Gliomas represent the most common and aggressive malignant tumors of the central nervous system, with glioblastoma (GBM) being the most lethal subtype. The highly heterogeneous biology of GBM, coupled with its resistance to conventional therapies, contributes to the dismal prognosis associated with this disease. Despite advancements in multimodal treatment approaches, including surgical resection, radiotherapy, and chemotherapy, the median survival of glioblastoma patients remains under two years, with a five-year survival rate that remains exceedingly low (1). With advancements in molecular biology and immunology, researchers have increasingly recognized that gliomas are not merely the result of uncontrolled tumor cell proliferation; rather, the complexity of their microenvironment plays a pivotal role in tumor initiation, progression, and therapeutic resistance. Consequently, a deeper exploration of the key factors influencing glioma progression, particularly inflammation-related mechanisms, is essential for the development of more effective therapeutic strategies.

Inflammation plays a central role in the initiation and progression of gliomas and is regarded as a critical factor shaping the tumor microenvironment and influencing therapeutic responses (2). The glioma microenvironment is enriched with pro-inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, along with tumor-associated macrophages (TAMs) and microglia. Together, these immune components shape an immunosuppressive milieu that enables tumor cells to evade immune surveillance (3–5). Moreover, inflammatory signaling pathways, including NF-κB, JAK-STAT, and NLRP3, are aberrantly activated within the tumor microenvironment, further driving tumor cell proliferation, invasion, and the development of therapeutic resistance (6–8). In recent years, targeted therapeutic strategies against inflammation-related molecules—such as immune checkpoint inhibitors, anti-inflammatory agents, and drugs targeting tumor-associated macrophages (TAMs)—have emerged as a major focus in glioma research (9–12). Therefore, a systematic review of the current research landscape and emerging trends in glioma-related inflammation can provide deeper insights into their interplay and offer new directions for future investigations.

Bibliometric analysis is a quantitative method based on large-scale literature data, enabling the identification of academic trends, research hotspots, key authors, leading institutions, and emerging frontiers within a specific research field (13). By leveraging bibliometric tools such as CiteSpace and VOSviewer, researchers can analyze glioma and inflammation-related literature from multiple perspectives, including citation networks of high-impact papers, collaboration networks, keyword co-occurrence analysis, and burst term detection. These analyses not only help identify research hotspots but also unveil academic collaboration patterns, interdisciplinary trends, and potential future directions. Furthermore, bibliometric analysis enables the tracking of emerging technologies and research paradigms, providing researchers with a more structured academic resource to support decision-making and strategic research planning.

This study employs a bibliometric approach to systematically analyze the literature on glioma and inflammation from the past two decades, aiming to uncover global research trends, key contributors, influential publications, research hotspots, and emerging advancements in the field. Through high-frequency keyword analysis, we identified core research directions within this domain. Additionally, we examined collaboration networks among research institutions and scholars to assess academic collaboration patterns and the degree of internationalization. The findings of this study not only provide researchers with a comprehensive academic overview but also offer valuable insights for future investigations, facilitating the exploration of inflammation’s role in glioma initiation, progression, and treatment. Furthermore, this study provides a theoretical foundation for optimizing personalized therapeutic strategies.

Bibliometric analysis, as an important research method, has been widely applied across multiple fields. By conducting quantitative analysis of publications, researchers can uncover research trends, hotspots, and academic impact within a specific domain (24). This method not only helps researchers gain a better understanding of the knowledge structure within a specific field but also offers insights into emerging trends and potential areas of interest for future research. However, bibliometric analysis has not yet been conducted in the field of glioma and inflammation. In this study, we performed a systematic literature search using the WoSCC database and, after screening, included 4,553 publications from the past two decades related to glioma and inflammation for bibliometric analysis and visualization. By systematically analyzing the research landscape in this field, we aim to reveal development trends, research hotspots, and emerging directions, enabling researchers to quickly grasp the evolution of this domain and providing valuable references for future studies.

We found that the most locally cited article is “Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma” (446 citations) (1). This clinical study demonstrated that combining radiotherapy with temozolomide (TMZ) after surgery extended the median survival of GBM patients from 12.1 months to 14.6 months. This research laid the foundation for the radiotherapy + TMZ regimen as the standard treatment for GBM, significantly transforming the clinical management of the disease.

While the radiotherapy + TMZ regimen has become the standard of care, its efficacy remains limited, highlighting the need for future research to explore more effective therapeutic strategies, such as combination immunotherapy and personalized precision medicine. In the citation burst analysis, we identified that the earliest highly cited paper with the strongest citation burst was published in 2010: “Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.” This study played a pivotal role in glioblastoma classification, revealing distinct molecular subtypes based on genetic alterations in PDGFRA, IDH1, EGFR, and NF1. Its strong citation burst in the early stages suggests that molecular subtyping was a major research focus at the time, laying the groundwork for subsequent advancements in targeted therapy and personalized treatment approaches for GBM (25). This groundbreaking study pioneered the molecular subtyping of GBM, significantly advancing the application of precision medicine in GBM research (26–28).

The regional distribution map reveals that the United States and China are the leading countries in terms of publication volume in the field of glioma and inflammation research, with their output significantly higher than that of other countries. However, despite both China and the U.S. leading in publication volume, total citations and average citations per paper show that the U.S. significantly outperforms China. This disparity points to differences in academic influence and research quality between the two countries in the glioma and inflammation field. For China to further enhance its academic influence, it will need to focus more on research innovation, international collaboration, and the publication of high-quality papers, aiming to increase the global academic impact of its research.

The distribution of corresponding authors by country reveals that China has the highest number of domestic collaboration publications (single-country publications), while the number of international collaboration publications (involving multiple countries) is lower than that of the United States. This phenomenon reflects the differences in academic collaboration models between China and the U.S., as well as the collaboration trend and academic influence of different countries in the field of glioma and inflammation research. From another perspective, due to the U.S.’s leading position, scholars worldwide tend to collaborate with U.S. researchers. In the future, China can enhance international cooperation and promote the publication of high-impact papers internationally to further strengthen its global academic influence in the glioma and inflammation research field. Author analysis helps us identify the core scientists, academic networks, collaboration patterns, research hotspots, and future trends in glioma and inflammation research. Through this analysis, we found that collaboration among researchers is primarily concentrated within the same country, institution, and field. This indicates that the research relies heavily on internal collaborations within laboratories or research teams, such as close ties between large medical research centers or university laboratories. The advantage of this collaboration model is that it allows for clear division of labor and easy resource sharing. However, it may also limit the breadth of research perspectives.

Inflammatory processes play a pivotal role in a wide range of diseases and are widely recognized as key drivers—and even initiators—of pathological changes. In tumors of the central nervous system, gliomas represent the most common and aggressive type, and their initiation and progression are closely associated with chronic inflammation (29). A growing body of evidence indicates that inflammatory signaling plays a critical role in the initiation, progression, immune evasion, and therapeutic resistance of glioma (30, 31). With the advancement of high-throughput sequencing technologies, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating the heterogeneity of the tumor microenvironment and intercellular interactions. Through single-cell transcriptomic and spatial transcriptomic analyses of glioma patient samples, murine glioma models, and organoids, researchers have uncovered how glioma cells interact with myeloid-derived cells—including tumor-associated macrophages (TAMs), tumor-associated neutrophils, microglia, and other immune cell types—to establish a highly immunosuppressive microenvironment that further promotes tumor progression and therapeutic resistance (32–35). Emerging evidence highlights the complex interplay between glioma cells and the inflammatory microenvironment. In this context, inflammation is not merely a host response to the tumor but also an active participant in shaping glioma biology. Tumor-associated macrophages (TAMs), along with cytokines such as IL-1β, IL-6, and transforming growth factor-β (TGF-β), contribute to the establishment of a highly immunosuppressive and tumor-promoting niche (36). Activation of inflammatory pathways such as NF-κB and STAT3 not only promotes glioma cell invasion and angiogenesis but also sustains the stem-like properties of glioma stem-like cells (GSCs), ultimately contributing to tumor recurrence and poor prognosis (37, 38).

Despite the detrimental role of inflammation in glioma progression, it also offers potential therapeutic opportunities. Targeting key inflammatory mediators—such as the CCL2/CCR2 axis, CSF1R, or the IL-1β signaling pathway—has demonstrated promising anti-tumor effects in preclinical models (39, 40). Similarly, reprogramming tumor-associated macrophages (TAMs) from a tumor-promoting M2-like phenotype to a tumor-suppressive M1-like phenotype has emerged as a viable strategy to overcome the immunosuppressive tumor microenvironment (41). Moreover, the combination of standard therapy with anti-inflammatory agents, such as ruxolitinib, has already entered phase II clinical trials for the treatment of glioma patients. However, due to the dual and context-dependent role of inflammation in glioma biology, translating these approaches into clinical benefit remains a significant challenge.

With the rapid advancement of artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have been widely applied in various aspects of glioma management, including diagnosis, grading, prognosis prediction, and molecular subtype classification. In the field of imaging diagnosis, deep learning models—particularly convolutional neural networks (CNNs) based on MRI and PET images—have demonstrated the ability to perform automatic tumor segmentation, boundary delineation, and grade classification, thereby significantly improving diagnostic efficiency and accuracy (42). At the pathological diagnostic level, AI models can automatically analyze digitized histopathological slides to predict tumor grade, quantify cell density, and assess cellular atypia, thereby assisting pathologists in making more objective and consistent evaluations (43).

In this study, we manually selected the 25 most prominent genes from the most popular research, including S100B, IDO, VEGFA, MGMT, TGFBI, CGAS, CSF1R, PTGS2, HMGB1, CHI3L1, ROS1, NR1D1, IDH1, VISTA, SOCS1, NFKB1, LGALS1, CXCL12, IL12, ALK, TSPO, CD47, YAP, IL10, CX3CR1. These genes have gained significant attention in the field of glioma and inflammation. Studies have shown that VEGFA plays a critical role in promoting angiogenesis, stemness maintenance, and immune evasion in glioma. Bevacizumab has already been used in the treatment of certain glioma patients, targeting VEGFA to inhibit blood vessel formation and tumor growth (44–47). IDH1 mutations are commonly found in low-grade gliomas, but are rare in glioblastomas (GBM). These mutations are associated with a relatively better prognosis for patients with low-grade gliomas (38–40). MGMT (O-6-methylguanine-DNA methyltransferase) methylation in patients significantly enhances the efficacy of temozolomide (TMZ) treatment. Moreover, MGMT also plays a role in regulating angiogenesis and might represent a potential predictive biomarker for glioma patients’ response to radiotherapy (48–52). TGFBI (Transforming Growth Factor Beta Induced) plays a crucial role in maintaining glioma stemness and promoting tumor growth. It is involved in regulating the tumor microenvironment, cell signaling pathways, and cellular interactions that support the growth and maintenance of glioma stem cells, which are critical for the tumor’s aggressive nature and resistance to treatment (38, 53). CSF1R (Colony Stimulating Factor 1 Receptor) promotes the recruitment of immune-suppressive macrophages to the tumor site, contributing to the immunosuppressive microenvironment that facilitates glioma progression and resistance to therapies. Targeting and blocking CSF1R has emerged as a promising therapeutic strategy for glioma, as it could inhibit macrophage recruitment, reprogram the immune landscape, and potentially enhance the effectiveness of other treatments, such as immunotherapy (40, 54). However, the presence of the immunosuppressive microenvironment in glioma makes its treatment less effective. In conclusion, the research focus in the field of glioma and inflammation is gradually shifting towards tumor immunity and the tumor microenvironment, which may serve as a potential breakthrough for glioma treatment.

Several limitations of this study should be acknowledged. First, the analysis was confined to publications written in English. This decision was made to ensure consistency and to facilitate accurate interpretation of the bibliometric data; however, it may have resulted in the exclusion of relevant research published in other languages, introducing a potential language bias. Second, the data source was limited to the Web of Science Core Collection (WoSCC), which was selected because of its rigorous indexing standards, comprehensive citation information, and widespread use in bibliometric research. Nevertheless, this choice may have led to the omission of pertinent studies indexed in other databases, such as Scopus, PubMed, or regional repositories. While these constraints were necessary to maintain the feasibility and comparability of the analysis, they may limit the completeness and generalizability of the findings. Future studies could address these limitations by incorporating literature from multiple databases and including non-English publications to provide a more comprehensive overview of the research landscape.

Overall, research on glioma and inflammation is receiving increasing attention, largely because tumor immunity and the tumor microenvironment are seen as promising solutions to the challenges in glioma treatment. This article summarizes the key papers, core journals, keywords, research institutions, and authors in the field of glioma and inflammation. In conclusion, inflammation is a double-edged sword in glioma pathogenesis. While it supports tumor growth and immune escape, it also provides potential vulnerabilities that can be exploited therapeutically. Future research should focus on deciphering the spatiotemporal dynamics of inflammatory signals in gliomas and developing precise strategies to modulate the glioma-associated inflammation for therapeutic gain.