Bibliometric Analysis of CD8⁺ T Cells in Colorectal Cancer Research

Bibliometric Analysis of CD8⁺ T Cells in Colorectal Cancer Research | 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 Bibliometric Analysis of CD8⁺ T Cells in Colorectal Cancer Research Xu Yang, Jing MA, Junfeng LUO, Juanjuan WANG, Maoyu LIAO, Haibo GAO, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8934222/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Colorectal cancer (CRC) remains a global health burden. Recent advances in tumor immunotherapy have reshaped the therapeutic landscape, highlighting the pivotal role of CD8 ་ T cells in tumor immune surveillance and elimination. However, this field is fragmented, warranting bibliometric evaluation. Methods Relevant articles and reviews were retrieved from the Web of Science Core Collection. Data on publications, countries, institutions, authors, journals, citations, and keywords were systematically analyzed using Microsoft Excel 2019 and CiteSpace 6.3.R3. Results A total of 2006 publications on CD8⁺ T cells and CRC published between 1992 and 2024 were included. Annual publications showed a significant upward trend, peaking in 2024 (n = 320). Frontiers in Immunology contributed the most articles (n = 84). China, the United States, Japan, and France were identified as the leading contributors, with China ranking first in publication volume. Among institutions, Sun Yat-sen University had the highest output (n = 91). The most prolific authors were Inti Zlobec and Francois Ghiringhelli (10 publications each). The 10 most influential publications appeared in high-impact journals, with landmark studies in Science and the New England Journal of Medicine exceeding 200 citations. Current research hotspots include reversing T-cell exhaustion, overcoming the immunosuppressive tumor microenvironment (TME), and elucidating mechanisms of immunotherapy resistance. Conclusion This bibliometric analysis systematically maps the knowledge framework and evolutionary trajectory of CD8⁺ T cell research in CRC. The findings provide critical insights into immune microenvironment mechanisms, novel biomarker discovery, and optimization of immunotherapeutic strategies for CRC. CD8 + T cell Colorectal Cancer bibliometric analysis CiteSpace knowledge graph Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Colorectal cancer (CRC) is among the most common malignancies of the digestive system [ 1 ] . Recent global statistics report over 1.9 million new CRC cases and approximately 900,000 related deaths annually, ranking it third in incidence and second in mortality among all cancers [ 2 ] . The development of CRC is driven by multiple interacting factors. Early-onset CRC (in patients younger than 50 years) has been linked to the adoption of Western dietary patterns, chronic psychological stress, and gut microbiota alterations associated with widespread antibiotic use [ 3 ] . Other established risk factors include sex, genetic susceptibility, smoking, obesity, unhealthy lifestyles, and family history [ 4 ] . Current therapeutic strategies for CRC include surgery, radiotherapy, targeted therapy, immunotherapy, and, in selected cases, hormonal therapy [ 5 ] . However, because CRC often remains asymptomatic in its early stages, many patients are diagnosed at advanced stages with distant organ or tissue metastases, thereby missing the optimal window for curative surgery [ 6 ] . Consequently, there is an urgent need for novel therapeutic approaches to improve patient outcomes. Cancer immunotherapy has gained unprecedented attention, particularly following the clinical success of immune checkpoint blockade therapy (ICBT) in multiple cancer types [ 7 ] . Within this context, CD8⁺ T cells are central to tumor immunity, and their roles in CRC have attracted considerable interest owing to their pivotal contribution to antitumor responses and their implications for immunotherapeutic strategies. CD8⁺ T cells are a critical component of the adaptive immune system, with effector, memory, and exhausted phenotypes that play distinct roles in anti-tumor immunity. Effector T cells are activated immune cells responsible for eliminating infected or malignant cells. They infiltrate tissues by upregulating surface molecules such as CD44 and CD69 and secrete cytotoxic mediators, including perforin and granzymes, to induce apoptosis in target cells (Fig. 1 A). Following antigen clearance, effector CD8⁺ T cells undergo contraction and differentiate into two subpopulations, KLRG1⁺ and KLRG1⁻ cells, with the former representing short-lived effector cells and the latter giving rise to long-lived memory cells [ 8 ] . This differentiation process is tightly regulated by transcription factors such as T-bet and Eomesodermin (Eomes) [ 9 ] . Under conditions of persistent antigenic stimulation, however, CD8⁺ T cells progressively lose their effector function and enter a dysfunctional state termed T-cell exhaustion. This phenotype is characterized by impaired cytokine production (e.g., IL-2, IFN-γ), diminished cytotoxicity, reduced proliferative potential, and eventual apoptosis (Fig. 1 B) [ 10 ] . In the tumor microenvironment (TME), CD8⁺ T-cell exhaustion is strongly associated with the upregulation of multiple inhibitory receptors, including programmed cell death protein-1 (PD-1), lymphocyte activation gene-3 (LAG-3), and T-cell immunoglobulin and mucin domain-containing protein-3 (TIM-3). These receptors transmit inhibitory signals upon binding to their respective ligands on tumor cells, such as PD-L1 or galectin-3 (Gal-3), thereby amplifying T-cell dysfunction [ 11 ] . The memory compartment of CD8⁺ T cells can be further subdivided into central memory T cells (T_CM) and effector memory T cells (T_EM). T_CM cells display superior antitumor activity compared with T_EM cells, which exhibit more limited protective capacity [ 12 ] . Tumor-specific T_CM and T_EM subpopulations have been identified in patients with breast cancer and CRC [ 10 ] . Within the CRC immune microenvironment, both the density and spatial distribution of CD8⁺ T cells—whether located in the tumor core or invasive margin—carry comparable prognostic value. These distributions, however, are dynamically shaped by stromal factors such as CXCL12 (SDF-1) [ 13 ] . The functional status of CD8⁺ T cells, particularly the balance between activation and exhaustion, is central to their tumor-controlling efficacy. Notably, exhausted CD8⁺ T cells often co-express high levels of inhibitory receptors such as PD-1 and TIM-3, reflecting a paradoxical state in which antitumor function is limited but can be at least partially restored through immune checkpoint blockade [ 14 ] . Advances in elucidating the molecular pathways that regulate CD8⁺ T-cell activity and their interactions with the TME provide promising opportunities to enhance immunotherapeutic strategies and improve clinical outcomes in CRC. Bibliometrics is a methodological approach for literature analysis that examines bibliographic systems and bibliometric characteristics to evaluate research output and trends from both quantitative and qualitative perspectives [ 15 , 16 ] . It can also examine research priorities and hotspots within research areas, reveal potential research patterns, and predict future trends, as well as assess the research output of countries, institutions, and researchers [ 17 , 18 ] . The role of CD8⁺ T cells in CRC has received increasing attention, with numerous studies investigating their infiltration, functional status, and regulatory mechanisms within the TME. These studies have revealed the complex cellular interactions that shape disease outcomes. For instance, O'Malley et al. (2018) demonstrated that PD-L1 expression by stromal cells suppresses CD8⁺ T cell activity and promotes tumor progression, underscoring the importance of stromal components in immune regulation and suggesting that targeting stromal PD-L1 could restore CD8⁺ T cell function [ 19 ] . Another study identified diverse CD8⁺ T-cell subpopulations with distinct functions and clonality, including IFNG-producing T1-like cells associated with effective antitumor immunity, highlighting the phenotypic heterogeneity of CD8⁺ T cells and the importance of understanding their functional states for therapeutic development [ 20 ] . In addition, TRIB3 has been identified as a negative regulator of CD8⁺ T-cell infiltration through suppression of the STAT1–CXCL10 axis, thereby facilitating immune evasion; these findings point to potential therapeutic targets for enhancing antitumor immunity [ 21 ] . Despite these advances, no bibliometric study has systematically analyzed the literature on CD8⁺ T cells in CRC, limiting comprehensive evaluation of research progress and future directions in this field. Therefore, the present study aims to assess the development, trends, and impact of publications related to CD8⁺ T cells in CRC, with the goal of clarifying research frontiers and hotspots and providing insights into their translational potential. 2 Materials and methods 2.1 Data source To improve the representativeness and accessibility of the data, this study utilized the Web of Science Core Collection (WoSCC) database enriched by the Science Citation Index as the primary data source. Web of Science is the leading research platform for information in the hard sciences, social sciences, arts, and humanities, and is the world's most trusted publisher-independent global citation database [ 22 ] . The WoSCC database is known for its comprehensive coverage, systematic methodology, and authority on more than 12,000 influential, high-quality journals worldwide. Use the search formula TS=("CD8* T cell*") AND TS=("Rectal Neoplasm" OR "Rectal Tumor" OR "Rectal Cancer" OR "Rectum Neoplasm" OR "Rectum Cancer" OR "Cancer of the Rectum" OR "Cancer of Rectum" OR "Colorectal Neoplasm" OR "Colorectal Tumor" OR "Colorectal Cancer" OR "Colorectal Carcinoma" OR "Colonic Neoplasm" OR "Colon Neoplasm" OR "Cancer of the Colon" OR "Colon Cancer" OR "Cancer of the Colon" OR "Colonic Cancer") for a comprehensive investigation, focusing on articles and reviews in the English language published until July 10, 2025, to avoid any potential bias due to the daily updates. This search resulted in a total of 2,006 relevant documents. 2.2 Data import and merging The complete data records were exported from the Web of Science Core Collection (WoSCC) with all the details, such as the annual research, countries/regions, source journal, institution, authors, keywords, citations, and Journal Citation Reports. Select bibliographic records in Plain Text File format and check "Record Content: Full Record" and "Cited References" in the export settings. The downloaded bibliographic records were then imported into NoteExpress, a document management software, where the records were filtered, validated, and the entries within each field were manually cleaned and standardized for subsequent analysis. At the same time, we identified three situations in which data needed to be merged and proposed solutions accordingly. These situations included (1) full names or abbreviations of the same country/region name, such as different writing conventions for the United States of America and USA; (2) different abbreviations of the same author's name or variations of the last name and first name order, which we solved through the use of ORCID information and the author's affiliation; and (3) synonymous expressions that were manually normalized to eliminate redundant entries, e.g., 'immune evasion' was normalized to 'immune escape'. 2.3 Data analysis and visualization After completing the data merging process, the integrated bibliographic records in Plain text format were imported into Microsoft Excel 2019 and CiteSpace V.6.3.R3 for multidimensional analysis. Microsoft Excel primarily conducts bibliometric analysis to statistically and visually represent trends in the number of publications per year, thereby illustrating the development of the research field over time. CiteSpace is used to construct and analyze scientific knowledge maps: to map the research cooperation networks among countries/regions, research institutions, and authors, and to reveal the patterns of cooperation and core subjects; and to conduct in-depth mining of citation data, which includes the construction of a co-citation network map and the identification of Highly Cited References (HCRs). Finally, we explore keywords from multiple perspectives, including keywords co-occurrence analysis and network visualization, keywords cluster analysis to extract research themes, and Keywords Burst Detection. We utilize Keywords Burst Detection to pinpoint research frontiers and emerging hotspots. The specific process is shown in Fig. 2 . 3 Results 3.1 Publication outputs and time trend From 1992 to 2024, a total of 2,006 publications were published in research related to CRC and CD8 + T cells. The number of publications has experienced a period of relative inactivity followed by rapid growth, as shown in Fig. 3 : the number of publications in the initial years was very low, with no publications appearing in 1994, and publications up to 2005 were mostly below 20; publications from 2006 to 2018 showed slight fluctuations but an overall upward trend; and from 2019 to 2024, there was explosive growth with an average increase of 43 publications per year. In addition, according to the linear regression results (y = 7.0431x-14082, R² = 0.6316), the number of publications shows a moderate positive correlation with the progression of years, indicating that this research field is in a phase of sustained development and has enormous potential for future growth. 3.2 Distribution of journals This study analyzed 2,006 publications were sourced from 484 distinct academic journals. Among these, Frontiers in Immunology exhibited the highest publication output, contributing 84 articles, which represent 4.19% of the total corpus. This ranking was followed by the Journal for Immunotherapy of Cancer , which published 67 articles, accounting for 3.34% of the total. Furthermore, 38 journals, representing 7.85% of the total, published at least 10 articles, totaling 1,003 articles, or 50% of the total. Notably, the top 10 journals published 523 articles, accounting for 26.07% of the total publications. These top 10 journals, representing merely 2.07% of the total journals, contributed over a quarter of the publications. These data indicate a pronounced core journal effect within this research field. Figure 4 displays the top 10 journals ranked by publication volume. 3.3 Analysis of countries/regions and institutions A total of 64 countries and regions participated in this study, A total of 2,542 studies have been published, with 25 of them publishing more than 10 studies each; China had the largest contribution at 36.2% of the total studies, followed by the United States at 18.1% and Japan at 7.2%. Table 1 shows in detail the top 10 major contributing countries/regions of research output and their corresponding bibliometric data. Subsequently, we used CiteSpace to visualize and analyze national collaborations, a collaborative network consisting of 64 nodes and 200 edges, presenting academic collaborations among highly productive countries; see Fig. 5 . The top 10 countries/regions are China, the United States, Japan, Germany, the United Kingdom, France, South Korea, Italy, Switzerland, and the Netherlands, and it is noteworthy that Canada and the Netherlands are jointly ranked 10th. Among them, the top three in terms of centrality are the United States, France, and China, with centralities of 0.42, 0.23, and 0.22, respectively. The combination of the number of publications and the centrality index can be analyzed to show that China, the United States, Japan, and France are the major research forces in this study. Table 1 Top 10 countries and institutions in the field of CD8 + T cell in Colorectal Cancer among 2006 included studies (1992–2024). Rank Country Count Centrality Institute (Country) Count Centrality 1 CHINA 921 0.22 Sun Yat-sen University (China) 91 0.08 2 USA 460 0.42 Fudan University (China) 68 0.03 3 JAPAN 183 0.16 Shanghai Jiao Tong University (China) 63 0.03 4 GERMANY 156 0.08 Zhejiang University (China) 44 0.05 5 UK 85 0.13 Chinese Academy of Sciences (China) 40 0.07 6 FRANCE 79 0.23 National Cancer Institute (USA) 37 0.12 7 SOUTH KOREA 76 0 Nanjing Medical University (China) 35 0.01 8 ITALY 58 0 Soochow University (China) 34 0.03 9 SWITZERLAND 52 0.03 Southern Medical University (China) 33 0.01 10 NETHERLANDS 47 0.1 Chinese Academy of Medical Sciences & Peking Union Medical College (China) 33 0.02 11 CANADA 47 0.05 Peking University (China) 33 0.02 In this study, a total of 1078 institutions were involved in the publication of research papers in this field, of which 185 universities (17.16%) contributed more than five papers. The top 10 universities had a threshold of 33 publications, with Sun Yat-sen University leading the way with the highest number of 91. In terms of centralities, National Cancer Institute ranked first with a centrality of 0.12, while Nanjing Medical University and Southern Medical University tied for 10th with a centrality of 0.01. The publication volume and centrality of the top 10 universities are detailed in Table 1 . The results of Institution Collaboration Network demonstrated 1397 nodes and 3734 links, and these data revealed solid collaborative relationships. In this network diagram, the top five institutions with the most active collaborative relationships are Sun Yat-sen University, Fudan University, Shanghai Jiao Tong University, Zhejiang University, and Chinese Academy of Sciences. The results of the Collaborative Institution Mapping are illustrated in Fig. 6 . 3.4 Analysis of authors and co-cited representative literature In the results of the analysis of authors and co-cited reference analysis, a total of 17107 authors were identified, of which the top 10 authors with a cumulative number of 90 publications were affiliated with 8 different research institutions, of which Zlobec, Inti, and Ghiringhelli, Francois, tied for the first place with 10 publications each, followed by Snook, Adam E., who published 9 articles. Table 2 demonstrates the data related to the top 10 most prolific authors. Table 3 summarizes the 10 most highly cited original articles on CD8 + T cells in CRC research. Among the top 10 highly cited original articles in this field, Science and New England Journal of Medicine had a significant impact with more than 200 citations each, with Science reaching the first place with 296 citations, followed by New England Journal of Medicine with 205 citations, and Nature Medicine tied for the 10th place in terms of the number of total citations with Ca-a Cancer Journal For Clinicians ; Galon J et al. results published in Science were the most highly cited document with 296 citations [ 23 ] . demonstrating its central influence in the field. The cumulative citations of the top 10 publications totaled 1,445, highlighting their core influence within the field. Figure 7 demonstrates the CiteSpace-based co-citation network analysis of the literature, identifying a total of 1453 nodes and 4368 co-occurring links, The high-density network structure reveals the complexity of the domain knowledge association. Table 2 Top 10 authors who published literature on CD8 + T cell in CRC among 2006 included studies (1992–2024). Rank Author Institute Count Centrality 1 Zlobec, Inti University of Bern 10 0.00 2 Ghiringhelli,Francois CHU Grenoble Alpes 10 0.00 3 Snook, Adam E. Jefferson Hlth 9 0.00 4 Scott A. Waldman Jefferson Hlth 9 0.00 5 Torigoe, Toshihiko Sapporo Medical University 9 0.00 6 Zheng, Xiao Soochow University - China 9 0.00 7 Chen, Lujun Gannan Medical University 9 0.00 8 Lugli, Alessandro University of Bern 9 0.00 9 Huang, Yizhou Central South University 8 0.00 10 Weitz, Juergen Technische Universitat Dresden 8 0.00 Table 3 Top 10 co-citation representative literature of CD8 + T cell in CRC among the 2006 articles included (1992–2024). Rank Cited number Title Type Year Centrality Journal JCR (2024) IF (2024) Reference 1 296 Type, density, and location of immune cells within human colorectal tumors predict clinical outcome Article 2006 0.07 Science Q1 45.8 [ 23 ] 2 205 PD-1 Blockade in Tumors with Mismatch-Repair Deficiency Article 2015 0.07 New England Journal of Medicine Q1 78.5 [ 33 ] 3 183 Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries Article 2021 0 Ca-a Cancer Journal For Clinicians Q1 232.4 [ 98 ] 4 123 Effector memory T cells, early metastasis, and survival in colorectal cancer Article 2005 0.08 New England Journal of Medicine Q1 78.5 [ 34 ] 5 117 CD8 + T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer Article 1998 0.13 Cancer Research Q1 16.6 [ 88 ] 6 114 International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study Article 2018 0.02 Lancet Q1 88.5 [ 99 ] 7 106 Immunotherapy in colorectal cancer: rationale, challenges and potential Review 2019 0.02 Nature Reviews Gastroenterology & Hepatologyt Q1 51.0 [ 100 ] 8 101 Safety, activity, and immune correlates of anti-PD-1 antibody in cancer Article 2012 0.05 New England Journal of Medicine Q1 78.5 [ 101 ] 9 100 The consensus molecular subtypes of colorectal cancer Article 2015 0.01 Nature Medicine Q1 50.0 [ 102 ] 10 100 Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries Article 2018 0.00 Ca-a Cancer Journal For Clinicians Q1 232.4 [ 103 ] * IF: impact factor; *JCR: Category Quartile of Journal Citation Report 3.5 Keyword analysis 3.5.1 Keywords co-occurrence analysis In this research analysis, we used CiteSpace software to identify and analyze keywords related to CD8 + T cells in CRC research papers during the publication period of 1992–2024. The top five keywords with the highest frequency were, in order, colorectal cancer (n = 554), immunotherapy (n = 262), tumor microenvironment (n = 161), colon cancer (n = 145), and prognosis (n = 136). In addition, a larger centrality value of a node represents a higher strength of its connection with other nodes. The centrality analysis indicated that the top five keywords were colorectal cancer (n = 0.55), immunotherapy (n = 0.33), colon cancer (n = 0.27), tumor microenvironment (n = 0.12), and apoptosis (0.08). All of these keywords show extremely high centrality and are key pivotal nodes in the network; among these keywords, colorectal cancer has the highest frequency and centrality, highlighting its centrality within the field, followed by the high centrality of immunotherapy, indicating its importance in research and its close relationship with colorectal cancer, which together promote the development of the field. 3.5.2 Keywords clusters analysis The keyword clustering graph reflects the structural features among the clusters, highlighting their key nodes and basic connections [ 24 ] . Based on the results of keyword co-occurrence analysis, we generated a keyword clustering network graph using LLR, and the network clustering structure is significant and plausible, and the Modularity Q = 0.536 > 0.5 and Mean Silhouette = 0.8128 > 0.7 of the keyword clustering graph are higher than the empirical thresholds, which proves that the clustering is clearly and reasonably delineated with high internal homogeneity. By reviewing and screening the clustered keywords, the labels of the final 10 core clusters (#0-#9) together outline the basic knowledge structure of the CRC and CD8 + T cell research field. Among the 10 keyword clusters, the research topics can be divided into three categories: (#5) and (#6) are the first category, which are mainly related to the CD8 + T cell-related diseases, reflecting the clinical background of the disease itself; (#1), (#2), (#8), and (#9) form the second group, which are mainly related to the microscopic mechanism of tumor immunity and TME characteristics; and (#0), (#3), (#4), and (#7) form the third group, which is mainly related to the application path of immunotherapy and its clinical effects. Among these clusters, the largest cluster #0 immunotherapy (n = 58) represents the core cluster, indicating that immunotherapy is a central theme in tumor research, encompassing both mechanism exploration and clinical application. As is shown in Fig. 8 . 3.5.3 Keywords citation burst analysis Analysis of keyword citation bursts can reveal research trends over a specific time frame [ 25 ] . This study integrates keyword co-occurrence analysis and keyword citation burst detection methods to quantitatively identify the 25 core keywords with the most explosive growth in citation frequency. As shown in Fig. 9 , in the Keywords citation burst analysis, "Begin" and "End" indicate the start and end time of the keyword burst. The "Strength" represents the burst strength, which reflects the statistical significance of the keyword's citation growth over time. By identifying keywords with high burst strength, researchers can effectively track the current research hotspots and cutting-edge dynamics in the field. In the Keywords citation burst analysis, the analysis reveals three phases of results. The first phase, from 1993 to 2002, highlights keywords such as "interleukin-2, CTL, cytotoxicity, gene therapy, adenovirus," which mainly focus on the underlying immune mechanisms and gene therapy techniques, reflecting the initial exploration of cancer immunotherapy. For example, CTLs are important anti-tumor effector cells in the immune system, capable of performing immunosurveillance functions by recognizing and killing tumor cells. The prognosis of patients with CRC closely correlates with the degree of CTL infiltration. Studies have shown that the infiltration density of CD8 + T cells is an important predictor of prognosis in CRC patients [ 23 ] . The second stage of mid-term development, which occurred from 2003 to 2012, shifted its focus to clinical applications such as vaccine development, antibody therapy, and tumor immunology, emphasizing terms like "vaccination, cancer, antibody, cancer vaccine, tumor immunology, immune response, cea, costimulation." The third phase spans from 2013 to 2024, focusing on keywords such as tumor-infiltrating, scRNA-seq, immune checkpoint inhibitors, biomarkers, tumor-infiltrating lymphocytes, PD-1, immunosuppression, NKG2D, cancer immunotherapy, CD8 + T cells, immunoscore, and immunomodulation. This stage mainly focused on the cutting-edge fields of immune checkpoints, TME, and single-cell technology, reflecting the technology-driven research trend. It has been found that combining anti-PD-1/PD-L1 therapies with chemotherapy, anti-angiogenic drugs, or targeted drugs improves treatment efficacy, especially in Microsatellite stable (MSS) CRC [ 26 ] . Phase III keywords are still relevant today and represent emerging trends and future research directions. 4 Discussion 4.1 General information Over the past three decades, research on CD8 + T cells in CRC has expanded from a marginal topic to a rapidly growing hotspot. Our bibliometric analysis of 2,006 publications (based on CiteSpace) revealed three distinct phases of development. Before 2005, annual publication output remained persistently low, with some years producing fewer than 20 papers, including none in 1994. From 2006 to 2018, the field entered a period of fluctuating but steady growth, largely driven by seminal findings. Notably, in 2006, Galon and colleagues first demonstrated that tumor-infiltrating CD8⁺ T-cell density positively correlated with survival in CRC patients [ 23 ] , establishing the prognostic value of adaptive immunity in this malignancy. After 2019, research activity surged, with an average annual increase of 43 publications, highlighting the expanding significance of CD8⁺ T cells in CRC. Clinical studies further confirmed that responses to ICBT, such as anti-PD-1 treatment, in Microsatellite-unstable (MSI) CRC subpopulations are strongly dependent on CD8⁺ T-cell-mediated antitumor immunity, underscoring their central role in immunotherapy [ 27 ] . More recently, the integration of single-cell sequencing and spatial transcriptomics has facilitated deeper mechanistic insights, reinforcing the importance of CD8⁺ T cells in developing next-generation immunotherapeutic strategies. This study also characterizes the global collaborative landscape of CD8⁺ T cell research in CRC. A total of 64 countries/regions contributed relevant publications, with China (921 articles, 36.2%), the United States (460 articles, 18.1%), and Japan (183 articles, 7.2%) leading the output. The national collaboration network constructed in CiteSpace (64 nodes, 200 edges) revealed that, apart from China, the top 10 most productive countries were all developed nations, reflecting the research dominance of developed countries. Although China ranked first in publication volume, its network centrality (0.22) and number of international collaborations (3) were lower than those of the United States (centrality 0.42, 40 collaborations). This indicates that China’s global engagement remains limited compared with developed countries. By contrast, the United States, with the highest centrality and most extensive partnerships, serves as the hub of the global collaboration network. These differences are largely attributable to disparities in research resources, including funding and infrastructure, between developed and developing countries. Institutional network analysis identified 1,078 institutions engaged in CD8⁺ T cell and CRC research, of which 185 (17.2%) published more than five papers. Sun Yat-sen University (91 papers), Fudan University (68 papers), and Shanghai Jiao Tong University (63 papers) were the top three contributors. Along with Zhejiang University (44 papers) and the Chinese Academy of Sciences (40 papers), they represent the core research power in this field (Table 1 ). In terms of collaboration, the National Cancer Institute (NCI) emerged as the central hub with the highest centrality (0.12). By comparison, Chinese institutions, despite ranking among the top in publication output, generally had lower centrality values (< 0.08). The network, consisting of 1,397 nodes and 3,734 links, indicates a high level of internationalization and collaboration. While Chinese institutions dominate in publication volume, developed institutions such as NCI continue to lead global collaboration networks, reflecting an imbalance between quantitative output and qualitative influence in international research integration. The analysis of author clusters indicates that research on CD8⁺ T cells in CRC is characterized by a high concentration of core contributors. Among 17,107 participating authors, the top 10 most productive scholars collectively published 90 articles across eight research institutions. The most influential authors include Zlobec, Inti, and Ghiringhelli, François (10 publications each), as well as Snook, Adam E. (9 publications). Zlobec’s group, in their work on tumor budding, demonstrated that in rectal cancer patients undergoing neoadjuvant radiotherapy, tumor buds represent highly aggressive residual lesions that are strongly associated with adverse pathological features and poor survival outcomes, whereas CD8⁺ T cells did not show clear prognostic significance in this therapeutic context [ 28 ] . In another study, they reported that RHAMM-positive tumor regions (including budding areas) were significantly associated with higher PD-1/PD-L1 expression and increased CD8⁺ T cell infiltration in mismatch repair-proficient (MMRp) stage IV colorectal cancer with liver metastases, suggesting that RHAMM may define a more immunoreactive tumor microenvironment and could serve as a potential biomarker for predicting response to immune checkpoint inhibitors (ICIs) [ 29 ] . Ghiringhelli’s team highlighted the limited efficacy of PD-1/PD-L1 blockade in microsatellite-stable (MSS) CRC but demonstrated that combined immune checkpoint inhibition may overcome this resistance. Specifically, they showed that atezolizumab (anti–PD-L1) combined with tiragolumab (anti–TIGIT) restored the immune function of tumor-infiltrating lymphocytes (TILs) in 46% of MSS-CRC samples [ 30 ] . Snook’s group investigated the split-tolerization mechanism and provided evidence for the safety and specificity of GUCY2C-targeted vaccines in CRC immunotherapy, supporting their potential clinical utility [ 31 ] . Analysis of publication outputs and citations revealed that Science and The New England Journal of Medicine are the leading journals in this field, exerting significant influence on CD8⁺ T cell and CRC research. Science, with an impact factor of 45.8 in 2024. The journal has played an important role in research in the field of CD8 + T cells and CRC. For example, Le DT et al. [ 32 ] reported in Science that cancers with defective mismatch repair (dMMR) accumulate abundant mutant neoantigens due to high mutational burden, rendering them highly sensitive to PD-1 blockade. In a cohort of 12 advanced solid tumor types, PD-1 antibody therapy induced objective responses in 53% of dMMR patients, with 21% achieving complete remission. Notably, these responses were durable, as median progression-free and overall survival were not reached at study end, underscoring the long-lasting efficacy of PD-1 blockade. Mechanistic studies demonstrated that treatment responders exhibited rapid clonal expansion of neoantigen-specific T cells with robust tumor-specific immunity. This finding established dMMR status as a genetic biomarker for immunotherapy response and highlighted that treatment efficacy depends on the quality and quantity of neoantigens rather than tumor tissue origin. Other high-impact journals, including CA: A Cancer Journal for Clinicians , Cancer Research , Lancet , and Nature Reviews Gastroenterology & Hepatology , have also contributed significantly to advancing CD8⁺ T cell and CRC research. 4.2 Important research findings Citation analysis identified the landmark study by Galon J et al., Science (2006), entitled “Type, density, and location of immune cells within human colorectal tumors predict clinical outcome”, as the most highly cited publication in this field (296 citations) [ 23 ] . This pioneering work demonstrated that the type, density, and spatial distribution of immune cells—particularly tumor-infiltrating CD8⁺ T cells—within the TME are critical predictors of clinical prognosis in CRC. A high density of CD8⁺ T cells, especially in the tumor core and invasive margins, was strongly associated with improved patient survival. This study laid the foundation for the Immunoscore concept and firmly established the central role of CD8⁺ T cell-mediated anti-tumor immunity in CRC progression and patient outcomes. The second most cited clinical study, “PD-1 Blockade in Tumors with Mismatch-Repair Deficiency” by Le DT et al., published in The New England Journal of Medicine (2015), received 205 citations [ 33 ] . This seminal trial provided the first clinical evidence that ICBT targeting PD-1 can induce robust and durable clinical responses in metastatic CRC patients with dMMR. The investigators showed that the elevated tumor mutational burden associated with dMMR generates abundant neoantigens, rendering these tumors highly susceptible to CD8⁺ T cell-mediated cytotoxicity. PD-1 inhibitors effectively reversed the functional inhibition of CD8⁺ T cells within the TME, thereby validating the therapeutic potential of immunotherapies that rely on CD8⁺ T-cell activity. Together, the foundational work by Galon J et al. (2006), which established tumor-infiltrating CD8⁺ T cells as a core prognostic biomarker, and the clinical breakthrough of Le DT et al. (2015), which translated this principle into a successful therapeutic strategy for dMMR CRC, highlight the pivotal role of CD8⁺ T cells in CRC biology and management. These studies exemplify a research continuum spanning mechanistic insights to clinical application. Further evidence was provided by Pagès F et al. in New England Journal of Medicine [ 34 ] , who reported that high infiltration of effector memory T cells (e.g., CD8⁺CD45RO⁺) in CRC tissues was strongly correlated with reduced early metastatic features. Such immune-enriched tumors tended to present at earlier clinical stages and were associated with significantly prolonged survival. This study underscores that the local immune microenvironment, particularly the presence and spatial distribution of memory T cells, plays a decisive role in tumor progression, metastasis, and patient prognosis. 4.3 Research hotspots and frontiers Keyword analysis is a valuable tool for accurately identifying emerging themes and research trends within a given field [ 35 ] . In this study, we conducted keyword burst analysis using CiteSpace and identified five keywords that have shown strong citation bursts in the past five years. Based on these findings, we provide an outlook on potential research hotspots concerning CD8⁺ T cells in CRC. 4.3.1 Tumor immune microenvironment The tumor immune microenvironment (TIME) is a critical determinant of CRC progression, therapeutic responsiveness, and patient prognosis. Its defining features include immune cell infiltration dynamics, metabolic reprogramming, and regulation of immunosuppressive molecules [ 36 ] . Clinical evidence demonstrates that in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy (nCRT), dense CD8⁺ T-cell infiltration correlates with an “Inflammation” immune phenotype and significantly prolongs recurrence-free survival (RFS). Conversely, an “immune-desert” phenotype is associated with poor prognosis despite adjuvant chemotherapy [ 37 ] . Single-cell analyses further revealed that CD15-overexpressing neutrophils can promote CRC progression by engaging CD8⁺ T cells through CXCL12, driving their differentiation into the GZMK⁺ effector memory phenotype [ 38 ] . These findings underscore the profound impact of immune cell interactions within TIME on tumor progression and clinical outcomes. The immunosuppressive nature of TIME is also reflected in the enrichment of regulatory T cells (Tregs), PMN myeloid-derived suppressor cells (PMN-MDSCs), and M2-polarized tumor-associated macrophages (M2-TAMs). For instance, the intestinal microbial metabolite 4-hydroxyphenylacetic acid (4-HPA) induces CXCL3 expression via the JAK2/STAT3 pathway, which subsequently recruits PMN-MDSCs through the CXCL3–CXCR2 axis, thereby suppressing CD8⁺ T-cell anti-tumor function and accelerating CRC progression [ 39 ] . Similarly, immune checkpoint molecules such as B7-H3 amplify immunosuppression by inhibiting CD8⁺ T-cell infiltration and activating the CCL2–CCR2–M2 macrophage axis [ 40 ] . Metabolic abnormalities are a central mechanism driving TIME immunosuppression. Estrogen synthesis mediated by the key enzyme of lipid metabolism, CYP19A1, upregulates PD-L1, IL-6, and TGF-β through the GPR30-AKT pathway and inhibits CD8 + T cell function, and targeted inhibition of CYP19A1 enhances anti-PD-1 efficacy [ 41 ] . In parallel, tumor-derived exosomal ENTPD2 hydrolyzes ATP to generate adenosine, which activates the CD39–CD73–A2AR pathway and directly suppresses CD8⁺ T-cell activity [ 42 ] . Accordingly, dual-targeting strategies that combine metabolic and immune checkpoint interventions have shown promise. For example, the VE-cadherin agonist CD5-2 normalizes vascular function, thereby facilitating CD8⁺ T-cell infiltration and synergizing with anti-PD-1 therapy [ 43 ] . Curcumin, which activates CD8 + T cells and induces ferroptosis by enriching short-chain fatty acid (SCFA)-producing microbe, modulates the microbe-immune axis [ 44 ] . Secondly, epigenetic interventions, such as atorvastatin-mediated RAS isoprenylation inhibition and induction of immunogenic cell death [ 45 ] . In addition, combinatorial strategies with radiotherapy or nanotechnology-based delivery platforms (e.g., liposomal oxaliplatin [ 46 ] , GRP78 nanotoxin [ 47 ] , and TAS-115 kinase inhibitor [ 48 ] ) to remodel the TIME and enhance therapeutic efficacy. The spatial heterogeneity of TIME exerts a decisive influence on treatment response. Highly multiplexed imaging techniques have enabled investigators to preserve spatial information in TIME in clinical samples, thereby revealing the spatial co-localization between immune cells (especially CD8 + T cells) and tumor cells, a spatial organization feature that has been suggested as an important biomarker for predicting response to immunotherapy [ 49 ] . Notably, left-sided and right-sided colon cancers display distinct TIME characteristics: right-sided colon cancer (RCC) exhibits higher CD8⁺ T-cell infiltration and a stronger association between TIME and oncogenic mutations (e.g., BRAF, KRAS), whereas left-sided colon cancer (LCC) demonstrates weaker correlations [ 50 ] . Furthermore, several molecular markers—including IGSF6 [ 51 ] , MDM4 [ 52 ] , and MAPK14 [ 53 ] —have been identified as novel predictors of immune infiltration in CRC. 4.3.2 Biomarkers Among immune microenvironmental indicators, CD8⁺ T cell density represents a core prognostic marker in stage III colon cancer. High CD8⁺ infiltration has been established as an independent prognostic factor, correlating with significantly improved overall and disease-free survival [ 54 ] . Tissue-resident memory T cells (CD103⁺CD8⁺ TRM cells) suppress colorectal cancer liver metastasis by strengthening anti-tumor immunity; their high infiltration independently predicted a reduced risk of liver metastasis and enhanced the efficacy of anti-angiogenic therapy by inducing vascular normalization [ 55 ] . Furthermore, co-expression of immune checkpoints on tumor-infiltrating CD8⁺ T cells—such as PD-1 with TIGIT or PD-1 with TIM-3—has emerged as a novel biomarker associated with prolonged disease-free survival [ 56 ] . At the epigenetic level, expression of RUNX family transcription factors (RUNX1 and RUNX3) in CD8⁺ T cells has been positively correlated with anti-tumor activity, while epigenetic modulation of RUNX3 further enhanced CD8⁺ T-cell function [ 57 , 58 ] . Similarly, the DNA methylation-derived score CD8⁺ MeTIL (Methylation-derived Tumor-Infiltrating Lymphocytes) has been linked to improved disease-free survival. By quantifying CD8⁺ T-cell-specific methylation sites in tumor tissue, this biomarker provides a non-invasive, tissue-based approach for assessing the immune microenvironment and predicting prognosis [ 59 ] . The multi-parameter combination biomarker model significantly improves predictive accuracy. For example, the histopathological risk score (HRS), which cmbinoes TP53 aberrant expression, CD8⁺ T cell density, and intratumoral budding (ITB), serves as an independent predictor of resistance to neoadjuvant therapy in rectal cancer [ 60 ] . Current research hotspots emphasize dynamic biomarkers derived from multi-omics technologies, which construct predictive models capable of real-time monitoring of therapeutic responses by integrating genomic, epigenomic, and microenvironmental heterogeneity. Standardized immune scoring systems such as the Immunoscore [ 61 ] and automated digital pathology approaches [ 62 ] facilitate objective quantification of tissue-resident immune markers. Meanwhile, circulating immune cell markers—such as the proportion of circulating CD4⁺ T cells determined via methylation assays [ 63 ] —overcome tissue-related limitations and enable systemic immune surveillance. Emerging molecular targets are increasingly being recognized as companion diagnostic markers to predict responses to immunotherapy. For instance, CMTM6 expression in M2 macrophages predicts responsiveness to PD-1 blockade [ 64 ] , while RIG-I upregulation has been associated with enhanced efficacy of combination therapies [ 65 ] . Collectively, these multidimensional and dynamic biomarkers are advancing the paradigm of precision immunotherapy. Moreover, spatial immuno-mapping technologies, such as CODEX multiplex imaging, enable individualized therapeutic strategies by resolving the spatial proximity of CD8⁺ T cells to tumor cells and quantifying activation-related markers (e.g., CD38⁺ MFI) [ 66 ] . These advances highlight a shift toward dynamic functional assessment, incorporation of circulating biomarkers, and integration of spatial multi-omics in immunotherapy research. 4.3.3 Single-cell RNA sequencing Single-cell RNA sequencing (scRNA-seq) has become a core technology for dissecting tumor microenvironment (TME) heterogeneity in CRC, with research hotspots converging on three major directions. First, regarding immune cell functional states and exhaustion mechanisms, scRNA-seq has identified highly heterogeneous exhausted subpopulations of CD8⁺ T cells and γδ T cells in CRC. TCF-1⁺PD-1⁺ CD8⁺ T cells (T-pex) were shown to positively correlate with patient survival and display a unique transcriptional profile [ 67 ] . In contrast, γδ T cells exhibited PD-1⁺TIM-3⁺ terminally exhausted subsets, which were even more functionally suppressed than CD8⁺ T cells [ 68 ] . Second, in elucidating therapeutic resistance mechanisms and guiding combination strategies, scRNA-seq uncovered the cellular basis of ICI resistance. For example, increased infiltration of IL-1β⁺ myeloid-derived suppressor cells (MDSCs) in tumors from anti-PD-1-resistant patients contributed to CD8⁺ T-cell inactivation [ 69 ] . Trehan et al. [ 70 ] further demonstrated that antigen-specific CD8⁺ T cells in liver metastases are numerically abundant but functionally impaired, while intermediate SPP1⁺ macrophages, associated with TGF-β signaling, suppress T-cell activity via immunosuppressive pathways. Liu et al. subsequently showed that SPP1⁺ macrophages activate NF-κB signaling through the SPP1–CD44 axis, driving PD-1/TIM-3 upregulation in CD8⁺ T cells and reinforcing their exhausted phenotype [ 71 ] . Based on these findings, novel combination strategies have been proposed. Targeting the m⁶A reader protein YTHDF1 reduces MDSC migration and significantly enhances anti-PD-1 efficacy [ 72 ] , while microwave ablation combined with TIGIT blockade remodels the TME and promotes CD8⁺ T-cell proliferation and functional activation [ 73 ] . Finally, in the domain of spatial heterogeneity and prognostic modeling, scRNA-seq integrated with spatial transcriptomics has revealed site-specific immune landscapes. For example, SPP1⁺ macrophages dominate the immunosuppressive microenvironment in metastatic hepatocellular carcinoma, whereas DC3 dendritic cells are enriched in primary CRC [ 71 ] . Moreover, terminally exhausted CD8⁺ T cells exhibit lipid metabolic reprogramming and immunosuppressive features [ 74 ] . Leveraging these key cellular subsets (e.g., exhausted T cells, CAFs), researchers have constructed prognostic models for CRC and identified the MIF–CD74/CXCR4 pathway as a critical regulatory axis of T-cell exhaustion. 4.3.4 Immune checkpoint inhibitors ICIs, including antibodies against PD-1/PD-L1 and CTLA-4, are generally ineffective CRC, particularly in patients with MSS disease, where primary resistance is common [ 75 ] . The underlying resistance mechanisms can be categorized into three major aspects. First, CD8 + T cell exhaustion, characterized by sustained antigenic stimulation in the TME, leads to the loss of effector function and acquisition of an exhausted phenotype including PD-1 + TIM-3+ [ 76 ] . Second, an immunosuppressive TME, primarily mediated by Treg infiltration and the upregulation of immunosuppressive mediators such as PCSK9 and TGF-β, suppresses antitumor immunity [ 77 ] . Third, tumor-intrinsic escape mechanisms, such as activation of β-catenin signaling, inhibit CCL4 expression, thereby reducing CD103 + dendritic cell recruitment and limiting CD8 + T cell priming and infiltration [ 78 ] . To overcome ICI resistance, recent research has focused on synergistic strategies. One approach involves TME-targeted interventions, such as promoting vascular normalization to facilitate T-cell infiltration via endostatin [ 79 ] or CXCL10 overexpression [ 80 ] , and employing outer membrane vesicles derived from Akkermansia muciniphila to deliver Amuc_1434 protein, which downregulates PD-L1 expression and activates CD8 + T cells [ 81 ] . Another promising avenue is the development of novel immune checkpoint targets, including LAG3 blockade to reverse T-cell exhaustion [ 75 ] , CD200R inhibition to restore NK and CD8 + T cell function [ 82 ] , and activation of the cGAS/STING/IFN-β axis (e.g., with riluzole) to enhance CXCL10 secretion and T-cell recruitment [ 83 ] . In addition, epigenetic and metabolic reprogramming strategies have shown potential; for example, cordycepin reduces the Treg ratio and reverses T-cell exhaustion [ 84 ] , while metformin rescues CD8 + T-cell metabolic dysfunction [ 85 ] . Furthermore, individualized combination therapies are being explored. These include the use of neoantigen vaccines with regorafenib to induce infiltration of Rgs2 + CD8+ T cells [ 86 ] , or intratumoral administration of neoadjuvanted influenza vaccine to enhance CD8 + T-cell infiltration and upregulate PD-L1 expression [ 87 ] . Collectively, these approaches aim to reinvigorate CD8 + T cell responses through multi-pronged strategies to overcome resistance. Future research should focus on elucidating subtype-specific mechanisms and identifying pan-cancer biomarkers to guide precision immunotherapy. 4.3.5 tumor-infiltrating lymphocytes Tumor-infiltrating lymphocytes (TILs) particularly CD8 + T cells, represent a central focus in studies of the TIME. High-density CD8 + T-cell infiltration within tumor nests serves as an independent prognostic factor in CRC, strongly correlating with improved patient survival and demonstrating superior predictive value compared with conventional histological staging [ 88 ] . These localized CD8 + T cells exhibit activated cytotoxic characteristics, underscoring their pivotal role in antitumor immunity. Synergistic infiltration of CD4 + and CD8 + T cells (CD4/8 double-positive) has also been shown to significantly improve survival in patients with esophageal squamous carcinoma [ 89 ] . In contrast, Tregs facilitate immune evasion by suppressing CD8 + T-cell activity [ 90 ] , while exhausted TIGIT+ CD8 + T cells are associated with advanced disease stage and early relapse in CRC [ 91 ] . Microenvironmental modulation further influences TIL composition. For example, neoadjuvant radiotherapy decreases the total number of TILs but selectively enriches cytotoxic CD8 + Granzyme B+ (GrzB+) T cells [ 92 ] . Interestingly, although low Granzyme B expression in CRC is linked to early metastatic potential, its prognostic value is weaker compared to FoxP3 + Treg infiltration [ 93 ] . In Lynch syndrome–associated CRC, high infiltration of activated CD8 + T cells is correlated with early-stage tumors and the absence of lymph node metastasis [ 94 ] . At the molecular level, studies on T-cell exhaustion have identified the poor prognosis CD8 gene signature (ppCD8sig) as a predictive biomarker [ 95 ] . Importantly, blockade of the PD-1/TIM-3 pathway has been shown to restore T-cell function and reverse exhaustion [ 96 ] . Additionally, the novel marker LAYN influences CRC progression by modulating both Treg and TAM activity, and its expression has been proposed as a prognostic indicator of immune infiltration and patient outcomes [ 97 ] . Collectively, these findings suggest that targeting key regulators of T-cell function represents a promising therapeutic avenue. Moreover, multidimensional approaches integrating dynamic monitoring of T-cell subsets with novel gene signatures or molecular tags are expected to improve patient stratification and enhance the prediction of immunotherapy responsiveness in CRC. 4.4 Limitations Although this study provides important insights into the research landscape of CD8 + T cells in colorectal cancer, several limitations should be acknowledged. First, this analysis included only publications indexed in the Web of Science Core Collection, indicating that future studies should consider incorporating additional databases such as Scopus and PubMed. Second, owing to citation accrual lag, the citation counts and scholarly impact of recently published articles may be systematically underestimated. Finally, as an analysis article, this study is primarily descriptive and hypothesis generating; no experimental or clinical validation was performed to substantiate the mechanistic interpretations or translational implications inferred from the hotspot and frontier analyses. 5 Conclusion Research on CD8 + T cells in CRC is rapidly advancing. Global trend analyses predict substantial growth in both the number of publications and the pool of active researchers in this field. Significant progress has been made, particularly in countries such as China, the United States, and Japan, highlighting the international importance of this research area. The existing literature can be broadly categorized into two main directions: "mechanism exploration" and "application of therapeutic strategies and efficacy assessment," and future research is expected to explore these directions in greater depth and coherence, especially in the areas of the dynamics of the TIME, the discovery and validation of novel biomarkers, the in-depth application of scRNA-seq technology, the optimal combination of immune checkpoint inhibitors, and therapies to enhance the function of CD8 + TILs, thus pointing out new research directions for the immunotherapy of CRC. Declarations 6.1 Ethics approval and consent to participate This study did not involve human participants, animal subjects, or any tissue/data that requires ethical approval. Therefore, ethics approval and consent to participate are not applicable. 6.2 Consent for publication This manuscript contains no individual person’s data in any form. Consent for publication is not applicable. 6.3 Availability of data and materials The datasets generated and analyzed during the current study are available in the [Zenodo] repository, accessible at https://doi.org/10.5281/zenodo.19389635. The raw data were originally retrieved from the Web of Science Core Collection (WoSCC). 6.4 Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 6.5 Funding This study was supported by the National Natural Science Foundation of China (82460924); Guizhou High-level Innovative Talent Cultivation Plan (Qiankehe Talent (2020) No. 6016); Guizhou Clinical Research Center for Digestive Diseases [grant no. Qian Ke He Platform-LCZX (2025) 001]. 6.6 Author Contributions Xu YANG, Jing MA: Data curation, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Junfeng LUO, Juanjuan WANG, Maoyu LIAO: Data curation, Investigation, Methodology, Visualization, Writing – original draft. Haibo GAO and Zilong LIU: Software, Validation, Visualization, Writing – original draft. Mingliang CHU and Jiemin LIU: Funding acquisition, Supervision, Writing – review & editing. 6.7 Acknowledgements We thank all participating authors for their efforts. References MATSUDA T, FUJIMOTO A, IGARASHI Y. Colorectal Cancer: Epidemiology, Risk Factors, and Public Health Strategies [J]. Digestion, 2025, 106(2): 91-9. BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2024, 74(3): 229-63. 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Metformin treatment rescues CD8(+) T-cell response to immune checkpoint inhibitor therapy in mice with NAFLD [J]. J Hepatol, 2022, 77(3): 748-60. CHEN H, CHEN B, YANG Y, et al. Personalized Neoantigen Vaccine plus Regorafenib Increases Rgs2⁺CD8⁺ T Cells Infiltration and Reprograms the Tumor Microenvironment in Microsatellite Stable Colorectal Cancer Liver Metastases [J]. Adv Sci (Weinh), 2025: e08040. GöGENUR M, BALSEVICIUS L, BULUT M, et al. Neoadjuvant intratumoral influenza vaccine treatment in patients with proficient mismatch repair colorectal cancer leads to increased tumor infiltration of CD8+ T cells and upregulation of PD-L1: a phase 1/2 clinical trial [J]. J Immunother Cancer, 2023, 11(5). NAITO Y, SAITO K, SHIIBA K, et al. CD8+ T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer [J]. Cancer Res, 1998, 58(16): 3491-4. CHO Y, MIYAMOTO M, KATO K, et al. CD4+ and CD8+ T cells cooperate to improve prognosis of patients with esophageal squamous cell carcinoma [J]. Cancer Res, 2003, 63(7): 1555-9. DENG L, ZHANG H, LUAN Y, et al. Accumulation of foxp3+ T regulatory cells in draining lymph nodes correlates with disease progression and immune suppression in colorectal cancer patients [J]. Clin Cancer Res, 2010, 16(16): 4105-12. LIANG R, ZHU X, LAN T, et al. TIGIT promotes CD8(+)T cells exhaustion and predicts poor prognosis of colorectal cancer [J]. Cancer Immunol Immunother, 2021, 70(10): 2781-93. JAROSCH A, SOMMER U, BOGNER A, et al. Neoadjuvant radiochemotherapy decreases the total amount of tumor infiltrating lymphocytes, but increases the number of CD8+/Granzyme B+ (GrzB) cytotoxic T-cells in rectal cancer [J]. Oncoimmunology, 2018, 7(2): e1393133. SALAMA P, PHILLIPS M, PLATELL C, et al. Low expression of Granzyme B in colorectal cancer is associated with signs of early metastastic invasion [J]. Histopathology, 2011, 59(2): 207-15. DE MIRANDA N F, GOUDKADE D, JORDANOVA E S, et al. Infiltration of Lynch colorectal cancers by activated immune cells associates with early staging of the primary tumor and absence of lymph node metastases [J]. Clin Cancer Res, 2012, 18(5): 1237-45. SALEH R, SASIDHARAN NAIR V, TOOR S M, et al. Differential gene expression of tumor-infiltrating CD8(+) T cells in advanced versus early-stage colorectal cancer and identification of a gene signature of poor prognosis [J]. J Immunother Cancer, 2020, 8(2). LIU J, ZHANG S, HU Y, et al. Targeting PD-1 and Tim-3 Pathways to Reverse CD8 T-Cell Exhaustion and Enhance Ex Vivo T-Cell Responses to Autologous Dendritic/Tumor Vaccines [J]. J Immunother, 2016, 39(4): 171-80. PAN J H, ZHOU H, COOPER L, et al. LAYN Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Gastric and Colon Cancers [J]. Front Immunol, 2019, 10: 6. SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209-49. PAGèS F, MLECNIK B, MARLIOT F, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study [J]. Lancet, 2018, 391(10135): 2128-39. GANESH K, STADLER Z K, CERCEK A, et al. Immunotherapy in colorectal cancer: rationale, challenges and potential [J]. Nat Rev Gastroenterol Hepatol, 2019, 16(6): 361-75. TOPALIAN S L, HODI F S, BRAHMER J R, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer [J]. N Engl J Med, 2012, 366(26): 2443-54. GUINNEY J, DIENSTMANN R, WANG X, et al. The consensus molecular subtypes of colorectal cancer [J]. Nat Med, 2015, 21(11): 1350-6. BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2018, 68(6): 394-424. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 15 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 2026 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8934222","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633674497,"identity":"f430eb5b-b281-4e17-8865-20375a953178","order_by":0,"name":"Xu Yang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Yang","suffix":""},{"id":633674498,"identity":"e55e41de-e71b-4044-a63a-5f334af88ec7","order_by":1,"name":"Jing MA","email":"","orcid":"","institution":"Zunyi Medical 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14:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8934222/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8934222/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108431832,"identity":"017d30aa-4412-4969-898e-63396d761574","added_by":"auto","created_at":"2026-05-04 14:52:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68767,"visible":true,"origin":"","legend":"\u003cp\u003eThe role of CD8+ T cells in colorectal cancer immunity.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/a30233728a8ad162c86d41c8.jpeg"},{"id":108492832,"identity":"259022d7-188d-4b69-b211-72bfa32f4b15","added_by":"auto","created_at":"2026-05-05 09:58:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43016,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of CD8+ T cells and CRC.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/8d6138960418464cec8dd111.jpeg"},{"id":108976628,"identity":"33dc708f-6d80-4b4e-9746-379a2e0477cb","added_by":"auto","created_at":"2026-05-11 11:26:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41877,"visible":true,"origin":"","legend":"\u003cp\u003eModel fitting curves for the number of publications and total annual publications.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/0202b26aab5db564cfa2a56b.jpeg"},{"id":108493003,"identity":"e3fa95b4-552e-41e1-9628-31021370e28a","added_by":"auto","created_at":"2026-05-05 09:59:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51948,"visible":true,"origin":"","legend":"\u003cp\u003ethe top 15 journals ranked by publication volume\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/1b455d42c45a150521f57457.jpeg"},{"id":108431835,"identity":"baf9c858-6b16-446c-98e6-26190110dff8","added_by":"auto","created_at":"2026-05-04 14:52:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":193984,"visible":true,"origin":"","legend":"\u003cp\u003eCiteSpace network visualization of countries/regions involved in CD8+ T cells and CRC.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/80b4ebb684f5512c6103a5be.png"},{"id":108493838,"identity":"48dc6448-aef3-4ee5-a2f2-7b4307e186b2","added_by":"auto","created_at":"2026-05-05 10:01:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":167365,"visible":true,"origin":"","legend":"\u003cp\u003eCiteSpace network visualization of the collaborative organization between CD8+ T cells and CRC.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/655b5ffb2999b3b14da691e5.png"},{"id":108431838,"identity":"72468ece-ab03-4d3f-b5c7-caff11aca275","added_by":"auto","created_at":"2026-05-04 14:52:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":264877,"visible":true,"origin":"","legend":"\u003cp\u003eCiteSpace network visualization of co-citations involving CD8+ T cells and CRC\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/8d3f65ded2eefdb51adade9f.png"},{"id":108493626,"identity":"6a81173b-80c3-4a03-99e8-f3000a719da2","added_by":"auto","created_at":"2026-05-05 10:01:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":283710,"visible":true,"origin":"","legend":"\u003cp\u003eshows the CiteSpace network visualization of the keyword clustering analysis of CD8+ T cell analysis involved in CRC research.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/857d2c9ae3632d6124e95ba0.png"},{"id":108431839,"identity":"2a2645be-99e5-427d-b649-fdef380651f0","added_by":"auto","created_at":"2026-05-04 14:52:33","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":106728,"visible":true,"origin":"","legend":"\u003cp\u003eKeyword citation burst and related literature analysis. The red line shows the time range of the keyword discovery burst, while the blue line shows the time interval.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/f7452a0787da787affca7fa0.jpeg"},{"id":108979458,"identity":"048e2efc-6c39-4908-905f-b90ac51ff232","added_by":"auto","created_at":"2026-05-11 11:58:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1616024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8934222/v1/f1cd5745-b402-44e0-a352-2d1946de49a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bibliometric Analysis of CD8⁺ T Cells in Colorectal Cancer Research","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is among the most common malignancies of the digestive system \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Recent global statistics report over 1.9\u0026nbsp;million new CRC cases and approximately 900,000 related deaths annually, ranking it third in incidence and second in mortality among all cancers \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The development of CRC is driven by multiple interacting factors. Early-onset CRC (in patients younger than 50 years) has been linked to the adoption of Western dietary patterns, chronic psychological stress, and gut microbiota alterations associated with widespread antibiotic use \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Other established risk factors include sex, genetic susceptibility, smoking, obesity, unhealthy lifestyles, and family history \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Current therapeutic strategies for CRC include surgery, radiotherapy, targeted therapy, immunotherapy, and, in selected cases, hormonal therapy \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, because CRC often remains asymptomatic in its early stages, many patients are diagnosed at advanced stages with distant organ or tissue metastases, thereby missing the optimal window for curative surgery \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Consequently, there is an urgent need for novel therapeutic approaches to improve patient outcomes. Cancer immunotherapy has gained unprecedented attention, particularly following the clinical success of immune checkpoint blockade therapy (ICBT) in multiple cancer types \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Within this context, CD8⁺ T cells are central to tumor immunity, and their roles in CRC have attracted considerable interest owing to their pivotal contribution to antitumor responses and their implications for immunotherapeutic strategies.\u003c/p\u003e \u003cp\u003eCD8⁺ T cells are a critical component of the adaptive immune system, with effector, memory, and exhausted phenotypes that play distinct roles in anti-tumor immunity. Effector T cells are activated immune cells responsible for eliminating infected or malignant cells. They infiltrate tissues by upregulating surface molecules such as CD44 and CD69 and secrete cytotoxic mediators, including perforin and granzymes, to induce apoptosis in target cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Following antigen clearance, effector CD8⁺ T cells undergo contraction and differentiate into two subpopulations, KLRG1⁺ and KLRG1⁻ cells, with the former representing short-lived effector cells and the latter giving rise to long-lived memory cells \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This differentiation process is tightly regulated by transcription factors such as T-bet and Eomesodermin (Eomes) \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Under conditions of persistent antigenic stimulation, however, CD8⁺ T cells progressively lose their effector function and enter a dysfunctional state termed T-cell exhaustion. This phenotype is characterized by impaired cytokine production (e.g., IL-2, IFN-γ), diminished cytotoxicity, reduced proliferative potential, and eventual apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In the tumor microenvironment (TME), CD8⁺ T-cell exhaustion is strongly associated with the upregulation of multiple inhibitory receptors, including programmed cell death protein-1 (PD-1), lymphocyte activation gene-3 (LAG-3), and T-cell immunoglobulin and mucin domain-containing protein-3 (TIM-3). These receptors transmit inhibitory signals upon binding to their respective ligands on tumor cells, such as PD-L1 or galectin-3 (Gal-3), thereby amplifying T-cell dysfunction \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The memory compartment of CD8⁺ T cells can be further subdivided into central memory T cells (T_CM) and effector memory T cells (T_EM). T_CM cells display superior antitumor activity compared with T_EM cells, which exhibit more limited protective capacity \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Tumor-specific T_CM and T_EM subpopulations have been identified in patients with breast cancer and CRC \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Within the CRC immune microenvironment, both the density and spatial distribution of CD8⁺ T cells\u0026mdash;whether located in the tumor core or invasive margin\u0026mdash;carry comparable prognostic value. These distributions, however, are dynamically shaped by stromal factors such as CXCL12 (SDF-1) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The functional status of CD8⁺ T cells, particularly the balance between activation and exhaustion, is central to their tumor-controlling efficacy. Notably, exhausted CD8⁺ T cells often co-express high levels of inhibitory receptors such as PD-1 and TIM-3, reflecting a paradoxical state in which antitumor function is limited but can be at least partially restored through immune checkpoint blockade \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Advances in elucidating the molecular pathways that regulate CD8⁺ T-cell activity and their interactions with the TME provide promising opportunities to enhance immunotherapeutic strategies and improve clinical outcomes in CRC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBibliometrics is a methodological approach for literature analysis that examines bibliographic systems and bibliometric characteristics to evaluate research output and trends from both quantitative and qualitative perspectives \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. It can also examine research priorities and hotspots within research areas, reveal potential research patterns, and predict future trends, as well as assess the research output of countries, institutions, and researchers \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The role of CD8⁺ T cells in CRC has received increasing attention, with numerous studies investigating their infiltration, functional status, and regulatory mechanisms within the TME. These studies have revealed the complex cellular interactions that shape disease outcomes. For instance, O'Malley et al. (2018) demonstrated that PD-L1 expression by stromal cells suppresses CD8⁺ T cell activity and promotes tumor progression, underscoring the importance of stromal components in immune regulation and suggesting that targeting stromal PD-L1 could restore CD8⁺ T cell function \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Another study identified diverse CD8⁺ T-cell subpopulations with distinct functions and clonality, including IFNG-producing T1-like cells associated with effective antitumor immunity, highlighting the phenotypic heterogeneity of CD8⁺ T cells and the importance of understanding their functional states for therapeutic development \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In addition, TRIB3 has been identified as a negative regulator of CD8⁺ T-cell infiltration through suppression of the STAT1\u0026ndash;CXCL10 axis, thereby facilitating immune evasion; these findings point to potential therapeutic targets for enhancing antitumor immunity \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Despite these advances, no bibliometric study has systematically analyzed the literature on CD8⁺ T cells in CRC, limiting comprehensive evaluation of research progress and future directions in this field. Therefore, the present study aims to assess the development, trends, and impact of publications related to CD8⁺ T cells in CRC, with the goal of clarifying research frontiers and hotspots and providing insights into their translational potential.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eTo improve the representativeness and accessibility of the data, this study utilized the Web of Science Core Collection (WoSCC) database enriched by the Science Citation Index as the primary data source. Web of Science is the leading research platform for information in the hard sciences, social sciences, arts, and humanities, and is the world's most trusted publisher-independent global citation database \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The WoSCC database is known for its comprehensive coverage, systematic methodology, and authority on more than 12,000 influential, high-quality journals worldwide. Use the search formula TS=(\"CD8* T cell*\") AND TS=(\"Rectal Neoplasm\" OR \"Rectal Tumor\" OR \"Rectal Cancer\" OR \"Rectum Neoplasm\" OR \"Rectum Cancer\" OR \"Cancer of the Rectum\" OR \"Cancer of Rectum\" OR \"Colorectal Neoplasm\" OR \"Colorectal Tumor\" OR \"Colorectal Cancer\" OR \"Colorectal Carcinoma\" OR \"Colonic Neoplasm\" OR \"Colon Neoplasm\" OR \"Cancer of the Colon\" OR \"Colon Cancer\" OR \"Cancer of the Colon\" OR \"Colonic Cancer\") for a comprehensive investigation, focusing on articles and reviews in the English language published until July 10, 2025, to avoid any potential bias due to the daily updates. This search resulted in a total of 2,006 relevant documents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data import and merging\u003c/h2\u003e \u003cp\u003eThe complete data records were exported from the Web of Science Core Collection (WoSCC) with all the details, such as the annual research, countries/regions, source journal, institution, authors, keywords, citations, and Journal Citation Reports. Select bibliographic records in Plain Text File format and check \"Record Content: Full Record\" and \"Cited References\" in the export settings. The downloaded bibliographic records were then imported into NoteExpress, a document management software, where the records were filtered, validated, and the entries within each field were manually cleaned and standardized for subsequent analysis. At the same time, we identified three situations in which data needed to be merged and proposed solutions accordingly. These situations included (1) full names or abbreviations of the same country/region name, such as different writing conventions for the United States of America and USA; (2) different abbreviations of the same author's name or variations of the last name and first name order, which we solved through the use of ORCID information and the author's affiliation; and (3) synonymous expressions that were manually normalized to eliminate redundant entries, e.g., 'immune evasion' was normalized to 'immune escape'.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analysis and visualization\u003c/h2\u003e \u003cp\u003eAfter completing the data merging process, the integrated bibliographic records in Plain text format were imported into Microsoft Excel 2019 and CiteSpace V.6.3.R3 for multidimensional analysis. Microsoft Excel primarily conducts bibliometric analysis to statistically and visually represent trends in the number of publications per year, thereby illustrating the development of the research field over time. CiteSpace is used to construct and analyze scientific knowledge maps: to map the research cooperation networks among countries/regions, research institutions, and authors, and to reveal the patterns of cooperation and core subjects; and to conduct in-depth mining of citation data, which includes the construction of a co-citation network map and the identification of Highly Cited References (HCRs). Finally, we explore keywords from multiple perspectives, including keywords co-occurrence analysis and network visualization, keywords cluster analysis to extract research themes, and Keywords Burst Detection. We utilize Keywords Burst Detection to pinpoint research frontiers and emerging hotspots. The specific process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Publication outputs and time trend\u003c/h2\u003e \u003cp\u003eFrom 1992 to 2024, a total of 2,006 publications were published in research related to CRC and CD8\u0026thinsp;+\u0026thinsp;T cells. The number of publications has experienced a period of relative inactivity followed by rapid growth, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: the number of publications in the initial years was very low, with no publications appearing in 1994, and publications up to 2005 were mostly below 20; publications from 2006 to 2018 showed slight fluctuations but an overall upward trend; and from 2019 to 2024, there was explosive growth with an average increase of 43 publications per year. In addition, according to the linear regression results (y\u0026thinsp;=\u0026thinsp;7.0431x-14082, R\u0026sup2; = 0.6316), the number of publications shows a moderate positive correlation with the progression of years, indicating that this research field is in a phase of sustained development and has enormous potential for future growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Distribution of journals\u003c/h2\u003e \u003cp\u003eThis study analyzed 2,006 publications were sourced from 484 distinct academic journals. Among these, \u003cem\u003eFrontiers in Immunology\u003c/em\u003e exhibited the highest publication output, contributing 84 articles, which represent 4.19% of the total corpus. This ranking was followed by the \u003cem\u003eJournal for Immunotherapy of Cancer\u003c/em\u003e, which published 67 articles, accounting for 3.34% of the total. Furthermore, 38 journals, representing 7.85% of the total, published at least 10 articles, totaling 1,003 articles, or 50% of the total. Notably, the top 10 journals published 523 articles, accounting for 26.07% of the total publications. These top 10 journals, representing merely 2.07% of the total journals, contributed over a quarter of the publications. These data indicate a pronounced core journal effect within this research field. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the top 10 journals ranked by publication volume.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of countries/regions and institutions\u003c/h2\u003e \u003cp\u003eA total of 64 countries and regions participated in this study, A total of 2,542 studies have been published, with 25 of them publishing more than 10 studies each; China had the largest contribution at 36.2% of the total studies, followed by the United States at 18.1% and Japan at 7.2%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows in detail the top 10 major contributing countries/regions of research output and their corresponding bibliometric data. Subsequently, we used CiteSpace to visualize and analyze national collaborations, a collaborative network consisting of 64 nodes and 200 edges, presenting academic collaborations among highly productive countries; see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The top 10 countries/regions are China, the United States, Japan, Germany, the United Kingdom, France, South Korea, Italy, Switzerland, and the Netherlands, and it is noteworthy that Canada and the Netherlands are jointly ranked 10th. Among them, the top three in terms of centrality are the United States, France, and China, with centralities of 0.42, 0.23, and 0.22, respectively. The combination of the number of publications and the centrality index can be analyzed to show that China, the United States, Japan, and France are the major research forces in this study.\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 and institutions in the field of CD8\u0026thinsp;+\u0026thinsp;T cell in Colorectal Cancer among 2006 included studies (1992\u0026ndash;2024).\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=\"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=\"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 \u003cdiv align=\"char\" char=\".\" 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\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInstitute (Country)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCentrality\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\u003eCHINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSun Yat-sen University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\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\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFudan University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\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\u003eJAPAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShanghai Jiao Tong University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\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\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhejiang University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\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\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChinese Academy of Sciences (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\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\u003eFRANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNational Cancer Institute (USA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\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\u003eSOUTH KOREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNanjing Medical University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\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\u003eITALY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSoochow University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\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\u003eSWITZERLAND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSouthern Medical University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\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\u003eNETHERLANDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChinese Academy of Medical Sciences \u0026amp; Peking Union Medical College (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\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\u003eCANADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeking University (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\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 \u003c/p\u003e \u003cp\u003eIn this study, a total of 1078 institutions were involved in the publication of research papers in this field, of which 185 universities (17.16%) contributed more than five papers. The top 10 universities had a threshold of 33 publications, with Sun Yat-sen University leading the way with the highest number of 91. In terms of centralities, National Cancer Institute ranked first with a centrality of 0.12, while Nanjing Medical University and Southern Medical University tied for 10th with a centrality of 0.01. The publication volume and centrality of the top 10 universities are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results of Institution Collaboration Network demonstrated 1397 nodes and 3734 links, and these data revealed solid collaborative relationships. In this network diagram, the top five institutions with the most active collaborative relationships are Sun Yat-sen University, Fudan University, Shanghai Jiao Tong University, Zhejiang University, and Chinese Academy of Sciences. The results of the Collaborative Institution Mapping are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of authors and co-cited representative literature\u003c/h2\u003e \u003cp\u003eIn the results of the analysis of authors and co-cited reference analysis, a total of 17107 authors were identified, of which the top 10 authors with a cumulative number of 90 publications were affiliated with 8 different research institutions, of which Zlobec, Inti, and Ghiringhelli, Francois, tied for the first place with 10 publications each, followed by Snook, Adam E., who published 9 articles. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the data related to the top 10 most prolific authors. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the 10 most highly cited original articles on CD8\u0026thinsp;+\u0026thinsp;T cells in CRC research. Among the top 10 highly cited original articles in this field, \u003cem\u003eScience\u003c/em\u003e and \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e had a significant impact with more than 200 citations each, with \u003cem\u003eScience\u003c/em\u003e reaching the first place with 296 citations, followed by \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e with 205 citations, and \u003cem\u003eNature Medicine\u003c/em\u003e tied for the 10th place in terms of the number of total citations with \u003cem\u003eCa-a Cancer Journal For Clinicians\u003c/em\u003e; Galon J et al. results published in \u003cem\u003eScience\u003c/em\u003e were the most highly cited document with 296 citations \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. demonstrating its central influence in the field. The cumulative citations of the top 10 publications totaled 1,445, highlighting their core influence within the field. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrates the CiteSpace-based co-citation network analysis of the literature, identifying a total of 1453 nodes and 4368 co-occurring links, The high-density network structure reveals the complexity of the domain knowledge association.\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 authors who published literature on CD8\u0026thinsp;+\u0026thinsp;T cell in CRC among 2006 included studies (1992\u0026ndash;2024).\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=\"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 \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\u003eInstitute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentrality\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\u003eZlobec, Inti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniversity of Bern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eGhiringhelli,Francois\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHU Grenoble Alpes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eSnook, Adam E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJefferson Hlth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eScott A. Waldman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJefferson Hlth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eTorigoe, Toshihiko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSapporo Medical University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eZheng, Xiao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoochow University - China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eChen, Lujun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGannan Medical University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eLugli, Alessandro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniversity of Bern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eHuang, Yizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral South University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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\u003eWeitz, Juergen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnische Universitat Dresden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\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=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 co-citation representative literature of CD8\u0026thinsp;+\u0026thinsp;T cell in CRC among the 2006 articles included (1992\u0026ndash;2024).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\u003eCited number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType\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\u003eCentrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJCR (2024)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIF (2024)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eReference\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType, density, and location of immune cells within human colorectal tumors predict clinical outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD-1 Blockade in Tumors with Mismatch-Repair Deficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNew England Journal of Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCa-a Cancer Journal For Clinicians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e232.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffector memory T cells, early metastasis, and survival in colorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNew England Journal of Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCancer Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternational validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLancet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmunotherapy in colorectal cancer: rationale, challenges and potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNature Reviews Gastroenterology \u0026amp; Hepatologyt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafety, activity, and immune correlates of anti-PD-1 antibody in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNew England Journal of Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe consensus molecular subtypes of colorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNature Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]\u003c/sup\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlobal cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCa-a Cancer Journal For Clinicians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e232.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* IF: impact factor; *JCR: Category Quartile of Journal Citation Report\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.5 Keyword analysis\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Keywords co-occurrence analysis\u003c/h2\u003e \u003cp\u003eIn this research analysis, we used CiteSpace software to identify and analyze keywords related to CD8\u0026thinsp;+\u0026thinsp;T cells in CRC research papers during the publication period of 1992\u0026ndash;2024. The top five keywords with the highest frequency were, in order, colorectal cancer (n\u0026thinsp;=\u0026thinsp;554), immunotherapy (n\u0026thinsp;=\u0026thinsp;262), tumor microenvironment (n\u0026thinsp;=\u0026thinsp;161), colon cancer (n\u0026thinsp;=\u0026thinsp;145), and prognosis (n\u0026thinsp;=\u0026thinsp;136). In addition, a larger centrality value of a node represents a higher strength of its connection with other nodes. The centrality analysis indicated that the top five keywords were colorectal cancer (n\u0026thinsp;=\u0026thinsp;0.55), immunotherapy (n\u0026thinsp;=\u0026thinsp;0.33), colon cancer (n\u0026thinsp;=\u0026thinsp;0.27), tumor microenvironment (n\u0026thinsp;=\u0026thinsp;0.12), and apoptosis (0.08). All of these keywords show extremely high centrality and are key pivotal nodes in the network; among these keywords, colorectal cancer has the highest frequency and centrality, highlighting its centrality within the field, followed by the high centrality of immunotherapy, indicating its importance in research and its close relationship with colorectal cancer, which together promote the development of the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Keywords clusters analysis\u003c/h2\u003e \u003cp\u003eThe keyword clustering graph reflects the structural features among the clusters, highlighting their key nodes and basic connections\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Based on the results of keyword co-occurrence analysis, we generated a keyword clustering network graph using LLR, and the network clustering structure is significant and plausible, and the Modularity Q\u0026thinsp;=\u0026thinsp;0.536\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and Mean Silhouette\u0026thinsp;=\u0026thinsp;0.8128\u0026thinsp;\u0026gt;\u0026thinsp;0.7 of the keyword clustering graph are higher than the empirical thresholds, which proves that the clustering is clearly and reasonably delineated with high internal homogeneity. By reviewing and screening the clustered keywords, the labels of the final 10 core clusters (#0-#9) together outline the basic knowledge structure of the CRC and CD8\u0026thinsp;+\u0026thinsp;T cell research field. Among the 10 keyword clusters, the research topics can be divided into three categories: (#5) and (#6) are the first category, which are mainly related to the CD8\u0026thinsp;+\u0026thinsp;T cell-related diseases, reflecting the clinical background of the disease itself; (#1), (#2), (#8), and (#9) form the second group, which are mainly related to the microscopic mechanism of tumor immunity and TME characteristics; and (#0), (#3), (#4), and (#7) form the third group, which is mainly related to the application path of immunotherapy and its clinical effects. Among these clusters, the largest cluster #0 immunotherapy (n\u0026thinsp;=\u0026thinsp;58) represents the core cluster, indicating that immunotherapy is a central theme in tumor research, encompassing both mechanism exploration and clinical application. As is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Keywords citation burst analysis\u003c/h2\u003e \u003cp\u003eAnalysis of keyword citation bursts can reveal research trends over a specific time frame \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This study integrates keyword co-occurrence analysis and keyword citation burst detection methods to quantitatively identify the 25 core keywords with the most explosive growth in citation frequency. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, in the Keywords citation burst analysis, \"Begin\" and \"End\" indicate the start and end time of the keyword burst. The \"Strength\" represents the burst strength, which reflects the statistical significance of the keyword's citation growth over time. By identifying keywords with high burst strength, researchers can effectively track the current research hotspots and cutting-edge dynamics in the field.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the Keywords citation burst analysis, the analysis reveals three phases of results. The first phase, from 1993 to 2002, highlights keywords such as \"interleukin-2, CTL, cytotoxicity, gene therapy, adenovirus,\" which mainly focus on the underlying immune mechanisms and gene therapy techniques, reflecting the initial exploration of cancer immunotherapy. For example, CTLs are important anti-tumor effector cells in the immune system, capable of performing immunosurveillance functions by recognizing and killing tumor cells. The prognosis of patients with CRC closely correlates with the degree of CTL infiltration. Studies have shown that the infiltration density of CD8\u0026thinsp;+\u0026thinsp;T cells is an important predictor of prognosis in CRC patients \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The second stage of mid-term development, which occurred from 2003 to 2012, shifted its focus to clinical applications such as vaccine development, antibody therapy, and tumor immunology, emphasizing terms like \"vaccination, cancer, antibody, cancer vaccine, tumor immunology, immune response, cea, costimulation.\" The third phase spans from 2013 to 2024, focusing on keywords such as tumor-infiltrating, scRNA-seq, immune checkpoint inhibitors, biomarkers, tumor-infiltrating lymphocytes, PD-1, immunosuppression, NKG2D, cancer immunotherapy, CD8\u0026thinsp;+\u0026thinsp;T cells, immunoscore, and immunomodulation. This stage mainly focused on the cutting-edge fields of immune checkpoints, TME, and single-cell technology, reflecting the technology-driven research trend. It has been found that combining anti-PD-1/PD-L1 therapies with chemotherapy, anti-angiogenic drugs, or targeted drugs improves treatment efficacy, especially in Microsatellite stable (MSS) CRC \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Phase III keywords are still relevant today and represent emerging trends and future research directions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 General information\u003c/h2\u003e \u003cp\u003eOver the past three decades, research on CD8\u0026thinsp;+\u0026thinsp;T cells in CRC has expanded from a marginal topic to a rapidly growing hotspot. Our bibliometric analysis of 2,006 publications (based on CiteSpace) revealed three distinct phases of development. Before 2005, annual publication output remained persistently low, with some years producing fewer than 20 papers, including none in 1994. From 2006 to 2018, the field entered a period of fluctuating but steady growth, largely driven by seminal findings. Notably, in 2006, Galon and colleagues first demonstrated that tumor-infiltrating CD8⁺ T-cell density positively correlated with survival in CRC patients \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, establishing the prognostic value of adaptive immunity in this malignancy. After 2019, research activity surged, with an average annual increase of 43 publications, highlighting the expanding significance of CD8⁺ T cells in CRC. Clinical studies further confirmed that responses to ICBT, such as anti-PD-1 treatment, in Microsatellite-unstable (MSI) CRC subpopulations are strongly dependent on CD8⁺ T-cell-mediated antitumor immunity, underscoring their central role in immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. More recently, the integration of single-cell sequencing and spatial transcriptomics has facilitated deeper mechanistic insights, reinforcing the importance of CD8⁺ T cells in developing next-generation immunotherapeutic strategies.\u003c/p\u003e \u003cp\u003eThis study also characterizes the global collaborative landscape of CD8⁺ T cell research in CRC. A total of 64 countries/regions contributed relevant publications, with China (921 articles, 36.2%), the United States (460 articles, 18.1%), and Japan (183 articles, 7.2%) leading the output. The national collaboration network constructed in CiteSpace (64 nodes, 200 edges) revealed that, apart from China, the top 10 most productive countries were all developed nations, reflecting the research dominance of developed countries. Although China ranked first in publication volume, its network centrality (0.22) and number of international collaborations (3) were lower than those of the United States (centrality 0.42, 40 collaborations). This indicates that China\u0026rsquo;s global engagement remains limited compared with developed countries. By contrast, the United States, with the highest centrality and most extensive partnerships, serves as the hub of the global collaboration network. These differences are largely attributable to disparities in research resources, including funding and infrastructure, between developed and developing countries.\u003c/p\u003e \u003cp\u003eInstitutional network analysis identified 1,078 institutions engaged in CD8⁺ T cell and CRC research, of which 185 (17.2%) published more than five papers. Sun Yat-sen University (91 papers), Fudan University (68 papers), and Shanghai Jiao Tong University (63 papers) were the top three contributors. Along with Zhejiang University (44 papers) and the Chinese Academy of Sciences (40 papers), they represent the core research power in this field (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In terms of collaboration, the National Cancer Institute (NCI) emerged as the central hub with the highest centrality (0.12). By comparison, Chinese institutions, despite ranking among the top in publication output, generally had lower centrality values (\u0026lt;\u0026thinsp;0.08). The network, consisting of 1,397 nodes and 3,734 links, indicates a high level of internationalization and collaboration. While Chinese institutions dominate in publication volume, developed institutions such as NCI continue to lead global collaboration networks, reflecting an imbalance between quantitative output and qualitative influence in international research integration.\u003c/p\u003e \u003cp\u003eThe analysis of author clusters indicates that research on CD8⁺ T cells in CRC is characterized by a high concentration of core contributors. Among 17,107 participating authors, the top 10 most productive scholars collectively published 90 articles across eight research institutions. The most influential authors include Zlobec, Inti, and Ghiringhelli, Fran\u0026ccedil;ois (10 publications each), as well as Snook, Adam E. (9 publications). Zlobec\u0026rsquo;s group, in their work on tumor budding, demonstrated that in rectal cancer patients undergoing neoadjuvant radiotherapy, tumor buds represent highly aggressive residual lesions that are strongly associated with adverse pathological features and poor survival outcomes, whereas CD8⁺ T cells did not show clear prognostic significance in this therapeutic context \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In another study, they reported that RHAMM-positive tumor regions (including budding areas) were significantly associated with higher PD-1/PD-L1 expression and increased CD8⁺ T cell infiltration in mismatch repair-proficient (MMRp) stage IV colorectal cancer with liver metastases, suggesting that RHAMM may define a more immunoreactive tumor microenvironment and could serve as a potential biomarker for predicting response to immune checkpoint inhibitors (ICIs) \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Ghiringhelli\u0026rsquo;s team highlighted the limited efficacy of PD-1/PD-L1 blockade in microsatellite-stable (MSS) CRC but demonstrated that combined immune checkpoint inhibition may overcome this resistance. Specifically, they showed that atezolizumab (anti\u0026ndash;PD-L1) combined with tiragolumab (anti\u0026ndash;TIGIT) restored the immune function of tumor-infiltrating lymphocytes (TILs) in 46% of MSS-CRC samples \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Snook\u0026rsquo;s group investigated the split-tolerization mechanism and provided evidence for the safety and specificity of GUCY2C-targeted vaccines in CRC immunotherapy, supporting their potential clinical utility \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnalysis of publication outputs and citations revealed that \u003cem\u003eScience\u003c/em\u003e and \u003cem\u003eThe New England Journal of Medicine\u003c/em\u003e are the leading journals in this field, exerting significant influence on CD8⁺ T cell and CRC research. Science, with an impact factor of 45.8 in 2024. The journal has played an important role in research in the field of CD8\u0026thinsp;+\u0026thinsp;T cells and CRC. For example, Le DT et al. \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e reported in Science that cancers with defective mismatch repair (dMMR) accumulate abundant mutant neoantigens due to high mutational burden, rendering them highly sensitive to PD-1 blockade. In a cohort of 12 advanced solid tumor types, PD-1 antibody therapy induced objective responses in 53% of dMMR patients, with 21% achieving complete remission. Notably, these responses were durable, as median progression-free and overall survival were not reached at study end, underscoring the long-lasting efficacy of PD-1 blockade. Mechanistic studies demonstrated that treatment responders exhibited rapid clonal expansion of neoantigen-specific T cells with robust tumor-specific immunity. This finding established dMMR status as a genetic biomarker for immunotherapy response and highlighted that treatment efficacy depends on the quality and quantity of neoantigens rather than tumor tissue origin. Other high-impact journals, including \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e, \u003cem\u003eCancer Research\u003c/em\u003e, \u003cem\u003eLancet\u003c/em\u003e, and \u003cem\u003eNature Reviews Gastroenterology \u0026amp; Hepatology\u003c/em\u003e, have also contributed significantly to advancing CD8⁺ T cell and CRC research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Important research findings\u003c/h2\u003e \u003cp\u003eCitation analysis identified the landmark study by Galon J et al., \u003cem\u003eScience\u003c/em\u003e (2006), entitled \u0026ldquo;Type, density, and location of immune cells within human colorectal tumors predict clinical outcome\u0026rdquo;, as the most highly cited publication in this field (296 citations) \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This pioneering work demonstrated that the type, density, and spatial distribution of immune cells\u0026mdash;particularly tumor-infiltrating CD8⁺ T cells\u0026mdash;within the TME are critical predictors of clinical prognosis in CRC. A high density of CD8⁺ T cells, especially in the tumor core and invasive margins, was strongly associated with improved patient survival. This study laid the foundation for the Immunoscore concept and firmly established the central role of CD8⁺ T cell-mediated anti-tumor immunity in CRC progression and patient outcomes. The second most cited clinical study, \u0026ldquo;PD-1 Blockade in Tumors with Mismatch-Repair Deficiency\u0026rdquo; by Le DT et al., published in The New England Journal of Medicine (2015), received 205 citations \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This seminal trial provided the first clinical evidence that ICBT targeting PD-1 can induce robust and durable clinical responses in metastatic CRC patients with dMMR. The investigators showed that the elevated tumor mutational burden associated with dMMR generates abundant neoantigens, rendering these tumors highly susceptible to CD8⁺ T cell-mediated cytotoxicity. PD-1 inhibitors effectively reversed the functional inhibition of CD8⁺ T cells within the TME, thereby validating the therapeutic potential of immunotherapies that rely on CD8⁺ T-cell activity. Together, the foundational work by Galon J et al. (2006), which established tumor-infiltrating CD8⁺ T cells as a core prognostic biomarker, and the clinical breakthrough of Le DT et al. (2015), which translated this principle into a successful therapeutic strategy for dMMR CRC, highlight the pivotal role of CD8⁺ T cells in CRC biology and management. These studies exemplify a research continuum spanning mechanistic insights to clinical application. Further evidence was provided by Pag\u0026egrave;s F et al. in \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, who reported that high infiltration of effector memory T cells (e.g., CD8⁺CD45RO⁺) in CRC tissues was strongly correlated with reduced early metastatic features. Such immune-enriched tumors tended to present at earlier clinical stages and were associated with significantly prolonged survival. This study underscores that the local immune microenvironment, particularly the presence and spatial distribution of memory T cells, plays a decisive role in tumor progression, metastasis, and patient prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Research hotspots and frontiers\u003c/h2\u003e \u003cp\u003eKeyword analysis is a valuable tool for accurately identifying emerging themes and research trends within a given field \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In this study, we conducted keyword burst analysis using CiteSpace and identified five keywords that have shown strong citation bursts in the past five years. Based on these findings, we provide an outlook on potential research hotspots concerning CD8⁺ T cells in CRC.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Tumor immune microenvironment\u003c/h2\u003e \u003cp\u003eThe tumor immune microenvironment (TIME) is a critical determinant of CRC progression, therapeutic responsiveness, and patient prognosis. Its defining features include immune cell infiltration dynamics, metabolic reprogramming, and regulation of immunosuppressive molecules \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Clinical evidence demonstrates that in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy (nCRT), dense CD8⁺ T-cell infiltration correlates with an \u0026ldquo;Inflammation\u0026rdquo; immune phenotype and significantly prolongs recurrence-free survival (RFS). Conversely, an \u0026ldquo;immune-desert\u0026rdquo; phenotype is associated with poor prognosis despite adjuvant chemotherapy \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Single-cell analyses further revealed that CD15-overexpressing neutrophils can promote CRC progression by engaging CD8⁺ T cells through CXCL12, driving their differentiation into the GZMK⁺ effector memory phenotype \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the profound impact of immune cell interactions within TIME on tumor progression and clinical outcomes. The immunosuppressive nature of TIME is also reflected in the enrichment of regulatory T cells (Tregs), PMN myeloid-derived suppressor cells (PMN-MDSCs), and M2-polarized tumor-associated macrophages (M2-TAMs). For instance, the intestinal microbial metabolite 4-hydroxyphenylacetic acid (4-HPA) induces CXCL3 expression via the JAK2/STAT3 pathway, which subsequently recruits PMN-MDSCs through the CXCL3\u0026ndash;CXCR2 axis, thereby suppressing CD8⁺ T-cell anti-tumor function and accelerating CRC progression \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Similarly, immune checkpoint molecules such as B7-H3 amplify immunosuppression by inhibiting CD8⁺ T-cell infiltration and activating the CCL2\u0026ndash;CCR2\u0026ndash;M2 macrophage axis \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetabolic abnormalities are a central mechanism driving TIME immunosuppression. Estrogen synthesis mediated by the key enzyme of lipid metabolism, CYP19A1, upregulates PD-L1, IL-6, and TGF-β through the GPR30-AKT pathway and inhibits CD8\u0026thinsp;+\u0026thinsp;T cell function, and targeted inhibition of CYP19A1 enhances anti-PD-1 efficacy \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. In parallel, tumor-derived exosomal ENTPD2 hydrolyzes ATP to generate adenosine, which activates the CD39\u0026ndash;CD73\u0026ndash;A2AR pathway and directly suppresses CD8⁺ T-cell activity \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Accordingly, dual-targeting strategies that combine metabolic and immune checkpoint interventions have shown promise. For example, the VE-cadherin agonist CD5-2 normalizes vascular function, thereby facilitating CD8⁺ T-cell infiltration and synergizing with anti-PD-1 therapy \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Curcumin, which activates CD8\u0026thinsp;+\u0026thinsp;T cells and induces ferroptosis by enriching short-chain fatty acid (SCFA)-producing microbe, modulates the microbe-immune axis \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Secondly, epigenetic interventions, such as atorvastatin-mediated RAS isoprenylation inhibition and induction of immunogenic cell death \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. In addition, combinatorial strategies with radiotherapy or nanotechnology-based delivery platforms (e.g., liposomal oxaliplatin \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, GRP78 nanotoxin \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e, and TAS-115 kinase inhibitor \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e) to remodel the TIME and enhance therapeutic efficacy.\u003c/p\u003e \u003cp\u003eThe spatial heterogeneity of TIME exerts a decisive influence on treatment response. Highly multiplexed imaging techniques have enabled investigators to preserve spatial information in TIME in clinical samples, thereby revealing the spatial co-localization between immune cells (especially CD8\u0026thinsp;+\u0026thinsp;T cells) and tumor cells, a spatial organization feature that has been suggested as an important biomarker for predicting response to immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Notably, left-sided and right-sided colon cancers display distinct TIME characteristics: right-sided colon cancer (RCC) exhibits higher CD8⁺ T-cell infiltration and a stronger association between TIME and oncogenic mutations (e.g., BRAF, KRAS), whereas left-sided colon cancer (LCC) demonstrates weaker correlations\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Furthermore, several molecular markers\u0026mdash;including IGSF6 \u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e, MDM4 \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e, and MAPK14 \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;have been identified as novel predictors of immune infiltration in CRC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Biomarkers\u003c/h2\u003e \u003cp\u003eAmong immune microenvironmental indicators, CD8⁺ T cell density represents a core prognostic marker in stage III colon cancer. High CD8⁺ infiltration has been established as an independent prognostic factor, correlating with significantly improved overall and disease-free survival \u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Tissue-resident memory T cells (CD103⁺CD8⁺ TRM cells) suppress colorectal cancer liver metastasis by strengthening anti-tumor immunity; their high infiltration independently predicted a reduced risk of liver metastasis and enhanced the efficacy of anti-angiogenic therapy by inducing vascular normalization \u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Furthermore, co-expression of immune checkpoints on tumor-infiltrating CD8⁺ T cells\u0026mdash;such as PD-1 with TIGIT or PD-1 with TIM-3\u0026mdash;has emerged as a novel biomarker associated with prolonged disease-free survival \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. At the epigenetic level, expression of RUNX family transcription factors (RUNX1 and RUNX3) in CD8⁺ T cells has been positively correlated with anti-tumor activity, while epigenetic modulation of RUNX3 further enhanced CD8⁺ T-cell function \u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Similarly, the DNA methylation-derived score CD8⁺ MeTIL (Methylation-derived Tumor-Infiltrating Lymphocytes) has been linked to improved disease-free survival. By quantifying CD8⁺ T-cell-specific methylation sites in tumor tissue, this biomarker provides a non-invasive, tissue-based approach for assessing the immune microenvironment and predicting prognosis \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe multi-parameter combination biomarker model significantly improves predictive accuracy. For example, the histopathological risk score (HRS), which cmbinoes TP53 aberrant expression, CD8⁺ T cell density, and intratumoral budding (ITB), serves as an independent predictor of resistance to neoadjuvant therapy in rectal cancer \u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. Current research hotspots emphasize dynamic biomarkers derived from multi-omics technologies, which construct predictive models capable of real-time monitoring of therapeutic responses by integrating genomic, epigenomic, and microenvironmental heterogeneity. Standardized immune scoring systems such as the Immunoscore \u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e and automated digital pathology approaches \u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e facilitate objective quantification of tissue-resident immune markers. Meanwhile, circulating immune cell markers\u0026mdash;such as the proportion of circulating CD4⁺ T cells determined via methylation assays \u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e \u0026mdash;overcome tissue-related limitations and enable systemic immune surveillance. Emerging molecular targets are increasingly being recognized as companion diagnostic markers to predict responses to immunotherapy. For instance, CMTM6 expression in M2 macrophages predicts responsiveness to PD-1 blockade \u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e, while RIG-I upregulation has been associated with enhanced efficacy of combination therapies \u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e. Collectively, these multidimensional and dynamic biomarkers are advancing the paradigm of precision immunotherapy. Moreover, spatial immuno-mapping technologies, such as CODEX multiplex imaging, enable individualized therapeutic strategies by resolving the spatial proximity of CD8⁺ T cells to tumor cells and quantifying activation-related markers (e.g., CD38⁺ MFI) \u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. These advances highlight a shift toward dynamic functional assessment, incorporation of circulating biomarkers, and integration of spatial multi-omics in immunotherapy research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Single-cell RNA sequencing\u003c/h2\u003e \u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) has become a core technology for dissecting tumor microenvironment (TME) heterogeneity in CRC, with research hotspots converging on three major directions. First, regarding immune cell functional states and exhaustion mechanisms, scRNA-seq has identified highly heterogeneous exhausted subpopulations of CD8⁺ T cells and γδ T cells in CRC. TCF-1⁺PD-1⁺ CD8⁺ T cells (T-pex) were shown to positively correlate with patient survival and display a unique transcriptional profile \u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. In contrast, γδ T cells exhibited PD-1⁺TIM-3⁺ terminally exhausted subsets, which were even more functionally suppressed than CD8⁺ T cells \u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e. Second, in elucidating therapeutic resistance mechanisms and guiding combination strategies, scRNA-seq uncovered the cellular basis of ICI resistance. For example, increased infiltration of IL-1β⁺ myeloid-derived suppressor cells (MDSCs) in tumors from anti-PD-1-resistant patients contributed to CD8⁺ T-cell inactivation \u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e. Trehan et al. \u003csup\u003e[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e further demonstrated that antigen-specific CD8⁺ T cells in liver metastases are numerically abundant but functionally impaired, while intermediate SPP1⁺ macrophages, associated with TGF-β signaling, suppress T-cell activity via immunosuppressive pathways. Liu et al. subsequently showed that SPP1⁺ macrophages activate NF-κB signaling through the SPP1\u0026ndash;CD44 axis, driving PD-1/TIM-3 upregulation in CD8⁺ T cells and reinforcing their exhausted phenotype \u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e. Based on these findings, novel combination strategies have been proposed. Targeting the m⁶A reader protein YTHDF1 reduces MDSC migration and significantly enhances anti-PD-1 efficacy \u003csup\u003e[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e, while microwave ablation combined with TIGIT blockade remodels the TME and promotes CD8⁺ T-cell proliferation and functional activation \u003csup\u003e[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e. Finally, in the domain of spatial heterogeneity and prognostic modeling, scRNA-seq integrated with spatial transcriptomics has revealed site-specific immune landscapes. For example, SPP1⁺ macrophages dominate the immunosuppressive microenvironment in metastatic hepatocellular carcinoma, whereas DC3 dendritic cells are enriched in primary CRC \u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e. Moreover, terminally exhausted CD8⁺ T cells exhibit lipid metabolic reprogramming and immunosuppressive features \u003csup\u003e[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. Leveraging these key cellular subsets (e.g., exhausted T cells, CAFs), researchers have constructed prognostic models for CRC and identified the MIF\u0026ndash;CD74/CXCR4 pathway as a critical regulatory axis of T-cell exhaustion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Immune checkpoint inhibitors\u003c/h2\u003e \u003cp\u003eICIs, including antibodies against PD-1/PD-L1 and CTLA-4, are generally ineffective CRC, particularly in patients with MSS disease, where primary resistance is common \u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. The underlying resistance mechanisms can be categorized into three major aspects. First, CD8\u0026thinsp;+\u0026thinsp;T cell exhaustion, characterized by sustained antigenic stimulation in the TME, leads to the loss of effector function and acquisition of an exhausted phenotype including PD-1\u0026thinsp;+\u0026thinsp;TIM-3+ \u003csup\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e. Second, an immunosuppressive TME, primarily mediated by Treg infiltration and the upregulation of immunosuppressive mediators such as PCSK9 and TGF-β, suppresses antitumor immunity \u003csup\u003e[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/sup\u003e. Third, tumor-intrinsic escape mechanisms, such as activation of β-catenin signaling, inhibit CCL4 expression, thereby reducing CD103\u0026thinsp;+\u0026thinsp;dendritic cell recruitment and limiting CD8\u0026thinsp;+\u0026thinsp;T cell priming and infiltration \u003csup\u003e[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e. To overcome ICI resistance, recent research has focused on synergistic strategies. One approach involves TME-targeted interventions, such as promoting vascular normalization to facilitate T-cell infiltration via endostatin \u003csup\u003e[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e or CXCL10 overexpression \u003csup\u003e[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/sup\u003e, and employing outer membrane vesicles derived from \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e to deliver Amuc_1434 protein, which downregulates PD-L1 expression and activates CD8\u0026thinsp;+\u0026thinsp;T cells \u003csup\u003e[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/sup\u003e. Another promising avenue is the development of novel immune checkpoint targets, including LAG3 blockade to reverse T-cell exhaustion \u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e, CD200R inhibition to restore NK and CD8\u0026thinsp;+\u0026thinsp;T cell function \u003csup\u003e[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/sup\u003e, and activation of the cGAS/STING/IFN-β axis (e.g., with riluzole) to enhance CXCL10 secretion and T-cell recruitment \u003csup\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/sup\u003e. In addition, epigenetic and metabolic reprogramming strategies have shown potential; for example, cordycepin reduces the Treg ratio and reverses T-cell exhaustion \u003csup\u003e[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/sup\u003e, while metformin rescues CD8\u0026thinsp;+\u0026thinsp;T-cell metabolic dysfunction \u003csup\u003e[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/sup\u003e. Furthermore, individualized combination therapies are being explored. These include the use of neoantigen vaccines with regorafenib to induce infiltration of Rgs2\u0026thinsp;+\u0026thinsp;CD8+ T cells \u003csup\u003e[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/sup\u003e, or intratumoral administration of neoadjuvanted influenza vaccine to enhance CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration and upregulate PD-L1 expression \u003csup\u003e[\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/sup\u003e. Collectively, these approaches aim to reinvigorate CD8\u0026thinsp;+\u0026thinsp;T cell responses through multi-pronged strategies to overcome resistance. Future research should focus on elucidating subtype-specific mechanisms and identifying pan-cancer biomarkers to guide precision immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.5 tumor-infiltrating lymphocytes\u003c/h2\u003e \u003cp\u003eTumor-infiltrating lymphocytes (TILs) particularly CD8\u003csup\u003e+\u003c/sup\u003e T cells, represent a central focus in studies of the TIME. High-density CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration within tumor nests serves as an independent prognostic factor in CRC, strongly correlating with improved patient survival and demonstrating superior predictive value compared with conventional histological staging \u003csup\u003e[\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/sup\u003e. These localized CD8\u0026thinsp;+\u0026thinsp;T cells exhibit activated cytotoxic characteristics, underscoring their pivotal role in antitumor immunity. Synergistic infiltration of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells (CD4/8 double-positive) has also been shown to significantly improve survival in patients with esophageal squamous carcinoma \u003csup\u003e[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]\u003c/sup\u003e. In contrast, Tregs facilitate immune evasion by suppressing CD8\u0026thinsp;+\u0026thinsp;T-cell activity \u003csup\u003e[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]\u003c/sup\u003e, while exhausted TIGIT+ CD8\u0026thinsp;+\u0026thinsp;T cells are associated with advanced disease stage and early relapse in CRC \u003csup\u003e[\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/sup\u003e. Microenvironmental modulation further influences TIL composition. For example, neoadjuvant radiotherapy decreases the total number of TILs but selectively enriches cytotoxic CD8\u0026thinsp;+\u0026thinsp;Granzyme B+ (GrzB+) T cells \u003csup\u003e[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]\u003c/sup\u003e. Interestingly, although low Granzyme B expression in CRC is linked to early metastatic potential, its prognostic value is weaker compared to FoxP3\u0026thinsp;+\u0026thinsp;Treg infiltration \u003csup\u003e[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/sup\u003e. In Lynch syndrome\u0026ndash;associated CRC, high infiltration of activated CD8\u0026thinsp;+\u0026thinsp;T cells is correlated with early-stage tumors and the absence of lymph node metastasis \u003csup\u003e[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/sup\u003e. At the molecular level, studies on T-cell exhaustion have identified the poor prognosis CD8 gene signature (ppCD8sig) as a predictive biomarker \u003csup\u003e[\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]\u003c/sup\u003e. Importantly, blockade of the PD-1/TIM-3 pathway has been shown to restore T-cell function and reverse exhaustion \u003csup\u003e[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]\u003c/sup\u003e. Additionally, the novel marker LAYN influences CRC progression by modulating both Treg and TAM activity, and its expression has been proposed as a prognostic indicator of immune infiltration and patient outcomes \u003csup\u003e[\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]\u003c/sup\u003e. Collectively, these findings suggest that targeting key regulators of T-cell function represents a promising therapeutic avenue. Moreover, multidimensional approaches integrating dynamic monitoring of T-cell subsets with novel gene signatures or molecular tags are expected to improve patient stratification and enhance the prediction of immunotherapy responsiveness in CRC.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations\u003c/h2\u003e \u003cp\u003eAlthough this study provides important insights into the research landscape of CD8\u0026thinsp;+\u0026thinsp;T cells in colorectal cancer, several limitations should be acknowledged. First, this analysis included only publications indexed in the Web of Science Core Collection, indicating that future studies should consider incorporating additional databases such as Scopus and PubMed. Second, owing to citation accrual lag, the citation counts and scholarly impact of recently published articles may be systematically underestimated. Finally, as an analysis article, this study is primarily descriptive and hypothesis generating; no experimental or clinical validation was performed to substantiate the mechanistic interpretations or translational implications inferred from the hotspot and frontier analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eResearch on CD8\u0026thinsp;+\u0026thinsp;T cells in CRC is rapidly advancing. Global trend analyses predict substantial growth in both the number of publications and the pool of active researchers in this field. Significant progress has been made, particularly in countries such as China, the United States, and Japan, highlighting the international importance of this research area. The existing literature can be broadly categorized into two main directions: \"mechanism exploration\" and \"application of therapeutic strategies and efficacy assessment,\" and future research is expected to explore these directions in greater depth and coherence, especially in the areas of the dynamics of the TIME, the discovery and validation of novel biomarkers, the in-depth application of scRNA-seq technology, the optimal combination of immune checkpoint inhibitors, and therapies to enhance the function of CD8\u0026thinsp;+\u0026thinsp;TILs, thus pointing out new research directions for the immunotherapy of CRC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1 Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human participants, animal subjects, or any tissue/data that requires ethical approval. Therefore, ethics approval and consent to participate are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript contains no individual person\u0026rsquo;s data in any form. Consent for publication is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the [Zenodo] repository, accessible at https://doi.org/10.5281/zenodo.19389635. The raw data were originally retrieved from the Web of Science Core Collection (WoSCC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5 Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82460924); Guizhou High-level Innovative Talent Cultivation Plan (Qiankehe Talent (2020) No. 6016); Guizhou Clinical Research Center for Digestive Diseases [grant no. Qian Ke He Platform-LCZX (2025) 001].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXu YANG, Jing MA: Data curation, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft, Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing. Junfeng LUO, Juanjuan WANG, Maoyu LIAO: Data curation, Investigation, Methodology, Visualization, Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft. Haibo GAO and Zilong LIU: Software, Validation, Visualization, Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft. Mingliang CHU and Jiemin LIU: Funding acquisition, Supervision, Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.7\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participating authors for their efforts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMATSUDA T, FUJIMOTO A, IGARASHI Y. Colorectal Cancer: Epidemiology, Risk Factors, and Public Health Strategies [J]. Digestion, 2025, 106(2): 91-9.\u003c/li\u003e\n \u003cli\u003eBRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2024, 74(3): 229-63.\u003c/li\u003e\n \u003cli\u003eHOFSETH L J, HEBERT J R, CHANDA A, et al. Early-onset colorectal cancer: initial clues and current views [J]. 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CA Cancer J Clin, 2018, 68(6): 394-424.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CD8 + T cell, Colorectal Cancer, bibliometric analysis, CiteSpace, knowledge graph","lastPublishedDoi":"10.21203/rs.3.rs-8934222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8934222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eColorectal cancer (CRC) remains a global health burden. Recent advances in tumor immunotherapy have reshaped the therapeutic landscape, highlighting the pivotal role of CD8\u003csup\u003e་\u003c/sup\u003e T cells in tumor immune surveillance and elimination. However, this field is fragmented, warranting bibliometric evaluation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRelevant articles and reviews were retrieved from the Web of Science Core Collection. Data on publications, countries, institutions, authors, journals, citations, and keywords were systematically analyzed using Microsoft Excel 2019 and CiteSpace 6.3.R3.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2006 publications on CD8⁺ T cells and CRC published between 1992 and 2024 were included. Annual publications showed a significant upward trend, peaking in 2024 (n\u0026thinsp;=\u0026thinsp;320). Frontiers in Immunology contributed the most articles (n\u0026thinsp;=\u0026thinsp;84). China, the United States, Japan, and France were identified as the leading contributors, with China ranking first in publication volume. Among institutions, Sun Yat-sen University had the highest output (n\u0026thinsp;=\u0026thinsp;91). The most prolific authors were Inti Zlobec and Francois Ghiringhelli (10 publications each). The 10 most influential publications appeared in high-impact journals, with landmark studies in Science and the New England Journal of Medicine exceeding 200 citations. Current research hotspots include reversing T-cell exhaustion, overcoming the immunosuppressive tumor microenvironment (TME), and elucidating mechanisms of immunotherapy resistance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis bibliometric analysis systematically maps the knowledge framework and evolutionary trajectory of CD8⁺ T cell research in CRC. The findings provide critical insights into immune microenvironment mechanisms, novel biomarker discovery, and optimization of immunotherapeutic strategies for CRC.\u003c/p\u003e","manuscriptTitle":"Bibliometric Analysis of CD8⁺ T Cells in Colorectal Cancer Research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 14:52:28","doi":"10.21203/rs.3.rs-8934222/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"260764027196149882911557322046719953280","date":"2026-04-30T17:47:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T16:35:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T12:52:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T06:07:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T23:41:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-04-02T23:35:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"178b25f0-cd04-4aa6-b1bb-b59f3ec64a3a","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"260764027196149882911557322046719953280","date":"2026-04-30T17:47:06+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T14:52:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 14:52:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8934222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8934222","identity":"rs-8934222","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}