. Rucker RB, Kosonen T, Clegg MS, Mitchell AE, Rucker BR, Uriu-Hare JY, Keen CL. Copper, lysyl oxidase, and extracellular matrix protein cross-linking. Am J Clin Nutr. 1998.

    Article  PubMed  Google Scholar 

  32. Marinković A, Liu F, Tschumperlin DJ. Matrices of physiologic stiffness potently inactivate idiopathic pulmonary fibrosis fibroblasts. Am J Resp Cell Mol. 2012.

    Article  Google Scholar 

  33. Tjin G, White ES, Faiz A, Sicard D, Tschumperlin DJ, Mahar A, Kable EP, Burgess JK. Lysyl oxidases regulate fibrillar collagen remodelling in idiopathic pulmonary fibrosis. Dis Model Mech. 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Tjin G, Mahar A, Kable E, Burgess J. Lysyl oxidases in idiopathic pulmonary fibrosis: a key participant in collagen I matrix remodelling. Eur Respir J. 2015.

    Article  Google Scholar 

  35. Chen L, Li S, Li W. LOX/LOXL in pulmonary fibrosis: potential therapeutic targets. J Drug Target. 2019.

    Article  PubMed  Google Scholar 

  36. Chen CC, Lau LF. Functions and mechanisms of action of CCN matricellular proteins. Int J Biochem Cell Biol. 2009.

    Article  PubMed  Google Scholar 

  37. Brigstock DR. Connective tissue growth factor (CCN2, CTGF) and organ fibrosis: lessons from transgenic animals. J Cell Commun Signal. 2010.

    Article  PubMed  Google Scholar 

  38. Mori T, Kawara S, Shinozaki M, Hayashi N, Kakinuma T, Igarashi A, Takigawa M, Nakanishi T, Takehara K. Role and interaction of connective tissue growth factor with transforming growth factor-beta in persistent fibrosis: a mouse fibrosis model. J Cell Physiol. 1999.<153::AID-JCP16>3.0.CO;2-K.

  39. Liao X, Bu Y, Jiang S, Chang F, Jia F, Xiao X, Song G, Zhang M, Ning P, Jia Q. CCN2-MAPK-Id-1 loop feedback amplification is involved in maintaining stemness in oxaliplatin-resistant hepatocellular carcinoma. Hepatol Int. 2019.

    Article  PubMed  Google Scholar 

  40. Greenspan DS, Byers MG, Eddy RL, Cheng W, Jani-Sait S, Shows TB. Human collagen gene COL5A1 maps to the q34.2→q34.3 region of chromosome 9, near the locus for nail-patella syndrome. Genomics. 1992.

    Article  PubMed  Google Scholar 

  41. Ilies RF, Aioanei CS, Cătană A, Halmagyi SR, Lukacs I, Tokes RE, Rotar IC, Pop IV. Involvement of COL5A2 and TGF–β1 in pathological scarring. Exp Ther Med. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Mak KM, Png CYM, Lee DJ. Type V collagen in health, disease, and fibrosis. Anat Rec. 2016.

    Article  Google Scholar 

  43. Velosa APP, Brito L, de Jesus Queiroz ZA, Carrasco S, Tomaz de Miranda J, Farhat C, Goldenstein-Schainberg C, Parra ER, de Oliveira DC, Silva PL, Capelozzi VL, Teodoro1WR. Identification of autoimmunity to peptides of collagen V α1 chain as newly biomarkers of early stage of systemic sclerosis. Front Immunol. 2020.

    Article  PubMed  Google Scholar 

  44. Velázquez-Enríquez JM, Santos-Álvarez JC, Ramírez-Hernández AA, Reyes-Jiménez E, López-Martínez A, Pina-Canseco S, Aguilar-Ruiz SR, Romero-Tlalolini MÁ, Castro-Sánchez L, Arellanes-Robledo J, Vásquez-Garzón VR, Baltiérrez-Hoyos R. Proteomic analysis reveals key proteins in extracellular vesicles cargo associated with idiopathic pulmonary fibrosis in vitro. Biomedicines. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  45. White ES, Thannickal VJ, Carskadon SL, Dickie EG, Livant DL, Markwart S, Toews GB, Arenberg DA. Integrin alpha4beta1 regulates migration across basement membranes by lung fibroblasts: a role for phosphatase and tensin homologue deleted on chromosome 10. Am J Resp Crit Care. 2003.

    Article  Google Scholar 

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This work was financially supported by the Jiangxi Provincial Department of Education (GJJ160786).

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Jun Chang and Du Zhu perform the analyze the Geo data, and Shaoqing Zou and Yiwen Xiao carried out the in vitro validation of the inhibition effects on the targets. All authors read and approved the final manuscript.

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Correspondence to Jun Chang or Du Zhu.

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Chang, J., Zou, S., Xiao, Y. et al. Identification and validation of targets of swertiamarin on idiopathic pulmonary fibrosis through bioinformatics and molecular docking-based approach. BMC Complement Med Ther 23, 352 (2023).

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