Current trends and landscape of drug resistance in renal cell carcinoma: a bibliometric analysis

Abstract

Introduction: Renal cell carcinoma (RCC) is a common type of kidney cancer, and the prognosis for patients with advanced-stage disease remains poor. One major obstacle is the development of drug resistance, which severely limits the effectiveness of therapeutic interventions. This bibliometric study aims to provide a comprehensive overview of current research trends on drug resistance in RCC.

Methods: This study examines publications on drug resistance in RCC from 2000 to 2023, sourced from the Web of Science Core Collection (WoSCC). Detailed analyses were conducted to identify research hotspots, academic collaborations, and emerging trends. CiteSpace, SCImago Graphica, and VOSviewer were utilized to conduct these analyses comprehensively.

Results: This study analyzed a total of 2,804 publications from the WoSCC database. The number of annual publications showed a consistent upward trend, with an average annual growth rate of 8.12%. The United States had the highest number of publications, followed by China and Japan. The most productive institutions were the University of Texas System, Harvard University, and the National Institutes of Health (NIH). Alfred H. Schinkel emerged as the most prolific author, also having the highest H-index. The three most frequent research categories were oncology, pharmacology and pharmacy, and biochemistry and molecular biology. The evolution of research topics was assessed in 5-year intervals, revealing that recent themes such as ferroptosis and immunotherapy have gained increasing attention. Keyword analysis indicated a shift in research focus toward cell lipid metabolism, androgen receptor and specific molecular signatures.

Conclusion: This study offers the first comprehensive bibliometric analysis specifically focused on drug resistance in RCC. It identifies current research trends, highlights emerging hotspots, and provides insights into key contributors and ongoing challenges in the field. Our study provides a theoretical reference and guidance to guide future research efforts to address drug resistance in RCC more effectively.

Keywords: Bibliometric analysis; Drug resistance; Immunotherapy; Renal cell carcinoma; Targeted therapy; Visualization.

Fig. 1

Flowchart depicting the publication screening strategy

Fig. 2

Overview of publication data. A Annual and cumulative number of publications from 2000 to 2023, with dashed lines indicating growth trends using a non-linear fitting method. B Average citations per publication per year from 2000 to 2023. C Summary of key information related to drug resistance in the treatment of renal cell carcinoma

Fig. 3

Analysis of countries/regions, institutional and author collaboration. A In the network of national/regional cooperation, node size indicates the number of publications, while node color depth reflects the intensity of collaboration. B In the institutional collaboration network, node size represents the number of publications. Blue nodes indicate early-stage research activities, whereas yellow nodes indicate recent research efforts. C Co-authorship network map of authors. Node size indicates the number of publications, and the color depth reflects the publication year, with darker colors representing more recent publications. D Clustering analysis of authors, where nodes of the same color belong to the same cluster

Fig. 4

Visualization of journal analysis results. A Top 10 journals related to drug resistance in RCC ranked by publication counts. B Trends in publication output of the top 10 journals from 2000 to 2023. C Classification of core and non-core journals based on Bradford’s law

Fig. 5

Research category and themes analysis. A Research category visualization. The size of the nodes indicates the number of publications, while the color gradient represents the publication years. B The progression of research themes using the R bibliometric thematic evolution tool. Time segmentation points were established in 2005, 2010, 2015, and 2020

Fig. 6

Visualization results of references on drug resistance in RCC research. A Co-citation analysis of references. The size of the nodes represents the number of citations for each reference, and the depth of the color indicates the publication year of the references. B Clustering analysis of references, with the same color representing a cluster. C Burst analysis of references. The red portion of the blue line represents the burst duration

Fig. 7

Visualization results of keywords on drug resistance in RCC research. A Keyword network visualization. B Clustering analysis of keywords. C Keyword timeline visualization. D Burst analysis of keywords. The red portion of the blue line indicates the burst duration of the keywords