Analyzing the research landscape: Mapping frontiers and hot spots in anti-cancer research using bibliometric analysis and research network pharmacology

Abstract

Introduction: Network pharmacology has emerged as a forefront and hotspot in anti-cancer. Traditional anti-cancer drugs are limited by the paradigm of "one cancer, one target, one drug," making it difficult to address the challenges of recurrence and drug resistance. However, the main advantage of network pharmacology lies in its approach from the perspective of molecular network relationships, employing a "one arrow, multiple targets" strategy, which provides a novel pathway for developing anti-cancer drugs. This study employed a bibliometric analysis method to examine network pharmacology's application and research progress in cancer treatment from January 2008 to May 2023. This research will contribute to revealing its forefront and hotspots, offering new insights and methodologies for future investigations. Methods: We conducted a literature search on network pharmacology research in anti-cancer (NPART) from January 2008 to May 2023, utilizing scientific databases such as Web of Science Core Collection (WoSCC) and PubMed to retrieve relevant research articles and reviews. Additionally, we employed visualization tools such as Citespace, SCImago Graphica, and VOSviewer to perform bibliometric analysis. Results: This study encompassed 3,018 articles, with 2,210 articles from WoSCC and 808 from PubMed. Firstly, an analysis of the annual national publication trends and citation counts indicated that China and the United States are the primary contributing countries in this field. Secondly, the recent keyword analysis revealed emerging research hotspots in "tumor microenvironment," "anti-cancer drugs," and "traditional Chinese medicine (TCM). " Furthermore, the literature clustering analysis demonstrated that "calycosin," "molecular mechanism," "molecular docking," and "anti-cancer agents" were widely recognized research hotspots and forefront areas in 2023, garnering significant attention and citations in this field. Ultimately, we analyzed the application of NPART and the challenges. Conclusion: This study represents the first comprehensive analysis paper based on bibliometric methods, aiming to investigate the forefront hotspots of network pharmacology in anti-cancer research. The findings of this study will facilitate researchers in swiftly comprehending the current research trends and forefront hotspots in the domain of network pharmacology in cancer research.

Keywords: anti-cancer; bibliometric analysis; citespace; network pharmacology; research frontiers.

FIGURE 1

A flowchart of the literature strategy and data analysis.

FIGURE 2

Annual publication trends and future predictions of NPART in PubMed and WoSCC databases. (A) GM model indicates significant increases in PubMed publications from 2023 to 2026. (B) WoSCC publications from 2023 to 2026 are higher than PubMed publications, but growth trends remain similar.

FIGURE 3

The Distribution of Collaborative Publications and Institutions among Countries. (A) Top 30 countries in terms of publication output and citation frequency. (B) Three clusters of the national collaborative network are based on similarity of research. China and the United States have the highest level of collaboration intensity. (C) Three clusters are formed based on similar research, with extensive collaboration between clusters. Above each node are the institutions’ publications.

FIGURE 4

Analysis of the Citations of Authors’ Publications and Collaboration. (A) Top 10 authors based on the number of publications and citations. (B) Four researchers were identified as the intersection of the Top 10 authors based on the number of publications and citations they have received. (C) Collaboration network diagram of 4 authors with high publication and citation counts. Zheng Chunli is an important node for collaboration between Li Yan and Wang Yonghua.

FIGURE 5

Comprehensive analysis of keywords. (A) Keyword co-occurrence and burstness. Node size is directly proportional to keyword frequency. (B) Select #0-#7 clusters for keyword clustering analysis. From Cluster #0 to Cluster #7, the number of keywords decreases sequentially. (C) Keyword clusters #2 and #5 have a mutual correlation. Cluster #2 exhibited keywords related to breast cancer, growth factor receptor, and cancer therapy in 2008. Cluster #5 featured keywords related to anticancer drugs in 2015. (D) Heatmap analysis of the annual and cumulative frequency of keywords in PubMed literature. Red labels indicate emerging research hotspots. Purple to yellow color blocks represent increasing standardized keyword frequency.

FIGURE 6

A dual-map overlay shows journals. Z-scores standardize citation frequency for comparative citation analysis by mitigating disciplinary differences. Citation paths are displayed by discipline, with widths proportional to z-score-scaled citation frequency. Labels represent research subjects, while the wavy curve connects citing articles (on the left) and cited articles (on the right).

FIGURE 7

Co-citation and clustering analysis network. (A) Co-citation reference network among BC. The size of the article node is proportional to the number of its co-citations, while nodes with high BC are marked by a purple outer ring. (B) Co-citation reference network with a burst of citations. Burst nodes are labeled with a red circle. (C) A circular view of the cluster of #0-#10 literature commonly cited, obtained through reference clustering based on the similarity between references. (D) Clusters #0, #1, #4, and #9 show a dense connection in 2023, indicating cutting-edge research directions.

FIGURE 8

The alluvial flow map of landmark literature between 2018 and 2023. During their individual citation impact periods of 4 years, these 8 papers made significant contributions to NPART research. In particular, Szklarczyk_D and Daina_A papers demonstrate strong continuity and lasting influence.