Bibliometric Analysis of Metabolic Reprogramming In T Lymphocytes Reveals Research Trends And Emerging Hotspots, 2016-2025

Summary

This bibliometric analysis used CiteSpace, VOSviewer, and Scimago Graphica to evaluate publication trends, collaboration networks, and emerging research hotspots in T-lymphocyte metabolic reprogramming research from 2016 to 2025.

Abstract

T lymphocytes are central regulators of immune responses, and recent studies have shown that metabolic processes influence T-lymphocyte activation, differentiation, and effector function in cancer and autoimmune diseases. This study performed a bibliometric analysis of publications from January 1, 2016, to December 31, 2025, retrieved from the Web of Science Core Collection. CiteSpace was used for co-occurrence, co-citation, and clustering analyses of references and keywords. VOSviewer was applied to analyze journals, authors, references, countries, and institutions, while Scimago Graphica visualized national collaboration networks and Excel was used to evaluate annual publication trends. A total of 1,882 publications were included. Annual publication output increased steadily, with notable growth observed in 2021 and 2025. China contributed the highest publication volume, whereas the United States demonstrated greater citation impact. Frontiers in Immunology was the most productive journal, while Nature showed the highest citation influence. Keyword clustering and citation analyses identified evolving research hotspots focused on T-lymphocyte metabolic reprogramming, glucose and amino acid metabolism, and emerging interest in immune-cell metabolic interactions. Overall, this bibliometric analysis demonstrates continued expansion of research in T-lymphocyte immunometabolism and highlights emerging thematic directions, including intercellular metabolic regulation and lipid metabolism.

Introduction

Naïve T lymphocytes primarily rely on mitochondrial oxidative phosphorylation (OXPHOS) to maintain their quiescent and low-energy metabolic state¹. Upon antigen stimulation, however, activated T lymphocytes undergo a metabolic shift toward aerobic glycolysis to support rapid proliferation and effector function². Effector T lymphocytes, particularly cytotoxic subsets, depend on glycolysis to meet the increased bioenergetic and biosynthetic demands associated with cytokine production and cellular expansion³. Although glycolysis produces less adenosine triphosphate (ATP) per glucose molecule than OXPHOS, its rapid metabolic rate supports the functional requirements of activated immune cells⁴. Excessive glycolytic activity may also contribute to detrimental effects within the immune microenvironment. Lactate accumulation can impair glucose-derived serine synthesis and alter intracellular redox balance, thereby suppressing T-lymphocyte proliferation and function⁵. In addition, elevated lactate levels disrupt glycolysis-dependent migration pathways and reduce the motility of CD4⁺ and CD8⁺ T lymphocytes^6,7.

Cellular metabolism dynamically adapts to environmental and physiological stimuli through a process known as metabolic reprogramming^8,9. Increasing evidence indicates that metabolic reprogramming plays a central role in regulating T-lymphocyte activation, differentiation, and functional plasticity^10,11. These metabolic alterations are particularly relevant in cancer and autoimmune diseases, where changes in the immune microenvironment influence T-lymphocyte function and immune regulation^12,13. Although several bibliometric studies have evaluated immunometabolism in broader contexts, including cancer immunometabolism and disease-associated immune metabolism, these analyses mainly focus on generalized immune-metabolic interactions rather than T-lymphocyte-specific metabolic reprogramming^14,15. A comprehensive bibliometric evaluation dedicated specifically to T-lymphocyte immunometabolism remains limited, particularly with respect to research trends, collaboration networks, and evolving thematic hotspots within this field.

To address this gap, this study performed a bibliometric analysis of publications indexed in the Web of Science Core Collection (WOScc) from January 1, 2016 to December 31, 2025. CiteSpace, VOSviewer, and Scimago Graphica were used to quantitatively analyze publication output, collaboration patterns, co-citation relationships, and thematic evolution in T-lymphocyte metabolic reprogramming research. Unlike traditional narrative reviews, bibliometric analysis provides a structured and data-driven overview of the scientific landscape through citation, clustering, and keyword analyses. The objective of this study was to systematically characterize global research trends and emerging hotspots in T-lymphocyte immunometabolism and to provide an organized overview of the current knowledge landscape in this rapidly evolving field.

Protocol

This study exclusively analyzed bibliographic data retrieved from publicly accessible databases and did not involve human participants, animals, or identifiable personal data; therefore, institutional ethics approval and informed consent were not required.

Data Collection and Search Strategy
Data were extracted from the WoSCC (https://www.webofscience.com/wos/woscc/) on January 27, 2026. The database editions selected were the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index within WoSCC. The search was conducted using the Advanced Search interface. The search strategy was applied in the “Topic (TS)” field, and the complete search query and resulting records are presented in Table 1. The search period was restricted to publications indexed between January 1, 2016 and December 31, 2025 using the WoSCC “Timespan” filter. Only original research articles and review articles were included using the “Document Types” filter. The language was restricted to English, and no restrictions were applied regarding country, institution, or research area. All records were exported in plain text format with “Full Record and Cited References” selected during export. When the number of retrieved records exceeded 500, records were downloaded in multiple batches and subsequently merged into a single dataset prior to analysis. After retrieval, all records were imported into CiteSpace for preprocessing and bibliometric analysis. Duplicate records were identified and removed using the built-in deduplication function in CiteSpace based on bibliographic metadata, including title, authors, publication year, and source journal. When potential inconsistencies or incomplete bibliographic records were identified, manual verification was additionally performed to ensure data accuracy and consistency before analysis.

Set	Results	Search Query
#1	252,461	TS=("T cell*" OR "T-cell*" OR "T lymphocyte*" OR "T-lymphocyte*" OR "T-lymph*" OR "CD4+ T cell*" OR "CD8+ T cell*" OR "cytotoxic T cell*" OR "regulatory T cell*" OR "Treg*" OR "T helper cell*" OR "Th cell*" OR "naive T cell*" OR "memory T cell*" OR "effector T cell*")
#2	13,799	TS=("metabolic reprogram*" OR "metabolic remodel*" OR "metabolic rewiring")
#3	1,882	#1 AND #2

Table 1: Web of Science Core Collection search strategy used for bibliometric data retrieval. The table summarizes the detailed search strategy, keyword combinations, Boolean operators, and final retrieval counts used for literature collection from the WoSCC. Searches were performed using the “Topic (TS)” field in the Advanced Search interface. Wildcards (*) were used to retrieve multiple word endings and related terminology. The final dataset consisted of publications indexed between January 1, 2016 and December 31, 2025 after application of language and document-type filters.

Analytical Approaches
A multitool bibliometric approach was systematically implemented for data analysis. The analysis was conducted in a Windows 10 operating system environment. CiteSpace (v.5.7.R2) was used to analyze references and keywords by processing TXT-format data exported from WoSCC. The CiteSpace parameters were configured as follows: (1) time slicing was set from 2016 to 2025 with one year per slice; (2) selection criteria were configured using the g-index (k = 25); (3) pruning methods included Pathfinder, Pruning Sliced Networks, and Pruning the Merged Network to simplify network structures while preserving key relationships; and (4) clustering analysis was performed using the log-likelihood ratio algorithm¹⁶. This analysis generated co-citation networks, clustering maps, timeline visualizations, and burst detection maps for both references and keywords. In the visualization maps, node size represented frequency, link thickness indicated co-occurrence strength, and purple rings represented high betweenness centrality. Burst nodes reflected a sudden increase in citation or keyword frequency over time.

Subsequently, the exported files were re-saved in UTF-8 encoding using Windows Notepad and imported into VOSviewer (version 1.6.20) for bibliometric mapping, including co-authorship, co-citation, and co-occurrence analyses across authors, journals, institutions, and countries¹⁷. The full-counting method was applied, and normalization was performed using the association-strength method. Minimum occurrence thresholds were established according to dataset size (authors ≥ 5 publications; keywords ≥ 5 occurrences; institutions ≥ 3 publications). Network visualization was based on item weight and total link strength. For country-level collaboration analysis, GML files exported from VOSviewer were imported into Scimago Graphica (version 1.0.53), where nodes represented countries. Network visualization and clustering were generated using the default force-directed layout algorithm to represent spatial relationships and collaboration patterns among countries. WPS Excel was used for descriptive statistical analysis of annual publication trends and basic data summarization. Results were visualized using bar charts, column charts, and line graphs.

A supplementary time-sliced analysis was performed using the same search strategy described above, with the search restricted to publications indexed between January 1, 2025 and December 31, 2025. Within the same WoSCC query framework and analytical environment, recent publications and conference-related outputs were retrieved for contextual and descriptive purposes. This procedure was conducted entirely within the built-in analytical interface of the WoSCC. No independent dataset was created, and the retrieved information was not integrated into the primary bibliometric dataset or used for quantitative analysis. The overall study design and bibliometric analysis workflow are summarized in a flowchart (Figure 1).

Figure 1. Workflow of the bibliometric analysis of T-lymphocyte metabolic reprogramming research. Schematic overview of the study design and analytical workflow used in this bibliometric analysis. Publications were retrieved from the Web of Science Core Collection (WoSCC) using predefined search strategies and inclusion criteria. Retrieved records were exported in plain text format and subjected to preprocessing, including duplicate removal and bibliographic data standardization. Bibliometric analyses were conducted using CiteSpace for co-citation, keyword co-occurrence, clustering, and burst detection analyses, and VOSviewer for collaboration network analyses among authors, institutions, countries, and journals. Scimago Graphica was used to visualize international collaboration networks, and WPS Excel was used to analyze annual publication trends and descriptive statistics. Please click here to view a larger version of this figure.

Results

Publication Output and Temporal Trends
To characterize the temporal evolution of research activity in this field, a bibliometric analysis was performed using publications indexed in the WoSCC. A total of 1,991 records were initially retrieved over the past decade. After removal of duplicate and non-English records, 1,882 publications were ultimately included and analyzed, including 1,046 original research articles and 836 review articles. Annual publication and citation trends from 2016 to 2025 are p...

Discussion

Our bibliometric analysis of metabolic reprogramming in T cells revealed a sustained increase in research output over time. A noticeable increase in publication and citation counts occurred in 2020, with 40 additional publications and 1,989 additional citations compared with 2019. This increase coincided temporally with several highly cited publications, including Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion (published November 22, 2019)¹⁸, whic...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CiteSpace	Drexel University	v.5.7.R2	SCR_025121
Microsoft Excel	Microsoft Corporation	v.2604 Build 16.0.19929.20136	SCR_016137 (Microsoft Excel)
WPS Excel	Kingsoft Office	v.12.1.0.26375
Scimago Graphica	Scimago Lab	v.1.0.53	Not available
VOSviewer	Leiden University	v.1.6.20	SCR_023516
Web of Science Core Collection	Clarivate Analytics	accessed on January 27, 2026; version not applicable	Not available
Windows Operating System	Microsoft Corporation	Windows 10	Not available