Comprehensive landscape and future perspectives of non-coding RNAs in esophageal squamous cell carcinoma, a bibliometric analysis from 2008 to 2023

Abstract

Objectives: Summarize the progress and hot topic evolution of non-coding RNAs (ncRNAs) research in esophageal squamous cell carcinoma (ESCC) in recent years and predict future research directions. Methods: Relevant articles from the Web of Science until 31 October 2023 were obtained. Bibliometric analysis of included articles was performed using software (VOSviewer, CiteSpace, and Bibliometrix). The volume and citation of publications, as well as the country, institution, author, journal, keywords of the articles were used as variables to analyze the research trends and hot spot evolution. Results: 1,118 literature from 2008 to 2023 were retrieved from database, with 25 countries/regions, 793 institutions, 5,426 authors, 261 journals involved. Global cooperation was centered on China, Japan, and the United States. Zhengzhou University, an institution from China, had the highest publication. The most prolific author was Guo Wei, and the most prolific journal was Oncology Letters. Analysis of keywords revealed that the research in this field revolved around the role of ncRNAs in the occurrence, development, diagnosis, treatment, and prognosis of ESCC, mainly including micro RNAs, long non-coding RNAs, and then circular RNAs. Conclusion: Overall, research on ncRNAs in ESCC remains strong. Previous research has mainly focused on the basic research, with a focus on the mechanism of ncRNAs in the occurrence, development, diagnosis, treatment, and prognosis of ESCC. Combining current research with emerging disciplines to further explore its mechanisms of action or shifting the focus of research from preclinical research to clinical research based on diagnosis, treatment, and prognosis, will be the main breakthrough in this field in the future.

Keywords: VOSviewer; bibliometric analysis; digestive system cancer; non-coding RNAs; novel biomarker.

FIGURE 1

Flow diagram of literature inclusion.

FIGURE 2

Annual publications trend chart.

FIGURE 3

The co-authorship analysis of ncRNAs associated with ESCC. (A) Network plot of co-authorship analysis of countries/regions. (B) World map of co-authorship analysis of countries/regions. (C) Network plot of co-authorship analysis of organizations. The size of the circles or the depth of map color blocks represents the number of publications, and the thickness of the lines between the circles or the map color blocks represents the degree of collaboration.

FIGURE 4

The co-authorship analysis and co-occurrence analysis of ncRNAs associated with ESCC. (A) Network plot of co-authorship analysis of authors. (B) Overlay map of co-authorship analysis of authors. (C) Network plot of co-occurrence analysis of top 100 keywords. (D) Overlay map of co-occurrence analysis of top 100 keywords. In network plot, a circle represents an author or a keyword. The larger the circle, the more articles the author has published or the more frequently the keyword appears. The thickness of the connecting line between circles represents the strength of the association. In overlay map, cold colors indicate that the author was active earlier or the keywords appeared earlier, while warm colors indicate that the author was active more recently or the keywords appeared more recently.

FIGURE 5

Timeline chart of keywords. Each diamond represents a keyword, and its position on the timeline represents the time it appeared. In addition, the size of the diamond represents the number of times it appears in the total time period, while each layer inside the diamond represents the number of times it appears in the corresponding year.

FIGURE 6

Top 25 keywords with the strongest citation burst. Blue lines indicate the time period in which the keywords appeared and ended, and red lines indicate the time period in which the keywords at high frequencies.