This bibliometric study was mainly based on the Web of Science (WoS) database. Search term as follows was applied to collect data comprehensively from 15 May 2015 to 15 May 2025: ((TS = ((renal OR kidney) NEAR/2 (cancer* OR tumor* OR tumor* OR neoplasm* OR carcinoma* OR oncology))) AND ((TS = metabolism) OR (TS = metabolic) OR (TS = metabolome) OR (TS = metabolite) OR (TS = metabonomics) OR (TS = metabolomics))). A total of 3173 publications in Web of Science (Core Collection) were obtained and 3010 of them, comprising articles and reviews, were ultimately included in our study.

All data were integrated in one TXT file, and were uploaded to Biblioshiny, a web application based on Bibliometrix package (20) in R version 4.3.2 for further analysis. After systematically analyzing the data from the perspective of authors, countries, institutions, journals, publications, keywords, etc., a relatively comprehensive understanding of this field could be obtained. Here, we adopted number of publications and citations to provide a brief overview of this field. And H-index (21), which means H of one’s publications were cited at least H times, were used as a criterion for assessing both the quantity and quality of an author or a journal. The M-index of an author equals the H-index/the years since first publication (22). Publications of an author is ranked in decreased order of citations, and G-index is the largest sequence number (denoted as G) of a publication that has at least G² citations (23). Bradford’s law (24) divides journals into several zones in a ratio of 1: k: k2: … (k > 1), with each of the zones accounting for equal publications in a certain field. The first zone with the least journals is perceived as core collections. Lotka’s law (25) states that the number of authors with one publication is n² times as many as those with n publications. In co-citation network, the weight of lines between two articles indicates the frequency of which they are cited simultaneously. Similarly, keyword co-occurrence measures the correlation between keywords occurred in the identical publication. The topics could be classified into four groups by the thematic map with the “Density” axis representing the development degree and the “Centrality” axis signifying the relevance degree of these themes or fields: “Niche Themes” might be some small, specialized but well-developed territories with high “Density” but low “Centrality”. “Motor Themes” might be some core areas that received continuous attention in a field with both high “Density” and “Centrality”. “Basic Themes” might not be the hot spots at present, but served as corner stones in a field with high “Centrality” but low “Density”. “Emerging or Declining Themes” might refer to new ideas that existed for a relatively short period of time.

The parameters used during the analytical process were as follows:

The collaboration network between institutions adopted Walktrap algorithm with 30 nodes. The collaboration network between authors adopted Walktrap algorithm with 30 nodes. The co-citation network between publications adopted Walktrap algorithm with 50 nodes. The keywords co-occurrence network adopted Louvein algorithm with 50 nodes. The thematic evolution plot adopted Walktrap algorithm with 3 cutting points, namely 2019, 2021 and 2023. The remaining settings utilized custom settings, or could be intuitively seen from the plot.

The complete retrieval and analytic process was shown in Figure 1 . From 15 May 2015 to 15 May 2025, 3010 publications (reviews or articles) in Web of Science (core collection) were written by 16884 authors with an annual growth rate of 8.86%, published on 1002 journals. Annual scientific production could be utilized to partially measure the popularity of a specific field. As depicted in Supplementary Figure S1 , the number of publications and citations manifested steady growth from 2015 to 2024, indicating that the field of “metabolism in RCC” is gaining more and more attention. Although the number of publications and citations seemed to drop in 2025, this might be attributed to the statistics ended at 15 May 2025, which only accounted for less than a half of the whole year.

A total of 66 countries contributed to this field. USA, China, ITALY were the top 3 among these countries according to total citations. The USA dominated the list with a total citation per item of 44.1, while China ranked second with a total citation per item of 17.0 ( Table 1 ). Countries’ number of publications could be intuitively observed in Figure 2A , where the darkness of the country indicated the quantity of publications. China was the most productive country and USA, China and ITALY were also the top 3 countries with highest MCP (multiple countries publication), indicating that they were the countries with the most intention to cooperate ( Figure 2B ).

As of institutions, Figure 2C showed the top 20 most prolific institutes, among which Harvard University in USA dominated the list with a production of 257 publications. Fudan University, Shanghai Jiao Tong University and Huazhong University of Science and Technology in China ranked fourth, sixth and seventh, respectively. In Figure 2D , the institutions could be classified into different groups, and each group had close relationships or common features, like publications they have contributed to, etc.

Up to 16884 authors contributed to this field. The top 20 most productive authors were listed in Figure 3A , where WANG Y and WANG X dominated the first and second place with 46 and 33 publications, surpassing other authors. As was shown in Figure 3B , the H-index of the top 20 authors varied from 12 to 16, with WANG Y still ranked first and LINEHAN WM ranked second (H-index = 15). In terms of local citations, HAKIMI AA topped the list with 648 local citations, closely followed by HSIEH JJ ( Supplementary Figure S2A ). LINEHAN WM dominated the list of G-index ( Supplementary Figure S2B ), but the list of M-index was dominated by “LAIMON YN” ( Supplementary Figure S2C ). Figure 3C presented the timelines of authors’ related researches in this field. “WANG Y” published 46 publications with an H-index of 16, indicating that “WANG Y” was the most productive and prominent author. The distribution of different authors with different number of publications was roughly in accordance with Lotka’s law ( Supplementary Figure S2D ). The authors could also be divided into different groups ( Figure 3D ). The top 20 most productive authors were also listed in Table 2 with their number of publications, total citations, H-index, G-index and M-index.

Supplementary Figure S3A demonstrated the top 20 journals extracted from 1002 journals according to number of publications, Local citations, total citations and H-index were also adopted to evaluate journals’ impact, as is shown in Supplementary Figures S3B–D . Supplementary Figure S3E displayed 41 core journals according to the Bradford’s law in the field of metabolism in RCC.

Detailed information of the top 20 journals with highest number of publications, including their impact factor, Quartile in category, H-index and total citations, was shown in Supplementary Table S1 .

Figure 4A demonstrated the top 20 publications based on total citations, Obesity and inflammation: the linking mechanism and the complications (total citations = 1240), Epidemiology of Renal Cell Carcinoma (total citations = 1061), Shared Risk Factors in Cardiovascular Disease and Cancer (total citations = 1036) had the most total citations, surpassing other publications whose total citations were all below 1000. Figure 4B presented the top 20 publications with the largest local citations, An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma with local citations of 244 topped the list, while local citations of other publications were all lower than 200. Co-cited publications were put in the same cluster with identical color, and different clusters of publications might indicate different hotspots in this field ( Figure 4C ).

As for references, Supplementary Table S2 illustrated the top 20 references out of more than 144,000 references in total according to local citations. Comprehensive molecular characterization of clear cell renal cell carcinoma, An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma, Hallmarks of cancer: the next generation were the top 3 in the list, indicating their fundamental impact in this fields. These publications could probably be regarded as the cornerstone of this fields.

Keywords could be regarded as the essence of publications, which concisely summarized major topics and main information of a publications. A total of more than 6000 keywords were extracted from our retrieval data. “Cancer”, “expression”, “metabolism”, “renal cell carcinoma” were the top 4 keywords with frequencies over 400, while those of other keywords were lower than 200, which could also be intuitively observed in Supplementary Figure S4A .

In keyword co-occurrence network, as is shown in Figure 5A , the red cluster was characterized by terms “metabolism”, “expression”, “cancer”, “activation”, “hypoxia”, “mutations”, indicating metabolic alterations in RCC and their influences on RCC cells. The purple cluster focused possibly on the correlations between metabolic syndrome and RCC, as terms like “metabolic syndrome”, “body-mass index”, “obesity”, “kidney cancer” could be seen in this cluster. The green cluster mainly reflected therapy techniques and potential mechanisms in this field, as terms like “sunitinib”, “therapy” could be observed. The blue cluster might focus on some shared mechanisms among RCC and other cancer types. We would make further explorations in the discussion part later about the red and purple clusters.

Trend shifts in a particular field could be succinctly presented by keywords dynamics. As can be observed in Figure 5B , “metabolic syndrome”, as well as its related term “body-mass index”, were emerging keywords recent years from 2020, indicating metabolic syndrome and RCC might be a hotspot of this field recent years. Development of trend topics overtime was shown in Supplementary Figure S4B . The classified topics were displayed in Supplementary Figure S4C by a thematic map.

The annual publications and citation in this field increased steadily from 2015 to 2024 with a drop in 2025, possibly for the statistic end point of 15 May 2025. As for countries, USA, China and UK were in the top 3 list according to total citations, and they were also the countries with most cooperation as indicated by their high MCP. For institutions’ productivity, Harvard University in USA dominated the list. Fudan University, Shanghai Jiao Tong University and Huazhong University of Science and Technology in China ranked fourth, sixth and seventh, respectively. As for authors, WANG Y dominated the list of productivity and H-index, while HAKIMI AA, LINEHAN WM, LAIMON YN ranked the first in terms of local citations, G-index, M-index, respectively. Journals were analyzed based on number of publications, total citations, local citations and h-index. Core journals were identified with Bradford’s law. Publications on these journals would be conductive to keep pace with the latest development in this field, as well as obtain basic knowledge in this field. After analyzing the keyword co-occurrence network, combined with targeted literature reading, we identified several major points and hotspots over the past decade in the field of metabolism in RCC, namely the metabolic alterations in RCC, the metabolic syndrome and RCC, the microbiome and RCC.

Metabolic syndrome was defined by WHO as a pathological condition consisting abdominal obesity, insulin resistance, hypertension, and hyperlipidemia (74). A growing body of evidence suggested a close association between metabolic syndrome and RCC. Metabolic syndrome has already been identified as an independent prognostic factor in localized ccRCC patients (75) back to 2019, and a meta-analysis (76) composed of six studies in 2025 reconfirmed its independent correlation with RCC. In addition, some specific diseases related to metabolic syndrome, including diabetes, obesity and hypertension was discovered to be correlated with RCC as well. Diabetes was identified as an independent risk factor for TNM staging in ccRCC patients (77). Obesity was a well-recognized risk factor for RCC, and BMI was positively correlated with the incidence (78, 79) and pathological upstaging (80) of RCC. Hypertension was identified as an independent prognostic factor for RCC survival (81), and a positive link between both diastolic blood pressure and systolic blood pressure and RCC risk (82) was discovered.

Mechanically, hyperinsulinemia was thought to be the shared potential mechanism between diabetes, obesity and RCC. Notably, hyperinsulinemia was not only seen in diabetes, but also existed in in obesity state (83). Insulin can enhance IGF-1 synthesis and activation. Both insulin and IGF-1 have the effects of fostering cell proliferation and restraining apoptosis (84). Upon activation of insulin receptors (INSR) and IGF1 receptors (IGF1R), a series of signaling pathways is triggered. These include pathways such as PI3K/AKT, cyclin D1, HIF-1α and VEGF. The activation of these pathways could contribute to key characteristics of cancer, including enhancing cell proliferation, stimulating angiogenesis, and diminishing apoptosis (85–87). However, some researchers observed a positive correlation between better prognosis and increased IGF-1 levels (88), which might suggest a more complicated mechanisms behind IGF-1 and RCC.

High level of glucose might play a role between diabetes and RCC by stimulating the metabolism of RCC cells (84). It has been proposed that hyperglycemia can enhance cancer cell proliferation via elevating the levels of protein kinase C (PKC) and peroxisome proliferator-activated receptors (PPARs), which can in turn accelerate cellular metabolism and thereby foster cell proliferation (89).

Pro-inflammatory factors and leptin might play roles between obesity and RCC. It was believed that chronic inflammation existed in adipose tissue under obesity state (90). Obesity-related chronic inflammation might promote tumor by releasing pro-inflammatory factors, such as IL-6 with anti-apoptotic and cell proliferation effect via JAK2 and PI3K/AKT (91, 92), as well as TNF-α with anti-apoptotic effect via NF-kB (87). Notably, glutamine, which was correlated with decreasing inflammation and proinflammatory cytokines, was downregulated in obesity (93), which further implied the role of inflammation in mediating the link between obesity and RCC. Moreover, leptin, an adipocyte-specific protein functions to regulate satiety and bodyweight (94), might also promote RCC by promoting cell proliferation, upregulating VEGF and inhibiting apoptosis, possibly through HIF-1α and NF-κB (95). Leptin was also shown to be correlated with worse OS and migration of ccRCC cells (96).

Though the precise and detailed mechanism between RCC and hypertension has not been fully understood, some researchers (97) hypothesized that renin-angiotensin-system might play a potential role. Ang- (1-7) (generated from Ang I and Ang II) could promote xenograft tumor growth in nude mice and migration of caki-1 and caki-2 cell lines in vitro (98), and combined administration of sunitinib and telmisartan (a renin-angiotensin-system antagonist) was observed to induce more necrosis and less neo-angiogenesis in 786-O cell line xenograft in mice, indicating the potential role and therapeutic value of renin-angiotensin-system in RCC (99).

Studies have demonstrated that there were significant differences in the relative abundance of 20 gut bacteria species between RCC patients and control group (100). Besides, antibiotic use may reduce the efficacy of immunotherapy in metastatic renal cell carcinoma (101), yet using antibiotics to target Bacteroides spp. in stool can improve progression-free survival (PFS) in metastatic RCC patients receiving first-line VEGF-TKI therapy (102). Additionally, CBM588, a live bacterial product, enhance clinical outcomes in patients with metastatic renal cell carcinoma treated with nivolumab-ipilimumab (103). These findings suggest that microbiome may play significant roles in RCC.

The precise and comprehensive mechanism behind microbiome and RCC were not fully understood yet. However, there seemed to be some indirect evidences revealing potential mechanisms.

The microbiome may impact RCC through tryptophan metabolism. Some endogenous tryptophan metabolites can act as ligands for aryl hydrocarbon receptor (AhR) signaling (104), and AhR has been linked to the invasion of RCC (105). Researchers investigated the relationship between gut bacterial abundance and the expression levels of certain tryptophan metabolites (106). Kynurenic acid showed a negative correlation with the gut bacteria Prevotella-9 and Akkermansia, and kynurenine (Kyn) was found to enhance the migration and invasion of 786-O cells via AhR. These findings suggest that gut microbiota may activate AhR through its tryptophan metabolite kynurenine, thereby mediating RCC metastasis. The microbiome might also influence RCC by modulating immunity through lipid metabolism. In 2025, a research team (107) discovered that low intra-tumoral mycobiome abundance was associated with suppressed lipid catabolism, CD8+ T cell depletion, and poor prognosis. This implies that the intra-tumoral mycobiome might affect RCC via immune regulation mediated by lipid metabolism. Carbohydrate metabolism and related molecules may represent another target through which the microbiome affect RCC. SUCNR1, the receptor of succinate, an important molecule during TCA cycle, was linked to a variety of microbes, including beneficial bacteria possibly contributing to a better disease-specific survival rate in ccRCC (108). Additionally, taurine might also play a intermediate role between microbiome and RCC. Upregulated bacteria desulfovibrionaceae downregulated Lactobacillus was observed in ccRCC (100) while the serum level of taurine, which was considered to be consumed by bacteria desulfovibrionaceae (109) and promoted by Lactobacillus (110), also decreased in the ccRCC (100).

In the future, with the aid of continuously advancing new technologies, it may be possible for researchers to further map the spatial and temporal landscapes of metabolism in RCC, thereby more comprehensively elucidating the role of metabolism throughout the entire process of cancer initiation. Additionally, leveraging sophisticated omics technologies and the progress in microbiome-related research, it may be feasible in the future to construct a holistic metabolic map of the human-microbiome ecosystem, providing a more comprehensive understanding of the role of metabolism in renal cancer. Lastly, it is anticipated that more studies will focus on the role of metabolism in renal cancer treatment and drug resistance, revealing the close connection between metabolism and renal cancer from a more clinically significant perspective.