Progress and trends in myocardial infarction-related long non-coding RNAs: a bibliometric analysis

FIGURE 1. Flowchart of data collection and study design. This figure outlines the step-by-step process used in our study to collect and analyze data on MI-related lncRNAs. The process begins with a comprehensive search of relevant database Web of Science, using specific keywords. Articles were then screened based on predefined inclusion and exclusion criteria. Eligible studies were subjected to data extraction and quality assessment. The extracted data were analyzed using bibliometric methods to identify trends, key research areas, and influential publications in the field of MI-related lncRNAs. Additionally, the figure includes notes on the criteria used for study selection and the statistical methods applied for data analysis.

FIGURE 2. Number of annual articles on MI-related lncRNAs. The bar graph shows the number of publications per year. The line graph overlays the total number of citations received each year. Notably, there is a sharp rise in both publications and citations in the last decade, suggesting an accelerating pace of discovery and scholarly attention.

FIGURE 3. (A), The collaborative research between countries and national literature published in the last 23 years; (B), The map of global scientific collaboration. Each country is color-coded based on the number of publications originating from that region, with darker shades indicating higher publication counts. The map highlights key contributing countries demonstrating the global nature of research efforts in this field. Additional notes provide information on the sources of geographic data, the methodology for categorizing and visualizing publication counts, and any notable trends or patterns observed in the distribution of research activities.

FIGURE 4. The knowledge flow in the field of MI-related lncRNAs in the last 23 years at the journal level. The overlay map is constructed based on journal citation data, visually representing the interdisciplinary connections and citation dynamics within the scientific community. The color coding of the nodes signifies different journal categories, such as cardiovascular research, molecular biology, and genetics. Edges between nodes indicate citation relationships, where the thickness of the edges reflects the frequency of citations between journals.

FIGURE 5. Overlay maps on the basis of a global map of science based on aggregated journal citation data. The nodes in the graph represent different scientific fields. Red: biology and medicine, blue: chemistry and physics, green: psychology and social sciences, purple: engineering and mathematics, yellow: ecology and environmental S&T. The connections between the nodes represent the connections between the fields.

FIGURE 6. (A) Keyword wordcloud: Top 50 keywords in MI-related lncRNAs in the last 23 years. (B) Network visualization map of keyword co-occurrence on MI-related lncRNAs. (C) Network visualization map of the current tendency towards MI-related lncRNAs based on keywords analysis. (D) Top 20 keywords with the strongest citations bursts. The size of each node represents the frequency of a keyword, while the link between nodes indicates the relationship between nodes, with the distance between them indicating the strength of the connection. The color indicates the co-occurrence of keywords (B), while the color change indicates the time change of keywords (C). “Begin” and “End” refer to the start and end of the keyword emergence, respectively. Strength is a measure of the intensity of the cited change. Red or blue bars represent time intervals. Red bars indicate bursts of citations (D).

FIGURE 7. Timeline visualization map of keyword clustering in the field of MI-related lncRNAs research. The size of the nodes represents the number of citations of the word, the position of the nodes represents the point in time when it was noted, the thickness of the lines between the nodes represents the strength of the correlation, and the color represents the clustering of research hotspots.