The landscape of research on B cell and immunotherapy in solid tumors over the past 20 years

Abstract

Background

Immunotherapy, remarkably immune checkpoint inhibitors, has shown significant efficacy and survival benefits in patients with various solid tumors. Although T lymphocytes have been extensively studied as the primary target cells, the role and application of B cells in solid tumor immunotherapy remain less understood.

Methods

In this study, we conducted a bibliometric analysis of articles on solid tumor immunotherapy related to B cells, published from 2003 to March 28, 2024, in the Web of Science Core Collection. Co-authorship and keyword co-occurrence analyses were performed using VOSviewer, while CiteSpace was used to identify burst keywords and references.

Results

A total of 1995 publications were analyzed, revealing a year-on-year increase in articles. The most common keywords are “immunotherapy” and “expression”. The most frequently referenced publications focused on tertiary lymphoid structures and B-cell markers. The United States and China are the largest contributors to this field, with Frontiers in Immunology being the most prolific journal. This study provides a comprehensive overview of over 20 years of B cell and immunotherapy research in solid tumors, identifying key countries, institutions, authors, journals, and publications.

Conclusions

The findings offer valuable insights into the relationship between B cells and immunotherapy in solid tumors. This study primarily unveils the current research hotspots concerning the role of B cells in immunotherapy, encompassing both fundamental research and clinical trials, as well as the accelerating emergence of an increasing number of B-cell-centric immunotherapies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03458-3.

Strength:

1. Utilized multiple complementary bibliometric tools (VOSviewer, CiteSpace, and R’s Bibliometrix) to enhance the robustness and multidimensionality of data analysis.

2. Implemented a rigorous, independent dual-reviewer screening process with third-party arbitration to minimize selection bias during data extraction.

3. Analyzed a 20-year longitudinal dataset (2003–2024) from the Web of Science Core Collection, capturing evolving trends in the field.

Limitation:

1. Restricted data sources to English-language articles in the Web of Science, potentially excluding relevant non-English studies or gray literature.

2. Excluded reviews and conference abstracts, which may overlook emerging hypotheses or unpublished findings critical to understanding the field’s trajectory.

Background

The advent of immunotherapies has heralded a transformative era in cancer treatment, particularly for patients with metastatic cancers previously deemed incurable [1]. Treatments ranging from immune-checkpoint blockade therapy to adoptive cellular therapy have demonstrated sustained clinical responses across various cancer types, significantly altering the landscape of oncology [2]. Despite these advancements, the response rates to immunotherapy remain limited, and the underlying mechanisms are not fully understood, necessitating further investigation into the tumor-immune microenvironment (TIME) and its complex interplay with therapeutic interventions [3].

Immunotherapy remodels the TIME by directly altering immune cell functions and enhancing their anti-tumor effects [4]. The TIME is highly intricate, encompassing diverse cells, including T cells, B cells, macrophages, dendritic cells, and other stromal components. In recent years, significant progress has been made in understanding how these components interact and contribute to the therapeutic outcomes of immunotherapy [5]. Notably, while the reinvigoration of effector T lymphocytes through immune checkpoint inhibitors (ICIs) has been a focal point, emerging evidence suggests that B cells may play a pivotal role in mediating the efficacy of these treatments [6].

Tumor-infiltrating B cells (TIBs), with the ability to produce antibodies [7], secrete cytokines [8], and interact with other immune cells [9–11], are reported to have a powerful prognostic value in various cancer types [7]. Recently, a review showed that the prognosis of 26 cancer types in the cancer genome atlas was positively associated with the proportion of TIBs [8]. In some studies, TIBs have been associated with poor prognosis [9, 10]. This is mainly because of the difficulty distinguishing effector B cells from regulatory B cells. Indeed, within the tumor microenvironment, TIBs display considerable functional heterogeneity, broadly spanning naïve B cells, memory B cells, germinal center B cells, and antibody-secreting cells [11]. After response to inflammatory factors, B cells could differentiate into regulatory B cells (Bregs), which, like regulatory T cells, served as an immunosuppressive factor [12]. By secreting cytokines such as IL-10 and IL-35 and transforming growth factor beta or releasing metabolites such as gamma-aminobutyric acid, Bregs can suppress pro-inflammatory cells via programmed cell death 1 (PD-1)/ programmed cell death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte associated protein 4 (CTLA-4) pathways [13–15]. Except Bregs; multiple studies have found double-negative B cells, or atypical memory (AtM) B cells in aging mice, autoimmune diseases, and chronic infection models characterized by IgD⁻CD21⁻ [16, 17]. Similarly to T cell exhaustion, tumor AtM B cells exhibited an exhausted phenotype. These cells could expand in non-small cell lung cancer (NSCLC) and inversely correlate with affinity-matured B cell populations, leading to a worse prognosis [18].

When it comes to structures associated with TIB responses, tertiary lymphoid structures (TLSs) are found to play an essential role in B cell differentiation, class switch, and somatic hypermutation [19], which are associated with improved survival in several cancers [19–22]. As for the relationship between immunotherapy and TLSs, a study has shown that TLSs and B cell signatures, rather than T cell signatures, are predictive of therapeutic responses to ICIs such as pembrolizumab and ipilimumab [23]. Moreover, the density of B cells and TLSs has been found to increase during treatment in patients who respond to immunotherapy, but not in non-responding patients [24]. In the study of soft tissue sarcomas, the patients’ group featuring the presence of tumor-infiltrating lymphocytes (TILs) exhibited a significant response to pembrolizumab treatment, with 50% of patients showing favorable therapeutic outcomes. In stark contrast, patients belonging to the immunologically deserted group did not demonstrate any response to the same treatment [25]. These correlations between B cell activity and therapeutic success point to a potential role of B cells in modulating the TIME to favor immunotherapy responses. Research related to B lymphocytes and immunotherapy has gradually become a hot topic.

Bibliometrics is a field of study that uses quantitative and statistical methods to analyze the production and dissemination of research literature [26]. It involves collecting, organizing, and analyzing bibliographic data, such as the number of citations, co-authorship patterns, and publication venues [27]. Bibliometrics has several advantages, including the ability to identify and quantify the impact of research, provide evidence-based assessments of scientific productivity, and track the dissemination and influence of study over time. Bibliometrics can also support the identification of research trends, emerging fields, and collaborations and inform strategic planning and resource allocation in research organizations [28]. Bao et al. recently conducted a bibliometric analysis of tertiary lymphoid structures (TLS), revealing that recent studies have primarily focused on TLS-related cancer mechanisms, including immunotherapy, tumor microenvironment, tumor-infiltrating lymphocytes, prognosis, and immune checkpoint inhibition. As a key component of TLS, B cells play a critical role in tumor immunity and immunotherapy; however, bibliometric analyses specifically targeting B cells remain limited. Given their significance, a dedicated bibliometric study on B cells is warranted. With the growing volume of scientific literature and the increasing importance of research impact, bibliometrics will continue to play a significant role in evaluating and assessing research.

This research has uncovered compelling evidence that B cells played an important role in solid tumor immunotherapies. Several studies have demonstrated that B cells could predict and affect the effectiveness of immunotherapies. However, the existing literature also highlights the need for further research to thoroughly understand the specific mechanisms or regulatory networks underlying the correlation between B cells and immunotherapies. This research aims to synthesize the present findings and trends in this field, providing a comprehensive overview of the current state of knowledge and understanding of the interplay between B cells and immunotherapies in solid tumors [90].

Materials and methods

Data connection and screening

This study was designed as a cross-sectional study and was conducted on March 28, 2024, using the Web of Science Core Collection. Web of Science is widely regarded as the most influential scientific research database and one of the most frequently accessed academic databases, as it includes journals that are typically considered to have the highest quality [29, 30]. It is a widely employed database for conducting bibliometric analysis of scientific research in the healthcare realm. All relevant literature was retrieved and downloaded in “Plain text” format to avoid biases introduced by updates [31].

The retrieval and screening strategy for the literature is as follows: 1. The literature was limited using the TS (“topic,” including title, abstract, author’s keywords, and keywords Plus) keyword search strategy. The search strategy used was “immunotherapy” OR “immunotherapies” OR “immunotherapeutic” OR “immunotherapeutics” OR “ICI” OR “ICIs” OR “CPI” OR “immune-checkpoint inhibitor” OR “immune checkpoint inhibitor” OR “immune-checkpoint blockade” OR “immune checkpoint blockade” (Topic) and “Bursa Dependent Lymphocyte” OR “Bursa Dependent Lymphocytes” OR “Lymphocyte, Bursa-Dependent” OR “Lymphocytes, Bursa-Dependent” OR “B Lymphocyte” OR “B Lymphocytes” OR “Lymphocyte, B” OR “Lymphocytes, B” OR “B Cell” OR “B Cells” OR “Cell, B” OR “Cells, B” OR “B1 cell” OR “B2 cell” OR “B1 Cells” OR “B2 Cells” OR “plasma cell” OR “cell, plasma” OR “plasma cells” OR “cells, plasma” (Topic) and English (Language) and “neoplasm” OR “cancer” OR “neoplasia” OR “tumor” OR “malignancy” (Topic). The publication date range was set from January 1, 2003, to March 28, 2024. We initially excluded non-English literature as well as reviews and meetings. The screening of publications was conducted by three researchers specializing in relevant fields. The specific process was as follows: two researchers independently screened all the publications. If their opinions on a particular piece of publication were conflicting, the third researcher was consulted for the final evaluation. This ensured the exclusion of non-solid tumors and other irrelevant studies from the publications. The detailed process is illustrated in Fig. 1.

Fig. 1.

Flowchart of bibliometric analysis based on B lymphocytes and immunotherapy in solid tumor

Data analysis and mapping

We performed bibliometric analysis using the software Citespace (version 6.1.R3) [32], VOSviewer (version 1.6.18) [33], Microsoft Office Excel 2021 and the Bibliometrix package (version 3.2.1) in R (4.0.3, https://www.r-project.org/) [34]. We utilized “Bibliometrix” to figure out basic bibliometric features and illustrate the collaboration between countries and authors. Running this R package after importing the target publications, we can also count the number of these publications in different countries, organizations, journals, etc. We used VOSviewer to analyze co-authorship among countries and institutions and to analyze keyword co-occurrences, which focuses on cluster analysis of scientific and technical papers at the aggregate level [35]. After selecting the analysis type and analysis unit and removing duplicate words, VOSviewer will generate the co-occurrence network, time-overlapping co-occurrence analysis network, and heat map. CiteSpace is a citation analysis and visualization software. The software visualizes the structure and trends in the literature using “scientific knowledge mapping” [36]. CiteSpace was employed to analyze citation bursts of keywords and cited references. Additionally, we used Microsoft Office Excel 2021 to build an exponential model fitting the cumulative publication numbers and frequency of keyword occurrence.

No participant data was involved in the study, as all original data were sourced from publicly available databases, thus obviating the need for ethical review.

Results

Time distribution of publications

As shown in Fig. 1, a total of 6549 relevant papers have been screened, and after excluding papers that did not adhere to the specified topic and article type requirements, a total of 1,995 papers were ultimately included in this study. Figure 2 displays a general upward trend in the number of publications focused on B lymphocytes and immunotherapy over the past two decades. This research area has become increasingly prominent, with 377 articles published in 2022, marking a 2217.65% increase from 17 articles in 2003. An exponential growth function was applied to analyze the publication trends over time, which reflected a similar pattern to actual growth (R² = 0.7731), indicating notable growth and expansion in the field of B cell and immunotherapy research.

Fig. 2.

Clinical Events Timeline of B Cell-Based Immunotherapy

The earliest publication in 2003 by Correa et al. investigated MUC1 mucin on B- and T-lymphocytes, identifying its role in T-cell and endothelial cell interactions and anti-tumor immunity [37]. A noteworthy study conducted in 2005 revealed that, in tumor patients, gemcitabine treatment led to a significant reduction in myeloid suppressor cells in the spleens while preserving the population of B cells, suggesting gemcitabine could mitigate the immunosuppressive TIME and enhance the efficacy of cancer immunotherapy [38]. Another study reported in 2011 showed that B cell depletion could impair the function and proliferation of CD4(+) and CD8(+) T cells and thus enhance B16 melanoma growth in mice [39]. In 2016, a study reported that memory B cells were associated with pathological complete response to neoadjuvant chemotherapy in estrogen receptor-negative breast tumors, illustrating the anti-tumor effect of B cells [40]. In the early days before the emergence of ICIs, researchers focused much of their studies on B lymphocytes on cell number changes, protein-protein interactions, and cell-cell interactions in other antitumor therapies, and these roles served as theoretical support for the exploration of original therapeutic modalities such as chemotherapy plus immunotherapy.

Analysis of national publication counts

National publication analysis reveals the volume of articles published on B cells and immunotherapy across different countries and international collaboration among authors. A total of 63 countries and territories are represented in the study. Figure 3 shows that if we count the number of publications by country of corresponding author, China and the United States top the list with 828 and 484 articles, followed by Germany and Japan in that order. Comparing the trends in the number of articles published by different countries, both China and the United States are rapidly emerging in this field, and the number of Chinese authors involved in related research has gradually surpassed that of the United States in recent years. Sorting by country of all authors yields approximately the same results. Chinese authors lead the way with 3,091 publications, followed by the United States with 2537 publications, and Germany with 567 publications. Other notable contributors include France, Japan, and the UK, each with more than 250 publications. Figure 4 illustrates international collaborations, with the most frequent collaboration occurring between China and the United States (82 instances). Other significant collaborations involve the United States with Germany (29), the United States with Canada (27), and the United States with France (25). Figure S1 provides additional insights into countries/regions’ co-authorship analysis, including cluster and time-overlapping network analyses, which show similar results.

Fig. 3.

Top 10 countries with publications in the field of B-lymphocyte and immunotherapy. A Number of publications by country of corresponding author; B Changes in trends in the number of articles issued in different countries (C). Number of publications by country of full authorship

Fig. 4.

Global Collaborations in B cells and Immunotherapy and the Frequency of Collaboration Among Nations

Analysis of institution publications

In our study on B lymphocytes and immunotherapy in solid tumors, we analyzed publications from 2,036 institutions worldwide. Figure 5 highlights the top 20 institutions by publication volume. The UTMD Anderson Cancer Center leads the list, with 193 publications, followed by the University of Texas System (191) and Harvard University (157). Sun Yat-sen University (140) and Fudan University (123) from China rank fourth and fifth, respectively. Most of these leading institutions are in the United States, with six originating from China. Apart from the United States and China, France is represented by Université Paris Cité, Unicancer and Institut National de la Santé et de la Recherche Médicale. The UK and Germany are represented by the University of London and the Helmholtz Association, respectively. Figure S2 presents an institutional co-authorship analysis, showcasing cluster and time-overlapping networks. U.S. institutions predominantly conducted early research, but in recent years, Chinese institutions like Sun Yat-sen University and Shanghai Jiaotong University have become increasingly active in this field.

Fig. 5.

Top 20 institutions involved in research on B cells and Immunotherapy

Analysis of publication quantity and journal impact

This study encompasses 1,995 articles published across 514 journals. Table 1 enumerates the top 10 journals by publication volume and their latest 2023 impact factors (IF) [32, 41]. Among these, six journals are classified in the first quartile (Q1) of the Journal Citation Reports (JCR). The top journals include Frontiers in Immunology (n = 130, IF = 7.3, Q1), Frontiers in Oncology (n = 77, IF = 4.7, Q2), and Journal for Immunotherapy of Cancer (n = 73, IF = 10.9, Q1). This study also highlights the number of articles published in high-impact journals such as Nature (n = 8) and Cell (n = 5). The journal analysis demonstrates that publications in B-lymphocyte and solid tumor immunotherapy are mostly published in immunology and oncology-related journals, and there is no shortage of articles with great impact and major scientific and technological breakthroughs published in top-tier journals.

Table 1.

Top 10 journals in the field of B lymphocyte and immunotherapy

Rank	Source	Articles	Country	IF	JCR-c
1	FRONTIERS IN IMMUNOLOGY	130	Switzerland	7.3	Q1
2	FRONTIERS IN ONCOLOGY	77	Switzerland	4.7	Q2
3	JOURNAL FOR IMMUNOTHERAPY OF CANCER	73	United Kingdom	10.9	Q1
4	ONCOIMMUNOLOGY	67	United States	7.2	Q1
5	CANCER IMMUNOLOGY IMMUNOTHERAPY	49	United States	5.8	Q1
6	CANCERS	48	Switzerland	5.2	Q1
7	CLINICAL CANCER RESEARCH	44	United States	11.5	Q1
8	JOURNAL OF IMMUNOLOGY	36	United States	4.4	Q2
9	FRONTIERS IN GENETICS	34	Switzerland	3.7	Q2
10	SCIENTIFIC REPORTS	32	United Kingdom	4.6	Q2

IF: impact factor; JCR: journal citation reports

Research hotspot analysis

Most cited publications

We used R to identify the top twenty most cited papers, as shown in Table 2. The most cited publication is “B cells and tertiary lymphoid structures promote immunotherapy response” by Beth A. Helmink, published in Nature in 2020 [24]. It has been cited 1219 times, averaging 243.8 citations per year. The second and third most cited papers are “Tertiary lymphoid structures improve immunotherapy and survival in melanoma” (2020, Nature) and “B cells are associated with survival and immunotherapy response in sarcoma” with 1008 and 979 citations respectively. Both focus on the role of TLSs and B cells in tumor immunotherapy [23, 25].

Table 2.

Top 20 highly cited articles in the field of B cell and immunotherapy

Rank	Paper	DOI	Total citations	Total citations per year	Normalized total citations
1	HELMINK BA, 2020, NATURE	10.1038/s41586-019-1922-8	1219	243.8	26.7665604
2	CABRITA R, 2020, NATURE	10.1038/s41586-019-1914-8	1008	201.6	22.1334642
3	PETITPREZ F, 2020, NATURE	10.1038/s41586-019-1906-8	979	195.8	21.4966879
4	VINCENT J, 2010, CANCER RES	10.1158/0008-5472.CAN-09-3690	899	59.9333333	9.47645819
5	SUZUKI E, 2005, CLIN CANCER RES	10.1158/1078 − 0432.CCR-05-0883	815	40.75	8.52906977
6	LINNEMANN C, 2015, NAT MED	10.1038/nm.3773	512	51.2	8.734571
7	ALI HR, 2016, PLOS MED	10.1371/journal.pmed.1002194	381	42.3333333	6.44002181
8	HERVAS-STUBBS S, 2011, CLIN CANCER RES	10.1158/1078 − 0432.CCR-10-1114	352	25.1428571	5.54520548
9	LINES JL, 2014, CANCER RES	10.1158/0008-5472.CAN-13-1504	336	30.5454545	7.06542056
10	THEMELI M, 2013, NAT BIOTECHNOL	10.1038/nbt.2678	307	25.5833333	6.58886653
11	KORTYLEWSKI M, 2009, NAT BIOTECHNOL	10.1038/nbt.1564	301	18.8125	5.94746988
12	ZHOU GY, 2017, GASTROENTEROLOGY	10.1053/j.gastro.2017.06.017	293	36.625	7.12844365
13	MESSINA JL, 2012, SCI REP-UK	10.1038/srep00765	277	21.3076923	4.83894333
14	YUAN JD, 2008, P NATL ACAD SCI USA	10.1073/pnas.0810114105	269	15.8235294	6.88436019
15	HOLLERN DP, 2019, CELL	10.1016/j.cell.2019.10.028	260	43.3333333	6.31530705
16	DAS R, 2018, J CLIN INVEST	10.1172/JCI96798	254	36.2857143	5.72932331
17	DILILLO DJ, 2010, J IMMUNOL	10.4049/jimmunol.0903009	245	16.3333333	2.58257203
18	COPPOLA D, 2011, AM J PATHOL	10.1016/j.ajpath.2011.03.007	234	16.71428571	3.68630137
19	IKUTANI M, 2012, J IMMUNOL	10.4049/jimmunol.1101270	233	17.9230769	4.07030251
20	DURANTE MA, 2020, NAT COMMUN	10.1038/s41467-019-14256-1	227	45.4	4.984421

Analysis of citation bursts

A citation burst indicates a significant increase in citations for a published article over a period of more than one year [32]. We analyzed the top 25 emerging keywords and references from 2003 to 2024, as depicted in Figs. 6 and 7. The blue line in the figure represents the time span of the analysis, while the red line indicates periods of notable content emergence. For keyword emergence, Fig. 6 enumerates the top 25 keywords. Terms such as “dendritic cells,” “monoclonal antibody,” “in vivo,” and “antigen” have demonstrated prolonged periods of prominence, highlighting these terms as key areas during the analyzed period. Figure 7 focuses on references, with the most prominent being a 2015 publication in Nature Methods titled “Robust enumeration of cell subsets from tissue expression profiles.”

Fig. 6.

Top 25 keywords with the strongest citation bursts on B cells and Immunotherapy

Fig. 7.

Top 25 references with the strongest citation bursts on B cells and Immunotherapy

Frequency and clustering analysis of keywords

Among the 6,261 identified keywords, we consolidated those with similar meanings and applied a minimum occurrence threshold of 30 in VOSviewer. This resulted in 100 keywords meeting the criteria for analysis, which facilitated the creation of a keyword co-occurrence network, time-overlapping co-occurrence analysis network, and heatmap, as illustrated in Fig. 8. Figure 8A presents the cluster analysis, categorizing the 100 keywords into three distinct clusters. Red Cluster contains 39 keywords, including “B cell”, “T cell”, and “dendritic cells”. This cluster reflects basic research and experiments at the cellular level concerning B-cells and organismal immunity. And green Cluster contains 37 keywords, such as “cancer”, “expression”, “prognosis” and “biomarkers”. It indicates a transition towards specific tumor types and clinical investigations, exploring B-cells and immune-related biomarkers and prognosis. Blue Cluster contains 24 keywords, including “immunotherapy”, “chemotherapy”, “pembrolizumab”, “survival”, “tertiary lymphoid structures” and “non-small cell lung cancer”. This cluster focuses on real-world immunotherapy cohorts and clinical trials investigating B-cells and survival outcomes in various types of immunotherapies.

Fig. 8.

Research hotspots on B cells and Immunotherapy. A Keyword co-occurrence network; B Time-overlapping co-occurrence analysis network of keywords; C Keyword co-occurrence heat map; D Wordcloud of keyword analysis

Figure 8B displays the temporal overlap visualization of the keywords. Keywords that emerged earlier are shown in purple, while more recent keywords are highlighted in yellow. This analysis suggests that from 2003 to 2024, research on B-cells and immunotherapy has transitioned from basic cellular studies to clinical applications. Recent research has increasingly focused on survival, prognosis, biomarkers, and other related components. Figure 8C shows the heat map analysis of 100 keywords, where a higher frequency of occurrence is indicated by more yellow shading. The word cloud in Fig. 8D visualizes keyword frequency, with larger words representing higher frequencies. Figure S3 also shows the trend of the top 10 keywords from 2003 to 2024. Figure S4 shows the changes in the main topics of interest and keywords from 2003 to 2024 in countries which have the highest number of articles. In addition, Figures S5 and S6 show the results of the authors’ co-authorship analysis and co-citation references analysis, respectively.

In conclusion, we can see a clear evolution in the research landscape, highlighting B-cell and immunotherapy studies’ growing complexity and clinical relevance.

Discussion

This review uses bibliometric analysis to synthesize the literature on the relationship and application of B lymphocytes in immunotherapy for solid tumors from 2003 to 2024. Combined with the trend changes in the number of publications in this field, the emergence of keywords, and highly cited references, it is obvious that the field pertaining to B cells and immunotherapy has undergone remarkable advancements in the era of ICIs.

From 2003 to 2016, the annual number of publications remained below 50, with minor fluctuations. However, there was a rapid increase starting from 2016, peaking in 2022–2023. This trend coincides with the approval of ipilimumab in 2011 as the first anti-CTLA-4 monoclonal antibody for treating unresectable and metastatic melanoma, leading to the onset of ICIs therapy in oncology [42]. Similarly, Nivolumab, an anti-PD-1 monoclonal antibody, was approved for melanoma treatment in 2014, further spurring research and clinical trials involving PD-1 inhibitors [43]. It’s shown that B cells could engage in immunotherapies by presenting antigens via T cell-dependent B cell receptors [44]. The antigen-presenting function of B cells has long been studied, mainly investigating how they capture exogenous antigens and present them to T cells to activate the immune response via T-cell receptors. In 2015, Carsten et al. evaluated neoantigen-specific CD4 + T cell responses in melanoma patients by co-culturing autologous B cells and T cells. They detected CD4 + T cell responses against tumor-specific mutations by loading these mutations onto autologous B cells and co-culturing these modified B cells with patients’ CD4 + T cells [45]. In 2017, Jill E. Slansky et al. demonstrated for the first time using fresh ex vivo tumor samples from NSCLC patients that TIBs can present antigens to CD4 + TILs and influence their activation or suppression phenotype. Moreover, TIBs themselves could exhibit a depletion phenotype, and their levels can be used as a marker for immunotherapy. Increasing the antigen presentation of TIBs to CD4 + TILs or reversing the depletion of the TIBs phenotype in the TIME could further enhance tumor-specific immune responses [46]. Furthermore, depending on the expression of major histocompatibility complex -II and after activating CD40, B lymphocytes can trigger secondary T cell responses and further stimulate anti-tumor immunity by antigen presentation [47]. The researchers also identified that a specific cell surface molecule named T cell immunoglobulin and mucin domain 1 is expressed on specific B lymphocyte subsets. Inhibition of its expression could enhance B cell activation and increase antigen presentation and co-stimulation, resulting in the expansion of tumor-specific effector T cells [48]. These molecules affecting B cell functions may be a promising research direction that can be further explored.

Publications from China and the United States dominate the top ten countries with the most publications, each exceeding 400. This underscores the significant contribution of Chinese and the United States institutions to high-output research. Notably, rising bibliometric trends—particularly the shift toward TLS and B-cell signatures as prognostic biomarkers—parallel emerging omics-driven advances. Single-cell transcriptomics and spatial proteomics now enable precise deconvolution of B-cell functional states within tumor microenvironments, while multi-omics integration elucidates context-dependent crosstalk between B cells and stromal components. Leveraging these technologies will be essential for translating descriptive bibliometric patterns into mechanistic insights that drive next-generation immunotherapies. Collaborations between countries are also noteworthy, particularly between China and the United States, followed by partnerships between the United States and Germany, Canada, and France.

China, the United States, and Germany share a similar focus in this area, with a gradual shift from early in vitro and in vivo topics to large-scale clinical trials and antibody-based drugs that have been conducted in recent years. In recent years, “ipilimumb”, “favorable prognosis”, “open label”. “pembrolizumab”, ‘survival’ and other topics have become more and more frequent in Chinese and American studies, suggesting that research on the efficacy and prognosis of various new drugs is becoming a hot topic. The conversion path from basic to clinical is very clear. In a globalized, digitized, and informatized economy, international collaboration enhances the rigor and reliability of academic results. China, in particular, should strive for broader cooperation with institutions in other countries to benefit more from global collaboration.

A comparative view highlights clear differences in research output between China (n = 828) and the United States (n = 484) in the field of B cells and immunotherapy. China’s substantial publication volume is largely attributable to sustained governmental investment and supportive national policies, which have facilitated the rapid expansion of research capacity. Moreover, an emphasis on enhancing academic visibility through high publication counts further drives output. In contrast, the United States benefits from a long-standing academic tradition, well-established research infrastructure, and extensive international collaborations, factors that collectively contribute to higher overall research quality and citation impact. Moving forward, China is expected to strengthen research innovation and deepen global partnerships, thereby enhancing its influence within the international scientific community.

The China–US collaboration, noted for 82 instances, indeed reflects significant cross-border research engagement. To assess the equity and depth of these partnerships, co-first authorships serve as a valuable indicator. In our study, many instances of co-first authorships between Chinese and US researchers were identified, such as the collaborative empirical study on faculty and student perceptions of academic incivility in Chinese nursing education involving US and Chinese faculty [49], and the conference report from the US Chinese Anti-Cancer Association (USCACA) meeting on genomics in personalized cancer medicine and its impact on early drug development in China [50], demonstrating genuine collaborative effort and shared contribution to the research output. These examples underscore the equitable nature and depth of China–US collaborations in this field.

JCR categorizes journals into four quartiles (Q1-Q4) based on IF, which served as strong indicators of the journal’s impact [51]. Frontiers in Immunology leads in publication volume (n = 130), driven in part by its open-access model, efficient review process (average 2-month cycle), and relatively low publication threshold. In contrast, Nature (IF = 50.5) published only eight articles, reflecting stringent selectivity and an emphasis on transformative research. Although Nature’s total citation count is lower due to its smaller output, its average citations per article are markedly higher (175.25 vs. 32.05 for Frontiers in Immunology), underscoring its exceptional per-article influence. Nature has fewer total citations but higher average citations per article; conversely, Frontiers in Immunology has a lower average citation per article but a larger total citation count. Conversely, Frontiers in Immunology, despite its lower per-article citation rate, achieves a higher cumulative citation count owing to its large publication volume. This divergence illustrates the quality–quantity spectrum: high-throughput journals facilitate rapid knowledge dissemination, whereas elite journals prioritize seminal impact. Among the top 10 journals, 60% are classified as Q1, with Clinical Cancer Research having the highest impact factor (IF = 10.4) in this group.

The number of citations of a publication can serve as a valuable indicator of research hotspots and significant breakthroughs. Our analysis of the top ten publications highlights the field of TLSs. TLSs are ectopic lymphoid organs that form under non-physiological conditions in non-lymphoid tissues during chronic inflammation, including autoimmune diseases and tumors [52]. In the TIME, TLSs promote immune cell infiltration into solid tumors, significantly correlating with survival in untreated patients [53]. TLSs are sites where initial/memory B-cells transform into plasma cells, producing antibodies (mainly IgG and IgA) directly against tumors [54]. The development of TLSs are associated with improved responses to ICIs, highlighting the importance of B cells in immunotherapy [22].

The most cited publication in our study is “B cells and tertiary lymphoid structures promote immunotherapy response” by Beth A Helmink, published in Nature in 2020. It has been cited 1219 times so far, with an average of 243.8 citations per year. In this pioneering article, the authors demonstrate the role of B cells and TLSs, which serves as markers for ICIs therapy. Also, B cell markers are found to be the most differentially expressed genes in responding versus non-responding tumors. Using RNA sequencing and single-cell sequencing, the authors found that immune responders had significantly more B-cell receptor diversity than non-responders. Moreover, tumor tissues from responders had significantly higher memory B-cell frequency, whereas a significantly higher frequency of B-cells was observed in non-responders. However, this study has two main limitations: the relatively small cohort sample size and the homogeneity of the treatment regimen, which may limit the generalizability and robustness of the findings [24].

These highly cited publications mainly confirm the biomarker role of B-lymphocytes and TLSs and are one of the important future research directions explored in several cancer types in subsequent years [55]. Similar to the article above, by use of single-cell transcriptome sequence and high-plex proteomic analysis, Cabrita et al. showed that TLSs formation and the coexistence of CD8 + T cells and CD20 + B cells in metastatic melanoma was correlated with longer survival, which was independent of other clinical variables [23]. In another key publication, Petitprez et al. (2020) found that B cells could strongly predict the prognosis of soft tissue sarcoma, independent of T lymphocyte and cytotoxic component levels. The presence of TLSs was also beneficial for pembrolizumab immunotherapy treatment [25]. The three aforementioned highly cited papers, released around the same time, all employed multi-omics analysis to demonstrate the promising potential of B-cells and tertiary lymphoid structures as markers for immunotherapy in different tumor types. Although the mechanisms were still not well understood at that time, they still proposed relevant ideas and future research directions, e.g., secretion of cytokines by B-cells for recruiting immune cells and differentiation of memory B-cells into plasma cells for antibody production. Indeed, TIL-B can produce in situ antibodies within tumor tissues, especially the tumor-specific IgG [56, 57]. Also, the dynamic changes and the predictive values of B-lymphocytes after treatment with ICIs have been confirmed in different tumor types like NSCLC and intrahepatic cholangiocarcinoma [58–61]. Although the specific immune response network is still poorly understood, the humoral antitumor immune response detected in TLSs and its characterization opens the way for developing new vaccines and antibody therapeutic strategies [62].

Tertiary lymphoid structures (TLS) serve as critical hubs for B cell activation and antigen presentation, significantly enhancing antitumor immunity and predicting favorable responses to immune checkpoint inhibitors. However, TLS formation is not universal across all tumor types; it exhibits substantial heterogeneity dependent on anatomical and immunosuppressive niche characteristics. For instance, glioblastoma’s immune-excluded niche—marked by the blood-brain barrier, immunosuppressive cytokines, and restricted T-cell infiltration—severely impedes TLS development. Similarly, pancreatic ductal adenocarcinoma, characterized by a dense desmoplastic stroma and an immunosuppressive microenvironment, also shows limited TLS formation. This tumor-specific variation necessitates caution when generalizing TLS as a pan-cancer biomarker, urging context-dependent evaluation of its clinical applicability.

B cells may play a positive role in the TIME, such as the observation that B cell-depleting can result in the progression and metastasis of B16 melanoma. In this model, researchers proved that the INF-γ production role of the T helper type 1 cells was impaired after B cell depletion [39]. What’s more, after treating with ICIs in the mouse breast model, both B cells and follicular helper T cells were activated. The researchers also illustrated that B cell activation of T cells and the generation of antibody are key to immunotherapy response and propose a new biomarker for immune checkpoint therapy [63]. As for in vivo research, for soft tissue sarcoma patients treated with PD-1, B cells was considered as the strongest prognostic factor no matter in high or low CD8 + T cells contents [25]. In melanoma, patients with higher levels of TLSs had better prognosis when treated by ICIs. Treatment responders had a significant higher level of the CXCR4 signaling, cytokine receptor interaction and chemokine signaling pathways than non-responders, which may improve the antigen expression ability of B cells to activate T cell functions [23, 24].

Regulatory B cells (Bregs) typically exert immunosuppressive functions via secreting IL-10 and TGF-β, contributing to tumor immune escape. Consequently, higher Breg infiltration correlates with poor prognosis in melanoma and gastric cancer. While regulatory B cells (Bregs) are frequently characterized by their immunosuppressive functions—mediated through cytokines like IL-10 and TGF-β—this dichotomous view oversimplifies their functional plasticity. Growing evidence highlights context-dependent Breg roles: in breast cancer models, PD-1/CTLA-4 blockade expanded IgG + plasma cells derived from Breg precursors, enhancing antitumor responses. Such plasticity, driven by dynamic microenvironmental cues (e.g., cytokine gradients, metabolic stress), necessitates reevaluating Bregs as adaptable immune modulators rather than uniformly suppressive entities. Additionally, in lung cancer immunotherapy, Bregs have been shown to reduce the occurrence of adverse immune-related reactions.

On the other hand, numerous studies have demonstrated that B cells may play a pivotal role in promoting tumor progression and metastasis. By mediating antibody-induced inflammation, the complement system can exert protumorigenic effects in both CMT and TC1 lung cancer models [64, 65]. In the studies of melanoma patients treated with ICIs, it was observed that non-responders exhibited significantly elevated levels of Bregs compared to responders. Additionally, a higher Breg cell score was found to be correlated with a shorter overall survival [24, 66]. In other kinds of tumors, the frequencies of Bregs correlated with shorter overall survival in bladder cancer and gastric cancer [67, 68], and the coexistence of Bregs with Tregs correlates with shorter metastasis-free survival in breast cancer [69]. Bregs may exert their immunomodulatory effects by secreting cytokines, such as IL-10, IL-35, and TGF-beta, or by releasing metabolites like gamma-aminobutyric acid, thereby suppressing the activation and function of pro-inflammatory cells, encompassing monocytes, dendritic cells, T helper cells, and CD8 + T lymphocytes [70–73].

In recent years, with the development of single-cell sequencing, various subclusters of B-lymphocytes capable of affecting TIME have been gradually identified [74]. In mouse models of breast cancer, PD-1/CTLA-4 antibody therapy resulted in the expansion of B cells, especially the IgG + plasma cells, leading to a favorable ICIs response [63]. In human patients with breast cancer, TIL-B was divided into five clusters and unusual cell types, in which follicular B cells have a strong correlation with immunotherapy efficacy [75]. In cutaneous melanoma, CD20 + TIBs were observed in patients responding to ICIs. These B cells enriched for genes such as FCRL5, IDO1, IFNG, and BTLA displayed a prognostic value [24]. In renal carcinoma, potential predictive biomarkers expressed on B cells in ICIs responders were also identified [76]. Intratumoral B-cell receptor diversity in TIME is also a biomarker for predicting response to immunotherapy [77]. The importance of TIBs as anti-tumor immune drivers and their potential role in regulating myeloid-derived suppressor cells is also known as one of the hotspots in recent years [78]. As for the immunotherapy-related adverse events (irAEs), changes in some B-lymphocyte subsets have also been shown to correlate with the incidence rate of irAEs [79–81]. Additionally, due to the heterogeneity of antigen expression in solid tumors and the challenges T cells face in infiltrating these tumors, CAR-T therapy remains in the exploratory stage for solid tumors [82]. Most CAR-T products are still in phase I clinical trials, and thus, there are fewer studies related to B lymphocytes [83, 84].

As for keyword frequency analysis and word clouds, keywords like “immunotherapy”, “expression”, “cancer”, “b-cells”, “survival”, “t-cells”, “dendritic cells”, “lymphocytes” and “therapy” were identified, reflecting the main themes discussed.

Citation burst analysis using CiteSpace highlighted significant changes in citations over time. Researchers can use keywords and cited references with burst features to explore hotspots of research. In this study, since 2018, keywords like “antitumor activity,” “inflammation,” and “melanoma” have become prominent. The keyword “multicenter” appearing in 2022 indicates the importance of large-scale clinical trials and multicenter cooperation. The reference with the highest burst strength was “Robust enumeration of cell subsets from tissue expression profiles,” introducing CIBERSORT, a method for identifying cell subsets from gene expression profiles. This method has been pivotal for analyzing B-cell-related markers and therapeutic targets [85]. The result also highlights the reference of “xCell: digitally portraying the tissue cellular heterogeneity landscape”, which also provided a valuable method to enumerate cell subsets from transcriptomes [86].

While our study utilized well-established bibliometric tools such as VOSviewer and CiteSpace for network visualization and clustering analysis, it is important to acknowledge certain limitations inherent to these tools. For example, VOSviewer’s clustering algorithm may tend to group high-frequency keywords more prominently, potentially overlooking emerging but less frequent terms. Similarly, CiteSpace’s citation burst detection is sensitive to temporal parameters, which could influence the identification of research frontiers. Future studies may consider using complementary methods or sensitivity analyses to validate these results and enhance robustness.

Previous studies have provided a comprehensive bibliometric perspective on cancer immunotherapy research, highlighting the dominant role of T-cell–based therapies in recent investigations [87]. In contrast, our work focuses on the emerging and distinctive role of B cells, suggesting a more diversified landscape of immune-cell research in the future [88]. Another study explored the tumor microenvironment, emphasizing the complex cell–cell interactions, which aligns closely with our findings that highlight the crucial interactions between B cells and other immune cells within the tumor milieu [89]. Additionally, some research has pointed out the presence of database selection bias in bibliometric analyses, reminding us that reliance on specific databases may overlook non-indexed or non-English literature, indicating an area for future improvement [90]. Furthermore, another study stressed the importance of integrating quantitative and qualitative analyses in bibliometric research on cancer drug discovery, inspiring us to consider incorporating qualitative evaluation methods in future studies of B-cell–related cancer immunotherapy to gain deeper insights [91]. Lastly, the necessity of regular updates in bibliometric analyses due to the rapid progress in cancer research has been emphasized, a principle that is equally applicable to our focus on B cells in cancer immunotherapy, underscoring the need for continuous monitoring to capture emerging trends and developments. We sincerely appreciate the reviewer’s constructive feedback, which has undoubtedly improved the quality of our manuscript.

Another limitation of this study is the data cutoff in March 2024, which may exclude some of the most recent publications; however, such a lag is common in bibliometric analyses due to the time required for indexing, analysis, and peer review.

Furthermore, the exclusive use of citation-based tools like VOSviewer and CiteSpace may favor high-frequency terms and mainstream research, limiting the detection of emerging or niche topics. The exclusion of gray literature and non-English publications may further bias the results, underscoring the need for more inclusive data sources and analytical approaches in future studies.

Conclusion

In recent years, research on B lymphocytes and immunotherapy in relation to solid tumors has garnered increasing attention. The volume of publications has grown annually over the past 20 years, with a significant surge in the last three years. This study exhibits the countries and institutions leading global research in B lymphocyte and immunotherapy studies. Frontiers in Immunology emerges as the most active journal, while China and the United States are the most prolific countries in this field.

B-lymphocytes, with the ability to produce antibodies, present antigens, secrete cytokines, and interact with other immune cells, can directly or indirectly activate the anti-tumor immunity in immunotherapies. Key research areas include the role of B lymphocytes and intratumoral TLSs in predicting and affecting immunotherapy efficacy and exploring related mechanisms. Another hot topic is the studies of B lymphocytes as target cells to enhance the efficacy of ICIs, which may be pivotal for future research. To sum up, this comprehensive overview of the evolution and frontiers of this field serves as a valuable resource for researchers and policymakers new to this domain [90].

Abbreviations

TIME

Tumor-immune microenvironment

TIBs

Tumor-infiltrating B-lymphocytes

Bregs

Regulatory B cells

Tregs

Regulatory T cells

TILs

Tumor-infiltrating lymphocytes

IF

Impact factor

JCR

Journal citation report

AtM

Atypical memory

TLSs

Tertiary lymphoid structures

ICIs

Immune checkpoint inhibitors

PD-1

Programmed cell death 1

PD-L1

Programmed cell death-ligand 1

CTLA-4

Cytotoxic T-lymphocyte associated protein 4

NSCLC

Non-small cell lung cancer

irAEs

Immunotherapy-related adverse events

Author contributions

The authors confirm their contribution to the paper as follows: Study conception and design: SK, MP, NZ, JL, CL. Data collection: SK, MP, CL. Analysis and interpretation of results: SK, MP, CL. Draft manuscript preparation: SK. References research and critical revision of the manuscript: ZX, SL, SW, BT, LZ. All authors reviewed the results and approved the final version of the manuscript. LZ、HZ acts as a guarantor of the study.

Funding

This work was supported by Beijing Natural Science Foundation (L248022), National High Level Hospital Clinical Research Funding[2022-PUMCH-B-128], CAMS Innovation Fund for Medical Sciences (CIFMS) [2021-I2M-1-061] [2021-I2M-1-003], CSCO-hengrui Cancer Research Fund [Y-HR2019-0239][Y-HR2020MS-0415][Y-HR2020QN-0414], CSCO-MSD Cancer Research Fund [Y-MSDZD2021-0213]. Lijin Zhao is supported by the National Natural Science Foundation of China (NSFC) (No. 81960125) and the Department of Science and Technology of Guizhou Province (No. Qiankehe Foundation [2020] 1Y302).

Data availability

All data supporting the findings of this study are available within the paper. For more information, please contact the corresponding author.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.