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

Abstract

Background: Myocardial infarction (MI), a critical condition, substantially affects patient outcomes and mortality rates. Long non-coding RNAs (lncRNAs) play a critical role in the onset and progression of MI. This study aimed to explore the related research on MI-related lncRNAs from a bibliometric perspective, providing new clues and directions for researchers in the field.

Methods: A comprehensive search was conducted on 7 August 2023, using the Web of Science Core Collection (WoSCC) database to compile a dataset of all English-language scientific journals. The search gathered all relevant publications from January 2000 to August 2023 that pertain to MI-related lncRNAs. Data on countries, institutions, journals, authors, and keywords were collected, sorted, statistically analyzed, and visualized using CiteSpace 6.2.R4, VOSviewer 1.6.19, an online bibliometric analysis platform (http://bibliometric.com), and the bibliometric package in R-Studio 4.3.1. Articles were screened by two independent reviewers.

Results: Between January 2000 and August 2023, a total of 1,452 papers were published in the research field of MI-related lncRNAs. The year with the most publications was 2020, accounting for 256 papers. The publication volume displayed an exponential growth trend, fitting the equation y = 2.0215e^0.2786x, R^{^2} = 0.97. In this domain, China leads in both the number of published papers (N = 1,034) and total citations, followed by the United States, Germany, Iran, and Italy. The most productive institution is Harbin Medical University (N = 144). The European Review for Medical and Pharmacological Sciences had the highest number of publications (N = 46), while Circulation Research had the most citations (TC = 4,537), indicating its irreplaceable standing in this field. Research mainly focuses on the cardiovascular system, cellular biology, physiology, etc. The most productive author is Zhang Y. Apart from "Myocardial Infarction" and "LncRNA," the most frequent keywords include "expression," "atherosclerosis," and "apoptosis." Cluster analysis suggests current research themes concentrate on cardiovascular diseases and gene expression, cardiac ischemia/reperfusion injury and protection, expression and proliferation, atherosclerosis and inflammatory response, among others. Keyword bursts indicate recent hot topics as targeting, autophagy, etc.

Conclusion: This bibliometric analysis reveals that research on MI-related lncRNAs has rapidly expanded between January 2000 and August 2023, primarily led by China and the United States. Our study highlights the significant biological roles of lncRNAs in the pathogenesis and progression of MI, including their involvement in gene expression regulation, atherosclerosis development, and apoptosis. These findings underscore the potential of lncRNAs as therapeutic targets and biomarkers for MI. Additionally, our study provides insights into the features and quality of related publications, as well as the future directions in this research field. There is a long road ahead, highlighting the urgent need for enhanced global academic exchange.

Keywords: bibliometric; cluster analysis; long non-coding RNAs (lncRNAs); myocardial infarction (MI); web of science core collection.

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.