Global trends and hotspots of tumor necrosis factor-alpha in pulmonary tuberculosis: a bibliometric and visualization analysis

Introduction

Pulmonary tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains one of the most significant global health challenges (1). According to the World Health Organization (WHO), there were an estimated 10.6 million new pulmonary tuberculosis cases and 1.6 million deaths globally in 2021, making pulmonary tuberculosis the leading cause of death from a single infectious agent (2). Despite advances in diagnostics and treatment, pulmonary tuberculosis continues to impose a substantial burden, particularly in low- and middle-income countries where healthcare resources are limited (3). The disease manifests as a chronic pulmonary infection characterized by persistent cough, fever, weight loss, and night sweats (4). Its pathogenesis is driven by complex host-pathogen interactions, with immune responses playing a dual role in bacterial containment and tissue damage (5). The balance between effective immune defense and inflammatory tissue damage is crucial in determining disease severity and patient outcomes.

Tumor necrosis factor-alpha (TNF-α) plays a pivotal role in the immune response to Mtb infection. It is essential for the activation of macrophages and the regulation of cytokines, both of which are critical for the clearance of the bacteria (6). TNF-α plays a dual role in pulmonary tuberculosis pathogenesis: facilitating granuloma formation and maintenance to contain bacterial spread while also contributing to immunopathology through excessive inflammation (7). Granuloma-associated tissue damage in pulmonary tuberculosis is partly mediated by matrix metalloproteinase (MMP), particularly MMP-9. Elevated MMP-9 levels contribute to extracellular matrix degradation and caseation necrosis. Anti-TNF therapies have been shown to reduce MMP-9 secretion, highlighting the cytokine’s role in regulating granuloma-associated tissue remodeling (8). Despite the growing body of research on TNF-α in pulmonary tuberculosis, there is no systematic analysis evaluating trends, collaborations, and emerging hotspots in this field. A systematic analysis of these trends provides actionable insights for prioritizing research directions and addressing gaps in therapeutic innovation and global collaboration.

Bibliometric analysis is a quantitative method used to evaluate research trends, collaborations, and emerging hotspots in a specific scientific field (9). Islam et al. conducted a bibliometric analysis on antibiotic-resistant active pulmonary tuberculosis research from 1996 to 2020, identifying key topics such as diagnostic advancements, drug resistance mechanisms, and collaborative networks (10). TNF-α warrants exclusive focus due to its role as the central orchestrator of granulomatous inflammation and key therapeutic target in pulmonary tuberculosis pathogenesis. Crucially, no prior bibliometric studies examine TNF-α’s research evolution in pulmonary tuberculosis pathogenesis, nor do any systematically analyze alternative biomarkers in pulmonary tuberculosis pathogenesis. Therefore, this study aims to address this gap by conducting a comprehensive bibliometric analysis of TNF-α research in pulmonary tuberculosis to identify key trends, influential studies, and potential future directions in this developing area. We present this article in accordance with the BIBLIO reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-620/rc).

Methods

Search strategies and data collection

A literature search on TNF-α in pulmonary tuberculosis, spanning from 1990 to 2024, was conducted utilizing the Web of Science Core Collection (WoSCC). The WoSCC is acknowledged as a comprehensive and authoritative database that provides access to high-quality academic publications across various disciplines (11). The search formula utilized was: (((TS=(tuberculos*) AND TS=(pulmonary OR lung)) OR TS=(“tuberculous pneumonia”)) AND TS=(“TNF-α” OR “TNF alpha” OR “anti-TNF-α” OR “tumor necrosis factor-alpha” OR “anti-tumor necrosis factor-α” OR “anti-tumor necrosis factor α”)) NOT TS=(“Rheumatoid arthritis” OR “ankylosing spondylitis” OR “Behcet’s disease” OR “Psoriasis” OR “Inflammatory bowel disease” OR “Crohn’s Disease” OR “Ulcerative colitis”) (12). Only articles in English were included. To mitigate discrepancies arising from database updates, literature retrieval was performed on December 2, 2024. During the filtering process, bibliographic information was exported in both “Full record and cited references” and “plain text” formats. The text format data encompassed publication and citation counts, titles, author affiliations, institutions, countries/regions, keywords, and journals for bibliometric analysis.

Statistical analysis

For the visualization and comprehensive analysis of academic data, three bibliometric tools were utilized: VOSviewer (version 1.6.20), CiteSpace (version 6.3.R1), and R 4.3.3. VOSviewer, recognized for its versatility, was essential for mapping collaborations among institutions and authors, as well as analyzing co-authorship, citation patterns, keyword co-occurrence networks, and co-citation networks (13). This tool enabled the visualization and analysis of collaborative networks within academia, providing significant insights into the relationships among authors, institutions, and publications. CiteSpace was utilized to detect keyword bursts, enhancing the understanding of emerging trends and research hotspots. The parameters were set as follows: time slicing was defined from January 1990 to November 2024, with keywords designated as the node type. A keyword node threshold of 5 was established prior to each fragment, and pruning was performed using the pathfinder and clip merge network methods. Visualization analysis was conducted according to these parameters to produce a keyword timeline within the research domain of TNF-α in pulmonary tuberculosis. R 4.3.3 was employed for an extensive bibliometric analysis. This tool was instrumental in analyzing and mapping the global distribution of research output, revealing significant trends and patterns, as well as evaluating the impact of authors, journals, and institutions within the dataset. The H-index quantifies the academic impact of individuals and journals, functioning as a balanced measure of scholarly influence (14,15). Additionally, the G-index and M-index were integrated into the analysis. The G-index assigns greater weight to highly cited publications, providing a more nuanced assessment of a researcher’s citation impact (16). The M-index normalizes the H-index based on the number of years since a researcher’s first publication, offering a valuable measure of the consistency and sustainability of their academic contributions (17). Journal Citation Reports (JCR) quartiles and the impact factor (IF) were utilized to assess journal prestige and citation influence. JCR quartiles categorize journals into four tiers, with Q1 denoting the highest academic impact; concurrently, the IF measures the average citations received by a journal’s articles over the preceding two years. The most recent 2023 JCR and IF data were utilized to ensure an updated assessment of journal prestige and citation influence.

Results

An overview of publications

A total of 1,324 studies on TNF-α in pulmonary tuberculosis were identified from the WoSCC. After excluding 124 records (66 review articles, 9 meeting abstracts, 4 early access papers, 3 editorial materials, 27 proceeding papers, 1 letter, 3 notes, 1 book chapter, and 11 non-English articles), 1,200 studies were included in the final analysis (Figure 1). The research spanned from 1990 to 2024, with an annual growth rate of 10.73%. The analysis revealed contributions from 6,129 authors affiliated with 1,581 institutions across 83 countries/regions. These manuscripts were published across 328 journals and referenced 31,674 citations in total. The average number of citations per article was 37.14, reflecting the significant impact of this body of work (Figure 2A).

Figure 1 Flowchart of the literature screening process. TNF-α, tumor necrosis factor-alpha.

Figure 2 Annual publication trends on TNF-α in pulmonary tuberculo sis from 1990 to 2024. (A) Overview of publications. (B) Annual number of publications on TNF-α in pulmonary tuberculosis. TNF-α, tumor necrosis factor-alpha.

The annual number of publications on TNF-α in pulmonary tuberculosis has exhibited a consistent upward trend since 1990. Early research activity was limited, with fewer than 20 publications per year until the early 2000s. A steady increase followed, with annual publications surpassing 50 by 2007. The field gained significant momentum from 2016 onward, and the peak was observed in 2020, with 71 articles published (Figure 2B). Although there was a slight decline in subsequent years, the overall trajectory highlights sustained research interest and continued advancements in this area.

One of the most highly cited studies in this field is titled “Virulence of a Mycobacterium tuberculosis clinical isolate in mice is determined by failure to induce Th1 type immunity and is associated with induction of IFN-alpha/beta”. Published in 2001 in Proceedings of the National Academy of Sciences of the United States of America (IF: 9.4), this influential article has garnered 490 citations, offering critical insights into immune responses against tuberculosis (18). Another notable work is “Exosomes released from macrophages infected with intracellular pathogens stimulate a proinflammatory response in vitro and in vivo”, published in 2007 in Blood (IF: 21.0), which has accumulated 489 citations. This research highlights the role of exosomes in immune modulation during infections (19). Additionally, the third most cited article, “IL-17-mediated regulation of innate and acquired immune response against pulmonary Mycobacterium bovis bacille Calmette-Guerin infection”, was published in 2007 in Journal of Immunology (IF: 3.6) and has received 437 citations, shedding light on IL-17’s role in tuberculosis immunity (20).

Analysis of countries

The USA is the leading contributor to research on TNF-α in pulmonary tuberculosis, with 277 articles, ranking first in both total publications (TP =923) and total citations (TC =14,911). The country demonstrates a moderate multiple country publications (MCP) ratio of 0.224, highlighting its balanced approach to international collaboration. China ranks second in both TP [604] and TC [3,613], although it has a lower MCP ratio of 0.16, reflecting a stronger focus on domestic research efforts. India follows as the third most productive country (TP =319) and ranks fourth in citations (TC =2,327), with an MCP ratio of 0.291, showing moderate international collaboration. The UK ranks fourth in terms of TP [209] but third in TCs [3,099], with a high MCP ratio of 0.578, indicating significant international partnerships (Figure 3A and Table S1).

Figure 3 Geographical distribution and international collaboration of research on TNF-α in pulmonary tuberculosis. (A) Distribution of corresponding author’s publications by country. (B) Visualization map depicting the collaboration among different countries. MCP, multiple country publications; SCP, single country publications; TNF-α, tumor necrosis factor-alpha.

The visualization map of international collaborations in TNF-α research on pulmonary tuberculosis highlights the global network of co-authorships (Figure 3B). The USA stands out with the highest number of collaborations, achieving a total link strength of 229. The UK follows with a total link strength of 132, demonstrating significant collaborative efforts, particularly with India and South Africa. South Africa ranks third with a total link strength of 87, indicating its active engagement in global research networks.

Analysis of institutions

The top ten institutions by article count in TNF-α research on pulmonary tuberculosis showed that the Indian Council of Medical Research (ICMR) leads with 83 articles. The University System of Ohio ranks second with 70 articles, reflecting its significant contributions to the research landscape. The University of London follows closely with 68 articles, showcasing its active involvement in this area. Other notable institutions include the ICMR-National Institute for Research in Tuberculosis (NIRT) with 62 articles and the University of Cape Town with 61 articles (Figure 4A).

Figure 4 Institutional contributions and collaborations in TNF-α-related pulmonary tuberculosis research. (A) Top ten institutions by article count and rank. (B) Visualization map depicting the collaboration among different institutions. TNF-α, tumor necrosis factor-alpha.

The institutional collaborative network for TNF-α research pertaining to pulmonary tuberculosis comprised 121 institutions engaged in international collaborations, with each institution contributing a minimum of five articles (Figure 4B). The National Institute of Allergy and Infectious Diseases (NIAID) leads with the highest total link strength of 55. The University of Cape Town ranks second with a total link strength of 37, reflecting its significant role in fostering international research collaborations. The NIRT follows closely with a total link strength of 32. Other notable institutions include Case Western Reserve University [31] and Government Stanley Medical Hospital [23].

Analysis of authors

The top 20 high-impact authors in TNF-α research on pulmonary tuberculosis showed that Kaplan G. leads with an H-index of 17 and a G-index of 18. He ranks fourth in TP [18] but first in TC [2,348], underscoring the high impact of his work. Actor J.K. follows closely, with the highest TP [21] and an H-index of 14, ranking him eleventh in citations (TC =664). Ellner J.J. also stands out with an H-index of 14 and a G-index of 14, coupled with a citation count of 1,229, ranking second in total citations despite having fewer publications (TP =14) (Table S2).

The author collaboration network comprised 199 authors involved in international collaborations, each with a minimum of 4 articles (Figure 5). Subash Babu leads with the highest total link strength of 67. Sung Jae Shin follows with a total link strength of 62, reflecting his significant role in fostering research partnerships. Woo Sik Kim ranks third with a total link strength of 54, indicating his active engagement in global collaborations. Other notable contributors include Nathella Pavan Kumar [53], Kee Woong Kwon [52], and Selvakumar Subbian [42].

Figure 5 Visualization map depicting the collaboration among different authors.

Analysis of journals

A comprehensive overview of the top 20 high-impact journals in TNF-α research on pulmonary tuberculosis revealed that The Journal of Immunology stands out as the leading journal, with an H-index of 46, a G-index of 65, and a total of 65 publications, ranking second in TP but first in TC [4,332]. Infection and Immunity ranks second in TC [3,211] and third in TP [63], reflecting its strong influence in the field despite a lower IF (2.9). PLoS One leads in TPs with 80 articles and has an H-index of 31, ranking fifth in TC [1,179]. The American Journal of Respiratory and Critical Care Medicine is notable for its high IF (19.3) and ranks sixth in TP [26] while placing eighth in TC [887] (Table S3).

The co-occurrence network of journals related to TNF-α research on pulmonary tuberculosis illustrated that, among the 142 journals with at least two occurrences, the three key journals with the highest total link strength are Infection and Immunity [439], followed by Journal of Immunology [333], and PLoS One [307] (Figure 6A). These journals play a central role in connecting research within this field, reflecting their significant influence and contribution to advancing knowledge in TNF-α-related studies. The journal coupling network evaluates the extent to which journals are linked based on shared references in their articles (Figure 6B). Among the 142 journals with at least two couplings, the three key journals with the highest total link strength are Journal of Immunology, with a total link strength of 19,902, PLoS One, with a total link strength of 17,563, and Infection and Immunity, with a total link strength of 17,266.

Figure 6 Journal analysis of TNF-α research in pulmonary tuberculosis. (A) Journal co-occurrence network diagram. (B) Journal bibliography coupling network diagram. TNF-α, tumor necrosis factor-alpha.

Analysis of keywords

The keyword co-occurrence analysis provides insights into the relationships among frequently paired terms, and 138 keywords with a minimum of 12 occurrences were identified (Figure 7A). The keyword “mycobacterium-tuberculosis” emerged as the most frequently occurring term, with 341 occurrences and a total link strength of 1,735. Other prominent keywords include “infection” (284 occurrences, total link strength of 1,467) and “interferon-gamma” (200 occurrences, total link strength of 1,092). Purple nodes, such as “tumor-necrosis-factor” and “human macrophages”, represent earlier research topics [2008–2010], focusing on foundational immunological mechanisms like cytokine production and host defense. Green nodes, including “immune response” and “infection”, indicate mid-phase research [2011–2013], emphasizing immune activation and macrophage interactions. More recent research [2014–2016] is represented by yellow nodes such as “diagnosis”, “biomarkers”, and “vaccine”, signaling a shift toward clinical applications, diagnostics, and prevention strategies. A total of four clusters were identified in this analysis (Figure 7B). The largest cluster, the red cluster, consists of 42 items, focusing on immune responses, vaccination strategies, and diagnostic tools. Key terms include “active tuberculosis”, “BCG vaccination”, “cytokine”, “diagnosis”, and “protective immunity”. The green cluster contains 38 items related to cellular processes and the pathogenesis of tuberculosis. Prominent terms include “granulomas”, “autophagy”, “inflammation”, “nitric oxide”, and “macrophages”. The blue cluster comprises 30 items centered on host defense mechanisms and experimental models. Key terms include “host-defense”, “granuloma-formation”, “NF-kappa-B”, “murine model”, and “toll-like receptors”. The yellow cluster includes 28 items focused on biomarkers, gene polymorphisms, and clinical applications. Notable terms are “biomarkers”, “cytokine production”, “latent tuberculosis”, and “regulatory T-cells”.

Figure 7 Visual analysis of keywords. (A) Visual analysis of keyword co-occurrence network analysis. (B) Keyword cluster analysis.

A burst analysis of keywords reveals the evolving focus of research on TNF-α and pulmonary tuberculosis (Figure 8). Early bursts include “murine macrophages” [1995–2001] and “alveolar macrophages” [1995–2009], reflecting foundational studies on immune cell roles in tuberculosis pathogenesis. Similarly, “cytokine production” [1996–2005] and “interferon-gamma” [1997–2005] highlight early interest in cytokine signaling pathways and their importance in immune responses. Recent bursts indicate a shift toward applied and translational research. The keyword “risk” [2017–2021] experienced one of the strongest citation bursts, emphasizing the growing focus on identifying risk factors for tuberculosis infection and progression. Additionally, terms such as “BCG” [2017–2022] and “protection” [2018–2024] reflect sustained interest in vaccine efficacy and protective immunity. More recently, “biomarkers” [2019–2024] and “inflammation” [2022–2024] have emerged as key areas of focus, underscoring efforts to identify diagnostic tools and understand inflammatory processes in tuberculosis.

Figure 8 Top 20 keywords with the strongest citation bursts.

Discussion

General information

The bibliometric analysis highlights a growing research focus on TNF-α in pulmonary tuberculosis, with key themes centered on immune responses, vaccination strategies, and diagnostic advancements. Emerging trends, as indicated by keyword bursts like “biomarkers” [2019–2024] and “inflammation” [2022–2024], suggest a shift toward translational applications, emphasizing diagnostic tools and inflammatory processes. This progression illustrates the field’s evolution from basic immunological studies to applied clinical solutions for tuberculosis management.

The USA has solidified its position as the leading contributor to TNF-α research in pulmonary tuberculosis, with 277 publications and the highest total citations. This dominance reflects the country’s robust research infrastructure and its balanced approach to international collaboration. The USA also leads in global research networks, achieving the highest total link strength, which underscores its significant role in fostering international partnerships. This leadership aligns with the USA’s broader commitment to combating tuberculosis, a disease that remains a global public health challenge, with an estimated 10.6 million new cases and 1.6 million deaths worldwide in 2021, according to the WHO (2). The ICMR leads institutional contributions with 83 articles, underscoring its critical role in advancing pulmonary tuberculosis research globally. This prominence stems from India’s significant tuberculosis burden, which accounted for 26% of global tuberculosis cases, according to the WHO (21). The high prevalence of tuberculosis in India has driven the need for targeted research initiatives and robust public health strategies. The ICMR has played a pivotal role in addressing these challenges.

Kaplan G. stands out as the most influential author in TNF-α research on pulmonary tuberculosis, ranking first in total citations with an H-index of 17. His work has significantly shaped the field, focusing on critical themes such as immune responses and cytokine signaling. One of Kaplan’s highly cited studies demonstrated that the failure to induce Th1-type immunity is associated with increased virulence of Mtb clinical isolates in mice. This study underscored the role of TNF-α and interferon-gamma (IFN-γ) in granuloma formation and bacterial control, providing foundational insights into pulmonary tuberculosis pathogenesis (18). The Journal of Immunology has emerged as the leading journal in TNF-α research on pulmonary tuberculosis, ranking first in total citations [4,332] and achieving an H-index of 46. This reflects its significant academic influence and central role in disseminating high-impact research within the field. The journal’s focus on both foundational and translational studies has cemented its reputation as a key platform for advancing knowledge in tuberculosis immunology (22).

Emerging topics

The co-occurrence analysis of keywords related to TNF-α and pulmonary tuberculosis research has highlighted four significant hotspots that reflect the current trends and focal points within the field. The largest cluster, focusing on immune responses and vaccination strategies, includes key terms such as “BCG vaccination”, “cytokine”, and “protective immunity”. This cluster underscores the critical role of the immune system in controlling tuberculosis infections. TNF-α is essential for granuloma formation, macrophage activation, and bacterial containment, making it a key cytokine in the immune response to pulmonary tuberculosis. The Bacillus Calmette-Guérin (BCG) vaccine remains a cornerstone in tuberculosis prevention, and ongoing research aims to enhance its efficacy through novel approaches. Guinea pig models have demonstrated that BCG vaccination modulates TNF-α levels in pulmonary granulomas, reducing tissue damage while maintaining bacterial control (23). Recent advancements include recombinant BCG vaccines expressing Mtb-specific antigens such as early secreted antigenic target of 6 kDa (ESAT-6) and culture filtrate protein 10 kDa (CFP-10). These vaccines have been shown to induce stronger polyfunctional T-cell responses, including increased TNF-α production, resulting in enhanced protective immunity (24).

Cluster 2 emphasizes cellular processes and the pathogenesis of tuberculosis, with prominent terms such as “granulomas”, “autophagy”, “nitric oxide”, and “macrophages”. A computational modeling study demonstrated that TNF-α receptor dynamics are critical for granuloma outcomes, influencing bacterial clearance, granuloma stability, or excessive inflammation. The study highlighted that optimal TNF-α signaling is necessary to balance bacterial containment and immune-mediated tissue damage (25). TNF-α has been identified as a potent inducer of autophagy in macrophages infected with Mtb. Shen et al. found that TNF-α promotes autophagy by suppressing the Akt/mTOR signaling cascade, which alleviates pyroptosis and necroptosis in Mtb-infected macrophages. This regulatory mechanism highlights the protective role of TNF-α-mediated autophagy in controlling bacterial replication and reducing inflammatory damage (26). Nitric oxide (NO) is primarily produced by macrophages through the inducible nitric oxide synthase (iNOS) pathway in response to Mtb infection. An investigation into MDM2’s role in macrophage responses demonstrated that TNF-α signaling enhances NO production via iNOS in M1 macrophages, promoting inflammatory cytokines and protection against sepsis, but aggravating conditions like obesity-induced inflammation (27).

The third cluster focuses on host defense mechanisms, featuring key terms such as “host-defense”, “NF-kappa-B”, and “toll-like receptors”. Host defense mechanisms play a critical role in controlling Mtb infection, with TNF-α serving as a central mediator in orchestrating immune responses. TNF-α contributes to macrophage activation, granuloma formation, and the regulation of cytokine networks, all of which are essential for bacterial containment and immune regulation in pulmonary tuberculosis (28). Upon TNF-α binding to its receptor (TNFR1), the NF-κB pathway is activated, leading to the transcription of genes involved in cytokine production, chemokine expression, and cell survival. Mtb infection induced TNF-α-mediated NF-κB activation in macrophages, which enhances the production of pro-inflammatory cytokines such as IL-6 and IL-1β (29). Toll-like receptors (TLRs) are pattern recognition receptors that detect Mtb components and initiate innate immune responses. TLR2 and TLR4, in particular, are involved in recognizing Mtb antigens such as the 38-kDa glycolipoprotein. Neutralizing TLR2 or TLR4 has been shown to reduce TNF-α secretion in human monocytes exposed to Mtb antigens, highlighting the importance of TLRs in amplifying TNF-α-mediated immune responses during tuberculosis infection (30).

The fourth cluster highlights clinical applications, with notable terms such as “biomarkers”, “cytokine production”, “latent tuberculosis”, and “regulatory T-cells”. This area reflects a growing interest in translating research findings into clinical practice. Regulatory T-cells (Tregs) are key mediators of immune homeostasis during tuberculosis infection. Huang et al. demonstrated that the Mtb protein ESAT-6 significantly prolongs allograft survival by inducing CD4⁺Foxp3⁺ regulatory T cells through the IκBα/cRel signaling pathway, suggesting its potential as an alternative immunosuppressant (31).

The burst analysis of keywords provides valuable insights into the evolving research focus on TNF-α in pulmonary tuberculosis over time. The recent phase is characterized by a shift toward applied research, as evidenced by keywords such as “biomarkers” [2019–2024], and “inflammation” [2022–2024]. The emergence of “inflammation” underscores a growing interest in understanding inflammatory pathways’ role in pulmonary tuberculosis pathogenesis and their implications for therapeutic strategies. Elevated levels of TNF-α have been associated with increased radiological abnormalities and poorer quality of life in tuberculosis patients at diagnosis, suggesting that monitoring TNF-α levels could provide insights into disease severity and treatment response (32). The keyword “biomarkers” highlights efforts to identify reliable indicators for disease progression and treatment response. Identifying reliable biomarkers can aid in early diagnosis and monitoring treatment responses, particularly in latent tuberculosis cases, where patients may not exhibit symptoms but remain at risk for developing active disease. TNF-α secretion from Mtb-specific CD4⁺ T cells with a CD38⁺CD27⁻ phenotype showed high diagnostic accuracy for distinguishing active tuberculosis from latent tuberculosis, with an area under the curve (AUC) of 0.91 (33).

Future trends

Further research is needed to validate the use of TNF-α-based biomarkers across diverse populations and disease stages, ensuring their effectiveness in various clinical contexts. Burst analysis has revealed a significant increase in keywords related to “biomarkers” and “inflammation” in recent years, indicating a growing focus on identifying reliable diagnostic tools and understanding inflammatory processes in tuberculosis. This trend underscores the necessity of investigating the molecular mechanisms underlying TNF-α-mediated inflammation, which could guide the development of host-directed therapies that effectively balance protective immunity with inflammation control. Additionally, exploring the interactions between TNF-α and other cytokines may reveal synergistic effects that enhance therapeutic outcomes. Understanding genetic variations in TNF-α responses among different ethnic groups will be crucial for personalizing treatment approaches, as highlighted by recent studies emphasizing the importance of tailoring interventions to individual patient profiles. Longitudinal studies are also essential to assess the long-term efficacy of TNF-α-targeted therapies, especially in patients with co-morbid conditions. Overall, these future directions will not only advance our understanding of TNF-α’s role in pulmonary tuberculosis but also improve clinical management strategies, ultimately leading to better patient outcomes. To apply these trends in practice, clinicians and researchers can integrate genetic profiling into routine tuberculosis screening protocols—such as using affordable genotyping kits to identify TNF-α variants before initiating therapy—and conduct follow-up longitudinal monitoring via digital health tools to adjust treatments dynamically for diverse patient populations, thereby enhancing personalized care and reducing relapse rates.

Strengths and limitations

This study has several strengths. First, it provides a novel comprehensive bibliometric analysis of TNF-α in pulmonary tuberculosis spanning 1990 to 2024, offering unprecedented insights into long-term global research trends that have not been systematically mapped before. Second, by employing advanced visualization tools like VOSviewer and CiteSpace, the approach generates innovative keyword burst maps and co-occurrence networks, revealing emerging hotspots such as biomarkers and inflammation in ways traditional reviews cannot. Third, the integration of multi-metric evaluations including H-index, G-index, and M-index delivers a novel assessment of author, institution, and journal impacts, uncovering underrepresented contributors and paving the way for targeted future research in personalized TNF-α therapies. This study has several limitations. First, it relied exclusively on the WoSCC database, which, while comprehensive, may have excluded relevant publications from other databases, potentially limiting the scope of the analysis. Second, the analysis was restricted to English-language publications, which could have introduced a language bias and excluded significant research contributions published in other languages. Third, the analysis includes only studies published until December 2024, meaning more recent research beyond this date is not accounted for. Fourth, the exclusion of certain document types, such as conference abstracts and non-peer-reviewed articles, may limit the comprehensiveness of the findings.

Conclusions

This bibliometric analysis systematically assessed the scope and development of TNF-α research in pulmonary tuberculosis. It identified key research themes such as immune responses, vaccination strategies, and diagnostic advancements. Burst analysis highlighted a progression from foundational studies on immune mechanisms, such as cytokine production, to recent translational focuses like biomarkers. These findings provide a comprehensive overview of the field, offering valuable insights into emerging research trends and guiding future studies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-620/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-620/prf

Funding: The study was supported by the Chinese Medicine Science and Technology Project of Zhejiang Province (Chinese Medicine Health Service Research Project) (No. 2023ZF180) (project name: study on the clinical application of shelter CT based on image omics in the adjuvant treatment of pulmonary tuberculosis with Shashen Maodong Decoction).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-620/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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