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Method Article

Bibliometrics and Bioinformatics Exploration of Research Hotspots and Key Targets in Comorbidity: Interstitial Lung Disease and Pulmonary Hypertension

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DOI:

10.3791/71631

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August 7th, 2026

 ,  ,  , 
1Graduate School, Beijing University of Chinese Medicine, 2Physical Examination Center, China-Japan Friendship Hospital, 3Qihuang School, Beijing University of Chinese Medicine, 4National Center for Integrative Medicine, China-Japan Friendship Hospital, 5School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College

In This Article

Summary

This protocol uses interstitial lung disease and pulmonary hypertension as examples and integrates bibliometrics with bioinformatics to systematically and preliminarily explore their research trends, hotspots, key targets, and related pathways. This protocol could be replicated to analyze any disease pairs by modifying the prompts.

Abstract

Interstitial lung disease (ILD) and pulmonary hypertension (PH) frequently coexist as comorbidities, leading to poor clinical outcomes and limited therapeutic options. Identifying important research hotspots and uncovering key comorbidity targets is essential for improving disease management and developing novel therapeutic strategies. This protocol retrieves relevant publications from the Web of Science Core Collection and Scopus databases, and uses CiteSpace and VOSviewer to map research hotspots and evolving trends. Subsequently, overlapping genetic targets associated with both diseases are identified from the GeneCards database. Protein-protein interaction networks are constructed using STRING to identify hub genes, and KEGG pathway enrichment analysis is performed using the R language to elucidate key signaling pathways. The results show that bibliometrics identifies the developmental trajectory of this interdisciplinary field from a macro perspective; bioinformatics analysis reveals FN1, IL6, and TNF as potential core comorbidity targets; and KEGG enrichment analysis indicates that immune-related pathways centered on PI3K-Akt and MAPK are deeply involved in the pathogenesis of the comorbidity. By integrating the two approaches, this protocol systematically delineates the development trajectory and research hotspots in ILD-PH comorbidity research, while preliminarily screening potential comorbidity targets and pathways. It provides direction and a computational basis for future experimental research and clinical validation. This integrated framework offers a reproducible methodology applicable to the study of other complex disease comorbidities.

Introduction

Interstitial lung disease (ILD) refers to a group of diffuse lung diseases characterized pathologically by inflammation and fibrosis of the pulmonary interstitium. During its disease progression, it is often complicated by multiple comorbidities, among which pulmonary hypertension (PH) is one of the common comorbidities that significantly affects the prognosis1,2. Epidemiological data indicate that the incidence of PH in patients with ILD varies depending on the subtype and disease stage, ranging from 13% to 86%3,4,5. This comorbid condition not only significantly reduces patients' quality of life and increases their risk of mortality but also exacerbates the socioeconomic and healthcare burden6,7. In contrast to pulmonary arterial hypertension, the pathogenesis of interstitial lung disease-associated pulmonary hypertension is more complex, involving multiple pathological processes such as hypoxic pulmonary vasoconstriction, pro-fibrotic and inflammatory factor-mediated vascular remodeling, and extracellular matrix deposition8,9,10. At present, clinical diagnosis and treatment strategies for this comorbid condition remain relatively limited, and specific targeted drugs are lacking. Its underlying molecular mechanisms, key signaling pathways, and early biomarkers still need to be systematically elucidated11,12.

As an objective tool for detecting research focal points and evolutionary trajectories, bibliometrics has gained extensive application in recent years13,14. Bioinformatics analysis identifies key targets and core pathways by integrating previously discovered disease-associated targets15. For comorbidity research, integrating macroscopic research hotspots with molecular-level pathway and target information deepens understanding of comorbidity mechanisms and provides computational predictions and directional guidance for subsequent experimental studies. However, research on the comorbidity of interstitial lung disease and pulmonary hypertension still lacks a systematic integrated analysis that combines macroscopic knowledge mapping with microscopic molecular networks. Accordingly, this study used the Web of Science Core Collection (WoSCC) and Scopus databases to comprehensively outline the development trajectory and research hotspots of ILD-PH comorbidity research through bibliometric methods. At the same time, bioinformatics tools were employed to identify core genes and potential molecular pathways associated with the comorbidity, providing a candidate molecular basis and experimental design direction for subsequent experimental research and targeted exploration.

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Protocol

The study used publicly available secondary databases, including GeneCards, STRING, and KEGG. According to Article 32 of the Regulations on Human Genetic Resources of China, these databases do not involve direct collection of human genetic resources within China; therefore, no ethical approval or informed consent was required for this study. Detailed version information of the software and platforms used in this section is provided in the Table of Materials.

1. Literature collection and organization

  1. Access the MeSH database (Table of Materials).
  2. Search for the disease terms “Interstitial Lung Disease” and “Pulmonary Hypertension” separately to retrieve their associated subheadings.
  3. Develop a comprehensive search strategy based on the identified MeSH terms and subheadings.
  4. On March 9, 2026, open the WoSCC database (Table of Materials).
  5. Select Advanced Search. Enter the search strategy: TS=("Diffuse Parenchymal Lung Disease" OR "Diffuse Parenchymal Lung Diseases" OR "Interstitial Lung Disease" OR "Interstitial Lung Diseases") AND TS=("Pulmonary Hypertension"). Set the publication date range from January 1, 2016, to December 31, 2025, and click Search.
  6. Select Articles and Review Articles as the document types, and choose English as the language. Select Full Record and Cited References as the record content. Export the records in txt format and save as wos.txt, then convert to csv format and save as wos.csv.
  7. On March 9, 2026, open the Scopus database (Table of Materials).
  8. Set the search field to” Title”/ ”Abstract” / “Keywords”. Enter the search strategy: TITLE-ABS-KEY(("Diffuse Parenchymal Lung Disease" OR "Diffuse Parenchymal Lung Diseases" OR "Interstitial Lung Disease" OR "Interstitial Lung Diseases")) AND TITLE-ABS-KEY("Pulmonary Hypertension"). Select the year range from January 1, 2016, to December 31, 2025, choose Medicine as the subject area, and click Search (Supplementary Table 1).
  9. Click Export, select CSV format, export all retrieved records, and save as scopus.csv.
  10. Open wos.csv, copy the DOI column, and paste it into scopus.csv. Perform data comparison, use the Remove Duplicates function in Excel to mark duplicate records, delete the duplicates, and obtain scopus1.csv. For the very few records lacking a DOI, we manually cross-checked using a combination of "title + author + year" to ensure complete inclusion of the literature required for this study.
  11. Create new folders named input directory and output directory. Place scopus1.csv obtained after deduplication into the input directory. Open CiteSpace, click Data, select Scopus, specify the corresponding folders, and click the Scopus (csv) > WoS button to convert the Scopus data format, unifying the format of the two databases into txt format. Name the resulting file scopus1.txt.
  12. Merge the data from the two databases. Place scopus1.txt and wos.txt into CiteSpace for deduplication and merging to obtain uniformly formatted, deduplicated literature data: dataset.txt, for subsequent analysis (Supplementary Table 2).
  13. Check the obtained wos.txt and scopus.csv files to ensure that the deduplication process uses DOI as the matching criterion and that the dataset.txt file is successfully generated.

2. Annual publication output trend analysis

  1. In Microsoft Excel, count the number of publications by year to generate two columns of data (Year, Publications).
  2. Select the dataset, access Insert > Chart > Column Chart to generate a column chart of annual publication output.
  3. Designate the horizontal axis as Year and the vertical axis as Publications.
  4. Enable data labels to display the specific number of publications for each year.
  5. Export all figures in Tagged Image File Format (TIFF), ensuring a resolution of at least 300 dpi.

3. National/regional publication output and international collaboration analysis

  1. Launch VOSviewer, select Create > Create a map based on bibliographic data > Read data from bibliographic database files16.
  2. Import the plain text dataset generated in step 1.12.
  3. Select Co-authorship as the analysis type and Countries as the unit of analysis.
  4. Set the minimum number of documents for a country to 40, retaining the top 20 countries by publication output. To ensure that the collaboration network focuses on high-output countries while maintaining the clarity and readability of the map, making it easier for readers to intuitively grasp the core international research forces and collaboration patterns.
  5. In the Verify selected items interface, select all countries, copy the list to Excel, and export the number of publications and citation counts for each country.
  6. In the resulting Excel table, select the top 10 countries by publication output and generate a chart displaying their publication and citation counts.
  7. Select the "Finish" button to build the national collaboration network and save the network in GML format.
  8. Launch Scimago Graphica and import the GML file.
  9. Map the label field to Country and set the cluster field type to String.
  10. In the visualization configuration, place the publication output field (typically weight<Documents> or a similar name) into the Size box, set the cluster field as the Color box, and map the label field to the Label, Tooltip, and Unit boxes.
  11. Go to Marks > Map. Use Edges for line curvature adjustment, and Edge color for node color configuration.
  12. Export the final map in PNG format.
  13. Import the plain text dataset generated in step 1.12 into the Bibliometric Online Analysis Platform (https://bibliometric.com), click Upload citation data, and select Country relations to generate a national collaboration chord diagram for visualization.
  14. A key checkpoint is to adjust the minimum number of documents appropriately so that the resulting network is well-balanced and visually clear, while still reflecting the research objectives.

4. Journal publication output and citation analysis

  1. Import the same dataset into VOSviewer, select Citation as the analysis type, and select Sources as the unit of analysis.
  2. Set the minimum number of documents for a source to 5 and the minimum number of citations to 20.
  3. In the Verify selected items interface, copy the journal list to Excel to obtain the number of publications and total citation counts for each journal.
  4. Choose Finish, and the journal co-citation network map will be generated. Adjust the Attraction and Repulsion parameters to enhance the layout, refine the appearance using the Visualization panel, and save the map as a TIFF with a resolution of at least 300 dpi.
  5. In Excel, sort journals by the number of publications, select the top 10 journals by publication output. On March 9, 2026, access the official JCR platform (Table of Materials), search for each journal respectively, and record their latest Impact Factor and JCR quartile. Generate a chart displaying the top 10 journals by publication output.
  6. In Excel, sort journals by total citation count, select the top 10 journals by citation frequency. On March 9, 2026, retrieve and record their latest Impact Factor and JCR quartile from the official JCR platform. Generate a chart displaying the top 10 journals by citation frequency.

5. High-productivity author collaboration network analysis

  1. Import the dataset into VOSviewer, select Co-authorship as the analysis type, and select Authors as the unit of analysis.
  2. Set the minimum number of documents for an author to 3 to filter authors with a certain level of activity.
  3. In the Verify selected items interface, copy the author list and save the publication counts and citation counts of authors in an Excel file.
  4. Choose Finish to produce the author collaboration network, adjust the network layout and appearance, and save as TIFF format.
  5. In Excel, sort authors according to their number of publications, choose the 10 most productive researchers, and generate a donut chart to display their publication counts.
  6. From the original dataset, retrieve the top 10 authors by citation count, identify their respective countries, assign distinct colors based on country, and generate a chart displaying the top 10 authors by citation frequency.

6. Keyword co-occurrence and thematic evolution analysis

  1. Launch CiteSpace and navigate to Data > Import/Export.
  2. Place the txt file exported in step 1.12 into the input folder. In the Import/Export interface, set the input and output paths, select Article and Review as the document types, and click Start to perform data format conversion.
  3. After conversion, copy the files from the output folder to the data folder. In the CiteSpace main interface, click New, set the project directory and data directory, select WoS as the data source, and choose English as the language.
  4. Go to the time slicing panel on the right, set the span to 2016–2025, adopt a 1-year window, and choose Keyword as the node type.
  5. Apply the g-index (k=10) for network scaling, and select the three pruning strategies: Pathfinder, Pruning sliced networks, and Pruning the merged network.
  6. Click Start to run the analysis. After completion, use the One-Click Clustering Label Optimization function to create the keyword co-occurrence map.
  7. Set the display of node and cluster labels from the control panel, and optimize visual elements such as node size.
  8. Select Layout > Timeline to generate the keyword timeline view.
  9. In the control panel, select Burstness, click Refresh and View sequentially, set the number of top 25 keywords to display, and generate the chart of the top 25 keywords by burst strength. Export the generated images in TIFF format for storage.

7. Reference co-citation analysis

  1. Load the same dataset into CiteSpace. Set the time range and pruning strategies as described in steps 6.4–6.5, and select Cited Reference as the node type.
  2. After running the analysis, use the One-Click Clustering Label Optimization function to generate the reference co-occurrence map.
  3. Adjust node and cluster labels, and select Clusters to generate the reference clustering map.
  4. In the Burstness panel, set the display to show the top 20 cited references by citation frequency, and generate the chart of the top 20 co-cited references by burst strength.
  5. Export all images in TIFF format for storage.

8. PPI network analysis

  1. Access the GeneCards database (https://www.genecards.org)17on March 12, 2026, and search using “Interstitial Lung Disease” and “Pulmonary Hypertension” as keywords, respectively. The disease terms used for the search are consistent with those in the literature retrieval, and no additional term variants were expanded.
  2. Export the list of targets with a relevance score ≥ 1 for each search. The extraction method involved sorting by Relevance Score and exporting all targets with a score ≥ 1. The threshold of ≥ 1 was chosen because it captures the vast majority of literature-supported associated genes while minimizing false positives.
  3. Use R to obtain the intersection of the two target sets, resulting in the ILD-PH comorbidity-related targets.
  4. Access the Multiple Proteins module of the STRING database (https://cn.string-db.org/)18, enter the list of comorbidity targets into the search box, and select Homo sapiens as the species.
  5. Click Continue, then in the Settings panel, set the minimum required interaction score to High confidence (0.700), and enable hide disconnected nodes in the network19.
  6. After updating the network, download the interaction data as a TSV file from the Export menu.
  7. Start Cytoscape, and load the TSV file through File > Import > Network from File System.
  8. In Tools > NetworkAnalyzer > Network Analysis > Network Interpretation, select Treat the network as undirected, and calculate topological parameters, including node degree.
  9. Configure node shape, color, size, and other appearance settings in the Style panel, and manually adjust node positions to achieve a clear layout.
  10. Save the PPI network image via File > Export > Network Image to File, and export the network analysis results via File > Export > Table to File.
  11. Open the exported table in Excel, order by the Degree column descending, choose the top 20 key targets along with their degree values, create a bar chart with data labels, and export in TIFF format.

9. KEGG Pathway enrichment analysis

  1. Launch R, and enter the commands to install and load the required packages (Supplementary Table 3):
    install.packages("BiocManager")
    BiocManager::install(c("clusterProfiler", "org.Hs.eg.db"))
    library(clusterProfiler)
    library(org.Hs.eg.db)
  2. Convert the gene symbols of ILD-PH comorbidity targets to Entrez IDs using the bitr() function. In R, enter:
    coma_entrez <- bitr(coma_genes, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)
  3. Perform enrichment analysis using the enrichKEGG() function with the following parameters: organism = "hsa", pvalueCutoff = 0.05, qvalueCutoff = 0.05. The Benjamini-Hochberg (BH) method was used for multiple testing correction. Enter the following code:
    kegg_result <- enrichKEGG(gene = coma_entrez$ENTREZID,
    organism = "hsa",
    pvalueCutoff = 0.05,
    qvalueCutoff = 0.05,
    use_internal_data = FALSE)
    NOTE: p-value: the original p-value, representing the probability of observing the given level of enrichment; q-value: the FDR (False Discovery Rate) after Benjamini-Hochberg correction, controlling for the false positive rate. In this study, q < 0.05 was used as the criterion for significant enrichment.
  4. Filter the results using the keywords “Human Diseases” and “Metabolism” to remove pathways associated with human diseases and metabolism, retaining pathways associated with biological processes. Human Diseases and Metabolism pathways mainly describe the overall mechanisms of diseases and metabolic networks, which are weakly associated with the fibrosis, inflammation, and vascular remodeling mechanisms of ILD-PH comorbidity. Therefore, they were excluded to focus the results on key signaling pathways. Enter:
    kegg_result_df <- as.data.frame(kegg_result)
    kegg_filtered <- kegg_result_df[!grepl("Human Diseases|Metabolism", kegg_result_df$Description), ]
  5. Sort the pathways in descending order by gene count, select the top 10 pathways, generate a dot plot using the dotplot() function, and export the plot in PNG format. Enter:
    top10_pathways <- head(kegg_filtered[order(kegg_filtered$Count, decreasing = TRUE), ], 10)
    dotplot_result <- dotplot(enrichResult(select(top10_pathways)), showCategory = 10)
    ggsave("KEGG_dotplot.png", dotplot_result, width = 8, height = 6, dpi = 300)
  6. Extract the top 10 pathways and their associated target genes, construct a two‑column table (pathway name, target gene), and save the table as a tab‑delimited TXT file.
  7. Import the file into Cytoscape, configure the shape and color differences between pathway nodes and target nodes via the Style panel, manually refine the layout, and export the pathway‑target network image20.

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Results

Data retrieval and screening

In total, 2,400 records were collected from the Scopus database, while 696 were identified in the WoSCC database. After merging the two datasets and removing 533 duplicate records, a total of 2,563 unique publications were finally included for subsequent bibliometric analysis (Figure 1).

Annual publication output trends

The annual publication outp...

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Discussion

This study represents the first integrated application of bibliometrics and bioinformatics to explore the comorbidity of ILD and PH. It systematically reviews the developmental trajectory, knowledge structure, and cutting-edge trends in this field over the past decade, and identifies the hub genes and key signaling pathways underlying this comorbid state at the molecular level. From a macro-trend perspective, the annual number of publications in this field increased from 142 in 2016 to 402 in 2025, representing a nearly ...

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Disclosures

The authors report no conflicts of interest in this work.

Acknowledgements

The authors gratefully acknowledge the financial support from the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0522300, 2024ZD0522301) and the Non‑profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2022‑ZHCH330‑01).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Bibliometric Online Analysis PlatformK-Synth Srl (Bibliometric)https://bibliometric.comOnline analysis platform; module: Country relations; generates country collaboration chord diagram; accessed March 10, 2026
CiteSpaceDrexel University6.4.R1Bibliometric analysis; time slicing: 2016–2025; node types: keywords, references; pruning: Pathfinder + Pruning sliced networks
clusterProfiler (R package)Guangchuang Yu et al.4.13.2KEGG enrichment analysis; thresholds: p < 0.05, q < 0.2
CytoscapeCytoscape Consortium3.10.3Network visualization and analysis; plugin: CytoHubba; algorithm: degree centrality; used for hub gene identification
GeneCardsWeizmann Institute of Sciencehttps://www.genecards.orgGene information database; database version 5.22; accessed March 12, 2026; filtering criterion: Relevance score ≥ 1
Journal Citation Reports (JCR)Clarivate Analyticshttps://jcr.clarivate.comJournal impact factor and quartile retrieval; accessed March 9, 2026
KEGGKyoto Encyclopedia of Genes and Genomeshttps://www.kegg.jpPathway database; accessed via clusterProfiler API on March 13, 2026; species: hsa
MeSH DatabaseNational Library of Medicine (NLM)https://meshb.nlm.nih.govMeSH term query database; accessed March 9, 2026; used to construct search strategy
Microsoft ExcelMicrosoft Corporation16.88 (Microsoft 365)Data statistics and chart generation; used for publication count, deduplication, chart plotting
org.Hs.eg.db (R package)Bioconductor Core Team3.20.0Human genome annotation database; used for gene ID conversion
RR Core Team4.4.2Statistical computing and plotting environment; run date: March 13, 2026
Scimago GraphicaScimago Lab2Visualization tool; layout: map projection; edge curvature: 0.5; color mapping: by cluster
ScopusElsevierhttps://www.scopus.comLiterature retrieval database; Elsevier Scopus 2026 complete edition; retrieval date: March 9, 2026
STRINGEMBL12.0 / https://cn.string-db.orgProtein-protein interaction database; accessed March 12, 2026; interaction score threshold: 0.7 (high confidence); network type: physical + functional
VOSviewerLeiden University1.6.20Bibliometric analysis; co-authorship as analysis type; normalization method: association strength
Web of Science Core CollectionClarivate Analyticshttps://www.webofscience.comLiterature retrieval database; covers SCIE and SSCI sub-databases; retrieval date: March 9, 2026

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