A bibliometric analysis of RNA methylation in prostate cancer and its complications from 2003 to 2024

Database Search

We retrieved all literature data regarding the RNA Methylation in prostate indexed in the Web of Science (WOS) Core Collection (Clarivate Analysis, Boston, United States; http://apps.webofknowledge.com/ UA_GeneralSearch_input.do;jsessionid=6595538022ED93CBA430A07BA5F00A98?product=UA&search_mode=GeneralSearch&SID=6F7xdGTnu5jxLKKjsjL&preferencesSaved=). The articles from 2003 (January 1) to 2024 (December 31) were searched, the language type was set to English, and the document type was set to article and review. The search terms and strategies used for the WOS database are as follows: ("RNA methylation" OR "m6A" OR "N6-methyladenosine" OR "RNA methyl" OR "epitranscriptom") AND ("prostate cancer" OR "prostatic neoplas" OR "prostate carcinoma" OR "prostate tumor").

A total of 156 documents on RNA methylation related to prostate cancer (article or review type, in English, published 2003–2024) were retrieved from SCI-Expanded. The publication deadline was set as December 31, 2024. Finally, 122 documents were selected through manual screening based on the following criteria: relevance to both prostate cancer and methylation, along with significant research value.

Methods

WOS-based literature analysis provides useful research information including publication years, journals, organizations, authors, and research fields. The Standard Competition Ranking (SCR) method was used as the ranking criteria in Bibliometric indicators. Both VOSviewer (version 1.6.16) software and CiteSpace (6.3.R1) software were used to perform and visualization of WOS-based literature analysis, including annual output, country/region, institutions, funding agencies, contributions of co-authors, journal, reference, keywords. Citation data were obtained for all the retrieved records from the Web of Science using the citation rates and h-index. The keywords and references were selected and analyzed to predict the research prospects and research hotspots.

The parameter of the VOSviewer was set by association strength method. The parameters of CiteSpace were set by LSI Method, time slicing from 2003 January to 2024 December, slice every year. Text Processing: Title, Abstract, Author Keywords (DE), Keywords Plus (ID). Node Types: choose one at a time, and in selection criteria choose g-index k=1, Top N: select top 50 levels, Top N%, select 10.0% and the maximum number of selected items per slice 100, Thresholds (c=2, cc=2, ccv=20), Citations (Use TC Filter 5-10), Usage 180: select top 50 items, Usage2013: select top 50 items. Visualization by Cluster View- Static and show merged network.

The bibliometrix package was used to analyze the country collaboration map. bibliometric.com website (https://bibliometric.com/) was Citespace software and Excel were used for analyze the distribution and international cooperation of countries.

Annual Output

We identified 122 publications associated with PCa in the WoS from 2003 to 2024. Of these 106 publications were indexed as “articles”. English was the predominant language for publications on RNA Methylation, constituting 100% (122/122) of the total. The whole information on the RNA Methylation field is shown in Table 1. The annual publication outputs in the RNA Methylation field are shown in Figure 1A. The number of publications varied from year to year, with an average of around publications per year represented by an overall upward trend during the investigated period. There were 1 (0.82%) and 2 (1.64%) papers published in 2007 and 2018, respectively. The publication number was greater than 10 in 2020, was greater than 20 in 2022, and was the highest in 2024 (n = 34, 27.87%). The Average Article Citations per Year in the RNA Methylation field are shown in Figure 1B. The publication number was greater than 10 in 2004, was greater than 20 in 2017, and was the highest in 2020 (n = 35, 28.6%).

Country/Region Analysis

The global research output in RNA methylation was comprised of contributions from 19 countries/regions (Table 2). China was the predominant contributor with 132 publications, substantially surpassing the United States (n=11). A group of seven countries, including Australia, Canada, England, India, Portugal, and Saudi Arabia, each contributed 3 papers. Furthermore, Italy, Nigeria, Sweden, and Switzerland each contributed 2 papers, while the remaining seven nations (Finland, Greece, Netherlands, Pakistan, South Korea, Spain, and Zambia) each contributed a single paper. A total of 19 countries were distributed across four continents, especially in Asia and North America, by using the VOS viewer (Figure 2). The network map reflects the state of research activities and communication among these countries/regions.

Contributions of Institutions

A total of 138 institutions contributed to the publications on RNA Methylation research. Nanjing Medical University was the largest contributor in terms of the number of publications with 40 papers, followed by the Shanghai Jiao Tong University and Guangzhou Medical University, with 21 and 20 papers, respectively. The top 10 most influential institutions and the quantity of articles in each institution are presented in Table 3.

As shown in Figure 3, the network map of cooperation relationships between institutions was a median-density map (density = 0.0318)，this indicates a typical loose cooperative network: specific cooperation channels have been established among institutions, but overall cooperation is not widespread, with a large number of unconnected institutions, and cooperation is highly selective. The network is dominated by China's top universities and medical research institutions.

Contributions of Funding Agencies

Table 4. lists the world’s top 10 funding agencies that sponsored the output of RNA Methylation research. Among them, 8 agencies were from China. The National Natural Science Foundation of China (NSFC) ranked first, supporting the highest number of 68 studies. The China Postdoctoral Science Foundation ranked second (10), and the National Natural Science Foundation of Guangdong Province ranked third (6).

Contributions of Authors

Figure 4A lists the top 20 authors who published the greatest number of papers. From the WoS, a total of 64 publications by the top 20 authors accounted for 52.46%(N=122) of all literature in this field. Jianming Lu from China was the author with the most publications of 5, followed by Chuanfan Zhong from China the with 4 papers, Weide Zhong from China with 4 papers, Alsaleem Mansour from Saudi Arabia with 3 papers, and Archer Nathan from England with 3 papers. As shown in Figure 4B, the author’s co-authorship network map was a high-density map (density = 0.0171). Jianming Lu, Chuanfan Zhong, and Weide Zhong were located at a central position of the cooperating clusters by using the CiteSpace and the Node Labels by citation set by threshold 2. The co-citation network between authors was also analyzed (Figure 4C). The weighted mean silhouette value of clusters #0 to #6 was0.7834, showing good homogeneity. In the cluster map, there were 6 co-cited author clusters. The appearance authors’ research categories of 7 clusters including “binding” (#0), “biochemical recurrence” (#1), “castration”(#2), “malignant progression”(#3), “arsi therapy”(#4), “prostate adenocarcinoma”(#6). For burst monitoring of Journals (Figure 4D), the top three ranked items were Xu Huan from 2022 to 2022, followed by Wang Zhong burst from 2022 to 2022 and Zhong Chuanfan burst from 2023 to 2024.

Journal Analysis

By the data analysis, the documents on RNA Methylation in prostate cancer from 2003 to 2024 were mainly distributed in different journals. In total, 76 journals have emerged recently in this research field. The top 15 active journals published 45 papers on RNA Methylation, accounting for 36.89% of all 122 publications (Table 5). The most prolific journal was Frontiers in Genetics with 5 documents. Six of the fifteen Journals by the JCR partition analysis Q1 40% (6/15) in this ranking. To analyze the distribution of publications sources, may be helpful to find out the core journal, especially to judge by the number of publications and impact factors, Both Frontiers in Genetics and the Journal of Cancer are possibly the most popular journals in this field.

Reference Analysis

The reference co-citation relationship was visualized in a co-citation network (Figure 5A). The co-citation network consists of 286 nodes. The Q value of modularity is a measure to assess the significance of the community structure. The maximum Q value equal to or more than 0.3 indicates a significant community structure. In this study, modularity Q was 0.7826, indicating that the clusters of networks were reasonable. Figure 5B also shows the timeline view of the reference co-citation clusters, which could reflect the temporal characteristics of research hot spots in this field. The silhouette values from clusters #0 to #8 were all more than 0.6, indicating the good homogeneity of the clusters. The largest cluster was “m6A score” (#0), followed by “ythdf2” (#1) and “hedgehog” (#2). The development of cluster 1 (ythdf2) and cluster 2（hedgehog） occurred earliest, suggesting that early considerations focused on the ythdf2 value and the hedgehog value of RNA Methylation. Cluster 0 (m6a score) and cluster 6 (cellular senescence) are the current research hot spots, which indicates that more concerns are shifting to potential RNA stability. For burst monitoring of authors (Figure 5C), the top three ranked items were from Ma JZ burst from 2018 to 2022, followed by Alarcon CR from 2018 to 2020, and Lin SB from 2019 to 2021.

Keywords

The goal of keywords co-occurrence analysis was to determine the developing trends and hot topics. Keywords are one of the most important pieces of evidence in tracing scientific development. The top 20 keywords from the WoS in terms are listed in Table 6. Prostate cancer, expression and messenger RNA were the most frequent keywords, with 73, 33, and 33 co-occurrences, respectively, highly matching our research theme. Concerning the other keywords, some were related to therapies such as translation, invasion, metastasis, growth, and carcinoma. The High-frequency keywords were used to create a density map by using the VOS viewer software. A total of 524 keywords were involved in 122 documents and 23 met the threshold (minimum 10 keyword documents number). The network visualization map showed the cooccurrence relations of keywords, and the size of the circle indicates keyword occurrence (Figure 6A). Overlay visualization of the cooccurrence analysis indicated the keywords changed from earlier time with blue nodes including prostate cancer, m6A, promotes, METTL3 to resent time with yellow nodes including m6a modification, androgen receptor and metastasis (Figure 6B). The keywords point changed indicates the hotspot view in the future. The co-citation network between keywords was also analyzed (Figure 6C). In the cluster map, RNA Methylation owned the highest centrality. The weighted mean silhouette value of clusters #0 to #6 was 0.7573 during 2007 to 2024 years, showing good homogeneity. In the cluster map, there were 184 co-cited keyword clusters. The appearance authors’ research categories of 7 clusters including “gene signature”(#0), “androgen receptor”(#1), “translation”(#2), “malignant progression”(#3), “arsi therapy”(#4), “prostate adenocarcinoma”(#5), “complex”(#6). These clusters showed the most prominent topics in RNA Methylation research so far. For burst monitoring of keywords (Figure 6D), the top three ranked items were breast cancer, from 2007 to 2018, followed by m(6)a from 2018 to 2020, messenger rna methylation from 2018 to 2018.