From feasibility to clinical pathways: a bibliometric and knowledge-mapping analysis of urine-based liquid biopsy in urologic cancers (2015–2025)

From feasibility to clinical pathways: a bibliometric and knowledge-mapping analysis of urine-based liquid biopsy in urologic cancers (2015–2025) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From feasibility to clinical pathways: a bibliometric and knowledge-mapping analysis of urine-based liquid biopsy in urologic cancers (2015–2025) Hanlin Liu, Yuqiang Fu, Jianwei Yang, Wang Li, Junsheng Bao, Liyun Ding, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8287024/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background Urologic cancers, including prostate cancer, bladder cancer, upper tract urothelial carcinoma and renal cell carcinoma, are well suited to urine-based liquid biopsy because tumors lie close to the urinary tract and often require repeated, minimally invasive monitoring. Over the past decade, multiple urinary analytes and platforms have been explored, but an integrated view of their temporal evolution, global knowledge production and disease-specific application pathways is lacking. Methods Publications on urine-based liquid biopsy in urologic cancers were retrieved from the Web of Science Core Collection (2015–2025); 439 articles and reviews met predefined criteria. CiteSpace, VOSviewer and the bibliometrix R package were used to analyze publication trends, country–institution–author–journal networks, highly cited and burst references, and keyword co-occurrence and evolution. Temporal and thematic patterns were synthesized into a stage-wise framework linking research hotspots with core clinical scenarios. Results Global output increased steadily from 2016, with marked acceleration after 2021. Co-citation and burst analyses supported two phases: an initial “foundational evidence and methodological expansion” phase dominated by circulating tumor DNA and early urine-focused work, and a later “standardization and platform integration” phase characterized by guideline-oriented reviews, liquid biopsy frameworks and consensus documents. The United States and Europe formed the main evidentiary hubs, with East Asian institutions showing rapid recent growth. Keyword analyses revealed two dominant trajectories: diagnostic pathways in prostate cancer, centered on urinary RNA signatures, exosome-based multigene panels and composite risk models in the prostate-specific antigen grey zone; and monitoring pathways in bladder cancer and urothelial carcinoma, focused on urinary DNA methylation and mutation panels and upgraded cytology for recurrence surveillance and cystoscopy de-escalation. Renal cell carcinoma and other less common urologic cancers remained evidence-sparse but clinically important frontiers, with exploratory work on urinary methylation, extracellular vesicles and metabolomics. Conclusions Urine-based liquid biopsy in urologic cancers has progressed from feasibility testing to disease-specific model construction and early standardized implementation. The two-stage pattern and diagnostic/monitoring twin pathways identified here, together with key geographical and methodological gaps, provide a framework for future multicenter cohorts, standardization efforts and clinically oriented trials to embed urinary biomarkers into routine decision-making. Urine Liquid biopsy Urologic neoplasms Bibliometrics Circulating tumor DNA Extracellular vesicles Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Urologic cancers—including prostate cancer, bladder cancer, upper tract urothelial carcinoma (UTUC) and renal cell carcinoma (RCC)—are among the major solid tumors worldwide and account for an increasing share of cancer incidence and mortality(1,2). Current diagnostic and surveillance pathways rely heavily on imaging, serum biomarkers and histopathological biopsy. These approaches underpin early detection, risk stratification and postoperative follow-up, but they are invasive, constrained in sampling frequency, and suboptimal for capturing dynamic changes in tumor burden and molecular evolution. In several typical scenarios—such as the “grey zone” of elevated prostate-specific antigen (PSA) with non-definitive imaging or digital rectal examination, long-term cystoscopic surveillance of non-muscle-invasive bladder cancer, and postoperative follow-up and recurrence monitoring in RCC—clinicians must constantly balance the risks of over-testing against those of missing high-risk progression. This tension underscores an unmet need for accurate, non-invasive and repeatable molecular biomarkers to guide clinical decision-making(3–5). Liquid biopsy, which interrogates circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles (EVs) and other soluble analytes in body fluids, has shown considerable promise in early cancer detection, assessment of minimal residual disease (MRD), monitoring of treatment response and tracking of resistance evolution6. Large pan-cancer cohort studies have demonstrated that ctDNA levels correlate closely with tumor volume, stage and prognosis, thereby anchoring the clinical validity of circulating tumor-derived signals(6). Subsequent methodological and clinical reviews have refined ctDNA assay platforms, statistical analysis frameworks and clinical application scenarios, collectively paving the way for plasma-based liquid biopsy to enter clinical guidelines and regulatory pathways(7,8).However, most of this work has focused on plasma, which primarily reflects systemic tumor burden and may be less sensitive to localized microenvironmental changes in lesions arising from, or confined to, the urothelial mucosa and luminal surfaces of the urinary tract(9–12). In contrast to blood, urine is easy to collect, non-invasive and amenable to repeated sampling, making it particularly attractive in urologic cancers(10,12). Tumors of the bladder and upper urinary tract are in direct contact with the urinary stream, whereas the prostate and kidneys communicate with urine through multiple layers of cellular and molecular exchange. As a result, tumor-derived extracellular nucleic acids, proteins and metabolites in urine can capture both systemic signals and additional information from the urothelial mucosa and local tumor microenvironment(13). Accumulating basic and translational data suggest that diverse urinary analytes—including cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA), microRNAs, DNA methylation patterns, proteomic features and extracellular vesicle cargo—may be useful for risk stratification in prostate cancer, recurrence monitoring in bladder cancer, and non-invasive diagnosis and prognostication in UTUC and RCC(14–18). These characteristics make urologic cancers a particularly suitable disease model for testing the concept of liquid biopsy based on an “organ-specific fluid.” Against this backdrop, urine-based assays have rapidly expanded across the spectrum of urologic cancers. In prostate cancer, a range of urinary RNA- and exosome-based multigene signatures, together with models that integrate PSA and clinical variables, have been developed to refine biopsy indications and identify high-risk patients within the PSA “grey zone”(19–21). In bladder cancer and UTUC, the limited sensitivity of conventional urine cytology has prompted the development of new urinary tests centered on DNA methylation and hotspot mutation panels, as well as multiplex protein assays, which are being evaluated as tools to aid diagnosis and to reduce the frequency of cystoscopy during surveillance(15–17,22). In RCC and other less common urologic cancers, urinary biomarker panels spanning DNA methylation, microRNAs, peptides and metabolites have also been explored for non-invasive discrimination between malignant and benign lesions and for prognostic risk assessment(23,24). Nevertheless, most existing studies have focused on a single tumor type, analyte or platform. A field-level synthesis that examines “urine plus the full spectrum of urologic cancers” as an integrated research landscape is still lacking. Several fundamental questions therefore remain unresolved: (1) On the temporal axis, does urine-based liquid biopsy for urologic cancers exhibit distinct stages of development over the past decade, and if so, where are the inflection points and how are they linked to peaks in evidentiary output? (2) How are different countries, institutions, authors and journals positioned within the global network of knowledge production and dissemination, and to what extent have they shaped the structure of this field? (3) Within a bibliometric framework integrating co-citation and citation-burst analyses, which highly cited and burst references constitute the key evidentiary nodes that underpin dominant technological pathways and clinical application models? (4) Across the core clinical scenarios—PSA grey-zone diagnosis in prostate cancer, recurrence surveillance in bladder cancer and UTUC, and non-invasive assessment of RCC and other less common urologic cancers—how do current hotspots and frontiers differ and evolve in terms of the specific combinations of clinical context, analytes and technological platforms? Bibliometrics and scientific knowledge mapping provide a systematic toolkit for addressing these questions(25). By quantitatively analyzing publication trends, highly cited and burst references, country–institution–author–journal networks and the evolution of keyword clusters, such approaches can delineate global patterns of knowledge production and collaboration and identify the pivotal nodes and pathways in evidence evolution(26). In recent years, knowledge-mapping studies across medicine and public health have shown that these methods are well suited to uncovering research hotspots and frontiers and to linking bibliometric patterns with disease burden, policy agendas and real-world clinical scenarios(27,28). In this study, we performed an integrated bibliometric and knowledge-mapping analysis of urine-based liquid biopsy in urologic cancers using records retrieved from the Web of Science Core Collection (WoSCC) between 2015 and 2025. Drawing on three complementary perspectives—temporal evolution, global knowledge production and clinically anchored research scenarios—we sought to delineate stage-wise development and map the dominant technological trajectories in this field, providing a structured visual overview to inform future study design and clinical translation. 2. Methods 2.1 Data source and search strategy This bibliometric analysis was based on the Web of Science Core Collection (WoSCC), a widely used multidisciplinary citation database that provides broad, regularly updated coverage with complete citation and cited-reference information, making it suitable for knowledge-mapping studies(29). To capture the evolution of urine-based liquid biopsy in urologic cancers over the past decade, we restricted the publication period to 2015 to 2025. All searches were performed in WoSCC on 15 November 2025. The search strategy was structured around three conceptual modules: (1) urologic cancers; (2) urine-related terms; and (3) liquid biopsy–related terms. All search terms were queried as Topic (TS) fields and combined using Boolean operators. The final search string was: TS = (( "bladder cancer" OR "bladder carcinoma" OR "bladder tumor*" OR "urothelial carcinoma" OR "transitional cell carcinoma" OR "urinary bladder neoplasm*" ) OR ( "kidney cancer" OR "renal cancer" OR "renal cell carcinoma" OR "kidney neoplasm*" OR "renal neoplasm*" ) OR ( "prostate cancer" OR "prostatic neoplasm*" OR "prostate tumor*" OR "prostatic carcinoma" )) AND TS = (("urine" OR "urinary") AND ("liquid biopsy" OR "liquid biopsies")). The initial search retrieved 481 records. All records were exported from WoSCC in “Full Record and Cited References” plain-text format to ensure compatibility with CiteSpace, VOSviewer and R-based bibliometric packages. 2.2 Eligibility criteria and study selection 2.2.1 Inclusion and exclusion criteria Studies were included if they met all of the following criteria: (1) peer-reviewed English-language publications; (2) document type classified as original research article (Article) or review (Review); (3) study population involving urologic cancers (e.g. prostate cancer, bladder cancer, UTUC, RCC); (4) urine used as one of the main or core biological matrices; and (5) study content closely related to liquid biopsy or urinary molecular biomarkers/detection platforms. We excluded non-research or unstable publication types, including conference abstracts, editorials, corrigenda/errata, books and book chapters, short communications, case reports, clinical trial registrations, reference-only records, retracted publications and online-only items not yet assigned to a specific volume/issue (Early Access articles). 2.2.2 Study selection process The initial WoSCC search yielded 481 records. After restricting the dataset to English-language publications from 2015–2025 and to the document types Article and Review, 448 records remained. We then removed non-stable or non-research items, including 7 Early Access papers and 2 conference papers (Proceedings Paper), resulting in 439 publications retained for analysis. The study selection process is summarized in Fig. 1 . Two researchers independently screened titles and abstracts to exclude studies that were unrelated to urologic cancers, did not use urine as a main biological matrix, or did not clearly fall within the concept of liquid biopsy. Discrepancies were resolved through discussion, and, when necessary, a third researcher adjudicated. This study used only publicly available literature, did not involve individual patient data and did not include any animal or human experimentation; therefore, additional ethical approval was not required. 2.3 Bibliometric and knowledge-mapping analysis Descriptive statistics and trend plots were generated using Microsoft Excel 2021 and OriginPro 2024 (version 10.1.0.178). Visualization and knowledge-mapping analyses were performed using CiteSpace 6.3.R1, VOSviewer 1.6.20 and the bibliometrix package in R (version 4.5.1)31–33. Plain-text “Full Record and Cited References” files exported from WoSCC were imported into these tools to construct collaboration, citation and co-citation networks, as well as keyword maps(30–32). In VOSviewer, we built collaboration networks for countries/regions, institutions and authors, and generated co-citation maps of journals and authors to identify high-impact venues and core research groups(31). CiteSpace was used mainly for time-based evolutionary analyses, including burst detection of references and keywords and dual-map overlays of journals, to delineate temporal shifts in evidentiary peaks and research foci(30). Keyword analyses were based on Keywords Plus from WoSCC. VOSviewer was used to perform keyword co-occurrence and clustering to identify high-frequency themes and their cluster structure, while R and the bibliometrix package were employed to examine thematic evolution and trend topics, thereby capturing research hotspots and emerging fronts across different time windows(32). To reduce noise from synonyms and spelling variants, selected keywords were merged and normalized—for example, “exosomes/microvesicles/urinary exosomes” were unified as “extracellular vesicles.” Country-level publication output and geographic distribution were visualized as a world map in R, with the number of publications overlaid for each country to complement the depiction of the global research landscape. Taken together, this multi-software, multi-dimensional analytic framework allowed us to characterize publication volume, collaboration patterns, knowledge bases and research hotspots in urine-based liquid biopsy for urologic cancers. 3. Results 3.1 Global publication trends and overall characteristics A total of 439 publications on urine-based liquid biopsy in urologic cancers were retrieved from WoSCC (Fig. 1 ). No studies explicitly framed within the concept of “liquid biopsy” were identified before 2015. Relevant papers began to appear in 2016, and annual output increased steadily thereafter, peaking at 77 publications in 2024 (Fig. 2 ). Annual average citations per article showed two periods of elevation, around 2016–2019 and 2021–2022, followed by a plateau despite continued growth in publication volume. The small but highly cited body of work in 2016–2017 suggests the presence of seminal, field-anchoring studies in an early foundational phase, whereas the period around 2021 marked a turning point at which both publication counts and total citations rose in parallel. Because of the search cut-off, data for 2025 remain incomplete and are presented only as indicative of overall trends. Table 1 Top 10 countries in urine-based liquid biopsy research for urologic cancers Rank Country Publications (%) Citations Avg. Citations H-index 1 USA 105 (23.9%) 3742 35.6 33 2 China 99 (22.6%) 2771 28.0 22 3 Italy 49 (11.2%) 1557 31.8 20 4 Germany 40 (9.1%) 1846 46.2 19 5 Spain 37 (8.4%) 1145 31.0 16 6 United Kingdom 36 (8.2%) 1275 35.4 18 7 Netherlands 33 (7.5%) 1814 55.0 21 8 Japan 23 (5.2%) 648 28.3 13 9 Australia 22 (5.0%) 947 43.0 11 10 Canada 22 (5.0%) 1047 47.6 12 Table 2 Top 10 institutions in urine-based liquid biopsy research for urologic cancers Rank Institution (Country) Publications (%) Citations Avg. Citations H-index 1 Univ. Amsterdam (Netherlands) 13 (3%) 637 49.0 10 2 Vrije Univ. Amsterdam (Netherlands) 13 (3%) 742 57.1 10 3 Johns Hopkins Univ. (USA) 12 (2.7%) 650 54.2 10 4 Univ. Porto (Portugal) 12 (2.7%) 181 15.1 8 5 Univ. California System (USA) 12 (2.7%) 248 20.7 7 6 Univ. Toronto (Canada) 11 (2.5%) 656 59.6 8 7 Aarhus Univ. (Denmark) 10 (2.3%) 631 63.1 8 8 Osaka Univ. (Japan) 10 (2.3%) 201 20.1 7 9 Univ. Oslo (Norway) 10 (2.3%) 978 97.8 9 10 CAMS–PUMC (China) 9 (2.1%) 165 18.3 7 3.2 Distribution by country/region and institution The 439 publications originated from 57 countries/regions and 1,051 institutions, with research activity concentrated in North America, Europe and East Asia (Fig. 3 A). The United States and China were the leading contributors, publishing 105 (23.9%) and 99 (22.6%) papers, respectively, followed by Italy and Germany (Table 1 ). The United States ranked first in both total citations and H-index, whereas countries such as the Netherlands and Canada produced fewer papers but achieved relatively high average citations and H-indices (Table 1 ). A country-level collaboration network (threshold ≥ 5 publications; Fig. 3 B) highlighted the United States as the main hub, closely linked with the United Kingdom, Germany, the Netherlands, Canada and others, while European countries formed a dense regional cluster. At the institutional level, the top 10 institutions came from eight countries (Table 2 ). The University of Amsterdam and Vrije Universiteit Amsterdam (Netherlands) shared the highest publication counts (13 papers each) and had the highest H-indices. European institutions accounted for half of the top 10, with the University of Oslo (Norway) standing out for its high total and average citations. Overall, 41 institutions with ≥ 5 publications were included in the collaboration network (Fig. 4 ), in which the two Amsterdam universities and Johns Hopkins University formed the core and maintained intensive collaborations with partners in multiple countries. Coloring nodes by average publication year showed that institutions such as Vrije Universiteit Amsterdam and Johns Hopkins University tended to enter the field earlier, whereas Asian institutions including Fudan University and Osaka University were later but increasingly active contributors. Table 3 Top 10 productive and co-cited authors in urine-based liquid biopsy research for urologic cancers Rank Author Publications Co-cited author Co-citations 1 Ferro M 8 McKiernan J 106 2 Fujita K 8 Christensen E 89 3 Crocetto F 7 Bryzgunova OE 81 4 Nonomura N 7 Birkenkamp-Demtröder K 77 5 Cheng L 7 Babjuk M 76 6 Li G 7 Siegel RL 74 7 Terracciano D 6 Tomlins SA 61 8 Busetto GM 6 van Kessel KEM 57 9 Del Giudice F 6 Sung H 54 10 Lopez-Beltran A 6 Hayashi Y 53 3.3 Authors and co-cited authors In total, 3,181 authors contributed to publications on urine-based liquid biopsy in urologic cancers. The top 10 authors each published at least six papers (Table 3 ), with Ferro M and Fujita K being the most productive, contributing eight papers each. Using a threshold of ≥ 5 publications, 24 authors were included in the author collaboration network (Fig. 5 A), in which Ferro M and Fujita K had the most connections and occupied central positions within their respective clusters. The co-cited author analysis identified 14,852 authors, among whom 10 had at least 53 co-citations (Table 3 ). McKiernan J had the highest number of co-citations (106), followed by Christensen E (89) and Bryzgunova OE (81). Using a threshold of ≥ 30 co-citations, 76 authors were included in the co-cited author network (Fig. 5 B). McKiernan J, Christensen E and Bryzgunova OE formed the largest nodes with the greatest total link strength and occupied central positions in their respective clusters, while authors such as Birkenkamp-Demtröder K and Babjuk M acted as secondary hubs. Together, these groups constitute the core cluster of highly cited contributors in this field. Table 4 . Top 10 productive and co-cited journals in urine-based liquid biopsy research for urologic cancers(see end of document) Table 4 Top 10 productive and co-cited journals in urine-based liquid biopsy research for urologic cancers Rank Journal Publications (%) IF 2024 Q Co-cited Journal Co-citation IF 2024 Q 1 Cancers 54 (12.30%) 4.4 Q2 European Urology 1433 25.2 Q1 2 International Journal of Molecular Sciences 22 (5.01%) 4.9 Q2 Journal of Urology 805 6.8 Q1 3 Frontiers in Oncology 16 (3.64%) 4.0 Q2 Clinical Cancer Research 766 10.2 Q1 4 Translational Andrology and Urology 9 (2.05%) 2.1 Q3 Oncotarget 766 — — 5 Expert Review of Molecular Diagnostics 8 (1.82%) 5.3 Q1 PLOS ONE 755 2.6 Q1 6 Journal of Extracellular Vesicles 8 (1.82%) 17.7 Q1 Scientific Reports 564 3.9 Q1 7 Urologic Oncology 8 (1.82%) 3.2 Q2 Cancers 549 4.4 Q2 8 Scientific Reports 8 (1.82%) 4.6 Q2 Journal of Extracellular Vesicles 536 14.5 Q1 9 Diagnostics 8 (1.82%) 3.1 Q2 International Journal of Molecular Sciences 505 4.9 Q2 10 Analytical Chemistry 7 (1.59%) 7.0 Q1 Urologic Oncology 446 2.3 Q2 3.4 Publishing journals, co-cited journals and citation flows The 439 publications were distributed across 200 journals. Cancers published the largest number of articles (54, 12.30%), followed by International Journal of Molecular Sciences (22, 5.01%) and Frontiers in Oncology (16, 3.64%) (Table 4 ). Among the top 10 most productive journals, Journal of Extracellular Vesicles had the highest 2024 impact factor, followed by Analytical Chemistry and Expert Review of Molecular Diagnostics. Using a threshold of ≥ 3 publications, 36 journals were included in the journal co-occurrence network (Fig. 6 A). Cancers had the largest node size, the densest connections and a relatively recent average publication year, and occupied a central position in the network. International Journal of Molecular Sciences, Frontiers in Oncology, Scientific Reports, Diagnostics and Translational Andrology and Urology were located around it and formed several secondary hubs. In the co-cited journal analysis, European Urology had the highest number of co-citations (1,433), followed by Journal of Urology (805), Clinical Cancer Research (766) and Oncotarget (766) (Table 4 ). A co-cited journal network constructed with a threshold of ≥ 100 co-citations (Fig. 6 B) showed dense co-citation links between European Urology and other urologic oncology journals such as Journal of Urology and Urologic Oncology. The dual-map overlay of journals (Fig. 7 ) showed that citing journals clustered in “molecular / biology / immunology” and “medicine / medical / clinical” predominantly referenced journals in the “molecular / biology / genetics” cluster, reflecting citation flows from basic and translational research toward clinical disciplines. Table 5 . Top 15 most cited articles on urine-based liquid biopsy in urologic cancers(see end of document) Table 5 Top 15 most cited articles on urine-based liquid biopsy in urologic cancers Rank First author Title (shortened) Journal Year TC a ACPP b 1 Yu W Exosome-based liquid biopsies in cancer Ann Oncol 2021 662 132.4 2 Yu D Exosomes as a new frontier in cancer liquid biopsy Mol Cancer 2022 621 155.25 3 Locke WJ DNA methylation biomarkers for cancer diagnosis Front Genet 2019 336 48 4 Skotland T Lipid species in urinary exosomes for prostate cancer Eur J Cancer 2017 294 32.67 5 Van Neste L High-grade prostate cancer detection via urinary biomarkers Eur Urol 2016 278 27.8 6 Erdbrügger U Urinary extracellular vesicles: ISEV position paper J Extracell Vesicles 2021 275 55 7 Di Meo A Liquid biopsy for precision medicine in urologic cancer Mol Cancer 2017 272 30.22 8 Tivey A Circulating tumour DNA: beyond the blood Nat Rev Clin Oncol 2022 208 52 9 Rodriguez M Non-invasive urinary miRNA biomarkers for prostate cancer Mol Cancer 2017 199 22.11 10 Usuba W Circulating miRNA panels for early bladder cancer detection Cancer Sci 2019 193 27.57 11 Birkenkamp-Demtröder K Genomic alterations in bladder cancer liquid biopsies Eur Urol 2016 181 18.1 12 Smith JT Nanoscale DLD arrays for extracellular vesicle separation Lab Chip 2018 156 19.5 13 Christensen E FGFR3 and PIK3CA hotspot mutations in bladder cancer liquid biopsy Eur Urol 2017 148 16.44 14 Zhang W Serum exosomal microRNAs as biomarkers in clear-cell renal cell carcinoma Eur Urol Focus 2018 130 16.25 15 Birkenkamp-Demtröder K Liquid biopsy monitoring of treatment response and relapse in advanced bladder cancer Eur Urol 2018 129 16.12 a TC, total citations; b ACPP, average citations per publication. Table 6 . Main contents of the 15 strongest citation-burst references Table 6 Main contents of the 15 strongest citation-burst references Rank Reference (first author, year, journal) Strength* Main research content 1 Bettegowda C, 2014, Sci Transl Med 7.35 Translational multi-cancer cohort study on ctDNA : evaluated detectability of circulating tumour DNA in patients with early- and late-stage solid tumours and found that ctDNA levels correlate with tumour burden and are detectable in most advanced and in many localised cancers. 2 Birkenkamp-Demtröder K, 2016, Eur Urol 6.46 Clinical liquid-biopsy study in bladder cancer : developed personalised assays for genomic variants in plasma and urine cell-free tumour DNA and showed that detectable and rising levels of tumour DNA are associated with recurrence, progression and metastasis. 3 McKiernan J, 2016, JAMA Oncol 7.72 Prospective diagnostic validation study in prostate cancer : developed a urine exosome three-gene expression assay and showed that its score distinguishes high-grade prostate cancer from low-grade cancer and benign disease in men with elevated PSA. 4 Bryzgunova OE, 2015, Acta Naturae 5.69 Review article on urinary extracellular nucleic acids : summarised the sources, structure, stability and reported diagnostic applications of extracellular nucleic acids in urine as non-invasive biomarkers. 5 Christensen E, 2017, Eur Urol 6.77 Clinical biomarker cohort study in bladder cancer : used droplet digital PCR to measure FGFR3 and PIK3CA hotspot mutations in urine and plasma cell-free DNA and found that higher mutant DNA levels predict later progression and metastasis. 6 Tomlins SA, 2016, Eur Urol 5.11 Clinical risk-prediction study in prostate cancer : validated Mi-Prostate Score models that combine serum PSA with urinary TMPRSS2:ERG and PCA3 and showed improved prediction of overall and high-grade prostate cancer on biopsy compared with PSA alone. 7 Berrondo C, 2016, PLoS One 4.99 Translational biomarker study in bladder cancer : analysed expression of the long non-coding RNA HOTAIR in tumour tissue and urinary exosomes and found that higher HOTAIR levels are enriched in urinary exosomes and correlate with advanced stage and poorer prognosis. 8 Wan JCM, 2017, Nat Rev Cancer 4.65 Review article on ctDNA liquid biopsy : summarised sequencing and digital PCR methods for circulating tumour DNA analysis and collated evidence for its use in cancer prognostication, molecular profiling and disease monitoring. 9 Bray F, 2018, CA Cancer J Clin 7.76 Epidemiological report (GLOBOCAN 2018) : reported global estimates of cancer incidence and mortality for 36 cancers in 185 countries and described geographic variation in cancer burden. 10 McKiernan J, 2018, Eur Urol 4.8 Prospective adaptive clinical-utility trial in prostate cancer : evaluated the ExoDx Prostate (IntelliScore) urine exosome test in men with PSA 2–10 ng/mL undergoing initial biopsy and showed that the test predicts ≥ Grade Group 2 prostate cancer and can be used to defer some biopsies. 11 Skotland T, 2017, Eur J Cancer 4.57 Quantitative lipidomics biomarker study in prostate cancer : performed mass-spectrometry lipidomic profiling of urinary exosomes from patients and controls and identified individual lipid species and three-lipid combinations that differ between groups and classify prostate cancer. 12 Sung H, 2021, CA Cancer J Clin 9.86 Updated epidemiological report (GLOBOCAN 2020) : updated global estimates of new cancer cases and deaths for 36 cancers in 185 countries and presented regional patterns and projected future trends in cancer burden. 13 Ignatiadis M, 2021, Nat Rev Clin Oncol 5.41 Perspective review on liquid-biopsy implementation : discussed analytical validation, clinical-trial design, regulatory, reimbursement and logistical issues that must be addressed to integrate ctDNA- and CTC-based liquid-biopsy tests into routine oncology practice. 14 Kalluri R, 2020, Science 6.34 Review article on exosome biology : summarised current knowledge on exosome biogenesis, cargo composition and mechanisms of intercellular communication and outlined experimental and clinical applications of exosomes, including as diagnostic and therapeutic tools. 15 Lone SN, 2022, Mol Cancer 5.26 Review article on liquid biopsy in oncology : overviewed principal liquid-biopsy analytes and platforms and synthesised reported applications for cancer detection, prognostic stratification and treatment-response monitoring, as well as technical and clinical challenges. *Strength = burst intensity calculated by CiteSpace. 3.5 Highly cited and burst references The top 15 most cited references are listed in Table 5 , each with a total citation count of at least 129. These papers were mainly published within two time windows, 2016–2019 and 2021–2022. Molecular Cancer contributed the largest number of highly cited articles (three papers). Two reviews on exosome/extracellular vesicle–based liquid biopsy, authored by Yu W and Yu D and published in Annals of Oncology and Molecular Cancer, respectively, had the highest citation counts and represent landmark contributions that are repeatedly cited in urine-based liquid biopsy research. Using a threshold of ≥ 326 co-citations, we constructed a co-cited reference network that comprised 35 core publications (Fig. 8 A). References such as “McKiernan J, 2016, JAMA Oncol”, “Christensen E, 2017, Eur Urol”, “Bettegowda C, 2014, Sci Transl Med”, “Birkenkamp-Demtröder K, 2016, Eur Urol” and “Sung H, 2021, CA Cancer J Clin” formed large nodes with high total link strength and clustered into several tightly connected co-citation cores. Burst reference analysis was used to identify papers with rapidly increasing citation counts over short periods. CiteSpace detected 15 references with significant citation bursts (Fig. 8 B). The updated global cancer burden report by Sung H, published in CA: A Cancer Journal for Clinicians in 2021, showed the strongest and most recent burst, followed by the GLOBOCAN 2018 report by Bray F in the same journal. Bursts associated with key liquid biopsy studies such as Birkenkamp-Demtröder K, 2016 and McKiernan J, 2016 were sustained over several years, while more recent platform- and implementation-focused reviews (e.g. Sung H, 2021; Ignatiadis M, 2021; Lone SN, 2022) continued through 2025, indicating that these papers constitute core and still-active knowledge sources in the current time window. The main content of these burst references is summarized in Table 6 . Table 7 Top 20 keywords on research of urinary liquid biopsy in urologic cancers Rank Keyword Occurrences Rank Keyword Occurrences 1 liquid biopsy 298 11 cell-free dna 52 2 biomarkers 224 12 urothelial carcinoma 47 3 prostate cancer 148 13 dna methylation 44 4 bladder cancer 142 14 circulating tumor cells 43 5 extracellular vesicles 129 15 circulating tumor dna 40 6 urine 98 16 plasma 34 7 cancer 95 17 identification 32 8 diagnosis 86 18 cytology 27 9 micrornas 77 19 mutations 27 10 expression 57 20 prognosis 27 3.6 Keyword co-occurrence and evolution of research hotspots Keyword co-occurrence analysis was used to identify research hotspots in urine-based liquid biopsy for urologic cancers. A total of 1,794 keywords were detected, of which 46 with a frequency ≥ 13 were included in the co-occurrence network (Fig. 9 A). The top 20 high-frequency keywords are listed in Table 7 . “Liquid biopsy” (298 occurrences) and “biomarkers” (224 occurrences) were the two most frequent terms and represented overarching concepts across the field. They were followed by disease- and platform-related terms such as “prostate cancer” (148), “bladder cancer” (142) and “extracellular vesicles” (129), reflecting the main disease types and analytes that currently attract the greatest research attention. VOSviewer clustering divided the 46 high-frequency keywords into four interconnected clusters (Fig. 9 A). The red cluster aggregated terms such as “biomarkers”, “extracellular vesicles”, “microRNAs”, “PSA” and “prostate cancer”, and primarily corresponded to prostate cancer–focused studies built around multi-analyte biomarker panels. The green cluster, dominated by “liquid biopsy”, included “cancer”, “cell-free DNA”, “circulating tumor cells”, “circulating tumor DNA” and several solid tumor entities, representing cross-tumor cfDNA/ctDNA- and CTC-based liquid biopsy platforms. The blue cluster, centered on “bladder cancer”, “diagnosis”, “cytology” and “DNA methylation” and accompanied by terms such as “surveillance”, “recurrence” and “machine learning”, mainly represented research on diagnostic and surveillance strategies in bladder cancer. The yellow cluster contained “renal cell carcinoma”, “prognosis” and “survival”; although smaller in size, it showed dense internal connections, indicating a distinct but less developed line of work focused on prognosis and survival in RCC. The trend topics map (Fig. 9 B) illustrated a temporal shift from early feasibility studies centered on nucleic acid–based markers and next-generation sequencing toward more disease-focused work in prostate and bladder cancer around 2020–2022, followed more recently by growing interest in RCC/UTUC, machine learning and metabolomics-based urinary biomarker discovery. 4. Discussion 4.1 Stage-wise evolution of evidence and dominant technological pathways Our bibliometric findings indicate that research on urine-based liquid biopsy in urologic cancers has shown a sustained upward trajectory over the past decade (Fig. 2 ). However, changes in annual publication counts or average citations per article alone do not fully explain the underlying forces reshaping the field. By integrating highly cited references with citation-burst patterns (Table 5 , Fig. 8 B), we delineated a stage-wise evolution characterized by two broad phases: a “foundational evidence and methodological expansion” phase from 2016 to 2020 and a “standardization and platform integration” phase from 2021 onward. The first phase was marked by a cluster of highly cited and burst references around 2016–2017, which established key clinical and methodological cornerstones for liquid biopsy, whereas the second phase, centered around 2021, coincided with a second peak in citation activity and reflected a shift towards consolidating platforms, refining analytical pipelines and embedding liquid biopsy into guideline- and practice-oriented frameworks. 4.1.1 Phase I: building foundational evidence and expanding methods In the first phase, pan-cancer ctDNA studies demonstrated that circulating tumor DNA levels are tightly associated with tumor burden, stage and prognosis, thereby providing the clinical evidence base for liquid biopsy as a whole(6,7). Urine-focused reviews in turn systematically described the sources, stability and early applications of extracellular nucleic acids in urine, establishing the conceptual rationale for urine as a non-invasive reservoir of molecular biomarkers(13). Subsequent clinical studies in bladder and prostate cancer, evaluating urinary cfDNA and exosome-related markers, further validated these concepts at the practical level(16,20,33–37). At the same time, epidemiological reports such as GLOBOCAN 2018 highlighted the prominent contribution of prostate and bladder cancer to the global cancer burden, reinforcing their central position among urologic cancers(38). Together, these works formed an early “knowledge core” in the co-citation network and provided the upstream evidentiary basis on which later research on urine-based liquid biopsy has since been built. On this basis, a series of highly cited original studies published between 2016 and 2019 on prostate and bladder cancer constituted the technological backbone of the first phase. In prostate cancer, several studies used multigene mRNA panels measured in post–digital rectal examination (DRE) urine sediments and miRNA and lipid signatures derived from urinary exosomes to show that post-DRE urine can reliably capture molecular signals associated with high-risk disease(37,39). Risk scores based on these mRNA panels, quantified by RT-qPCR, were further integrated with PSA levels and clinical variables to construct multi-analyte biomarker models that support key decisions such as whether to perform biopsy and how to classify high-risk patients(40). In bladder cancer, studies of urinary cfDNA/ctDNA translated the cross-tumor ctDNA concept into the urothelial setting: driver mutations such as FGFR3 and PIK3CA were repeatedly detected in urine and plasma, and their abundance correlated with subsequent recurrence, progression and metastasis(16,33). Serial sampling outlined an early disease-monitoring pathway in which dynamic ctDNA curves served as a central indicator of residual disease and impending relapse(41). In parallel, advances in exosome isolation platforms, DNA methylation assay frameworks, miRNA panels and RCC-related urinary markers gradually extended the field from single tumor entities and single analytes to multi-tumor, multi-omics explorations(42–45). Taken together, the highly cited and burst references of this first phase collectively moved the field from asking “can tumor-derived signals be detected in body fluids?” to “can urine be established as a stable molecular carrier?”, laying the groundwork for subsequent efforts to build urine-based biomarker models for specific clinical scenarios. 4.1.2 Phase II: standardization and platform integration The defining feature of the second phase was a marked shift in the co-citation burst profile. A cluster of key reviews and position papers published between 2020 and 2022 showed burst peaks that were highly concentrated after 2021. Updated GLOBOCAN 2020 estimates underscored the continued increase in incidence and mortality of prostate and bladder cancer, highlighting from a demand-side perspective the urgent need for non-invasive biomarkers and early detection tools(46). Authoritative reviews on the clinical implementation of ctDNA discussed in a systematic manner how how liquid biopsy could be translated into practice across multiple dimensions—including analytical validation, trial design, regulatory approval and reimbursement frameworks—thereby elevating previously fragmented feasibility studies to the level of guideline- and pathway-oriented discourse(8). In parallel, a set of highly co-cited reviews focusing on multi-analyte integration (CTCs, ctDNA and tumor-derived extracellular vesicles) and on exosome biology established a cross-platform liquid biopsy framework that bridged technological platforms with underlying biological mechanisms(47,48). Collectively, these burst references formed a “translational hub” in the co-citation network and drove a shift in focus from local technical exploration towards system-level designs aimed at clinical implementation. At the same time, several cross-tumor reviews published in 2021–2022 on exosome-based liquid biopsy and “ctDNA beyond blood”, together with a position paper on urinary EVs, entered the ranks of highly cited references, with ACPP values among the top four in Table 5 . These platform-oriented papers represent the most influential methodological cornerstones of the second phase. On the one hand, they integrated ctDNA and extracellular vesicles from multiple body fluids—including urine—into a unified liquid biopsy framework, emphasizing their potential roles in early diagnosis, MRD assessment and individualized treatment(49–51). On the other hand, by proposing concrete recommendations on sample collection, pre-analytical processing and reporting standards, they provided a reproducible technical baseline for subsequent studies(14). The synchronized “uplift” of these high-impact platform papers and the co-citation burst spectrum around 2021 provides direct evidence for defining 2021 as the boundary between the first and second phases in this two-phase framework. 4.1.3 Technological trajectories and clinical focuses: twin diagnostic and monitoring pathways Within this temporal structure, urine-based liquid biopsy in prostate and bladder cancer has evolved into a pair of “twin pathways” characterized by shared technologies but distinct clinical uses. In both diseases, high-throughput omics approaches are used to screen candidate biomarkers; digital PCR and targeted sequencing are then applied to precisely quantify low-abundance signals; and multivariable models subsequently integrate urinary molecular features with PSA, stage and treatment information, embedding these signatures into specific clinical decision points for validation(7,48). The key difference lies in the clinical focus. In prostate cancer, the dominant trajectory is “diagnostic”: around the PSA grey zone and high-risk stratification, urinary exosome and cfDNA panels are used to improve diagnostic accuracy and reduce unnecessary biopsies(34,37,39). In bladder cancer, the trajectory is primarily “monitoring”: in the context of long-term follow-up and recurrence risk assessment, dynamic changes in urinary ctDNA/cfDNA and tracking of tumor-specific mutations are placed at the center, with the aim of alleviating the burden and cost associated with frequent cystoscopy(16,33,41). Representative studies from the first phase laid the groundwork for these two routes. In the second phase, platform-oriented work on exosome/EV standardization, the uEV consensus and multi-fluid ctDNA frameworks further incorporated these diagnostic and monitoring trajectories into a coherent, scalable system for urine-based liquid biopsy(47–51). Taken together, the two-stage evolution and the “diagnostic/monitoring” twin pathways jointly define the dominant technological landscape of urine-based liquid biopsy in urologic cancers. 4.2 Global knowledge production and dissemination: countries, institutions, authors and journals 4.2.1 Country- and institution-level patterns: Western leadership and East Asian catch-up Country- and institution-level analyses revealed a knowledge-production landscape characterized by Western leadership and East Asian catch-up (Fig. 3 , Tables 1 – 2 ). The United States occupied a central position in both publication output and citation impact. Although individual European countries produced fewer papers than the United States and China, their combined publication volume, the number of highly cited institutions and the density of inter-institutional collaboration networks together constitute a major knowledge block in this field. Several institutions in countries such as the Netherlands and Norway—for example, the Amsterdam universities and the University of Oslo—were prominently represented among highly cited centers, suggesting that high-impact hubs do not necessarily depend on very large country-level output. When average publication year by country is taken into account, many European and North American countries appear to have entered this field earlier and show clear first-mover advantages in long-term accumulation and sustained contributions. China and Japan represent emerging East Asian contributors, with more recent average publication years. China is currently the most productive country in terms of publication count, yet its average citations per paper rank relatively low among the top 10 countries, and only one institution enters the list of highly cited centers. This pattern points to a certain misalignment between overall research volume and academic impact. Japan shows a broadly similar profile to China in terms of average citations, institutional output and H-index. Overall, the field is still dominated by Western centers, whereas East Asia is catching up in volume but lags behind in impact, underscoring the need to improve research quality, foster cross-regional collaboration and build high-impact hubs in East Asian settings. 4.2.2 Prolific authors and highly co-cited authors: frontline teams and evidentiary hubs Author-level analyses revealed a pattern of multiple teams advancing in parallel, without a single dominant group (Fig. 5 A, Table 3 ). The Italian team represented by Ferro M and the Japanese team represented by Fujita K are typical examples of highly productive author clusters. The former has published several reviews and systematic analyses that map the application pathways of urinary biomarkers in urologic cancers(52–54), whereas the latter has conducted a series of original studies focusing on hotspot mutations such as TERT promoter and FGFR3, as well as urinary EVs, thereby establishing a relatively coherent research line spanning urinary cfDNA and urinary EVs(55–57). Overall, prolific authors mainly represent “frontline teams” that continually expand the evidence base and enrich clinical application scenarios in recent years. By contrast, the co-cited author network (Fig. 5 B) highlights shared evidentiary sources at the levels of methodology and clinical pathways. Representative works by highly co-cited authors such as McKiernan J, Christensen E and Bryzgunova OE are repeatedly cited together across multiple related papers, and are mainly concentrated in three areas: urinary exosome-based gene expression assays and risk-prediction models in prostate cancer; cohort studies of urinary and plasma cfDNA/ctDNA in bladder cancer; and basic and methodological studies on extracellular nucleic acids and EVs in urine17,59,60. These publications form tightly connected clusters of highly co-cited references and also account for a substantial portion of burst references, underscoring their role as “evidentiary hubs” within the knowledge structure of this field. In our dataset, prolific authors and highly co-cited authors showed minimal overlap. Prolific authors largely represent frontline teams that continuously generate new data, whereas highly co-cited authors act as evidentiary hubs supplying foundational clinical and methodological references; together, these two groups sustain the development of the field. 4.2.3 Journal ecosystem and knowledge flows: from publication platforms to cited cores At the journal level, oncology and molecular medicine journals such as Cancers, International Journal of Molecular Sciences and Frontiers in Oncology are the most common publication platforms for studies on urine-based liquid biopsy in urologic cancers (Fig. 6 A, Table 4 ). In contrast, Q1, high-impact urology and oncology journals—including European Urology, Journal of Urology and Clinical Cancer Research—occupy central positions in the co-cited journal network and serve as major knowledge sources (Fig. 6 B). Notably, Cancers, Journal of Extracellular Vesicles and International Journal of Molecular Sciences rank highly both among the most productive journals and among the most frequently co-cited journals, indicating high visibility and influence at the levels of “publishing outlet” and “cited core” alike. The dual-map overlay of journals (Fig. 7 ) further illustrates, from a disciplinary perspective, how urine-based liquid biopsy is situated at the interface between clinical demand and basic/translational support. On one side, clinical disciplines such as urologic surgery and oncology articulate needs for improved diagnostic and surveillance tools; on the other, basic and translational fields including molecular biology and genetics provide methodological and mechanistic evidence to meet these needs. Overall, this pattern reflects a typical translational interface in which basic molecular and genetic research underpins clinical problem-solving and aligns with the stage-wise evolution from feasibility testing toward standardized implementation. 4.3 Research hotspots and frontiers: keyword networks anchored to clinical scenarios As shown in Section 3.6 (Fig. 9 , Table 7 ), terms such as “liquid biopsy”, “biomarkers”, “prostate cancer”, “bladder cancer” and “extracellular vesicles” occupy central positions in the keyword network, indicating that the field has evolved into a multicentric structure organized around the triad of “liquid biopsy–biomarkers–specific clinical scenarios”. By combining cluster colors with temporal distribution, the current hotspots can be summarized, from a clinical perspective, into three main disease scenarios—biopsy decision-making and high-risk stratification in prostate cancer, recurrence surveillance and cystoscopy de-escalation in bladder cancer and upper tract urothelial carcinoma (UTUC), and early diagnosis and prognostic assessment in RCC and other less common, evidence-sparse urologic cancers—alongside a methodological theme centered on cfDNA/ctDNA, circulating tumor cells and extracellular vesicles that can be repurposed across multiple tumor types. Different diseases exhibit distinct “preferences” in analyte selection and clinical questions, whereas cross-tumor ctDNA/cfDNA and CTC frameworks provide the shared technical backbone underpinning these diverse strategies. 4.3.1 Prostate cancer: from the PSA grey zone to urinary multi-marker panels Within the red cluster, prostate cancer–related terms are highly concentrated, reflecting that one of the main application scenarios is biopsy indication and high-risk patient identification in men within the PSA grey zone, where the limited specificity of serum PSA drives a need to better detect clinically significant disease while avoiding unnecessary biopsies(3,58). From the perspective of analytes, recent studies have shown a clear preference for urinary exosomes and multigene urinary RNA panels. Early work mainly focused on single markers—such as urinary cfDNA concentration and integrity, or individual mRNAs and proteins like PCA3—to verify whether tumor-related signals could be stably detected in urine(59–61). Subsequently, attention shifted from single indicators to integrative models that combine “urinary molecular panels plus clinical variables”. On the one hand, a three-gene expression signature (ERG, PCA3 and SPDEF) carried by urinary exosomes was validated in multicenter prospective cohorts and shown to improve the detection of Grade Group ≥ 2 (Gleason score ≥ 7) clinically significant prostate cancer among men undergoing initial biopsy with PSA levels of 2–10 ng/mL, while reducing unnecessary biopsies under comparable safety thresholds(20,34). On the other hand, urinary molecular panels—such as the Mi-Prostate Score combining TMPRSS2:ERG and PCA3, the SelectMDx assay based on HOXC6 and DLX1 mRNA, and urinary exosomal miRNA signatures—are commonly entered into logistic regression models together with PSA, digital rectal examination (DRE) findings and prior biopsy history to predict biopsy positivity and the risk of high-grade prostate cancer(35,39,40). Building on these models, several urinary DNA methylation signatures have further extended study endpoints to include postoperative Gleason upgrading, pathological upstaging and composite adverse pathological features. For example, Bakavicius and colleagues developed a risk score based on methylation of RARB, RASSF1 and GSTP1 in urine combined with PSA, which can predict grade and stage migration as well as aggressive disease in radical prostatectomy specimens(62,63). Collectively, these studies converge on a common clinical objective: to refine biopsy decision-making in men with PSA in the grey zone through urinary biomarkers, while simultaneously providing complementary information for postoperative risk stratification and individualized management. 4.3.2 Bladder cancer and urothelial carcinoma: recurrence surveillance and de-escalation of cystoscopy Within the blue cluster, terms centered on bladder cancer are tightly interconnected, corresponding to a clinical scenario dominated by bladder cancer and upper tract urothelial carcinoma (UTUC) in which the primary goals are recurrence surveillance and de-escalation of cystoscopy. Current EAU and other guidelines recommend long-term, intensive cystoscopic follow-up for patients with high- and very high-risk non-muscle-invasive bladder cancer, resulting in frequent, invasive and costly examinations, while conventional urine cytology shows limited sensitivity for low-grade lesions(4,64). This mismatch between high surveillance burden and low sensitivity has directly driven the rapid development of urine-based liquid biopsy in this field. Against this background, a large body of work has focused on driver mutations and multi-locus DNA methylation panels carried by urinary cfDNA/ctDNA—particularly alterations in FGFR3, PIK3CA and the TERT promoter—to non-invasively detect residual disease and predict the risk of recurrence and progression(33,65–67). In parallel, traditional urine cytology has been continuously upgraded through cell-enrichment platforms and immunocytological markers (such as ImmunoCyt/uCyt + and UroVysion FISH). These advances have improved sensitivity for small-volume or low-grade disease, and, when combined with multigene expression assays and machine learning models, have enabled the integration of molecular markers, cytology and clinical variables into composite tools for recurrence-risk assessment(4,68–71). Overall, mutation and methylation assays, refined urine cytology platforms and integrated risk models serve a common clinical objective: to safely reduce the frequency of surveillance cystoscopy by leveraging urinary monitoring strategies with high negative predictive value, while maintaining vigilance against high-risk recurrence and progression events. In addition, these approaches are beginning to be explored in UTUC and related settings, extending the reach of urine-based liquid biopsy beyond bladder cancer alone(64,67). 4.3.3 Renal cell carcinoma and other urologic tumors: an emerging frontier with weak evidence but strong clinical need The yellow cluster is smaller in size but shows dense internal connections, indicating that renal cell carcinoma (RCC) constitutes a relatively independent yet still early-stage research theme within the overall network. Unlike bladder cancer and urothelial carcinoma, most RCC lesions arise in the renal parenchyma, and tumor cells are not continuously and directly exposed to the urinary tract. Strategies that rely on exfoliated cells or bulk cfDNA levels therefore have limited sensitivity, a limitation repeatedly emphasized in systematic reviews of liquid biopsy and urinary biomarkers in RCC, helping explain the current paucity of urine-based evidence and slow clinical translation(24,72). Existing studies show a clear exploratory pattern in terms of analytes, with a focus on epigenetic alterations, extracellular vesicles and metabolomics. On the epigenetic front, cfDNA methylation markers have started to enter the field. Studies using cfMeDIP-seq have shown that joint analysis of cfDNA methylation profiles in plasma and urine can achieve good discrimination between RCC patients at various stages and controls, and may hold promise for early disease detection76. Subsequent systematic reviews summarizing RCC methylation targets across multiple body fluids, including urine, have pointed out that although some loci and panels demonstrate encouraging diagnostic performance, most work remains at the discovery and preliminary validation stages(72). In parallel, nucleic acid–based biomarkers carried by urinary exosomes and free miRNAs have gradually emerged as another major line in RCC urine-based liquid biopsy. Several studies have used sequencing or microarray approaches to identify differentially expressed miRNAs in urine—particularly in urinary exosomes—and validated their diagnostic value for clear-cell RCC, as well as their associations with tumor stage, volume and high-risk pathological features. Some of these signatures have been combined into multi-miRNA urinary scores to estimate the malignant potential of small renal masses and the likelihood of adverse outcomes(18,73,74). Moreover, urinary metabolomic and proteomic studies have revealed RCC-specific metabolic reprogramming and microenvironmental changes. Multiple untargeted or semi-targeted metabolomic analyses have identified candidate metabolite combinations in the urine of patients with clear-cell RCC that are related to energy metabolism, the tricarboxylic acid cycle and fatty acid metabolism. Subsequent work has used these metabolites to construct multi-marker panels with relatively high AUCs and has completed small-scale external validation(75,76). Taken together, urinary diagnostic biomarkers for RCC now span metabolites, proteins, miRNAs and DNA methylation, encompassing hundreds of single markers and nearly 30 multi-marker panels(24,77). Overall, however, most studies remain exploratory or single-center with limited sample sizes and are not yet supported by multicenter, large-scale prospective cohorts or standardized workflows. RCC and related entities can therefore be regarded as an “emerging frontier” characterized by weak current evidence but a clearly defined and pressing clinical need. 4.3.4 Cross-tumor liquid biopsy methodologies and multi-analyte signatures Within the green cluster, “liquid biopsy” lies at the center and is connected to multiple assay terms and solid tumor types, representing a set of cross-tumor, shared methodological themes in liquid biopsy. Multicancer clinical studies and highly cited reviews have shown that platforms based on plasma ctDNA/cfDNA and circulating tumor cells have progressively established an evidence base across a range of solid tumors for supporting early detection of high-risk or localized disease, assessing tumor burden, monitoring treatment response and MRD, and predicting recurrence risk(6–8,78). These platforms provide reusable technical pathways and trial-design paradigms for different cancer types, including urologic cancers. Building on existing ctDNA/cfDNA and CTC platforms, a series of cross-tumor methodological studies in recent years have begun to integrate additional molecular layers into unified analytical frameworks. These include the cargo of exosomes and other extracellular vesicles, DNA methylation and fragmentomics features in liquid samples, and blood- and urine-based metabolomic profiles, which are then jointly modeled using machine-learning and other statistical learning algorithms(79–81). In the specific context of urine-based liquid biopsy for urologic tumors, this cross-tumor methodological trajectory is reflected in several ways. First, analytes such as urinary cfDNA/ctDNA, exosomal RNA/proteins, exfoliated cells and metabolites are combined in a tailored manner according to the clinical scenario. Second, high-dimensional omics data from urine and paired blood samples are subjected to feature-selection procedures and machine-learning algorithms to derive diagnostic models or recurrence/progression risk scores. Third, for tumor types such as bladder and prostate cancer, multi-gene or multi-marker urinary panels are being advanced into commercial assays and multicenter prospective validation studies(52,64,71,82). The relatively late average publication years of terms such as “extracellular vesicles”, “metabolomics” and “machine learning” in Fig. 9 B further support the notion that this methodological pathway is in a phase of rapid expansion. Taken together, these cross-tumor methodological developments and disease-specific applications indicate that urine-based liquid biopsy in urologic cancers is evolving within an intertwined “horizontal–vertical” framework: horizontally through reusable platforms based on ctDNA/cfDNA, CTCs, extracellular vesicles, DNA methylation and metabolites, and vertically through analyte and biomarker combinations tailored to concrete clinical scenarios such as PSA grey-zone biopsy decisions, bladder/urothelial cancer surveillance and RCC risk stratification. 4.4.1 Advantages of urine-based liquid biopsy and of the present study Compared with blood, the practical advantages of urine — non-invasive, easily repeatable sampling and direct anatomical proximity to the urothelial tract — become particularly salient in light of our findings that most current applications cluster around biopsy decision-making in the PSA grey zone and long-term surveillance in bladder cancer(83,84). As an organ-specific body fluid, urine can mirror systemic circulation while also reflecting local mucosal and tumor microenvironmental changes, which may underlie its emerging role in early lesion detection and assessment of local disease burden in urologic cancers(14,85). In this study, we analyzed 439 publications on urine-based liquid biopsy in urologic cancers indexed in WoSCC between 2015 and 2025. By integrating CiteSpace, VOSviewer and bibliometrix, we systematically delineated the evidentiary structure and evolutionary trajectory of this field from multiple perspectives, including publication trends, country–institution–author–journal networks and keyword evolution. On this basis, we proposed a two-stage framework of “foundational evidence and methodological expansion” followed by “standardization and platform integration”, and aligned this framework with concrete clinical scenarios such as prostate cancer diagnosis, bladder cancer surveillance and RCC and other less common urologic cancers. This approach provides a reusable temporal structure and an evidence map that may inform future reviews, guideline development and clinical study design. 4.4.2 Limitations of bibliometric research This study has several limitations. First, our data source was restricted to English-language publications indexed in WoSCC. We did not include other databases or non-English literature, and by omitting local-language journals and regional databases in high-output countries such as China, we may have underestimated the scientific contributions from certain regions. Second, our search strategy was limited to TS = (“urine” OR “urinary”) AND (“liquid biopsy” OR “liquid biopsies”), which helped to capture studies explicitly framed within the “liquid biopsy” concept but may have missed earlier or parallel work on urinary biomarkers that did not use this terminology, potentially introducing bias into the thematic structure of the early period. Third, we confined the time window to 2015 to 2025 and excluded Early Access articles and conference papers. While this choice helped us focus on formally published, relatively mature journal evidence, it may also have led to underestimation of very recent frontier studies that have not yet had time to accumulate citations, and to a truncation effect on the performance of 2024–2025 publications in highly cited and burst-reference analyses. Moreover, bibliometric tools are inherently limited as macro-level instruments and cannot substitute for systematic reviews and evidence syntheses addressing specific clinical questions; keyword normalization, threshold settings and clustering algorithms may also introduce additional methodological bias. 4.4.3 Future research directions and priorities for clinical translation Taken together with the evidentiary landscape described above, these limitations suggest several directions for future work. First, in prostate and bladder cancer, more prospective multicenter cohorts and randomized studies are needed to embed urinary biomarkers into real-world clinical pathways—such as biopsy decision-making in the PSA grey zone and adjustment of cystoscopy follow-up intervals—and to rigorously evaluate their incremental value over traditional indicators, including health-economic outcomes and patient-centered endpoints. Second, evidence gaps for RCC, UTUC and other rare urologic tumors need to be addressed. Future research should, while fully acknowledging the biological constraints of urine as a sample type in these settings, focus on the roles of cfDNA methylation profiling, multi-omics signatures of urinary extracellular vesicles and metabolomic signatures in postoperative surveillance and treatment response assessment, and increase the representation of populations from East Asia and low- and middle-income regions(86). Third, further efforts are required to advance standardization and platform-level implementation. Building on existing consensus documents such as those from ISEV, it will be important to refine protocols for sample collection, pre-analytical handling, assay platforms and reporting standards. For models that rely on machine learning, transparent and reproducible feature-selection procedures and robust external validation should be emphasized to enhance clinical interpretability and regulatory acceptability(87). Fourth, cross-disciplinary and cross-regional collaboration should be strengthened. Joint efforts involving urology, oncology, clinical laboratory medicine, molecular biology, data science and health economics will be essential to promote sample and data sharing, expand study scale and improve the generalizability of findings. Overall, urine-based liquid biopsy for urologic cancers has largely completed the transition from “molecular-level feasibility testing” to “disease-specific model construction” and is now at a critical juncture on the path toward “standardized implementation and integration into clinical pathways”. Whether the field can successfully move from a research hotspot into guideline recommendations and routine clinical practice will depend on its ability to strike a balance between high-quality evidence, standardized workflows and affordable, scalable platforms. 5. Conclusion Drawing on 439 publications indexed in WoSCC database between 2015 and 2025, this study mapped the research landscape and evidentiary evolution of urine-based liquid biopsy in urologic cancers. Over the past decade, the field has progressed from molecular-level feasibility testing and early disease-specific model building toward a second phase characterized by standardization efforts and integration into broader, multi-fluid liquid biopsy platforms. Within this trajectory, complementary diagnostic and monitoring pathways have emerged in prostate and bladder cancer, both centered on urinary multi-marker panels—one aiming to refine biopsy decision-making and high-risk stratification, the other to support recurrence surveillance and potential de-escalation of cystoscopy—while exploratory evidence for non-invasive assessment is gradually accumulating in RCC and other less common urologic cancers. Keyword networks and temporal patterns further indicate that current work is concentrated around PSA grey-zone biopsy decisions, bladder/urothelial cancer surveillance and cystoscopy de-escalation, and early diagnosis and prognostic stratification in RCC, underpinned by a cross-tumor, platform-based diagnostic route driven by multi-omics integration and machine-learning approaches. At the same time, our analysis highlights persistent gaps, including a shortage of high-quality prospective and real-world studies, limited representation of rare diseases and populations from low- and middle-income regions, and incomplete standardization of workflows and cost-effective testing platforms. Overall, urine-based liquid biopsy in urologic cancers combines non-invasiveness, repeatability and close anatomical proximity to the tumor burden, and is now entering a critical phase focused on how to implement standardized, interpretable and affordable assays within routine care. Our stage-wise framework and visual overview may help align future study design and standard-setting efforts with concrete clinical scenarios, thereby facilitating the incorporation of urine-based liquid biopsy into guidelines and reimbursement systems. Abbreviations ACPP Average citations per publication AUC Area under the curve cfDNA Cell-free DNA ctDNA Circulating tumor DNA CTCs Circulating tumor cells DRE Digital rectal examination EAU European Association of Urology EVs Extracellular vesicles ISEV International Society for Extracellular Vesicles MRD Minimal residual disease PSA Prostate-specific antigen RCC Renal cell carcinoma uEVs Urinary extracellular vesicles UTUC Upper tract urothelial carcinoma WoSCC Web of Science Core Collection Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Availability of data and materials The bibliometric dataset analysed during the current study was retrieved from the Web of Science Core Collection (WoSCC) using the search strategy described in the Methods section. The original WoSCC records are subject to database licensing restrictions and are therefore not publicly available. The processed data files and analysis scripts that support the findings of this study are available from the corresponding author on reasonable request. Funding This work was supported by the Gansu Provincial Natural Science Foundation (22JR11RA069 and 25JRRA584) and by the Gansu Province Health Commission Major Scientific Research Project for Scientific and Technological Innovation in the Health Industry (GSWSQNPY2025-15). Author contributions HL, YF and JY conceived and designed the study and developed the search strategy and analytical framework. HL, YF and WL performed the literature search, data retrieval and initial screening. JB, LD and WS conducted data extraction, cleaning and bibliometric analyses, and prepared the tables and figures. HL drafted the first version of the manuscript, and YF, JY, WL, JB, LD and WS contributed to data interpretation and critical revision of subsequent drafts. SC and XL supervised the overall project, provided methodological and clinical guidance, critically revised the manuscript for important intellectual content, and approved the final version of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. Leung DKW, Wong CHM, Ko ICH, Siu BWH, Liu AQY, Meng HYH, et al. Global trends in the incidence, mortality, and risk-attributable deaths for prostate, bladder, and kidney cancers: a systematic analysis from the global burden of disease study 2021. Eur Urol Oncol. 2025 May 28;S2588-9311(25)133-6. 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Théry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the international society for extracellular vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2018;7(1):1535750. Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 06 Dec, 2025 First submitted to journal 05 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":5288578,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study selection for urine-based liquid biopsy in urologic cancers.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/d9077d834a50156e42082ba5.png"},{"id":98438435,"identity":"b06073b9-c624-41ee-b689-d948b198280b","added_by":"auto","created_at":"2025-12-17 16:59:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12098098,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual trends in urine-based liquid biopsy publications for urologic cancers, 2015–2025.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/80ddc308296e7dc6d452a28d.png"},{"id":98324766,"identity":"4c4f3fe4-9243-4935-b5fc-77772e491101","added_by":"auto","created_at":"2025-12-16 14:33:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7118745,"visible":true,"origin":"","legend":"\u003cp\u003eCountry output and international collaboration in urine-based liquid biopsy for urologic cancers.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/fb98ab8cd0f7306a6d19e124.png"},{"id":98438099,"identity":"01719dc1-b947-479f-b01e-36e12d923136","added_by":"auto","created_at":"2025-12-17 16:58:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21579482,"visible":true,"origin":"","legend":"\u003cp\u003eInstitutional collaboration network in urine-based liquid biopsy for urologic cancers.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/112854f577c74e73c7028cd2.png"},{"id":98435910,"identity":"c5294d4b-553c-4434-9715-ffb972ee2bcf","added_by":"auto","created_at":"2025-12-17 16:54:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17686633,"visible":true,"origin":"","legend":"\u003cp\u003eAuthor co-authorship and co-citation networks in urine-based liquid biopsy for urologic cancers.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/e2d67552d89311dfd1c3bd66.png"},{"id":98324776,"identity":"ed0b6b07-b5ac-445c-a7e3-c561dbb3eab8","added_by":"auto","created_at":"2025-12-16 14:33:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":15095056,"visible":true,"origin":"","legend":"\u003cp\u003eJournal co-occurrence and co-citation networks in urine-based liquid biopsy for urologic cancers.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/de0a9a8c95a895998dc24de7.png"},{"id":98437928,"identity":"9e06447c-500e-427c-8341-36784f29b4a1","added_by":"auto","created_at":"2025-12-17 16:58:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":9852870,"visible":true,"origin":"","legend":"\u003cp\u003eDual-map overlay of journals on urine-based liquid biopsy for urologic cancers.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/0756084ad55a81114d826db7.png"},{"id":98436634,"identity":"bb562213-3fe4-4b15-b671-d4b06512c1e5","added_by":"auto","created_at":"2025-12-17 16:56:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":13602042,"visible":true,"origin":"","legend":"\u003cp\u003eReference co-citation network and top citation bursts in urine-based liquid biopsy for urologic cancers.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/b05f08ab1486c7c3bff7630c.png"},{"id":98324784,"identity":"d3e65b85-9ed1-45f0-8f62-c48a5cb7f282","added_by":"auto","created_at":"2025-12-16 14:33:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":8957976,"visible":true,"origin":"","legend":"\u003cp\u003eKeyword co-occurrence network and trend topics in urine-based liquid biopsy for urologic cancers.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/51261b0969e6047d802a34fa.png"},{"id":105755455,"identity":"80c49b3b-ba3a-4996-9873-b48d5295b544","added_by":"auto","created_at":"2026-03-30 16:27:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":102266955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8287024/v1/0e5fd39e-34f7-4350-8a26-5ffe1d52e501.pdf"}],"financialInterests":"","formattedTitle":"From feasibility to clinical pathways: a bibliometric and knowledge-mapping analysis of urine-based liquid biopsy in urologic cancers (2015–2025)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrologic cancers\u0026mdash;including prostate cancer, bladder cancer, upper tract urothelial carcinoma (UTUC) and renal cell carcinoma (RCC)\u0026mdash;are among the major solid tumors worldwide and account for an increasing share of cancer incidence and mortality(1,2). Current diagnostic and surveillance pathways rely heavily on imaging, serum biomarkers and histopathological biopsy. These approaches underpin early detection, risk stratification and postoperative follow-up, but they are invasive, constrained in sampling frequency, and suboptimal for capturing dynamic changes in tumor burden and molecular evolution. In several typical scenarios\u0026mdash;such as the \u0026ldquo;grey zone\u0026rdquo; of elevated prostate-specific antigen (PSA) with non-definitive imaging or digital rectal examination, long-term cystoscopic surveillance of non-muscle-invasive bladder cancer, and postoperative follow-up and recurrence monitoring in RCC\u0026mdash;clinicians must constantly balance the risks of over-testing against those of missing high-risk progression. This tension underscores an unmet need for accurate, non-invasive and repeatable molecular biomarkers to guide clinical decision-making(3\u0026ndash;5).\u003c/p\u003e \u003cp\u003eLiquid biopsy, which interrogates circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles (EVs) and other soluble analytes in body fluids, has shown considerable promise in early cancer detection, assessment of minimal residual disease (MRD), monitoring of treatment response and tracking of resistance evolution6. Large pan-cancer cohort studies have demonstrated that ctDNA levels correlate closely with tumor volume, stage and prognosis, thereby anchoring the clinical validity of circulating tumor-derived signals(6). Subsequent methodological and clinical reviews have refined ctDNA assay platforms, statistical analysis frameworks and clinical application scenarios, collectively paving the way for plasma-based liquid biopsy to enter clinical guidelines and regulatory pathways(7,8).However, most of this work has focused on plasma, which primarily reflects systemic tumor burden and may be less sensitive to localized microenvironmental changes in lesions arising from, or confined to, the urothelial mucosa and luminal surfaces of the urinary tract(9\u0026ndash;12).\u003c/p\u003e \u003cp\u003eIn contrast to blood, urine is easy to collect, non-invasive and amenable to repeated sampling, making it particularly attractive in urologic cancers(10,12). Tumors of the bladder and upper urinary tract are in direct contact with the urinary stream, whereas the prostate and kidneys communicate with urine through multiple layers of cellular and molecular exchange. As a result, tumor-derived extracellular nucleic acids, proteins and metabolites in urine can capture both systemic signals and additional information from the urothelial mucosa and local tumor microenvironment(13). Accumulating basic and translational data suggest that diverse urinary analytes\u0026mdash;including cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA), microRNAs, DNA methylation patterns, proteomic features and extracellular vesicle cargo\u0026mdash;may be useful for risk stratification in prostate cancer, recurrence monitoring in bladder cancer, and non-invasive diagnosis and prognostication in UTUC and RCC(14\u0026ndash;18). These characteristics make urologic cancers a particularly suitable disease model for testing the concept of liquid biopsy based on an \u0026ldquo;organ-specific fluid.\u0026rdquo;\u003c/p\u003e \u003cp\u003eAgainst this backdrop, urine-based assays have rapidly expanded across the spectrum of urologic cancers. In prostate cancer, a range of urinary RNA- and exosome-based multigene signatures, together with models that integrate PSA and clinical variables, have been developed to refine biopsy indications and identify high-risk patients within the PSA \u0026ldquo;grey zone\u0026rdquo;(19\u0026ndash;21). In bladder cancer and UTUC, the limited sensitivity of conventional urine cytology has prompted the development of new urinary tests centered on DNA methylation and hotspot mutation panels, as well as multiplex protein assays, which are being evaluated as tools to aid diagnosis and to reduce the frequency of cystoscopy during surveillance(15\u0026ndash;17,22). In RCC and other less common urologic cancers, urinary biomarker panels spanning DNA methylation, microRNAs, peptides and metabolites have also been explored for non-invasive discrimination between malignant and benign lesions and for prognostic risk assessment(23,24). Nevertheless, most existing studies have focused on a single tumor type, analyte or platform. A field-level synthesis that examines \u0026ldquo;urine plus the full spectrum of urologic cancers\u0026rdquo; as an integrated research landscape is still lacking.\u003c/p\u003e \u003cp\u003eSeveral fundamental questions therefore remain unresolved:\u003c/p\u003e \u003cp\u003e(1) On the temporal axis, does urine-based liquid biopsy for urologic cancers exhibit distinct stages of development over the past decade, and if so, where are the inflection points and how are they linked to peaks in evidentiary output?\u003c/p\u003e \u003cp\u003e(2) How are different countries, institutions, authors and journals positioned within the global network of knowledge production and dissemination, and to what extent have they shaped the structure of this field?\u003c/p\u003e \u003cp\u003e(3) Within a bibliometric framework integrating co-citation and citation-burst analyses, which highly cited and burst references constitute the key evidentiary nodes that underpin dominant technological pathways and clinical application models?\u003c/p\u003e \u003cp\u003e(4) Across the core clinical scenarios\u0026mdash;PSA grey-zone diagnosis in prostate cancer, recurrence surveillance in bladder cancer and UTUC, and non-invasive assessment of RCC and other less common urologic cancers\u0026mdash;how do current hotspots and frontiers differ and evolve in terms of the specific combinations of clinical context, analytes and technological platforms?\u003c/p\u003e \u003cp\u003eBibliometrics and scientific knowledge mapping provide a systematic toolkit for addressing these questions(25). By quantitatively analyzing publication trends, highly cited and burst references, country\u0026ndash;institution\u0026ndash;author\u0026ndash;journal networks and the evolution of keyword clusters, such approaches can delineate global patterns of knowledge production and collaboration and identify the pivotal nodes and pathways in evidence evolution(26). In recent years, knowledge-mapping studies across medicine and public health have shown that these methods are well suited to uncovering research hotspots and frontiers and to linking bibliometric patterns with disease burden, policy agendas and real-world clinical scenarios(27,28).\u003c/p\u003e \u003cp\u003eIn this study, we performed an integrated bibliometric and knowledge-mapping analysis of urine-based liquid biopsy in urologic cancers using records retrieved from the Web of Science Core Collection (WoSCC) between 2015 and 2025. Drawing on three complementary perspectives\u0026mdash;temporal evolution, global knowledge production and clinically anchored research scenarios\u0026mdash;we sought to delineate stage-wise development and map the dominant technological trajectories in this field, providing a structured visual overview to inform future study design and clinical translation.\u003c/p\u003e "},{"header":"2. Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and search strategy\u003c/h2\u003e \u003cp\u003eThis bibliometric analysis was based on the Web of Science Core Collection (WoSCC), a widely used multidisciplinary citation database that provides broad, regularly updated coverage with complete citation and cited-reference information, making it suitable for knowledge-mapping studies(29). To capture the evolution of urine-based liquid biopsy in urologic cancers over the past decade, we restricted the publication period to 2015 to 2025. All searches were performed in WoSCC on 15 November 2025.\u003c/p\u003e \u003cp\u003eThe search strategy was structured around three conceptual modules: (1) urologic cancers; (2) urine-related terms; and (3) liquid biopsy\u0026ndash;related terms. All search terms were queried as Topic (TS) fields and combined using Boolean operators. The final search string was:\u003c/p\u003e \u003cp\u003eTS = (( \"bladder cancer\" OR \"bladder carcinoma\" OR \"bladder tumor*\" OR \"urothelial carcinoma\" OR \"transitional cell carcinoma\" OR \"urinary bladder neoplasm*\" )\u003c/p\u003e \u003cp\u003eOR ( \"kidney cancer\" OR \"renal cancer\" OR \"renal cell carcinoma\" OR \"kidney neoplasm*\" OR \"renal neoplasm*\" ) OR ( \"prostate cancer\" OR \"prostatic neoplasm*\" OR \"prostate tumor*\" OR \"prostatic carcinoma\" )) AND TS = ((\"urine\" OR \"urinary\") AND (\"liquid biopsy\" OR \"liquid biopsies\")).\u003c/p\u003e \u003cp\u003eThe initial search retrieved 481 records. All records were exported from WoSCC in \u0026ldquo;Full Record and Cited References\u0026rdquo; plain-text format to ensure compatibility with CiteSpace, VOSviewer and R-based bibliometric packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Eligibility criteria and study selection\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eStudies were included if they met all of the following criteria: (1) peer-reviewed English-language publications; (2) document type classified as original research article (Article) or review (Review); (3) study population involving urologic cancers (e.g. prostate cancer, bladder cancer, UTUC, RCC); (4) urine used as one of the main or core biological matrices; and (5) study content closely related to liquid biopsy or urinary molecular biomarkers/detection platforms.\u003c/p\u003e \u003cp\u003eWe excluded non-research or unstable publication types, including conference abstracts, editorials, corrigenda/errata, books and book chapters, short communications, case reports, clinical trial registrations, reference-only records, retracted publications and online-only items not yet assigned to a specific volume/issue (Early Access articles).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Study selection process\u003c/h2\u003e \u003cp\u003eThe initial WoSCC search yielded 481 records. After restricting the dataset to English-language publications from 2015\u0026ndash;2025 and to the document types Article and Review, 448 records remained. We then removed non-stable or non-research items, including 7 Early Access papers and 2 conference papers (Proceedings Paper), resulting in 439 publications retained for analysis. The study selection process is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTwo researchers independently screened titles and abstracts to exclude studies that were unrelated to urologic cancers, did not use urine as a main biological matrix, or did not clearly fall within the concept of liquid biopsy. Discrepancies were resolved through discussion, and, when necessary, a third researcher adjudicated. This study used only publicly available literature, did not involve individual patient data and did not include any animal or human experimentation; therefore, additional ethical approval was not required.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Bibliometric and knowledge-mapping analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics and trend plots were generated using Microsoft Excel 2021 and OriginPro 2024 (version 10.1.0.178).\u003c/p\u003e \u003cp\u003eVisualization and knowledge-mapping analyses were performed using CiteSpace 6.3.R1, VOSviewer 1.6.20 and the bibliometrix package in R (version 4.5.1)31\u0026ndash;33. Plain-text \u0026ldquo;Full Record and Cited References\u0026rdquo; files exported from WoSCC were imported into these tools to construct collaboration, citation and co-citation networks, as well as keyword maps(30\u0026ndash;32).\u003c/p\u003e \u003cp\u003eIn VOSviewer, we built collaboration networks for countries/regions, institutions and authors, and generated co-citation maps of journals and authors to identify high-impact venues and core research groups(31). CiteSpace was used mainly for time-based evolutionary analyses, including burst detection of references and keywords and dual-map overlays of journals, to delineate temporal shifts in evidentiary peaks and research foci(30).\u003c/p\u003e \u003cp\u003eKeyword analyses were based on Keywords Plus from WoSCC. VOSviewer was used to perform keyword co-occurrence and clustering to identify high-frequency themes and their cluster structure, while R and the bibliometrix package were employed to examine thematic evolution and trend topics, thereby capturing research hotspots and emerging fronts across different time windows(32). To reduce noise from synonyms and spelling variants, selected keywords were merged and normalized\u0026mdash;for example, \u0026ldquo;exosomes/microvesicles/urinary exosomes\u0026rdquo; were unified as \u0026ldquo;extracellular vesicles.\u0026rdquo;\u003c/p\u003e \u003cp\u003eCountry-level publication output and geographic distribution were visualized as a world map in R, with the number of publications overlaid for each country to complement the depiction of the global research landscape. Taken together, this multi-software, multi-dimensional analytic framework allowed us to characterize publication volume, collaboration patterns, knowledge bases and research hotspots in urine-based liquid biopsy for urologic cancers.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Global publication trends and overall characteristics\u003c/h2\u003e \u003cp\u003eA total of 439 publications on urine-based liquid biopsy in urologic cancers were retrieved from WoSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No studies explicitly framed within the concept of \u0026ldquo;liquid biopsy\u0026rdquo; were identified before 2015. Relevant papers began to appear in 2016, and annual output increased steadily thereafter, peaking at 77 publications in 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnnual average citations per article showed two periods of elevation, around 2016\u0026ndash;2019 and 2021\u0026ndash;2022, followed by a plateau despite continued growth in publication volume. The small but highly cited body of work in 2016\u0026ndash;2017 suggests the presence of seminal, field-anchoring studies in an early foundational phase, whereas the period around 2021 marked a turning point at which both publication counts and total citations rose in parallel. Because of the search cut-off, data for 2025 remain incomplete and are presented only as indicative of overall trends.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 countries in urine-based liquid biopsy research for urologic cancers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublications (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCitations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvg. Citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 institutions in urine-based liquid biopsy research for urologic cancers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution (Country)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublications (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCitations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvg. Citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniv. Amsterdam\u003c/p\u003e \u003cp\u003e(Netherlands)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVrije Univ. Amsterdam\u003c/p\u003e \u003cp\u003e(Netherlands)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohns Hopkins Univ.\u003c/p\u003e \u003cp\u003e(USA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniv. Porto\u003c/p\u003e \u003cp\u003e(Portugal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniv. California System\u003c/p\u003e \u003cp\u003e(USA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniv. Toronto\u003c/p\u003e \u003cp\u003e(Canada)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAarhus Univ.\u003c/p\u003e \u003cp\u003e(Denmark)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsaka Univ.\u003c/p\u003e \u003cp\u003e(Japan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniv. Oslo\u003c/p\u003e \u003cp\u003e(Norway)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAMS\u0026ndash;PUMC\u003c/p\u003e \u003cp\u003e(China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Distribution by country/region and institution\u003c/h2\u003e \u003cp\u003eThe 439 publications originated from 57 countries/regions and 1,051 institutions, with research activity concentrated in North America, Europe and East Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The United States and China were the leading contributors, publishing 105 (23.9%) and 99 (22.6%) papers, respectively, followed by Italy and Germany (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The United States ranked first in both total citations and H-index, whereas countries such as the Netherlands and Canada produced fewer papers but achieved relatively high average citations and H-indices (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA country-level collaboration network (threshold\u0026thinsp;\u0026ge;\u0026thinsp;5 publications; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) highlighted the United States as the main hub, closely linked with the United Kingdom, Germany, the Netherlands, Canada and others, while European countries formed a dense regional cluster.\u003c/p\u003e \u003cp\u003eAt the institutional level, the top 10 institutions came from eight countries (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The University of Amsterdam and Vrije Universiteit Amsterdam (Netherlands) shared the highest publication counts (13 papers each) and had the highest H-indices. European institutions accounted for half of the top 10, with the University of Oslo (Norway) standing out for its high total and average citations. Overall, 41 institutions with \u0026ge;\u0026thinsp;5 publications were included in the collaboration network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), in which the two Amsterdam universities and Johns Hopkins University formed the core and maintained intensive collaborations with partners in multiple countries. Coloring nodes by average publication year showed that institutions such as Vrije Universiteit Amsterdam and Johns Hopkins University tended to enter the field earlier, whereas Asian institutions including Fudan University and Osaka University were later but increasingly active contributors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 productive and co-cited authors in urine-based liquid biopsy research for urologic cancers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCo-cited author\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCo-citations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFerro M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMcKiernan J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFujita K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChristensen E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrocetto F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBryzgunova OE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonomura N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBirkenkamp-Demtr\u0026ouml;der K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCheng L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBabjuk M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLi G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSiegel RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerracciano D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTomlins SA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusetto GM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003evan Kessel KEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDel Giudice F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSung H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLopez-Beltran A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHayashi Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Authors and co-cited authors\u003c/h2\u003e \u003cp\u003eIn total, 3,181 authors contributed to publications on urine-based liquid biopsy in urologic cancers. The top 10 authors each published at least six papers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with Ferro M and Fujita K being the most productive, contributing eight papers each. Using a threshold of \u0026ge;\u0026thinsp;5 publications, 24 authors were included in the author collaboration network (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), in which Ferro M and Fujita K had the most connections and occupied central positions within their respective clusters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe co-cited author analysis identified 14,852 authors, among whom 10 had at least 53 co-citations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). McKiernan J had the highest number of co-citations (106), followed by Christensen E (89) and Bryzgunova OE (81). Using a threshold of \u0026ge;\u0026thinsp;30 co-citations, 76 authors were included in the co-cited author network (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). McKiernan J, Christensen E and Bryzgunova OE formed the largest nodes with the greatest total link strength and occupied central positions in their respective clusters, while authors such as Birkenkamp-Demtr\u0026ouml;der K and Babjuk M acted as secondary hubs. Together, these groups constitute the core cluster of highly cited contributors in this field.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Top 10 productive and co-cited journals in urine-based liquid biopsy research for urologic cancers(see end of document)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 productive and co-cited journals in urine-based liquid biopsy research for urologic cancers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublications (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIF 2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCo-cited Journal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCo-citation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIF 2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCancers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (12.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEuropean Urology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInternational Journal of Molecular Sciences\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (5.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eJournal of Urology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFrontiers in Oncology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (3.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eClinical Cancer Research\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTranslational Andrology and Urology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (2.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOncotarget\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExpert Review of Molecular Diagnostics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (1.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePLOS ONE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJournal of Extracellular Vesicles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (1.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eScientific Reports\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrologic Oncology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (1.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCancers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eScientific Reports\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (1.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eJournal of Extracellular Vesicles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDiagnostics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (1.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eInternational Journal of Molecular Sciences\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnalytical Chemistry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (1.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUrologic Oncology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Publishing journals, co-cited journals and citation flows\u003c/h2\u003e \u003cp\u003eThe 439 publications were distributed across 200 journals. Cancers published the largest number of articles (54, 12.30%), followed by International Journal of Molecular Sciences (22, 5.01%) and Frontiers in Oncology (16, 3.64%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the top 10 most productive journals, Journal of Extracellular Vesicles had the highest 2024 impact factor, followed by Analytical Chemistry and Expert Review of Molecular Diagnostics.\u003c/p\u003e \u003cp\u003eUsing a threshold of \u0026ge;\u0026thinsp;3 publications, 36 journals were included in the journal co-occurrence network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Cancers had the largest node size, the densest connections and a relatively recent average publication year, and occupied a central position in the network. International Journal of Molecular Sciences, Frontiers in Oncology, Scientific Reports, Diagnostics and Translational Andrology and Urology were located around it and formed several secondary hubs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the co-cited journal analysis, European Urology had the highest number of co-citations (1,433), followed by Journal of Urology (805), Clinical Cancer Research (766) and Oncotarget (766) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A co-cited journal network constructed with a threshold of \u0026ge;\u0026thinsp;100 co-citations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) showed dense co-citation links between European Urology and other urologic oncology journals such as Journal of Urology and Urologic Oncology.\u003c/p\u003e \u003cp\u003eThe dual-map overlay of journals (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) showed that citing journals clustered in \u0026ldquo;molecular / biology / immunology\u0026rdquo; and \u0026ldquo;medicine / medical / clinical\u0026rdquo; predominantly referenced journals in the \u0026ldquo;molecular / biology / genetics\u0026rdquo; cluster, reflecting citation flows from basic and translational research toward clinical disciplines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Top 15 most cited articles on urine-based liquid biopsy in urologic cancers(see end of document)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 15 most cited articles on urine-based liquid biopsy in urologic cancers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst author\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitle (shortened)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTC\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACPP\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYu W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExosome-based liquid biopsies in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAnn Oncol\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e132.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYu D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExosomes as a new frontier in cancer liquid biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMol Cancer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e155.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocke WJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA methylation biomarkers for cancer diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFront Genet\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkotland T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLipid species in urinary exosomes for prostate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEur J Cancer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVan Neste L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-grade prostate cancer detection via urinary biomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEur Urol\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErdbr\u0026uuml;gger U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrinary extracellular vesicles: ISEV position paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eJ Extracell Vesicles\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDi Meo A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiquid biopsy for precision medicine in urologic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMol Cancer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTivey A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCirculating tumour DNA: beyond the blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNat Rev Clin Oncol\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRodriguez M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-invasive urinary miRNA biomarkers for prostate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMol Cancer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsuba W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCirculating miRNA panels for early bladder cancer detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCancer Sci\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirkenkamp-Demtr\u0026ouml;der K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenomic alterations in bladder cancer liquid biopsies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEur Urol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmith JT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNanoscale DLD arrays for extracellular vesicle separation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLab Chip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristensen E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFGFR3 and PIK3CA hotspot mutations in bladder cancer liquid biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEur Urol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhang W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerum exosomal microRNAs as biomarkers in clear-cell renal cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEur Urol Focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirkenkamp-Demtr\u0026ouml;der K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiquid biopsy monitoring of treatment response and relapse in advanced bladder cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEur Urol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eTC, total citations; \u003csup\u003eb\u003c/sup\u003eACPP, average citations per publication.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Main contents of the 15 strongest citation-burst references\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain contents of the 15 strongest citation-burst references\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference (first author, year, journal)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrength*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain research content\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBettegowda C, 2014, Sci Transl Med\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTranslational multi-cancer cohort study on ctDNA\u003c/b\u003e: evaluated detectability of circulating tumour DNA in patients with early- and late-stage solid tumours and found that ctDNA levels correlate with tumour burden and are detectable in most advanced and in many localised cancers.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirkenkamp-Demtr\u0026ouml;der K, 2016, Eur Urol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eClinical liquid-biopsy study in bladder cancer\u003c/b\u003e: developed personalised assays for genomic variants in plasma and urine cell-free tumour DNA and showed that detectable and rising levels of tumour DNA are associated with recurrence, progression and metastasis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMcKiernan J, 2016, JAMA Oncol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eProspective diagnostic validation study in prostate cancer\u003c/b\u003e: developed a urine exosome three-gene expression assay and showed that its score distinguishes high-grade prostate cancer from low-grade cancer and benign disease in men with elevated PSA.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBryzgunova OE, 2015, Acta Naturae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eReview article on urinary extracellular nucleic acids\u003c/b\u003e: summarised the sources, structure, stability and reported diagnostic applications of extracellular nucleic acids in urine as non-invasive biomarkers.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristensen E, 2017, Eur Urol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eClinical biomarker cohort study in bladder cancer\u003c/b\u003e: used droplet digital PCR to measure FGFR3 and PIK3CA hotspot mutations in urine and plasma cell-free DNA and found that higher mutant DNA levels predict later progression and metastasis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTomlins SA, 2016, Eur Urol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eClinical risk-prediction study in prostate cancer\u003c/b\u003e: validated Mi-Prostate Score models that combine serum PSA with urinary TMPRSS2:ERG and PCA3 and showed improved prediction of overall and high-grade prostate cancer on biopsy compared with PSA alone.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBerrondo C, 2016, PLoS One\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTranslational biomarker study in bladder cancer\u003c/b\u003e: analysed expression of the long non-coding RNA HOTAIR in tumour tissue and urinary exosomes and found that higher HOTAIR levels are enriched in urinary exosomes and correlate with advanced stage and poorer prognosis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWan JCM, 2017, Nat Rev Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eReview article on ctDNA liquid biopsy\u003c/b\u003e: summarised sequencing and digital PCR methods for circulating tumour DNA analysis and collated evidence for its use in cancer prognostication, molecular profiling and disease monitoring.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBray F, 2018, CA Cancer J Clin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEpidemiological report (GLOBOCAN 2018)\u003c/b\u003e: reported global estimates of cancer incidence and mortality for 36 cancers in 185 countries and described geographic variation in cancer burden.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMcKiernan J, 2018, Eur Urol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eProspective adaptive clinical-utility trial in prostate cancer\u003c/b\u003e: evaluated the ExoDx Prostate (IntelliScore) urine exosome test in men with PSA 2\u0026ndash;10 ng/mL undergoing initial biopsy and showed that the test predicts\u0026thinsp;\u0026ge;\u0026thinsp;Grade Group 2 prostate cancer and can be used to defer some biopsies.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkotland T, 2017, Eur J Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eQuantitative lipidomics biomarker study in prostate cancer\u003c/b\u003e: performed mass-spectrometry lipidomic profiling of urinary exosomes from patients and controls and identified individual lipid species and three-lipid combinations that differ between groups and classify prostate cancer.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSung H, 2021, CA Cancer J Clin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eUpdated epidemiological report (GLOBOCAN 2020)\u003c/b\u003e: updated global estimates of new cancer cases and deaths for 36 cancers in 185 countries and presented regional patterns and projected future trends in cancer burden.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgnatiadis M, 2021, Nat Rev Clin Oncol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePerspective review on liquid-biopsy implementation\u003c/b\u003e: discussed analytical validation, clinical-trial design, regulatory, reimbursement and logistical issues that must be addressed to integrate ctDNA- and CTC-based liquid-biopsy tests into routine oncology practice.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKalluri R, 2020, Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eReview article on exosome biology\u003c/b\u003e: summarised current knowledge on exosome biogenesis, cargo composition and mechanisms of intercellular communication and outlined experimental and clinical applications of exosomes, including as diagnostic and therapeutic tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLone SN, 2022, Mol Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eReview article on liquid biopsy in oncology\u003c/b\u003e: overviewed principal liquid-biopsy analytes and platforms and synthesised reported applications for cancer detection, prognostic stratification and treatment-response monitoring, as well as technical and clinical challenges.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Strength\u0026thinsp;=\u0026thinsp;burst intensity calculated by CiteSpace.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Highly cited and burst references\u003c/h2\u003e \u003cp\u003eThe top 15 most cited references are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, each with a total citation count of at least 129. These papers were mainly published within two time windows, 2016\u0026ndash;2019 and 2021\u0026ndash;2022. Molecular Cancer contributed the largest number of highly cited articles (three papers). Two reviews on exosome/extracellular vesicle\u0026ndash;based liquid biopsy, authored by Yu W and Yu D and published in Annals of Oncology and Molecular Cancer, respectively, had the highest citation counts and represent landmark contributions that are repeatedly cited in urine-based liquid biopsy research.\u003c/p\u003e \u003cp\u003eUsing a threshold of \u0026ge;\u0026thinsp;326 co-citations, we constructed a co-cited reference network that comprised 35 core publications (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). References such as \u0026ldquo;McKiernan J, 2016, JAMA Oncol\u0026rdquo;, \u0026ldquo;Christensen E, 2017, Eur Urol\u0026rdquo;, \u0026ldquo;Bettegowda C, 2014, Sci Transl Med\u0026rdquo;, \u0026ldquo;Birkenkamp-Demtr\u0026ouml;der K, 2016, Eur Urol\u0026rdquo; and \u0026ldquo;Sung H, 2021, CA Cancer J Clin\u0026rdquo; formed large nodes with high total link strength and clustered into several tightly connected co-citation cores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBurst reference analysis was used to identify papers with rapidly increasing citation counts over short periods. CiteSpace detected 15 references with significant citation bursts (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The updated global cancer burden report by Sung H, published in CA: A Cancer Journal for Clinicians in 2021, showed the strongest and most recent burst, followed by the GLOBOCAN 2018 report by Bray F in the same journal. Bursts associated with key liquid biopsy studies such as Birkenkamp-Demtr\u0026ouml;der K, 2016 and McKiernan J, 2016 were sustained over several years, while more recent platform- and implementation-focused reviews (e.g. Sung H, 2021; Ignatiadis M, 2021; Lone SN, 2022) continued through 2025, indicating that these papers constitute core and still-active knowledge sources in the current time window. The main content of these burst references is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 20 keywords on research of urinary liquid biopsy in urologic cancers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKeyword\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOccurrences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKeyword\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOccurrences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eliquid biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecell-free dna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebiomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eurothelial carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprostate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edna methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebladder cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecirculating tumor cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eextracellular vesicles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecirculating tumor dna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eurine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eplasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eidentification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ediagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecytology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emicrornas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emutations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexpression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprognosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.6 Keyword co-occurrence and evolution of research hotspots\u003c/p\u003e \u003cp\u003eKeyword co-occurrence analysis was used to identify research hotspots in urine-based liquid biopsy for urologic cancers. A total of 1,794 keywords were detected, of which 46 with a frequency\u0026thinsp;\u0026ge;\u0026thinsp;13 were included in the co-occurrence network (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The top 20 high-frequency keywords are listed in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. \u0026ldquo;Liquid biopsy\u0026rdquo; (298 occurrences) and \u0026ldquo;biomarkers\u0026rdquo; (224 occurrences) were the two most frequent terms and represented overarching concepts across the field. They were followed by disease- and platform-related terms such as \u0026ldquo;prostate cancer\u0026rdquo; (148), \u0026ldquo;bladder cancer\u0026rdquo; (142) and \u0026ldquo;extracellular vesicles\u0026rdquo; (129), reflecting the main disease types and analytes that currently attract the greatest research attention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVOSviewer clustering divided the 46 high-frequency keywords into four interconnected clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The red cluster aggregated terms such as \u0026ldquo;biomarkers\u0026rdquo;, \u0026ldquo;extracellular vesicles\u0026rdquo;, \u0026ldquo;microRNAs\u0026rdquo;, \u0026ldquo;PSA\u0026rdquo; and \u0026ldquo;prostate cancer\u0026rdquo;, and primarily corresponded to prostate cancer\u0026ndash;focused studies built around multi-analyte biomarker panels. The green cluster, dominated by \u0026ldquo;liquid biopsy\u0026rdquo;, included \u0026ldquo;cancer\u0026rdquo;, \u0026ldquo;cell-free DNA\u0026rdquo;, \u0026ldquo;circulating tumor cells\u0026rdquo;, \u0026ldquo;circulating tumor DNA\u0026rdquo; and several solid tumor entities, representing cross-tumor cfDNA/ctDNA- and CTC-based liquid biopsy platforms. The blue cluster, centered on \u0026ldquo;bladder cancer\u0026rdquo;, \u0026ldquo;diagnosis\u0026rdquo;, \u0026ldquo;cytology\u0026rdquo; and \u0026ldquo;DNA methylation\u0026rdquo; and accompanied by terms such as \u0026ldquo;surveillance\u0026rdquo;, \u0026ldquo;recurrence\u0026rdquo; and \u0026ldquo;machine learning\u0026rdquo;, mainly represented research on diagnostic and surveillance strategies in bladder cancer. The yellow cluster contained \u0026ldquo;renal cell carcinoma\u0026rdquo;, \u0026ldquo;prognosis\u0026rdquo; and \u0026ldquo;survival\u0026rdquo;; although smaller in size, it showed dense internal connections, indicating a distinct but less developed line of work focused on prognosis and survival in RCC.\u003c/p\u003e \u003cp\u003eThe trend topics map (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB) illustrated a temporal shift from early feasibility studies centered on nucleic acid\u0026ndash;based markers and next-generation sequencing toward more disease-focused work in prostate and bladder cancer around 2020\u0026ndash;2022, followed more recently by growing interest in RCC/UTUC, machine learning and metabolomics-based urinary biomarker discovery.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Stage-wise evolution of evidence and dominant technological pathways\u003c/h2\u003e \u003cp\u003eOur bibliometric findings indicate that research on urine-based liquid biopsy in urologic cancers has shown a sustained upward trajectory over the past decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, changes in annual publication counts or average citations per article alone do not fully explain the underlying forces reshaping the field. By integrating highly cited references with citation-burst patterns (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), we delineated a stage-wise evolution characterized by two broad phases: a \u0026ldquo;foundational evidence and methodological expansion\u0026rdquo; phase from 2016 to 2020 and a \u0026ldquo;standardization and platform integration\u0026rdquo; phase from 2021 onward. The first phase was marked by a cluster of highly cited and burst references around 2016\u0026ndash;2017, which established key clinical and methodological cornerstones for liquid biopsy, whereas the second phase, centered around 2021, coincided with a second peak in citation activity and reflected a shift towards consolidating platforms, refining analytical pipelines and embedding liquid biopsy into guideline- and practice-oriented frameworks.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Phase I: building foundational evidence and expanding methods\u003c/h2\u003e \u003cp\u003eIn the first phase, pan-cancer ctDNA studies demonstrated that circulating tumor DNA levels are tightly associated with tumor burden, stage and prognosis, thereby providing the clinical evidence base for liquid biopsy as a whole(6,7). Urine-focused reviews in turn systematically described the sources, stability and early applications of extracellular nucleic acids in urine, establishing the conceptual rationale for urine as a non-invasive reservoir of molecular biomarkers(13). Subsequent clinical studies in bladder and prostate cancer, evaluating urinary cfDNA and exosome-related markers, further validated these concepts at the practical level(16,20,33\u0026ndash;37).\u003c/p\u003e \u003cp\u003eAt the same time, epidemiological reports such as GLOBOCAN 2018 highlighted the prominent contribution of prostate and bladder cancer to the global cancer burden, reinforcing their central position among urologic cancers(38). Together, these works formed an early \u0026ldquo;knowledge core\u0026rdquo; in the co-citation network and provided the upstream evidentiary basis on which later research on urine-based liquid biopsy has since been built.\u003c/p\u003e \u003cp\u003eOn this basis, a series of highly cited original studies published between 2016 and 2019 on prostate and bladder cancer constituted the technological backbone of the first phase. In prostate cancer, several studies used multigene mRNA panels measured in post\u0026ndash;digital rectal examination (DRE) urine sediments and miRNA and lipid signatures derived from urinary exosomes to show that post-DRE urine can reliably capture molecular signals associated with high-risk disease(37,39). Risk scores based on these mRNA panels, quantified by RT-qPCR, were further integrated with PSA levels and clinical variables to construct multi-analyte biomarker models that support key decisions such as whether to perform biopsy and how to classify high-risk patients(40).\u003c/p\u003e \u003cp\u003eIn bladder cancer, studies of urinary cfDNA/ctDNA translated the cross-tumor ctDNA concept into the urothelial setting: driver mutations such as FGFR3 and PIK3CA were repeatedly detected in urine and plasma, and their abundance correlated with subsequent recurrence, progression and metastasis(16,33). Serial sampling outlined an early disease-monitoring pathway in which dynamic ctDNA curves served as a central indicator of residual disease and impending relapse(41). In parallel, advances in exosome isolation platforms, DNA methylation assay frameworks, miRNA panels and RCC-related urinary markers gradually extended the field from single tumor entities and single analytes to multi-tumor, multi-omics explorations(42\u0026ndash;45).\u003c/p\u003e \u003cp\u003eTaken together, the highly cited and burst references of this first phase collectively moved the field from asking \u0026ldquo;can tumor-derived signals be detected in body fluids?\u0026rdquo; to \u0026ldquo;can urine be established as a stable molecular carrier?\u0026rdquo;, laying the groundwork for subsequent efforts to build urine-based biomarker models for specific clinical scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Phase II: standardization and platform integration\u003c/h2\u003e \u003cp\u003eThe defining feature of the second phase was a marked shift in the co-citation burst profile. A cluster of key reviews and position papers published between 2020 and 2022 showed burst peaks that were highly concentrated after 2021. Updated GLOBOCAN 2020 estimates underscored the continued increase in incidence and mortality of prostate and bladder cancer, highlighting from a demand-side perspective the urgent need for non-invasive biomarkers and early detection tools(46). Authoritative reviews on the clinical implementation of ctDNA discussed in a systematic manner how how liquid biopsy could be translated into practice across multiple dimensions\u0026mdash;including analytical validation, trial design, regulatory approval and reimbursement frameworks\u0026mdash;thereby elevating previously fragmented feasibility studies to the level of guideline- and pathway-oriented discourse(8). In parallel, a set of highly co-cited reviews focusing on multi-analyte integration (CTCs, ctDNA and tumor-derived extracellular vesicles) and on exosome biology established a cross-platform liquid biopsy framework that bridged technological platforms with underlying biological mechanisms(47,48). Collectively, these burst references formed a \u0026ldquo;translational hub\u0026rdquo; in the co-citation network and drove a shift in focus from local technical exploration towards system-level designs aimed at clinical implementation.\u003c/p\u003e \u003cp\u003eAt the same time, several cross-tumor reviews published in 2021\u0026ndash;2022 on exosome-based liquid biopsy and \u0026ldquo;ctDNA beyond blood\u0026rdquo;, together with a position paper on urinary EVs, entered the ranks of highly cited references, with ACPP values among the top four in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. These platform-oriented papers represent the most influential methodological cornerstones of the second phase. On the one hand, they integrated ctDNA and extracellular vesicles from multiple body fluids\u0026mdash;including urine\u0026mdash;into a unified liquid biopsy framework, emphasizing their potential roles in early diagnosis, MRD assessment and individualized treatment(49\u0026ndash;51). On the other hand, by proposing concrete recommendations on sample collection, pre-analytical processing and reporting standards, they provided a reproducible technical baseline for subsequent studies(14).\u003c/p\u003e \u003cp\u003eThe synchronized \u0026ldquo;uplift\u0026rdquo; of these high-impact platform papers and the co-citation burst spectrum around 2021 provides direct evidence for defining 2021 as the boundary between the first and second phases in this two-phase framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Technological trajectories and clinical focuses: twin diagnostic and monitoring pathways\u003c/h2\u003e \u003cp\u003eWithin this temporal structure, urine-based liquid biopsy in prostate and bladder cancer has evolved into a pair of \u0026ldquo;twin pathways\u0026rdquo; characterized by shared technologies but distinct clinical uses. In both diseases, high-throughput omics approaches are used to screen candidate biomarkers; digital PCR and targeted sequencing are then applied to precisely quantify low-abundance signals; and multivariable models subsequently integrate urinary molecular features with PSA, stage and treatment information, embedding these signatures into specific clinical decision points for validation(7,48).\u003c/p\u003e \u003cp\u003eThe key difference lies in the clinical focus. In prostate cancer, the dominant trajectory is \u0026ldquo;diagnostic\u0026rdquo;: around the PSA grey zone and high-risk stratification, urinary exosome and cfDNA panels are used to improve diagnostic accuracy and reduce unnecessary biopsies(34,37,39). In bladder cancer, the trajectory is primarily \u0026ldquo;monitoring\u0026rdquo;: in the context of long-term follow-up and recurrence risk assessment, dynamic changes in urinary ctDNA/cfDNA and tracking of tumor-specific mutations are placed at the center, with the aim of alleviating the burden and cost associated with frequent cystoscopy(16,33,41). Representative studies from the first phase laid the groundwork for these two routes.\u003c/p\u003e \u003cp\u003eIn the second phase, platform-oriented work on exosome/EV standardization, the uEV consensus and multi-fluid ctDNA frameworks further incorporated these diagnostic and monitoring trajectories into a coherent, scalable system for urine-based liquid biopsy(47\u0026ndash;51).\u003c/p\u003e \u003cp\u003eTaken together, the two-stage evolution and the \u0026ldquo;diagnostic/monitoring\u0026rdquo; twin pathways jointly define the dominant technological landscape of urine-based liquid biopsy in urologic cancers.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Global knowledge production and dissemination: countries, institutions, authors and journals\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Country- and institution-level patterns: Western leadership and East Asian catch-up\u003c/h2\u003e \u003cp\u003eCountry- and institution-level analyses revealed a knowledge-production landscape characterized by Western leadership and East Asian catch-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The United States occupied a central position in both publication output and citation impact. Although individual European countries produced fewer papers than the United States and China, their combined publication volume, the number of highly cited institutions and the density of inter-institutional collaboration networks together constitute a major knowledge block in this field. Several institutions in countries such as the Netherlands and Norway\u0026mdash;for example, the Amsterdam universities and the University of Oslo\u0026mdash;were prominently represented among highly cited centers, suggesting that high-impact hubs do not necessarily depend on very large country-level output. When average publication year by country is taken into account, many European and North American countries appear to have entered this field earlier and show clear first-mover advantages in long-term accumulation and sustained contributions.\u003c/p\u003e \u003cp\u003eChina and Japan represent emerging East Asian contributors, with more recent average publication years. China is currently the most productive country in terms of publication count, yet its average citations per paper rank relatively low among the top 10 countries, and only one institution enters the list of highly cited centers. This pattern points to a certain misalignment between overall research volume and academic impact. Japan shows a broadly similar profile to China in terms of average citations, institutional output and H-index.\u003c/p\u003e \u003cp\u003eOverall, the field is still dominated by Western centers, whereas East Asia is catching up in volume but lags behind in impact, underscoring the need to improve research quality, foster cross-regional collaboration and build high-impact hubs in East Asian settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Prolific authors and highly co-cited authors: frontline teams and evidentiary hubs\u003c/h2\u003e \u003cp\u003eAuthor-level analyses revealed a pattern of multiple teams advancing in parallel, without a single dominant group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Italian team represented by Ferro M and the Japanese team represented by Fujita K are typical examples of highly productive author clusters. The former has published several reviews and systematic analyses that map the application pathways of urinary biomarkers in urologic cancers(52\u0026ndash;54), whereas the latter has conducted a series of original studies focusing on hotspot mutations such as TERT promoter and FGFR3, as well as urinary EVs, thereby establishing a relatively coherent research line spanning urinary cfDNA and urinary EVs(55\u0026ndash;57). Overall, prolific authors mainly represent \u0026ldquo;frontline teams\u0026rdquo; that continually expand the evidence base and enrich clinical application scenarios in recent years.\u003c/p\u003e \u003cp\u003eBy contrast, the co-cited author network (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) highlights shared evidentiary sources at the levels of methodology and clinical pathways. Representative works by highly co-cited authors such as McKiernan J, Christensen E and Bryzgunova OE are repeatedly cited together across multiple related papers, and are mainly concentrated in three areas: urinary exosome-based gene expression assays and risk-prediction models in prostate cancer; cohort studies of urinary and plasma cfDNA/ctDNA in bladder cancer; and basic and methodological studies on extracellular nucleic acids and EVs in urine17,59,60. These publications form tightly connected clusters of highly co-cited references and also account for a substantial portion of burst references, underscoring their role as \u0026ldquo;evidentiary hubs\u0026rdquo; within the knowledge structure of this field.\u003c/p\u003e \u003cp\u003eIn our dataset, prolific authors and highly co-cited authors showed minimal overlap. Prolific authors largely represent frontline teams that continuously generate new data, whereas highly co-cited authors act as evidentiary hubs supplying foundational clinical and methodological references; together, these two groups sustain the development of the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Journal ecosystem and knowledge flows: from publication platforms to cited cores\u003c/h2\u003e \u003cp\u003eAt the journal level, oncology and molecular medicine journals such as Cancers, International Journal of Molecular Sciences and Frontiers in Oncology are the most common publication platforms for studies on urine-based liquid biopsy in urologic cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, Q1, high-impact urology and oncology journals\u0026mdash;including European Urology, Journal of Urology and Clinical Cancer Research\u0026mdash;occupy central positions in the co-cited journal network and serve as major knowledge sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Notably, Cancers, Journal of Extracellular Vesicles and International Journal of Molecular Sciences rank highly both among the most productive journals and among the most frequently co-cited journals, indicating high visibility and influence at the levels of \u0026ldquo;publishing outlet\u0026rdquo; and \u0026ldquo;cited core\u0026rdquo; alike.\u003c/p\u003e \u003cp\u003eThe dual-map overlay of journals (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) further illustrates, from a disciplinary perspective, how urine-based liquid biopsy is situated at the interface between clinical demand and basic/translational support. On one side, clinical disciplines such as urologic surgery and oncology articulate needs for improved diagnostic and surveillance tools; on the other, basic and translational fields including molecular biology and genetics provide methodological and mechanistic evidence to meet these needs. Overall, this pattern reflects a typical translational interface in which basic molecular and genetic research underpins clinical problem-solving and aligns with the stage-wise evolution from feasibility testing toward standardized implementation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Research hotspots and frontiers: keyword networks anchored to clinical scenarios\u003c/h2\u003e \u003cp\u003eAs shown in Section 3.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), terms such as \u0026ldquo;liquid biopsy\u0026rdquo;, \u0026ldquo;biomarkers\u0026rdquo;, \u0026ldquo;prostate cancer\u0026rdquo;, \u0026ldquo;bladder cancer\u0026rdquo; and \u0026ldquo;extracellular vesicles\u0026rdquo; occupy central positions in the keyword network, indicating that the field has evolved into a multicentric structure organized around the triad of \u0026ldquo;liquid biopsy\u0026ndash;biomarkers\u0026ndash;specific clinical scenarios\u0026rdquo;. By combining cluster colors with temporal distribution, the current hotspots can be summarized, from a clinical perspective, into three main disease scenarios\u0026mdash;biopsy decision-making and high-risk stratification in prostate cancer, recurrence surveillance and cystoscopy de-escalation in bladder cancer and upper tract urothelial carcinoma (UTUC), and early diagnosis and prognostic assessment in RCC and other less common, evidence-sparse urologic cancers\u0026mdash;alongside a methodological theme centered on cfDNA/ctDNA, circulating tumor cells and extracellular vesicles that can be repurposed across multiple tumor types. Different diseases exhibit distinct \u0026ldquo;preferences\u0026rdquo; in analyte selection and clinical questions, whereas cross-tumor ctDNA/cfDNA and CTC frameworks provide the shared technical backbone underpinning these diverse strategies.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Prostate cancer: from the PSA grey zone to urinary multi-marker panels\u003c/h2\u003e \u003cp\u003eWithin the red cluster, prostate cancer\u0026ndash;related terms are highly concentrated, reflecting that one of the main application scenarios is biopsy indication and high-risk patient identification in men within the PSA grey zone, where the limited specificity of serum PSA drives a need to better detect clinically significant disease while avoiding unnecessary biopsies(3,58).\u003c/p\u003e \u003cp\u003eFrom the perspective of analytes, recent studies have shown a clear preference for urinary exosomes and multigene urinary RNA panels. Early work mainly focused on single markers\u0026mdash;such as urinary cfDNA concentration and integrity, or individual mRNAs and proteins like PCA3\u0026mdash;to verify whether tumor-related signals could be stably detected in urine(59\u0026ndash;61). Subsequently, attention shifted from single indicators to integrative models that combine \u0026ldquo;urinary molecular panels plus clinical variables\u0026rdquo;. On the one hand, a three-gene expression signature (ERG, PCA3 and SPDEF) carried by urinary exosomes was validated in multicenter prospective cohorts and shown to improve the detection of Grade Group\u0026thinsp;\u0026ge;\u0026thinsp;2 (Gleason score\u0026thinsp;\u0026ge;\u0026thinsp;7) clinically significant prostate cancer among men undergoing initial biopsy with PSA levels of 2\u0026ndash;10 ng/mL, while reducing unnecessary biopsies under comparable safety thresholds(20,34). On the other hand, urinary molecular panels\u0026mdash;such as the Mi-Prostate Score combining TMPRSS2:ERG and PCA3, the SelectMDx assay based on HOXC6 and DLX1 mRNA, and urinary exosomal miRNA signatures\u0026mdash;are commonly entered into logistic regression models together with PSA, digital rectal examination (DRE) findings and prior biopsy history to predict biopsy positivity and the risk of high-grade prostate cancer(35,39,40).\u003c/p\u003e \u003cp\u003eBuilding on these models, several urinary DNA methylation signatures have further extended study endpoints to include postoperative Gleason upgrading, pathological upstaging and composite adverse pathological features. For example, Bakavicius and colleagues developed a risk score based on methylation of RARB, RASSF1 and GSTP1 in urine combined with PSA, which can predict grade and stage migration as well as aggressive disease in radical prostatectomy specimens(62,63). Collectively, these studies converge on a common clinical objective: to refine biopsy decision-making in men with PSA in the grey zone through urinary biomarkers, while simultaneously providing complementary information for postoperative risk stratification and individualized management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Bladder cancer and urothelial carcinoma: recurrence surveillance and de-escalation of cystoscopy\u003c/h2\u003e \u003cp\u003eWithin the blue cluster, terms centered on bladder cancer are tightly interconnected, corresponding to a clinical scenario dominated by bladder cancer and upper tract urothelial carcinoma (UTUC) in which the primary goals are recurrence surveillance and de-escalation of cystoscopy. Current EAU and other guidelines recommend long-term, intensive cystoscopic follow-up for patients with high- and very high-risk non-muscle-invasive bladder cancer, resulting in frequent, invasive and costly examinations, while conventional urine cytology shows limited sensitivity for low-grade lesions(4,64). This mismatch between high surveillance burden and low sensitivity has directly driven the rapid development of urine-based liquid biopsy in this field.\u003c/p\u003e \u003cp\u003eAgainst this background, a large body of work has focused on driver mutations and multi-locus DNA methylation panels carried by urinary cfDNA/ctDNA\u0026mdash;particularly alterations in FGFR3, PIK3CA and the TERT promoter\u0026mdash;to non-invasively detect residual disease and predict the risk of recurrence and progression(33,65\u0026ndash;67). In parallel, traditional urine cytology has been continuously upgraded through cell-enrichment platforms and immunocytological markers (such as ImmunoCyt/uCyt\u0026thinsp;+\u0026thinsp;and UroVysion FISH). These advances have improved sensitivity for small-volume or low-grade disease, and, when combined with multigene expression assays and machine learning models, have enabled the integration of molecular markers, cytology and clinical variables into composite tools for recurrence-risk assessment(4,68\u0026ndash;71).\u003c/p\u003e \u003cp\u003eOverall, mutation and methylation assays, refined urine cytology platforms and integrated risk models serve a common clinical objective: to safely reduce the frequency of surveillance cystoscopy by leveraging urinary monitoring strategies with high negative predictive value, while maintaining vigilance against high-risk recurrence and progression events. In addition, these approaches are beginning to be explored in UTUC and related settings, extending the reach of urine-based liquid biopsy beyond bladder cancer alone(64,67).\u003c/p\u003e \u003cp\u003e4.3.3 Renal cell carcinoma and other urologic tumors: an emerging frontier with weak evidence but strong clinical need\u003c/p\u003e \u003cp\u003eThe yellow cluster is smaller in size but shows dense internal connections, indicating that renal cell carcinoma (RCC) constitutes a relatively independent yet still early-stage research theme within the overall network. Unlike bladder cancer and urothelial carcinoma, most RCC lesions arise in the renal parenchyma, and tumor cells are not continuously and directly exposed to the urinary tract. Strategies that rely on exfoliated cells or bulk cfDNA levels therefore have limited sensitivity, a limitation repeatedly emphasized in systematic reviews of liquid biopsy and urinary biomarkers in RCC, helping explain the current paucity of urine-based evidence and slow clinical translation(24,72).\u003c/p\u003e \u003cp\u003eExisting studies show a clear exploratory pattern in terms of analytes, with a focus on epigenetic alterations, extracellular vesicles and metabolomics. On the epigenetic front, cfDNA methylation markers have started to enter the field. Studies using cfMeDIP-seq have shown that joint analysis of cfDNA methylation profiles in plasma and urine can achieve good discrimination between RCC patients at various stages and controls, and may hold promise for early disease detection76. Subsequent systematic reviews summarizing RCC methylation targets across multiple body fluids, including urine, have pointed out that although some loci and panels demonstrate encouraging diagnostic performance, most work remains at the discovery and preliminary validation stages(72).\u003c/p\u003e \u003cp\u003eIn parallel, nucleic acid\u0026ndash;based biomarkers carried by urinary exosomes and free miRNAs have gradually emerged as another major line in RCC urine-based liquid biopsy. Several studies have used sequencing or microarray approaches to identify differentially expressed miRNAs in urine\u0026mdash;particularly in urinary exosomes\u0026mdash;and validated their diagnostic value for clear-cell RCC, as well as their associations with tumor stage, volume and high-risk pathological features. Some of these signatures have been combined into multi-miRNA urinary scores to estimate the malignant potential of small renal masses and the likelihood of adverse outcomes(18,73,74).\u003c/p\u003e \u003cp\u003eMoreover, urinary metabolomic and proteomic studies have revealed RCC-specific metabolic reprogramming and microenvironmental changes. Multiple untargeted or semi-targeted metabolomic analyses have identified candidate metabolite combinations in the urine of patients with clear-cell RCC that are related to energy metabolism, the tricarboxylic acid cycle and fatty acid metabolism. Subsequent work has used these metabolites to construct multi-marker panels with relatively high AUCs and has completed small-scale external validation(75,76).\u003c/p\u003e \u003cp\u003eTaken together, urinary diagnostic biomarkers for RCC now span metabolites, proteins, miRNAs and DNA methylation, encompassing hundreds of single markers and nearly 30 multi-marker panels(24,77). Overall, however, most studies remain exploratory or single-center with limited sample sizes and are not yet supported by multicenter, large-scale prospective cohorts or standardized workflows. RCC and related entities can therefore be regarded as an \u0026ldquo;emerging frontier\u0026rdquo; characterized by weak current evidence but a clearly defined and pressing clinical need.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Cross-tumor liquid biopsy methodologies and multi-analyte signatures\u003c/h2\u003e \u003cp\u003eWithin the green cluster, \u0026ldquo;liquid biopsy\u0026rdquo; lies at the center and is connected to multiple assay terms and solid tumor types, representing a set of cross-tumor, shared methodological themes in liquid biopsy. Multicancer clinical studies and highly cited reviews have shown that platforms based on plasma ctDNA/cfDNA and circulating tumor cells have progressively established an evidence base across a range of solid tumors for supporting early detection of high-risk or localized disease, assessing tumor burden, monitoring treatment response and MRD, and predicting recurrence risk(6\u0026ndash;8,78). These platforms provide reusable technical pathways and trial-design paradigms for different cancer types, including urologic cancers.\u003c/p\u003e \u003cp\u003eBuilding on existing ctDNA/cfDNA and CTC platforms, a series of cross-tumor methodological studies in recent years have begun to integrate additional molecular layers into unified analytical frameworks. These include the cargo of exosomes and other extracellular vesicles, DNA methylation and fragmentomics features in liquid samples, and blood- and urine-based metabolomic profiles, which are then jointly modeled using machine-learning and other statistical learning algorithms(79\u0026ndash;81).\u003c/p\u003e \u003cp\u003eIn the specific context of urine-based liquid biopsy for urologic tumors, this cross-tumor methodological trajectory is reflected in several ways. First, analytes such as urinary cfDNA/ctDNA, exosomal RNA/proteins, exfoliated cells and metabolites are combined in a tailored manner according to the clinical scenario. Second, high-dimensional omics data from urine and paired blood samples are subjected to feature-selection procedures and machine-learning algorithms to derive diagnostic models or recurrence/progression risk scores. Third, for tumor types such as bladder and prostate cancer, multi-gene or multi-marker urinary panels are being advanced into commercial assays and multicenter prospective validation studies(52,64,71,82). The relatively late average publication years of terms such as \u0026ldquo;extracellular vesicles\u0026rdquo;, \u0026ldquo;metabolomics\u0026rdquo; and \u0026ldquo;machine learning\u0026rdquo; in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB further support the notion that this methodological pathway is in a phase of rapid expansion.\u003c/p\u003e \u003cp\u003eTaken together, these cross-tumor methodological developments and disease-specific applications indicate that urine-based liquid biopsy in urologic cancers is evolving within an intertwined \u0026ldquo;horizontal\u0026ndash;vertical\u0026rdquo; framework: horizontally through reusable platforms based on ctDNA/cfDNA, CTCs, extracellular vesicles, DNA methylation and metabolites, and vertically through analyte and biomarker combinations tailored to concrete clinical scenarios such as PSA grey-zone biopsy decisions, bladder/urothelial cancer surveillance and RCC risk stratification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Advantages of urine-based liquid biopsy and of the present study\u003c/h2\u003e \u003cp\u003eCompared with blood, the practical advantages of urine \u0026mdash; non-invasive, easily repeatable sampling and direct anatomical proximity to the urothelial tract \u0026mdash; become particularly salient in light of our findings that most current applications cluster around biopsy decision-making in the PSA grey zone and long-term surveillance in bladder cancer(83,84). As an organ-specific body fluid, urine can mirror systemic circulation while also reflecting local mucosal and tumor microenvironmental changes, which may underlie its emerging role in early lesion detection and assessment of local disease burden in urologic cancers(14,85).\u003c/p\u003e \u003cp\u003eIn this study, we analyzed 439 publications on urine-based liquid biopsy in urologic cancers indexed in WoSCC between 2015 and 2025. By integrating CiteSpace, VOSviewer and bibliometrix, we systematically delineated the evidentiary structure and evolutionary trajectory of this field from multiple perspectives, including publication trends, country\u0026ndash;institution\u0026ndash;author\u0026ndash;journal networks and keyword evolution. On this basis, we proposed a two-stage framework of \u0026ldquo;foundational evidence and methodological expansion\u0026rdquo; followed by \u0026ldquo;standardization and platform integration\u0026rdquo;, and aligned this framework with concrete clinical scenarios such as prostate cancer diagnosis, bladder cancer surveillance and RCC and other less common urologic cancers. This approach provides a reusable temporal structure and an evidence map that may inform future reviews, guideline development and clinical study design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Limitations of bibliometric research\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, our data source was restricted to English-language publications indexed in WoSCC. We did not include other databases or non-English literature, and by omitting local-language journals and regional databases in high-output countries such as China, we may have underestimated the scientific contributions from certain regions. Second, our search strategy was limited to TS = (\u0026ldquo;urine\u0026rdquo; OR \u0026ldquo;urinary\u0026rdquo;) AND (\u0026ldquo;liquid biopsy\u0026rdquo; OR \u0026ldquo;liquid biopsies\u0026rdquo;), which helped to capture studies explicitly framed within the \u0026ldquo;liquid biopsy\u0026rdquo; concept but may have missed earlier or parallel work on urinary biomarkers that did not use this terminology, potentially introducing bias into the thematic structure of the early period.\u003c/p\u003e \u003cp\u003eThird, we confined the time window to 2015 to 2025 and excluded Early Access articles and conference papers. While this choice helped us focus on formally published, relatively mature journal evidence, it may also have led to underestimation of very recent frontier studies that have not yet had time to accumulate citations, and to a truncation effect on the performance of 2024\u0026ndash;2025 publications in highly cited and burst-reference analyses. Moreover, bibliometric tools are inherently limited as macro-level instruments and cannot substitute for systematic reviews and evidence syntheses addressing specific clinical questions; keyword normalization, threshold settings and clustering algorithms may also introduce additional methodological bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Future research directions and priorities for clinical translation\u003c/h2\u003e \u003cp\u003eTaken together with the evidentiary landscape described above, these limitations suggest several directions for future work.\u003c/p\u003e \u003cp\u003eFirst, in prostate and bladder cancer, more prospective multicenter cohorts and randomized studies are needed to embed urinary biomarkers into real-world clinical pathways\u0026mdash;such as biopsy decision-making in the PSA grey zone and adjustment of cystoscopy follow-up intervals\u0026mdash;and to rigorously evaluate their incremental value over traditional indicators, including health-economic outcomes and patient-centered endpoints.\u003c/p\u003e \u003cp\u003eSecond, evidence gaps for RCC, UTUC and other rare urologic tumors need to be addressed. Future research should, while fully acknowledging the biological constraints of urine as a sample type in these settings, focus on the roles of cfDNA methylation profiling, multi-omics signatures of urinary extracellular vesicles and metabolomic signatures in postoperative surveillance and treatment response assessment, and increase the representation of populations from East Asia and low- and middle-income regions(86).\u003c/p\u003e \u003cp\u003eThird, further efforts are required to advance standardization and platform-level implementation. Building on existing consensus documents such as those from ISEV, it will be important to refine protocols for sample collection, pre-analytical handling, assay platforms and reporting standards. For models that rely on machine learning, transparent and reproducible feature-selection procedures and robust external validation should be emphasized to enhance clinical interpretability and regulatory acceptability(87).\u003c/p\u003e \u003cp\u003eFourth, cross-disciplinary and cross-regional collaboration should be strengthened. Joint efforts involving urology, oncology, clinical laboratory medicine, molecular biology, data science and health economics will be essential to promote sample and data sharing, expand study scale and improve the generalizability of findings.\u003c/p\u003e \u003cp\u003eOverall, urine-based liquid biopsy for urologic cancers has largely completed the transition from \u0026ldquo;molecular-level feasibility testing\u0026rdquo; to \u0026ldquo;disease-specific model construction\u0026rdquo; and is now at a critical juncture on the path toward \u0026ldquo;standardized implementation and integration into clinical pathways\u0026rdquo;. Whether the field can successfully move from a research hotspot into guideline recommendations and routine clinical practice will depend on its ability to strike a balance between high-quality evidence, standardized workflows and affordable, scalable platforms.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eDrawing on 439 publications indexed in WoSCC database between 2015 and 2025, this study mapped the research landscape and evidentiary evolution of urine-based liquid biopsy in urologic cancers. Over the past decade, the field has progressed from molecular-level feasibility testing and early disease-specific model building toward a second phase characterized by standardization efforts and integration into broader, multi-fluid liquid biopsy platforms. Within this trajectory, complementary diagnostic and monitoring pathways have emerged in prostate and bladder cancer, both centered on urinary multi-marker panels\u0026mdash;one aiming to refine biopsy decision-making and high-risk stratification, the other to support recurrence surveillance and potential de-escalation of cystoscopy\u0026mdash;while exploratory evidence for non-invasive assessment is gradually accumulating in RCC and other less common urologic cancers.\u003c/p\u003e \u003cp\u003eKeyword networks and temporal patterns further indicate that current work is concentrated around PSA grey-zone biopsy decisions, bladder/urothelial cancer surveillance and cystoscopy de-escalation, and early diagnosis and prognostic stratification in RCC, underpinned by a cross-tumor, platform-based diagnostic route driven by multi-omics integration and machine-learning approaches. At the same time, our analysis highlights persistent gaps, including a shortage of high-quality prospective and real-world studies, limited representation of rare diseases and populations from low- and middle-income regions, and incomplete standardization of workflows and cost-effective testing platforms. Overall, urine-based liquid biopsy in urologic cancers combines non-invasiveness, repeatability and close anatomical proximity to the tumor burden, and is now entering a critical phase focused on how to implement standardized, interpretable and affordable assays within routine care. Our stage-wise framework and visual overview may help align future study design and standard-setting efforts with concrete clinical scenarios, thereby facilitating the incorporation of urine-based liquid biopsy into guidelines and reimbursement systems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage citations per publication\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecfDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCell-free DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ectDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCirculating tumor DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCirculating tumor cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigital rectal examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEAU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Association of Urology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Society for Extracellular Vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinimal residual disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate-specific antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenal cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003euEVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrinary extracellular vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUpper tract urothelial carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWoSCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeb of Science Core Collection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe bibliometric dataset analysed during the current study was retrieved from the Web of Science Core Collection (WoSCC) using the search strategy described in the Methods section. The original WoSCC records are subject to database licensing restrictions and are therefore not publicly available. The processed data files and analysis scripts that support the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Gansu Provincial Natural Science Foundation (22JR11RA069 and 25JRRA584) and by the Gansu Province Health Commission Major Scientific Research Project for Scientific and Technological Innovation in the Health Industry (GSWSQNPY2025-15).\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eHL, YF and JY conceived and designed the study and developed the search strategy and analytical framework. HL, YF and WL performed the literature search, data retrieval and initial screening. JB, LD and WS conducted data extraction, cleaning and bibliometric analyses, and prepared the tables and figures. HL drafted the first version of the manuscript, and YF, JY, WL, JB, LD and WS contributed to data interpretation and critical revision of subsequent drafts. SC and XL supervised the overall project, provided methodological and clinical guidance, critically revised the manuscript for important intellectual content, and approved the final version of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eLeung DKW, Wong CHM, Ko ICH, Siu BWH, Liu AQY, Meng HYH, et al. 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J Extracell Vesicles. 2018;7(1):1535750.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urine, Liquid biopsy, Urologic neoplasms, Bibliometrics, Circulating tumor DNA, Extracellular vesicles, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-8287024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8287024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUrologic cancers, including prostate cancer, bladder cancer, upper tract urothelial carcinoma and renal cell carcinoma, are well suited to urine-based liquid biopsy because tumors lie close to the urinary tract and often require repeated, minimally invasive monitoring. Over the past decade, multiple urinary analytes and platforms have been explored, but an integrated view of their temporal evolution, global knowledge production and disease-specific application pathways is lacking.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePublications on urine-based liquid biopsy in urologic cancers were retrieved from the Web of Science Core Collection (2015\u0026ndash;2025); 439 articles and reviews met predefined criteria. CiteSpace, VOSviewer and the bibliometrix R package were used to analyze publication trends, country\u0026ndash;institution\u0026ndash;author\u0026ndash;journal networks, highly cited and burst references, and keyword co-occurrence and evolution. Temporal and thematic patterns were synthesized into a stage-wise framework linking research hotspots with core clinical scenarios.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGlobal output increased steadily from 2016, with marked acceleration after 2021. Co-citation and burst analyses supported two phases: an initial \u0026ldquo;foundational evidence and methodological expansion\u0026rdquo; phase dominated by circulating tumor DNA and early urine-focused work, and a later \u0026ldquo;standardization and platform integration\u0026rdquo; phase characterized by guideline-oriented reviews, liquid biopsy frameworks and consensus documents. The United States and Europe formed the main evidentiary hubs, with East Asian institutions showing rapid recent growth. Keyword analyses revealed two dominant trajectories: diagnostic pathways in prostate cancer, centered on urinary RNA signatures, exosome-based multigene panels and composite risk models in the prostate-specific antigen grey zone; and monitoring pathways in bladder cancer and urothelial carcinoma, focused on urinary DNA methylation and mutation panels and upgraded cytology for recurrence surveillance and cystoscopy de-escalation. Renal cell carcinoma and other less common urologic cancers remained evidence-sparse but clinically important frontiers, with exploratory work on urinary methylation, extracellular vesicles and metabolomics.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUrine-based liquid biopsy in urologic cancers has progressed from feasibility testing to disease-specific model construction and early standardized implementation. The two-stage pattern and diagnostic/monitoring twin pathways identified here, together with key geographical and methodological gaps, provide a framework for future multicenter cohorts, standardization efforts and clinically oriented trials to embed urinary biomarkers into routine decision-making.\u003c/p\u003e","manuscriptTitle":"From feasibility to clinical pathways: a bibliometric and knowledge-mapping analysis of urine-based liquid biopsy in urologic cancers (2015–2025)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 14:33:28","doi":"10.21203/rs.3.rs-8287024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-12T05:48:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T18:54:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-06T09:02:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-12-05T06:00:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"002ab4b7-0608-4911-9add-f4d781145100","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:21:57+00:00","versionOfRecord":{"articleIdentity":"rs-8287024","link":"https://doi.org/10.1186/s12967-026-08043-y","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-03-25 16:13:17","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2025-12-16 14:33:28","video":"","vorDoi":"10.1186/s12967-026-08043-y","vorDoiUrl":"https://doi.org/10.1186/s12967-026-08043-y","workflowStages":[]},"version":"v1","identity":"rs-8287024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8287024","identity":"rs-8287024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}