Skip to main content
NCBI home page
Discover Oncology logoLink to Discover Oncology
. 2025 Nov 4;16:2018. doi: 10.1007/s12672-025-03854-9

Collaboration networks and knowledge structure in cancer and cellular senescence research (2000–2025): a bibliometric perspective

He Zhu 1,#, Shuangjia Pan 1,#, Yujia Zhou 1, Baozhu Yi 1, Yuqing Jing 1, Luyi Ruan 1, Lingxiao Huang 1, Xueqiong Zhu 1,✉
PMCID: PMC12586263  PMID: 41186845

Abstract

Cancer is a leading cause of death worldwide, posing substantial public health challenges. Cellular senescence, a stress-induced cell fate characterized by stable cell-cycle arrest and a hypersecretory state, plays dual roles in tissue repair and tumor suppression. Although the relationship between cancer and cellular senescence has been widely investigated, no comprehensive bibliometric analysis has been reported. This study aimed to map the global research landscape, identify hotspots, and reveal emerging trends in this field. Publications from 2000 to 2025 were retrieved from the Web of Science Core Collection, including only articles and reviews, and analyzed using VOSviewer and CiteSpace. A total of 5790 papers were identified, showing a steady increase in publications and citations over the past 25 years. The USA ranked first in publication output and institutional productivity, followed by China, while Judith Campisi was the most prolific and cited author. Hotspots included the senescence-associated secretory phenotype (SASP), immunotherapy, tumor microenvironment, secretory phenotype, and molecular hallmarks of senescence. These findings indicate that cancer and cellular senescence research is expanding rapidly, with secretory phenotypes and molecular hallmarks representing promising directions for future investigation.

Keywords: Cancer, Cellular senescence, Bibliometrics, Visualized analysis, CiteSpace

Introduction

Hayflick and Moorhead initially characterized cellular senescence as the finite replicative potential observed in cultured human fibroblasts [1]. Cellular senescence represents a stress-induced permanent growth arrest accompanied by enhanced secretory activity, serving as a physiological mechanism that facilitates tissue repair while providing tumor suppression [2, 3]. In contrast to proliferating cells, senescent cells exhibit significant alterations in both morphology and molecular biology. The senescent state is characterized by flattened cell morphology and enlarged nuclei, which are key hallmarks of cellular senescence [4]. Senescence-associated β-galactosidase (SA-β-gal), a lysosomal hydrolase, serves as a well-established biomarker for identifying senescent phenotypes in malignant cell populations [5, 6]. Notably, escaping the senescent state can also be a dangerous outcome [7–11], warranting further investigation.

Cancer has increasingly become a major cause of mortality in numerous countries [12]. Therapeutic induction of senescence in malignant cells represents a viable oncological intervention, effectively suppressing tumor progression and metastatic dissemination. Recent studies have shown that certain genes and pathways can regulate cellular senescence in cancer cells. For example, the Flap Endonuclease 1–Pre-B-cell Leukemia Homeobox 1 (FEN1–PBX1) axis has been functionally characterized as a critical modulator of senescent pathways in mammary carcinoma cells. Genetic suppression of FEN1 elevates endogenous ROS accumulation, resulting in PBX1 transcriptional repression and subsequent senescence induction [13]. The increasing number of publications in cancer and cellular senescence research, including cervical cancer [14], colorectal cancer [15], and breast cancer [16], highlights the growing interest in this field in recent years.

Bibliometrics research has been developed for many years and achieved a great many scientific achievements [17]. Utilizing bibliometric methods can uncover the latest trends in scientific research, identify emerging hot topics and interdisciplinary areas, and assess academic impact, thereby providing empirically-grounded guidance for science policy formulation [18]. However, bibliometric analysis on cancer and cellular senescence has not been conducted yet.

In this study, bibliometric methods were employed to systematically analyze research articles focusing on the relationship between cancer and cellular senescence from 2000 to 2025. The objectives are to outline current research trends in cancer and cellular senescence, and to identify and predict future research hot topics in this domain. By employing bibliometric techniques, this study seeks to systematically characterize the developmental trajectory of cellular senescence research and explore its significance and implications in the context of oncogenesis.

Methods

Sources of data and search techniques

To ensure the inclusion of high-quality literature and adherence to standardized citation formats, the Web of Science Core Collection (WoSCC) (https://www.webofscience.com/wos/) was employed as the primary source of data. The search terms and strategy in the WOS database was: (TS = cancer) AND (TI=(‘Cellular Senescence’ OR ‘Senescence, Cellular’ OR ‘Cell Senescence’ OR ‘Senescence, Cell’ OR ‘Cell Aging’ OR ‘Cellular Ageing’ OR ‘Ageing, Cellular’ OR ‘Aging, Cell’ OR ‘Cell Ageing’ OR ‘Ageing, Cell’ OR ‘Cellular Aging’ OR ‘Aging, Cellular’ OR ‘Replicative Senescence’ OR ‘Senescence, Replicative’) OR AK =(‘Cellular Senescence’ OR ‘Senescence, Cellular’ OR ‘Cell Senescence’ OR ‘Senescence, Cell’ OR ‘Cell Aging’ OR ‘Cellular Ageing’ OR ‘Ageing, Cellular’ OR ‘Aging, Cell’ OR ‘Cell Ageing’ OR ‘Ageing, Cell’ OR ‘Cellular Aging’ OR ‘Aging, Cellular’ OR ‘Replicative Senescence’ OR ‘Senescence, Replicative’)). The search strategy utilized three field tags in the Web of Science database to refine the literature retrieval: TS (Topic), encompassing the title, abstract, and keywords; TI (Title), specifying terms within article titles; and AK (Author Keywords), targeting keywords designated by the authors. Employing these tags enabled a comprehensive and precise collection of relevant publications on cancer and cellular senescence. The analysis covered publications from 1 January 2000 through 13 February 2025, with inclusion criteria restricted to English-language original articles and review papers. Publications such as book chapters, editorial material, biographical-item, book review, letter, meeting, retraction, news item, non-English literatures, early access, retracted publication, correction, proceeding paper, expression of concern, and meeting abstract were excluded. The conceptual framework and procedural flow of the study are illustrated in Fig. 1.

Fig. 1.

Fig. 1

Open in a new tab

Diagrammatic illustration of the literature search and selection process

Data and statistical analysis

VOSviewer (version 1.6.20), a bibliometric visualization software developed by Van Eck and Waltman at Leiden University in 2010, is extensively used for generating and analyzing scientific knowledge network maps [19]. primarily designed to help researchers analyze, visualize, and understand academic literature, knowledge networks, collaborative relationships, and research hotspots [20]. This study employed a multifaceted bibliometric approach to visualize four key analytical dimensions: (1) co-authorship patterns among countries, institutions, and individual researchers; (2) co-citation relationships involving journals and authors; (3) bibliographic coupling among publication sources; and (4) keyword co-occurrence to identify thematic associations.

CiteSpace (version 6.4.R1), a Java-based bibliometric analysis tool developed by Chaomei Chen at Drexel University, is widely utilized to explore research frontiers, evolutionary trends, and knowledge clusters within specific scientific domains [21]. Additionally, it facilitates the visualization of institutional collaboration networks, co-occurring subject categories, and reference co-citation patterns. Furthermore, CiteSpace offers a unique dual-map overlay function, which enables the depiction of citation pathways, knowledge diffusion trajectories, and the disciplinary distribution of scholarly journals [22]. The parameters of CiteSpace were as follows: time span (2000/01/01 to 2025/02/13), year of slice (1), selection criteria g-index (k = 25), selection criteria (Top 50), link retaining factor (LRF = 2.5), look back years (LBY = 5).

Microsoft Excel 2016 (Microsoft Corporation, USA), R software (version 4.3.0) and Graphpad Prism (version 10.1.2) and charticulator (https://ilfat-galiev.im/charticulator/) was employed to conduct descriptive statistical analyses and to generate visual representations of the data. The creation of high-quality maps was achieved using RStudio (version 4.3.0), in conjunction with packages such as rworldmap and ggplot2. The flow charts (Fig. 1) were meticulously constructed using Microsoft Excel 2016, leveraging its powerful data visualization and diagramming tools to clearly illustrate the processes and workflows involved in the study. GraphPad Prism, a leading graphing and statistical analysis software [23], was employed to systematically quantify the frequency of occurrence for each keyword.

The average citation index (ACI) serves as a valuable metric for assessing research quality, reflecting the impact and influence of scholarly work within the academic community [24, 25]. Higher ACI values typically indicate that research findings are widely recognized and frequently referenced, suggesting a greater contribution to the advancement of knowledge in the field. The H-index serves as an integrated metric to assess scholarly productivity and impact by accounting for both the quantity of publications and their citation frequency, thus reflecting the consistency and significance of a researcher’s academic contributions [26, 27]. Together, ACI and H-index provide a comprehensive evaluation of research quality, combining insights into the immediate impact of individual works with the long-term productivity and significance of a researcher’s contributions [28].

Results and discussion

Comprehensive analysis of cancer and cellular senescence research (2000–2025)

The growing number of publications in cancer and cellular senescence indicated that it is a hot topic of research, which in turn drives the development of the field [29]. A total of 5790 publications related to cancer and cellular senescence were retrieved from the Web of Science Core Collection (WoSCC) between January 1, 2000, and February 13, 2025. These comprised 4655 original research articles and 1135 review papers. The retrieved publications accumulated a total of 272,895 citations, of which 245,881 excluded self-citations. The ACI was 47.13, and the overall H-index for the selected literature reached 208. As illustrated in Fig. 2, both the annual publication output and citation frequency have exhibited a continuous upward trend from 2000 to 2025.

Fig. 2.

Fig. 2

Open in a new tab

This figure was obtained from the Web of Science Core Collection. The annual publication trended in the past 25 years. Purple bars represented the number of papers related to cancer and cellular senescence per year. The dark purple line represented the number of citations per year

This growth trend likely stems from deeper understanding of cellular senescence mechanisms such as therapy-induced senescence, SASP, and the development of senolytic and senomorphic agents, together with increasing interest in its potential as a diagnostic and therapeutic target. Cellular senescence was initially regarded as an important barrier to tumorigenesis, functioning as an intrinsic defense to prevent malignant transformation. However, increasing evidence shows that senescent cells can promote tumor progression through SASP, which includes chemokines, growth factors, cytokines, and stromal metalloproteinases that remodel the tumor microenvironment and facilitate immune evasion [30]. In some contexts, SASP can also enhance immunosurveillance to suppress cancers. This dual role highlights the complexity and heterogeneity of senescence and SASP in cancer, driving the development of therapeutic strategies to clear senescent cells or mitigate SASP’s harmful effects.

Global contributions and collaborations

The high volume of publications in a particular field by a country or region typically indicates a high level of research activity, strength, and influence in that field [31]. However, it is essential to combine this indicator with others such as citation counts, journal impact, and research quality to comprehensively evaluate the overall scientific research level [32, 33]. All publications covered 27 countries/regions. China and the United States (USA) emerged as the leading contributors to the global body of published research (Fig. 3A). USA had published the most publications with 1896(26.16%), followed by China [1461(20.16%)], Japan [435(6.00%)], Germany [428(5.91%)], and England [361(4.98%)] (Fig. 3B). USA held holded the highest Academic Citation Index (ACI) value of 77.23, followed by England (68.80), Germany (60.62), Japan (35.83), and China (23.96).

Fig. 3.

Fig. 3

Open in a new tab

Analysis of national publication volume in the field of cancer and cellular senescence. A Global distribution of publications on cancer and cellular senescence. B The Combined Chart illustrated the academic metrics of USA, China, Japan, Germany, and England, including ACI, H-index, and the number of publications. C Cooperation between contributed countries. Line thickness correlates with the intensity of the closeness. D Country co-authorship analysis. In this overlay visualization map, each node was a country, and links between countries represented co-authorship relationship. The size of each node was proportional to the total number of publications

In terms of the H-index, USA also led leaded with a score of 170, succeeded by England (82), Germany (82), China (78), and Japan (58). These metrics clearly demonstrated the USA’s substantial contribution in the field of cancer and cellular senescence. In contrast, China appeared to have a lower ACI value compared to the other countries listed, which suggests that there is scope for improvement in the quality of Chinese publications (Fig. 3B).

USA had collaborated with countries around the world, with the most extensive cooperation being with China (Fig. 3C). As illustrated in Fig. 3D, a country-level analysis was also performed using VOSviewer. A total of 94 countries and regions had contributed to publications in the field of cancer and cellular senescence, among which 27 have published more than 42 papers each, demonstrating their significant research output in this area. As can be seen as is shown in Fig. 3D, China and USA were situated in a central position of this overlay visualization map. Countries like China, Russia, and India are marked in yellow, meaning they had recently published research papers in this field. The USA, England, and Canada - highlighted in purple on the analysis map - represented pioneering nations in cellular senescence and cancer research. Their early entry into this field has enabled them to produce substantial high-impact publications, which has subsequently solidified their leading academic reputation and established significant influence within the international research community regarding senescence-related oncology studies. Therefore, it was essential for countries to strengthen international collaboration to enhance progress and impact in this field.

The USA holds a leading position worldwide in research output and academic influence in the field of cancer and cellular senescence. This advantage stems from a robust research infrastructure, substantial funding, and extensive interdisciplinary collaboration, enabling steady progress from fundamental studies to clinical translation. U.S. researchers have published a large volume of high-impact papers in top-tier journals, advancing knowledge on key topics such as therapy-induced senescence [34], SASP regulation [35], and strategies for senescent cell clearance [36]. Moreover, the USA actively participates in international collaborations and data sharing, which further expands its academic reach and leadership. This combination of high productivity and strong influence positions the U.S. as a global driver of innovation and translational application in cancer and senescence research.

Institutional achievement analysis

Academic collaboration among different institutions within a country can achieve multiple advantages, including resource optimization, knowledge complementarity, quality enhancement and talent cultivation. It is an important pathway for driving national scientific and technological progress and social development [29, 37–39]. As depicted in Fig. 4A, a collaboration network was constructed using CiteSpace analysis to visualize global contributions in the field of cancer and cellular senescence over the past 25 years. In this network, institutions were represented by text and circles, with lines indicating collaborative relationships. The thickness of the lines corresponds to the frequency of collaboration, while gradient colors reflect the quality and depth of these collaborative efforts. The diameter of each node corresponded proportionally to the publication output of the respective institution. Notably, institutions highlighted with a purple halo indicated their significant academic influence within this domain. These prominent institutions included the National Cancer Institute (NCI), the National Institutes of Health (NIH), and the University of California System in the USA; Assistance Publique – Hôpitaux de Paris (APHP) in France. Figure 4B illustrated the temporal changes in institutional research output on cancer and cellular senescence. For example, NCI was highlighted in purple, which indicated that the NCI was among the early entrants and has made significant foundational contributions. In contrast, Fudan University was highlighted in yellow, indicating its recent participation through relevant research publications in this area. These findings indicated that numerous institutions have successively entered the field of cancer and cellular senescence, thereby contributing to its development.

Fig. 4.

Fig. 4

Open in a new tab

Analysis of institution publication volume in the field of cancer and cellular senescence. A Institutional collaboration analysis. B Institution co-authorship analysis

The progressive involvement of long-standing leaders, such as the NCI and NIH, alongside emerging contributors, such as Fudan University, illustrates a vibrant and continuously evolving collaborative ecosystem. This diversification has broadened the thematic scope and methodological repertoire of the field while fostering the cross-pollination of ideas, which is crucial for driving innovative breakthroughs and expediting the translation of fundamental senescence research into clinical applications. Scientific collaboration can take multiple forms, and a widely adopted method to evaluate such collaboration within the scholarly community is the analysis of co-authorship, which not only quantifies the strength of collaborative networks but also reveals how cross-institutional and international partnerships enhance research productivity and impact [40].

Author contribution analysis

To rapidly identify prominent scholars in the field, a series of analysis among authors were conducted. The author analysis was generated by VOSviewer. This approach enables the identification of scholars who have significant influence within a specific research domain [41]. As was shown in Fig. 5A, among the 76 authors included in the analysis, each of whom had published more than nine papers. Judith Campisi contributed the most with 50 publications, followed by Krzysztof Ksiazek (25 publications) and Justyna Mikula-Pietrasik (23 publications). Regarding citation impact, the three authors with the highest average citations per publication are Judith Campisi (428.0 citations per publication), David A Gewirtz (72.5 citations per publication), and Ewa Sikora (58.5 citations per publication) (Fig. 5B).

Fig. 5.

Fig. 5

Open in a new tab

Analysis of author publication volume in the field of cancer and cellular senescence. A Analysis of author publication on cancer and cellular senescence. In this overlay visualization map, each node represented the name of author, and links between author represented co-authorship. The size of each node was proportional to the total number of publications. B Ring bar chart showing the number of publications and average citation (ACI) for the top 6 authors by publication count

Judith Campisi has clearly made substantial contributions and exerted considerable influence in this field. Judith Campisi, a pioneering geroscientist, is renowned for her leadership in cellular senescence and the SASP. Her groundbreaking research has transformed the perspective from a primarily cancer-focused framework to a comprehensive recognition of the complex roles these processes play in aging [42]. However, there remains a need for enhanced collaboration among authors to further advance the research. Strengthening cross-institutional and interdisciplinary partnerships could have helped connect different areas of research, integrate diverse methodological approaches, and accelerate the translation of senescence research into clinical practice. Furthermore, expanding collaborative networks beyond traditional research hubs to include emerging institutions and early-career investigators would have promoted knowledge exchange and fostered a more sustainable and inclusive growth of the field [43].

Journals and co-cited journals

As indicated in Fig. 6A, bibliographic coupling of sources was generated by VOSviewer. Among the 1250 journals contributing to this research field, 52 journals met the minimum publication threshold of 18 articles. The International Journal of Molecular Sciences ranked first in the number of publications within this field, followed by Aging-US, Aging Cell, and Cell Cycle.

Fig. 6.

Fig. 6

Open in a new tab

Analysis of journal publication volume in the field of cancer and cellular senescence. A Analysis of journal publication volume. B Journal co-citation analysis. C The dual-map overlay of journals related to cancer and cellular senescence. In the dual-map, the citing journals were located on the left, and the cited journal was on the right. Colored paths indicated the citation relationships, with the thicker lines representing main pathways

This analysis provided scholars with valuable insights into the journal landscape within the field, enhancing their understanding of publication trends and contributing to a more efficient and successful manuscript submission process [44]. Table 1 presented the top 20 journals ranked by their publication volume, providing a clear overview of the most active journals in the field. Cancer Research and Ageing Research Reviews both exhibited the highest impact factors in 2023, each with an IF of 12.5.

Table 1.

Top 20 most active journals in cancer and cellular senescence

Ranking Sources title Output % of 5790 IF 2023 Category quartile(JCR)
1 International Journal Of Molecular Sciences 143 2.47% 4.9 Q1/Q2
2 Aging-Us 139 2.40% 3.9 Q2/Q2
3 Aging Cell 118 2.04% 8 Q1/Q1
4 Cell Cycle 101 1.74% 3.4 Q3
5 Cancers 95 1.64% 4.5 Q1
6 Oncotarget 91 1.57% 5.168(2016) Q2/Q1
7 Plos One 85 1.47% 2.9 Q1
8 Oncogene 83 1.43% 6.9 Q1/Q1
9 Biochemical And Biophysical Research Communications 72 1.24% 2.5 Q3/Q3
10 Mechanisms Of Ageing And Development 71 1.23% 5.3 Q2/Q1
11 Frontiers In Immunology 68 1.17% 5.7 Q1
12 Cells 62 1.07% 5.1 Q2
13 Cancer Research 60 1.04% 12.5 Q1
14 Proceedings Of The National Academy Of Sciences Of The United States Of America 59 1.02% 9.4 Q1
15 Scientific Reports 59 1.02% 3.8 Q1
16 Journal Of Biological Chemistry 55 0.95% 4 Q2
17 Frontiers In Oncology 50 0.86% 3.5 Q2
18 Cell Death & Disease 48 0.83% 8.1 Q1
19 Ageing Research Reviews 43 0.74% 12.5 Q1
20 Experimental Gerontology 43 0.74% 3.3 Q2
Open in a new tab

Ranking: according to the number of total publications.

Figure 6B depicted the co-citation network map of journals. The threshold for the minimum number of citations was set at 600, with 88 journals qualifying. Nature was the most cited journal, followed by Cell. In addition, a dual-map overlay of journals was generated to visualize the subject distribution of academic journals [45], as illustrated in Fig. 6C. The map features labels that denoted thematic domains covered in the journals, with referenced journals on the right and citing journals on the left. The citation pathways between them were depicted by color-coded lines, with the thickness of these lines indicating the citation frequency on a z-score scale [46, 47]. The dual-map analysis revealed two primary citation paths. The majority of published articles appeared in journals specializing in molecular biology and immunology. While, the majority of cited articles originated from journals specializing in molecular, biology, and genetics. This visualization indicated a strong interdisciplinary connection between molecular mechanisms of cellular senescence and cancer development.

The journal distribution and citation patterns highlighted the interdisciplinary nature of cancer and cellular senescence research, bridging molecular biology, immunology, and genetics. This integration helped guide researchers in identifying both leading and related journals in the field, facilitating more informed publication and collaboration decisions.

Co-cited references analysis

Co-citation analysis was employed to identify 15 distinct research clusters by Citespace (Q = 0.703, S = 0.885) (Fig. 7A), each representing a key thematic focus within the field. These clusters included #0 senolytics, #1 p53, #2 telomerase, #3 sasp, #4 stem cells, #5 exhaustion, #6 mtor, #7 dna methylation, #8 oligoclonal expansions, #9 elderly, #10 prognosis, #11 phase ii study, #12 non-small-cell lung cancer, #13 anastasis, #14 age factors, #15 microrna, #16 mortality, #17 androgen receptor and #19 cancer stem cell. This analysis offered a thorough overview of the primary research domains and their interrelations, highlighting the diverse and multifaceted nature of current scientific inquiry. The analysis further revealed that these clusters are predominantly localized in fundamental research.

Fig. 7.

Fig. 7

Open in a new tab

Analysis of reference in the field of cancer and cellular senescence. A Reference co-citation analysis of publications related to cancer and cellular senescence. B Top 25 references with the strongest citation bursts. The blue line represented the timeline, while the red bars indicated periods of keyword bursts, showing the start year, end year, and duration of these bursts

Figure 7B was visualized the citation history of highly influential papers in this research field from 2000 to 2025 and listed the top 25 most cited papers, including author names, publication year, journal, volume, page numbers, and DOI links. Among them, it can be observed that the articles written by Gorgoulis V (2019, CELL, Strength = 75.35) [2], Calcinotto A (2019, PHYSIOL REV, Strength = 53.68) [48], Sung H (2021, CA-CANCER J CLIN, Strength = 48) [12], Di Micco R (2021, NAT REV MOL CELL BIO, Strength = 47.27) [49], and Wang LQ (2022, NAT REV CANCER, Strength = 45.95) [50] were still frequently appeared in 2025.

Co-cited references refer to the frequency with which two publications are cited together, serving as a valuable metric for uncovering the intellectual linkages and conceptual relationships that underpin scientific research [51]. These highly cited papers remained influential, establishing key foundations in cancer and cellular senescence mechanisms. Their focus on critical topics continued to guide the field, suggesting that future research should have built upon these works to deepen mechanistic understanding and drive innovative therapeutic strategies.

Co-occurrence analysis of keywords

Keywords represent the fundamental components of academic publications, encapsulating the main themes and focus of the research. As a result, keyword analysis serves as a crucial indicator for identifying research hotspots and trends within a specific field [52–54]. The keyword co-occurrence network was generated in VOSviewer after removing duplicate terms. Among the 5,790 publications, 10,168 keywords were identified. 99 keywords occurred at least 23 times each (Fig. 8A). Figure 8B illustrated that keywords coded in yellow represented current research hotspots, which included Senolytics, Senescence-associated secretory phenotype, Immunotherapy, Prognosis, and Overall Survival. Conversely, keywords coded in purple signified earlier research hotspots, such as p53, p16, tumor suppression, and telomeres.

Fig. 8.

Fig. 8

Open in a new tab

Analysis of keywords in the field of cancer and cellular senescence. A Density visualization of keywords based on occurrence times. B keywords co-occurrence analysis. The nodes marked in purple or blue indicated keywords that emerged relatively early, while those coded in yellow signified the current research focuses. C Top 25 keywords with the strongest citation bursts. The blue line represented the timeline, while the red bars indicated periods of keyword bursts, showing the start year, end year, and duration of these bursts. D Keywords analysis

Figure 8C illustrated the top 25 keywords that have experienced the most significant citation bursts from 2000 to 2025, highlighting the years of peak influence and the duration of each trend. It revealed that keywords such as “human fibroblasts (strength = 37.66),” “in vivo (strength = 34.87),” and “replicative senescence (strength = 33.49)” had the strongest and longest-lasting impacts. Since 2018, keywords like “clearance,” “secretory phenotype,” and “hallmarks” had exhibited significant citation bursts, highlighting the growing interest in these research areas. However, these citation bursts may also signal ongoing challenges in the field. For example, while the involvement of cellular senescence in cancer and aging-related diseases is well-documented, effectively targeting and clearing senescent cells for therapeutic benefit remains an unresolved issue. Figure 8D depicted a time-based analysis of keyword frequency clusters related to cancer and cellular senescence, generated using CiteSpace. In the diagram, the frequency of keyword occurrence was depicted by the size of each circle, with larger circles representing higher frequencies. The co-occurrence of keywords reflected how frequently they appear together, demonstrating their thematic connections. The timeline at the top showed the chronological progression of the initial keywords. This visualization elucidated the organization of keywords within each cluster, where larger clusters corresponded to higher thematic significance. It also tracked the lifespan of keyword trends within each cluster [55]. Keywords that belong to the same cluster are aligned horizontally to reflect their thematic associations and are grouped into 20 clusters (Q = 0.7278, S = 0.8793): #0 cancer stem cells, #1 non-small cell lung cancer, #2 DNA methylation, #3 DNA repair, #4 tumor microenvironment, #5 DNA damage, #6 disease, #7 breast cancer, #8 cell senescence, #9 stem cells, #10 replicative senescence, #11 ovarian cancer, #12 renal cell carcinoma, #13 T cells, #14 progression, #15 p53, #16 liver cancer, #17 tumorigenesis, #18 in vivo, #19 gene expression.

Keyword analysis plays an essential role in revealing the core themes of a publication, highlighting prevailing trends in a given field, and guiding researchers toward its main areas of focus [56]. The keyword analysis revealed that early research primarily focused on fundamental biology and cellular biology. Over time, the research hotspots have gradually shifted to areas such as “secretory phenotype,” and “immunotherapy,” which are closely related to aging, cancer, and therapeutic interventions. Therefore, researchers should stay updated on these changing trends and work together more closely to drive progress in these areas.

Advances and challenges in cellular senescence and cancer therapy

Currently, research in the fields of cancer and cellular senescence primarily focuses on mechanistic studies, with key markers such as p16, p21, lipofuscin and SA-β-gal being widely used to understand the underlying mechanisms of senescence [57–59]. These markers have offered essential insights into the pathways governing DNA damage response, cell cycle arrest, and the SASP, all of which play roles in both cancer progression and tumor suppression. Notably, lipofuscin has entered the market, allowing simplified senescence detection even in live animal models, and facilitating the development of innovative therapeutic platforms [60].

Emerging technologies are playing a pivotal role in advancing this field. For instance, organoid models derived from stem cells are now being utilized to simulate tumor development and senescence processes [61, 62]. These organoids serve as powerful tools for drug screening and mechanistic studies, offering a more physiologically relevant platform compared to traditional cell lines. Additionally, nanotechnology has enabled the development of targeted drug delivery systems using nanocarriers, which enhance therapeutic efficacy while minimizing off-target effects [63]. Machine learning is used to model SASP protein profiles from THP-1 cells and apply them to plasma samples to predict various age-related clinical traits [64]. In parallel, single-cell techniques have become indispensable for characterizing the unique features of cancer. Collectively, these advancements hold significant promise for improving cancer treatment strategies.

Despite these innovations, there is still a significant gap in translating these findings into clinically effective therapies. Specifically, no pharmacological agents have been successfully developed or approved for clinical use to specifically induce cancer cell senescence as a therapeutic strategy. At the same time, inhibiting the senescence of immune cells and macrophages in the tumor microenvironment has also always been a challenge. While inducing senescence in cancer cells could potentially halt tumor growth and prevent relapse, challenges remain in selectively targeting cancer cells without affecting normal cells and in managing the potential pro-tumorigenic effects of the SASP.

Conclusion

This study provides a extensive bibliometric evaluation of worldwide research focused on cancer and cellular senescence between 2000 and 2025 (Fig. 9). Utilizing advanced scientometric methods and data visualization tools, the research systematically explores international collaboration patterns, publication trends, disciplinary distributions, and knowledge diffusion pathways. The analysis identifies leading countries, institutions, and authors, while highlighting emerging research frontiers and paradigm shifts in this interdisciplinary field. A thorough understanding of these advances and their implications is crucial for guiding future research directions.

Fig. 9.

Fig. 9

Open in a new tab

Analysis in the field of cancer and cellular senescence (Draw by figdraw, www.figdraw.com)

The United States emerged as the most productive and influential country in the field, with NCI and NIH identified as the leading American institutions, while APHP was recognized as a significant institution representing France; Judith Campisi was recognized as the most prolific author; the International Journal of Molecular Sciences ranked first in publication volume; and since 2018, keywords such as “clearance,” “secretory phenotype,” and “hallmarks” have shown notable citation bursts, underscoring the growing research interest in these areas. However, the field of cancer-associated cellular senescence still faces several key challenges. The exact role of senescence in cancer remains complex - it can either suppress or promote tumors depending on specific biological contexts, and this dual nature is not yet fully understood. Additionally, the regulatory mechanisms controlling the senescence-associated secretory phenotype (SASP) require further investigation. Perhaps most importantly, the clinical application of senescence-targeting therapies is currently limited by insufficient data regarding their treatment efficacy, target specificity, and safety profiles.

Despite these limitations, cellular senescence remains a highly promising therapeutic target for both cancer treatment and age-related diseases. To break through current barriers, we must integrate multidisciplinary approaches combining oncology, immunology, senescence biology and drug development. Moving forward, research efforts should focus on three key priorities: (1) elucidating the fundamental mechanisms of senescence, (2) developing more effective senolytic and senomorphic agents, and (3) conducting rigorously designed clinical trials. This comprehensive strategy will accelerate the translation of senescence-based therapies from bench to bedside, ultimately bringing tangible benefits to cancer patients.

Author contributions

HZ and SP: Conceptualization, Data curation, Writing –original draft. YZ and BY: Methodology, Software, Writing – review & editing. YJ, LR and LH: Formal analysis, Methodology, Writing – review & editing. XZ: Formal analysis, Funding acquisition, Supervision, Visualization, Writing – review & editing.

Funding

This work was supported by zhejiang Key Laboratory of Traditional chinese Medicine for Diagnosis and Treatment of Gynecological Cancers (2022-11).

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

No application.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

He Zhu and Shuangjia Pan are equally contributed.

References

  • 1.Hayflick L, Moorhead PS. The serial cultivation of human diploid cell strains. Exp Cell Res. 1961;25:585–621. 10.1016/0014-4827(61)90192-6. [DOI] [PubMed] [Google Scholar]
  • 2.Gorgoulis V, Adams PD, Alimonti A, et al. Cellular senescence: defining a path forward. Cell. 2019;179(4):813–27. 10.1016/j.cell.2019.10.005. [DOI] [PubMed] [Google Scholar]
  • 3.López-Otín C, Blasco MA, Partridge L, et al. Hallmarks of aging: an expanding universe. Cell. 2023;186(2):243–78. 10.1016/j.cell.2022.11.001. [DOI] [PubMed] [Google Scholar]
  • 4.Muñoz-Espín D, Serrano M. Cellular senescence: from physiology to pathology. Nat Rev Mol Cell Biol. 2014;15(7):482–96. 10.1038/nrm3823. [DOI] [PubMed] [Google Scholar]
  • 5.Dimri GP, Lee X, Basile G, et al. A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc Natl Acad Sci USA. 1995;92(20):9363–7. 10.1073/pnas.92.20.9363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Lee BY, Han JA, Im JS, et al. Senescence-associated beta-galactosidase is lysosomal beta-galactosidase. Aging Cell. 2006;5(2):187–95. 10.1111/j.1474-9726.2006.00199.x. [DOI] [PubMed] [Google Scholar]
  • 7.Martínez-Zamudio RI, Stefa A, Freitas JA, et al. Escape from oncogene-induced senescence is controlled by POU2F2 and memorized by chromatin scars. Cell Genomics. 2023;3(4):100293. 10.1016/j.xgen.2023.100293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Zampetidis CP, Galanos P, Angelopoulou A, et al. A recurrent chromosomal inversion suffices for driving escape from oncogene-induced senescence via SubTAD reorganization. Mol Cell. 2021;81(23):4907-e49238. 10.1016/j.molcel.2021.10.017. [DOI] [PubMed] [Google Scholar]
  • 9.Galanos P, Vougas K, Walter D, et al. Chronic p53-independent p21 expression causes genomic instability by deregulating replication licensing. Nat Cell Biol. 2016;18(7):777–89. 10.1038/ncb3378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Patel PL, Suram A, Mirani N, et al. Derepression of hTERT gene expression promotes escape from oncogene-induced cellular senescence. Proc Natl Acad Sci USA. 2016;113(34):E5024-5033. 10.1073/pnas.1602379113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Milanovic M, Fan DNY, Belenki D, et al. Senescence-associated reprogramming promotes cancer stemness. Nature. 2018;553(7686):96–100. 10.1038/nature25167. [DOI] [PubMed] [Google Scholar]
  • 12.Sung H, Ferlay J. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
  • 13.Wu M, Wu B, Huang X, et al. Inhibition of the FEN1-PBX1 axis elicits cellular senescence in breast cancer via the increased intracellular reactive oxygen species levels. J Transl Med. 2025;23(1):248. 10.1186/s12967-025-06216-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.He Y, Qiu Y, Yang X, et al. Remodeling of tumor microenvironment by cellular senescence and Immunosenescence in cervical cancer. Sem Cancer Biol. 2025. 10.1016/j.semcancer.2024.11.002. [DOI] [PubMed] [Google Scholar]
  • 15.Chen Z, Zhou J, Wu Y, et al. METTL3 promotes cellular senescence of colorectal cancer via modulation of CDKN2B transcription and mRNA stability. Oncogene. 2024;43(13):976–91. 10.1038/s41388-024-02956-y. [DOI] [PubMed] [Google Scholar]
  • 16.Ye J, Baer JM, Faget DV, et al. Senescent CAFs mediate immunosuppression and drive breast cancer progression. Cancer Discov. 2024;14(7):1302–23. 10.1158/2159-8290.CD-23-0426. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Lyu P, Liu X. A bibliometric analysis of literature on bibliometrics in recent half-century. J Inform Sci. 2023. 10.1177/01655515231191233. [Google Scholar]
  • 18.Sun W, Zhou T, Ding P, et al. Bibliometric analysis of intestinal microbiota and lung diseases. Front Cell Infect Microbiol. 2024;14:1347110. 10.3389/fcimb.2024.1347110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523–38. 10.1007/s11192-009-0146-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Yeung AWK, Georgieva MG, Atanasov AG, et al. Monoamine oxidases (MAOs) as privileged molecular targets in neuroscience: research literature analysis. Front Mol Neurosci. 2019;12:143. 10.3389/fnmol.2019.00143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.CiteSpace II. Detecting and visualizing emerging trends and transient patterns in scientific literature - chen – 2006 - journal of the American society for information science and technology - wiley online library[EB/OL]. [2025-02-17]. 10.1002/asi.20317.
  • 22.Ou Z, Qiu L, Rong H, et al. Bibliometric analysis of chimeric antigen receptor-based immunotherapy in cancers from 2001 to 2021. Front Immunol. 2022;13:822004. 10.3389/fimmu.2022.822004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Software for graphing time-series data | request PDF. ResearchGate, 2024[2025-02-17]. https://www
  • 24.Sun G, Dong DONGD. Drug repositioning: a bibliometric analysis. Front Pharmacol. 2022;13:974849. 10.3389/fphar.2022.974849. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Xia DM, Wang XR. Research progress of heat stroke during 1989–2019: a bibliometric analysis. Military Med Res. 2021. 10.1186/s40779-021-00300-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Azar M, Lagacé F, Muntyanu A, et al. Measuring h-index and scholarly productivity in academic dermatology in Canada. Scientometrics. 2023;128(2):1071–90. 10.1007/s11192-022-04589-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bertran K, Cortey M, Díaz I. The use of H-index to assess research priorities in poultry diseases. Poult Sci. 2020;99(12):6503–12. 10.1016/j.psj.2020.09.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Jiang F, Su Y, Chang T. Knowledge mapping of global trends for myasthenia Gravis development: a bibliometrics analysis. Front Immunol. 2023;14:1132201. 10.3389/fimmu.2023.1132201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Jackson SR, Chambers S, Leslie S, et al. Prostate cancer, online health information and communication technology - bibliometric analysis of field with research frontiers. Patient Educ Couns. 2023;115:107887. 10.1016/j.pec.2023.107887. [DOI] [PubMed] [Google Scholar]
  • 30.Raynard C, Ma X, Huna A. NF-κB‐dependent secretome of senescent cells can trigger neuroendocrine transdifferentiation of breast cancer cells. [2025][2025-08-15]. 10.1111/acel.13632. [DOI] [PMC free article] [PubMed]
  • 31.Zheng S, Wang Y, Geng J, et al. Global trends in research on MOG antibody-associated disease: bibliometrics and visualization analysis. Front Immunol. 2024;15:1278867. 10.3389/fimmu.2024.1278867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Zare A, Izanloo S, Khaledi S, et al. A bibliometric and in silico-based analysis of anti-lung cancer compounds from sea cucumber. Mar Drugs. 2023;21(5):283. 10.3390/md21050283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Zhang L, Zheng H, Jiang ST, et al. Worldwide research trends on tumor burden and immunotherapy: a bibliometric analysis. Int J Surg (London England). 2024;110(3):1699–710. 10.1097/JS9.0000000000001022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Cellular senescence promotes adverse effects of chemotherapy and cancer relapse - PMC[EB/OL]. [2025-08-15]. https://pmc [DOI] [PMC free article] [PubMed]
  • 35.MTOR regulates the pro-tumorigenic senescence-associated secretory phenotype by promoting IL1A translation - PMC[EB/OL]. [2025-08-15]. https://pmc [DOI] [PMC free article] [PubMed]
  • 36.Baker DJ, Wijshake T, Tchkonia T, et al. Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders. Nature. 2011;479(7372):232–6. 10.1038/nature10600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Chen C, Yuan P, Zhang Z. Nanomedicine-based cancer immunotherapy: a bibliometric analysis of research progress and prospects. Front Immunol. 2024;15:1446532. 10.3389/fimmu.2024.1446532. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Wang J, Wang S, Zhang Y, et al. Bibliometric analysis of evolutionary trajectory and prospective directions of LAG-3 in cancer. Front Immunol. 2024;15:1329775. 10.3389/fimmu.2024.1329775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.He X, Xu S, Tang L, et al. Insights into the history and tendency of liver transplantation for liver cancer: a bibliometric-based visual analysis. Int J Surg. 2024;110(1):406–18. 10.1097/JS9.0000000000000806. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Uddin S, Hossain L, Rasmussen K. Network effects on scientific collaborations. PLoS ONE. 2013;8(2):e57546. 10.1371/journal.pone.0057546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Yang C, Liu H, Feng X, et al. Research hotspots and frontiers of neoadjuvant therapy in triple-negative breast cancer: a bibliometric analysis of publications between 2002 and 2023. Int J Surg. 2024;110(8):4976–92. 10.1097/JS9.0000000000001586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Jin K. Judith campisi (1950–2024) - a pioneering geroscientist in senescence and SASP. Aging Dis. 2024; 15(2): 444. 10.14336/AD.2024.0123.
  • 43.Wu PN, Liu JL, Fang MJ, et al. Global trends in colorectal cancer and metabolic syndrome research: a bibliometric and visualization analysis. Int J Surg. 2024;110(6):3723–33. 10.1097/JS9.0000000000001342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wan Y, Shen J, Ouyang J, et al. Bibliometric and visual analysis of neutrophil extracellular traps from 2004 to 2022. Front Immunol. 2022;13:1025861. 10.3389/fimmu.2022.1025861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Liu Y, Cheng X, Han X, et al. Global research landscape and trends of lung cancer immunotherapy: a bibliometric analysis. Front Immunol. 2022;13:1032747. 10.3389/fimmu.2022.1032747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Yuan R, Tan Y, Sun PH, et al. Emerging trends and research foci of Berberine on tumor from 2002 to 2021: A bibliometric Article of the literature from WoSCC. Front Pharmacol. 2023;14:1122890. 10.3389/fphar.2023.1122890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Zhang T, Zhang B, Tian W, et al. A bibliometric analysis of atrophic gastritis from 2011 to 2021. Front Med. 2022;9:843395. 10.3389/fmed.2022.843395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Calcinotto A, Kohli J. Cellular senescence: aging, cancer, and injury. Physiol Rev. 2019;99(2):1047–78. 10.1152/physrev.00020.2018. [DOI] [PubMed] [Google Scholar]
  • 49.Di Micco R, Krizhanovsky V. Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat Rev Mol Cell Biol. 2021;22(2):75–95. 10.1038/s41580-020-00314-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Wang L, Lankhorst L, Bernards R. Exploiting senescence for the treatment of cancer. Nat Rev Cancer. 2022;22(6):340–55. 10.1038/s41568-022-00450-9. [DOI] [PubMed] [Google Scholar]
  • 51.Sugimoto CR, Ahn YY, Smith E, et al. Factors affecting sex-related reporting in medical research: a cross-disciplinary bibliometric analysis. Lancet. 2019;393(10171):550–9. 10.1016/S0140-6736(18)32995-7. [DOI] [PubMed] [Google Scholar]
  • 52.Wang D, Yang Y, Yang L, et al. Bibliometric analysis and visualization of endocrine therapy for breast cancer research in the last two decade. Front Endocrinol. 2023;14:1287101. 10.3389/fendo.2023.1287101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Wang J, Zhao W, Zhang Z, et al. A journey of challenges and victories: a bibliometric worldview of nanomedicine since the 21st century. Adv Mater. 2024;36(15):e2308915. 10.1002/adma.202308915. [DOI] [PubMed] [Google Scholar]
  • 54.Li P, Li J, Tong X, et al. Global research trends and prospects related to tumor microenvironment within triple negative breast cancer: a bibliometric analysis. Front Immunol. 2023;14:1261290. 10.3389/fimmu.2023.1261290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Zhang R, Jiang Q, Zhuang Z, et al. A bibliometric analysis of drug resistance in immunotherapy for breast cancer: trends, themes, and research focus. Front Immunol. 2024;15:1452303. 10.3389/fimmu.2024.1452303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Xiao F, Li C, Sun J, et al. Knowledge domain and emerging trends in organic photovoltaic technology: a scientometric review based on CiteSpace analysis. Front Chem. 2017;5:67. 10.3389/fchem.2017.00067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.de Magalhães JP. Cellular senescence in normal physiology. Science. 2024;384:1300–1. 10.1126/science.adj7050. [DOI] [PubMed] [Google Scholar]
  • 58.Evangelou K, Lougiakis N. Robust, universal biomarker assay to detect senescent cells in biological specimens. Aging Cell. 2017;16(1):192–7. 10.1111/acel.12545. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Magkouta S, Veroutis D, Pousias A, et al. A fluorophore-conjugated reagent enabling rapid detection, isolation and live tracking of senescent cells. Mol Cell. 2023;83(19):3558-e35737. 10.1016/j.molcel.2023.09.006. [DOI] [PubMed] [Google Scholar]
  • 60.Magkouta S, Veroutis D, Papaspyropoulos A, et al. Generation of a selective senolytic platform using a micelle-encapsulated Sudan black B conjugated analog. Nat Aging. 2025;5(1):162–75. 10.1038/s43587-024-00747-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Vlachogiannis G, Hedayat S, Vatsiou A, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018;359:920–6. 10.1126/science.aao2774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Lehmann J, Baar MP, de Keizer PLJ. Senescent cells drive frailty through systemic signals. Trends Mol Med. 2018;24(11):917–8. 10.1016/j.molmed.2018.09.003. [DOI] [PubMed] [Google Scholar]
  • 63.Liu S, Khan AR, Yang X, et al. The reversal of chemotherapy-induced multidrug resistance by nanomedicine for cancer therapy. J Controlled Release. 2021;335:1–20. 10.1016/j.jconrel.2021.05.012. [DOI] [PubMed] [Google Scholar]
  • 64.Olinger B, Banarjee R, Dey A, et al. The secretome of senescent monocytes predicts age-related clinical outcomes in humans. Nat Aging. 2025;5(7):1266–79. 10.1038/s43587-025-00877-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Articles from Discover Oncology are provided here courtesy of Springer

RESOURCES