A bibliometric analysis of the research landscape of lenalidomide resistance in multiple myeloma

Abstract

Background

Multiple myeloma (MM), the second most common hematological malignancy, is often treated with lenalidomide. However, resistance to this cornerstone therapy inevitably develops in nearly all patients, adversely affecting treatment outcomes and prognosis. To map the knowledge landscape, this study aims to analyze publication trends and identify research hotspots in lenalidomide resistance in MM.

Methods

A comprehensive search was conducted in the Web of Science Core Collection database from 2005 to 2025. Bibliometric analysis was performed using CiteSpace, VOSviewer, and the R package “bibliometrix” software to identify publication trends, collaborations, and research clusters.

Results

This bibliometric analysis showed sustained growth in publications on MM and lenalidomide resistance. The USA accounted for the largest number of articles (961), followed by China (235) and Italy (183). Harvard University had the largest number of publications among institution (502) whereas Blood had the highest total citation count in this dataset (16,080). Author analysis identified Philippe Moreau as the researcher with the highest H-index. Keyword analysis revealed four major research clusters focusing on combination therapy, cellular mechanisms and apoptosis pathways, proteasome inhibitors, and monoclonal antibodies. Keyword burst analysis revealed “consensus”, “phase 3”, “open label”, “survival outcomes”, “oral ixazomib” and “multicenter” since 2020.

Conclusion

This bibliometric analysis highlights the rapidly evolving field of lenalidomide resistance in MM. Future efforts should focus on developing next-generation agents against resistance, designing rational combination therapies targeting key pathways like signal transducer and activator of transcription 3 (STAT3)/ nuclear factor kappa-B (NF-κB), and establishing evidence-based treatment consensus to improve outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-05344-y.

Introduction

Multiple myeloma (MM) is a malignant neoplasm characterized by the clonal proliferation of plasma cells in the bone marrow [1]. Though relatively rare, representing approximately 1% of all cancers worldwide, it constitutes the second most common hematological malignancy, accounting for about 10% of all blood cancers. According to recent global estimates, the worldwide incidence of MM reached approximately 148,755 new cases in 2021, with approximately 116,360 attributed deaths, underscoring its significant global health impact [2]. The epidemiology of MM shows notable demographic variations, with higher incidence rates among older adults, males, and non-Hispanic Black populations [3]. While incidence rates have shown a slight increase globally over recent decades, mortality rates have been declining by approximately 2.6% annually in the USA and similar developed regions, reflecting significant advances in therapeutic approaches [4]. This contrasting pattern of stable or slightly increasing incidence alongside decreasing mortality highlights the improved survival outcomes resulting from modern treatment modalities.

The treatment landscape for MM has evolved dramatically over the past two decades, with significant improvements in patient outcomes attributed to novel therapeutic agents. Treatment strategies have progressed from conventional chemotherapy and autologous stem cell transplantation to incorporate three major drug classes: proteasome inhibitors (bortezomib, carfilzomib), immunomodulatory drugs (IMiDs), and monoclonal antibodies (daratumumab, elotuzumab) [5]. Among these, lenalidomide, a second-generation IMiD, has emerged as a cornerstone in MM treatment regimens, widely used in various clinical settings including induction therapy, maintenance treatment following transplantation, and in relapsed/refractory disease [6]. Lenalidomide exerts its therapeutic effects through multiple mechanisms, including modulation of substrate specificity of the CRL4CRBN E3 ubiquitin ligase, leading to ubiquitination and subsequent proteasomal degradation of transcription factors IKZF1 and IKZF3, which are essential for myeloma cell survival [7]. This multifaceted mechanism of action has contributed to its efficacy in improving progression-free survival and overall survival in MM patients [8].

Despite the clinical success of lenalidomide, drug resistance remains a significant challenge in MM management. Almost all MM patients are exposed to lenalidomide, but despite its potent anti-MM activity, the vast majority of patients eventually relapse and become lenalidomide-resistant [9]. This progression to lenalidomide-resistant disease represents a distinct and clinically critical subcategory within the broader population of relapsed or refractory MM (RRMM), characterized by unique biological mechanisms and a more constrained set of subsequent therapeutic options. Several resistance mechanisms have been identified, including alterations in the cereblon (CRBN) pathway through genetic mutations, copy number losses, or expression of variant transcripts [10]. Additional resistance mechanisms involve changes in cell adhesion molecules, activation of alternative signaling pathways such as phosphoinositide 3-kinase (PI3K)/Protein Kinase B (PKB) and janus kinase (JAK)/ signal transducer and activator of transcription (STAT), and modulation of bone marrow microenvironment interactions [11]. The clinical implications of lenalidomide resistance are profound, as it significantly limits subsequent treatment options and is associated with poor prognosis in RRMM [12].

Bibliometric analysis provides a powerful, quantitative approach to evaluate the structure and dynamics of scientific fields by examining publication trends, collaborative networks, and research fronts [13]. While previous bibliometric studies in MM have mapped the broader therapeutic landscape, identifying “drug resistance” as an emerging keyword [14], the resistance to lenalidomide, a cornerstone of MM treatment, warrants focused investigation due to its distinct biological mechanisms and direct clinical implications. To date, a systematic bibliometric analysis dedicated specifically to lenalidomide resistance has not been conducted. This represents a meaningful opportunity to refine our understanding beyond general resistance categories. Therefore, this study performs the first targeted bibliometric analysis of lenalidomide resistance in MM, with the aim of delineating its unique intellectual structure, key research themes, and scientific collaborations. By illuminating the evolving landscape of this clinically pivotal form of resistance, we seek to provide a nuanced, mechanism‑informed perspective that complements broader resistance analyses and helps guide future research toward more tailored therapeutic strategies.

Methods

Search strategies and data collection

We performed a systematic literature search in the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) databases within the Web of Science Core Collection (WoSCC), a platform recognized for its wide multidisciplinary coverage of high-impact scientific literature [15]. To comprehensively capture publications on lenalidomide resistance in MM, we employed a broad search strategy incorporating terms related to disease progression, resistance, and relapse. Therefore, the resulting bibliometric landscape reflects studies centered on lenalidomide resistance within the wider clinical context of RRMM. The search formula was as follows: TS=((“multiple myeloma” OR “multiple myeloma*” OR “plasma cell myeloma” OR “Kahler disease” OR myelomatosis)) AND TS=((lenalidomide OR revlimid OR “cc-5013” OR cc5013 OR “3-(4-amino-1-oxoisoindolin-2-yl)piperidine-2,6-dione”)) AND TS=(resist* OR “drug resist*” OR “acquir* resist*” OR refractory OR refractor* OR relapse* OR “relapsed/refractory” OR RRMM OR “treatment fail*” OR “non-respon*” OR progression OR “cross-resist*”). Only English-language articles were included to improve the consistency and comparability of bibliometric analyses. To avoid database update-related deviations, all records were retrieved on a single day (October 17, 2025), and full records with cited references were exported in plain text format. Because all records were obtained from WoSCC, cross-database deduplication was unnecessary; nevertheless, potential duplicates were screened using title, DOI, and accession data where available. Obvious variants in author and institutional names were manually harmonized. For institutional names, the original WoSCC indexing form was retained in principle; only minor variants caused by abbreviations, capitalization, punctuation, or spelling differences were harmonized, whereas independently indexed affiliated hospitals, institutes, or companies were not merged across higher-level systems. Before formal screening, two evaluators manually checked a subset of retrieved records to assess whether the search strategy captured publications involving MM, lenalidomide exposure, and resistance-, refractory-, relapse-, progression-, or treatment-failure-related concepts. This procedure was used as a pragmatic relevance check to confirm the topical appropriateness of the retrieved dataset before formal screening. However, it should be noted that this process was not a formal validation against an external gold-standard set of publications. Subsequently, two evaluators independently conducted a two-stage screening process, including title/abstract screening and eligibility assessment based on predefined inclusion and exclusion criteria. Disagreements were resolved by a third evaluator through full-text review and discussion.

Statistical analysis

For comprehensive visualization and analysis of the bibliometric data, we utilized three specialized tools: VOSviewer (version 1.6.20), CiteSpace (version 6.3.R1), and R package “bibliometrix” (version 4.3.3). VOSviewer served as a versatile tool for mapping collaborative networks, enabling the visualization of institutional cooperation, author collaboration, co-authorship patterns, citation networks, and co-citation relationships [16]. For VOSviewer analyses, thresholds were set according to the analytical object of each network. The minimum threshold was set at 3 for institutional collaboration networks, 5 for author collaboration networks, 3 for journal/source-level networks, and 8 occurrences for keyword co-occurrence networks. The VOSviewer layout parameters were set as attraction = 3 and repulsion = − 3 for the institutional, author, and journal networks, and attraction = 2 and repulsion = − 1 for the keyword co-occurrence timeline map. The association strength normalization method and VOSviewer clustering algorithm were applied to construct and cluster the networks. Only items meeting the predefined thresholds and connected within the network were displayed in the final visualizations. This allowed us to examine the complex interconnections between authors, institutions, and publications in the field of MM and lenalidomide resistance. In the network visualizations, node size represents the volume of publications, line thickness indicates the strength of connections between entities, and node colors distinguish different clusters or temporal patterns.

To investigate emerging trends and research hotspots, keyword co-occurrence analysis was conducted using VOSviewer and keyword burst detection using CiteSpace [17]. For the CiteSpace analysis, the following parameters were configured: the time slice spanned from January 2005 to October 2025, with keywords selected as the node type. For keyword co-occurrence mapping, a 1-year time slice interval was applied. The top 25 most cited keywords per slice were retained as nodes, and the network was pruned using the Pathfinder algorithm combined with the Pruning Merged Network method. Visualization was then performed to generate burst word timeline diagrams.

The R package “bibliometrix” was employed for additional bibliometric analyses and visualization of publication patterns [18]. Specifically, the national ranking of publications is based on the countries of the corresponding author(s) to reflect primary correspondence affiliation, whereas the publication counts for authors, institutions, and journals are calculated based on all contributing authors. The collaboration network analysis is constructed using all author signatures and all institutional/country affiliations exported from WoSCC. Therefore, multiple authors or multiple institutional affiliations from the same publication are all included in the network mapping. To evaluate the academic impact of authors and journals, the H-index was employed, which quantifies an individual’s or journal’s scientific productivity and citation impact. The H-index is considered a reliable indicator for evaluating researchers’ academic contributions and predicting their future scientific achievements [19]. In this study, the H-index for each author was obtained directly from WoSCC. Additionally, G-index [20] and M-index [21] were utilized as complementary metrics to provide a more comprehensive assessment of scientific impact. For journal evaluation, the Journal Impact Factor 2024 (IF) values and Journal Citation Report 2024 (JCR) quartile rankings were incorporated to assess the influence and standing of journals publishing research on MM and lenalidomide resistance [22, 23].

Results

An overview of publications in research of MM and lenalidomide resistance

Following the established search strategy and screening process, 2,579 relevant articles on MM and lenalidomide resistance were identified for this bibliometric analysis (Fig. 1). The 2,579 documents were published across 402 sources and authored by 15,347 researchers. The international collaborative nature of this research is evident, with 33.23% of publications involving cross-national partnerships. The high average of 13.2 co-authors per document underscores the collaborative approach typical in clinical research (Fig. 2A).

Fig. 1.

Literature screening flowchart for MM and lenalidomide resistance research publications.

Fig. 2.

Main information. A Research Overview. B Annual publication trends of MM and lenalidomide resistance research from 2005 to 2025

The publication trend from 2005 to 2025, as shown in Fig. 2B, illustrates both the annual output and cumulative growth of literature in this field. The earliest relevant publications emerged in 2005, with a modest output of only 7 articles that year. A significant increase began in 2009, which recorded 49 publications, and this was followed by sustained growth over the subsequent decades. A notable peak occurred in 2021, with 230 articles. The calculated annual growth rate of 16.97% demonstrates a consistent upward trajectory in research productivity.

Geographic distribution and international collaboration patterns in research output

The geographical distribution of research productivity revealed significant contributions from multiple nations across the globe (Table S1, Fig. 3A). With 961 articles, the USA accounted for the largest number of publications, representing 37.3% of the total output. China ranked second with 235 articles (9.1%), followed by Italy with 183 articles (7.1%). The USA accumulated 59,623 citations in this field (ranked 1st), while France had the second-highest total citation count in this dataset (11,156 citations). Citation metrics provided additional insights into research influence. Netherlands achieved the highest average citation rate (87.1 citations per article), followed by France (83.3) and Greece (79). The collaborative patterns among countries were particularly noteworthy. The USA led in multiple country publications (MCP = 287), followed by France (MCP = 71). In the international collaboration network, the USA had the highest total link strength (2,446), followed by France (1,847) and Spain (1,663) (Fig. 3B).

Fig. 3.

Geographic distribution and international collaboration network. A Global distribution map of publications by country/region. B International collaboration network visualization, where node size indicates publication volume and line thickness represents collaboration strength

Analysis of journals in the field

The bibliometric analysis identified a total of 402 journals publishing research on MM and lenalidomide resistance from 2005 to 2025. The top 20 journals in this field, ranked by H-index, are presented in Table S2. Among all journals, Blood ranked first in H-index (90), total publications (TP = 157, ranked 1st), and the total citations (TC = 16,080, ranked 1st). Its citation performance in this dataset was reflected by a high G-index (151) and M-index (4.286), as well as its prestigious Q1 position in the Journal Citation Reports with a 2024 IF of 21. The Journal of Clinical Oncology followed, ranking second by H-index in this dataset (H-index = 54). It produced 77 publications and received 6,407 citations in this dataset. In third place was Leukemia (H-index = 46), with 85 publications (TP ranked 5th) and substantial citation counts (TC = 5,953, ranked 4th).

The co-occurrence network analysis illustrated the intellectual relationships between journals (Fig. 4A). This visualization revealed three journals with the strongest co-occurrence connections: Blood (total link strength = 4353), New England Journal of Medicine (total link strength = 3840), and Journal of Clinical Oncology (total link strength = 2,236). Complementary to this, the bibliographic coupling network (Fig. 4B) identified journals sharing common intellectual foundations based on reference overlap. Blood had the highest bibliographic coupling strength (total link strength = 246,667), followed by British Journal of Haematology (total link strength = 180,574) and Clinical Cancer Research (total link strength = 165,582).

Fig. 4.

Journal analysis network. A Distribution and relationship of publishing journals based on citation patterns. B Journal bibliographic coupling network showing research theme clusters

Key contributing authors and their citation impact

The bibliometric analysis identified a total of 15,347 authors contributing to research on MM and lenalidomide resistance. Moreau Philippe led the impact metrics in Table S3, with an H-index of 64, a G-index of 141, and an M-index of 3.20—reflecting both high productivity and substantial academic impact since his first publication in the field in 2006. He also ranked first in both total publications (154) and total citations (19,994). Richardson Paul G. followed in second place, with an H-index of 63, along with 121 publications and 15,297 citations, each ranked second. Anderson Kenneth C. placed third based on H-index (58), contributing 91 publications (ranked 5th) and accumulating 14,577 citations (ranked 3rd).

The co-authorship network visualization (Fig. 5) revealed distinctive collaborative patterns among researchers in this field. The network analysis identified several major collaborative clusters, represented by different colors in the visualization. Moreau Philippe demonstrated the strongest collaborative connections (total link strength = 591), followed by Dimopoulos Meletios A. (total link strength = 448) and Rajkumar S. Vincent (total link strength = 444).

Fig. 5.

Author collaboration network visualization, where node size represents publication count and connecting lines indicate co-authorship relationships

Research institutions and collaborative networks

The bibliometric analysis identified numerous research institutions contributing to the field of lenalidomide resistance in MM. The top ten institutions ranked by article count revealed the high representation of leading cancer research centers and academic medical institutions in the USA (Fig. 6A). Harvard University was the most productive institution, contributing 502 articles. Its affiliated medical centers also demonstrated substantial output, with the Mayo Clinic and Harvard University Medical Affiliates ranking second (438 articles) and third (428 articles), respectively.

Fig. 6.

Institutional distribution and collaboration patterns. A Distribution of publications by contributing institutions based on all author affiliations. B Collaboration network among institutions

The institutional collaboration network (Fig. 6B) illuminated the cooperative relationships among research centers working in this field. Mayo Clinic demonstrated the strongest collaborative ties with 581 connections to other institutions, followed closely by Dana-Farber Cancer Institute (541 connections) and Emory University (493 connections). These metrics reflect the frequency and strength of co-authorship relationships between institutions.

Analysis of the keywords

Keyword co-existence network analysis

The co-occurrence analysis of keywords revealed the conceptual structure and research focus areas in lenalidomide resistance in MM research. Based on the revised keyword clustering results, the visualized network was categorized into four distinct clusters, each represented by a unique color. The thematic labeling of these clusters was primarily informed by the semantic content of the high‑frequency keywords and representative terms within each group (Fig. 7A). Cluster 1 (red, 39 items) focused on combination therapy, featuring representative keywords such as “combination therapy”, “chemotherapy”, “induction therapy”, “stem-cell transplantation”, and “thalidomide plus dexamethasone”. Cluster 2 (green, 33 items) centered on cellular mechanisms and apoptosis pathways, with prominent keywords including “apoptosis”, “activation”, “proliferation”, “t-cells”, and “CRBN”. This cluster reflects studies examining cellular responses, immune cell interactions, and signaling pathways involved in drug response. Cluster 3 (blue, 26 items) concentrated on proteasome inhibitors, featuring keywords such as “bortezomib”, “carfilzomib”, “multicenter”, “safety”, and “efficacy”. Cluster 4 (yellow, 4 items) addressed monoclonal antibodies with keywords including “antibody daratumumab”, “monotherapy”, “cluster of differentiation 38 (cd38)”, and “phase-2”. This cluster encompasses research on the therapeutic potential of monoclonal antibodies, which are aimed at overcoming resistance and improving patient outcomes.

Fig. 7.

Research theme analysis. A Keyword co-occurrence network showing major research themes and their relationships. B Timeline view of the top 20 keywords with the strongest citation bursts from 2005 to 2025

The keyword burst analysis

The keyword burst analysis revealed significant temporal patterns in research focus within the MM and lenalidomide resistance field from 2005 to 2025 (Fig. 7B). This visualization identifies keywords that experienced periods of intensified research attention, with the red segments indicating burst periods. Early research (2005–2010) demonstrated strong interest in foundational therapeutic approaches, with “analogs” showing the highest early burst strength (7.22) during 2005–2011. This was followed by “combination therapy” (strength 32.86, 2006–2015), “high dose therapy” (strength 14.19, 2006–2013), and “chemotherapy” (strength 18.59, 2007–2013). The middle period (2010–2020) shifted toward clinical aspects and specific treatment protocols, evidenced by bursts in “thalidomide plus dexamethasone” (strength 7.12, 2012–2013), “stem cell transplantation” (strength 11.16, 2013–2015), and “low dose dexamethasone” (strength 7.12, 2012–2013). The most recent period (2020–2025) highlighted emerging topics and advanced therapeutic approaches, with several keywords showing strong citation bursts: “consensus” (strength 12.71, 2020–2025), “phase 3” (strength 9.71, 2021–2025), “open label” (strength 29.01, 2022–2025), “survival outcome” (strength 7.39, 2022–2025), “multicenter” (strength 9.49, 2023–2025), and “oral ixazomib” (strength 7.29, 2023–2025). These terms reflect the increasing emphasis on robust clinical trial methodologies (open label and multicenter studies) in contemporary MM research.

Discussion

General information

The continuous growth in publication volume over the past two decades reflects the increasing clinical significance of lenalidomide resistance in MM treatment, with an annual growth rate of 16.97%. The publication surge observed between 2009 and 2021 coincides with the period when lenalidomide resistance became a recognized clinical challenge. Notably, a decline in the annual number of publications has been observed since 2021, which may indicate a paradigm shift in research focus. Instead of predominantly investigating strategies to overcome resistance within the IMiD class, increasing attention is being directed toward lenalidomide‑independent therapeutic approaches, such as chimeric antigen receptor T‑cell immunotherapy (CAR‑T), bispecific antibodies, and CRBN E3 ligase modulators (CELMoDs) [24–26].

The high publication volume and citation performance of the USA may partly reflect sustained engagement in MM clinical research [27]. From a bibliometric perspective, Blood ranked first in several journal-level indicators in this dataset. Philippe Moreau, Richardson Paul G., and Anderson Kenneth C. were among the most productive and highly cited contributors in the retrieved literature [28, 29].

Current hotspots

Cluster 1 (red): combination therapy

The composition of Cluster 1 suggests that combination therapy and treatment sequencing represent major recurring themes in the retrieved literature. The co-occurrence of “chemotherapy”, “induction therapy”, and “stem-cell transplantation” indicates that studies in this cluster frequently examined lenalidomide-containing or lenalidomide-related regimens within broader treatment frameworks, including induction, transplantation, and post-relapse management [9, 30]. The data thus underscores that the efficacy of novel combinations depends critically on the treatment line and patient population, which reflects a more mature clinical trial methodology focused on validating strategies in lenalidomide-resistant settings.

Cluster 2 (green): cellular mechanisms and apoptosis pathways

The existence of this cluster indicates that, parallel to clinical strategy research, there is a substantial foundational research effort aimed at elucidating the cellular biological basis of drug resistance [31, 32]. Keywords such as “apoptosis”, “activation”, “proliferation”, and “t‑cells” collectively point to focused studies on myeloma cell survival, proliferation, death regulation, and immune‑cell interactions within the tumor microenvironment [33, 34]. In addition, the core biology of lenalidomide resistance involves CRBN mutations or loss, IKZF1/3 escape, and CRBN‑independent survival pathways such as STAT3 and NF‑κB signaling [7, 10, 34]. These alterations may correspond clinically to progressive loss of sensitivity to IMiD-based regimens after prolonged exposure. They also provide a biological rationale for the transition toward CELMoDs and other cereblon-independent therapeutic strategies in refractory settings. This body of work provides a mechanistic framework for understanding resistance and forms a “mechanism‑to‑application” knowledge structure that is complementary to the therapy‑focused Cluster 1. Notably, this mechanistic understanding directly informs the development of next-generation CELMoDs, such as iberdomide and mezigdomide, which were designed to enhance cereblon-mediated substrate degradation and potentially overcome resistance to earlier IMiDs. Clinical studies have reported meaningful activity of iberdomide plus dexamethasone and mezigdomide plus dexamethasone in heavily pretreated relapsed/refractory MM populations, supporting the translational relevance of this mechanism-focused research direction [35, 36].

Cluster 3 (blue): proteasome inhibitors

Cluster 3 was characterized by proteasome inhibitor-related terms, including “bortezomib”, “carfilzomib”, and “ixazomib”. This pattern indicates that proteasome inhibitor-based regimens form a major therapeutic theme in the literature related to lenalidomide resistance or refractoriness. Keywords such as “bortezomib”, “carfilzomib”, and “ixazomib” correspond to currently approved proteasome inhibitors for MM and other hematologic malignancies [37]. The co‑occurrence of “safety” and “efficacy” with these drug names suggests that a considerable portion of the literature evaluates the clinical performance and risk‑benefit profile of these agents, especially within combination regimens [38]. The prominent appearance of the term “multicenter” further implies that the evidence for these inhibitors is largely generated from large‑scale, collaborative clinical trials. Therefore, in this bibliometric context, terms such as “safety”, “efficacy”, and “multicenter” should be interpreted as indicators of clinical-evidence generation rather than as direct evidence that all proteasome inhibitor-based regimens are broadly generalizable.

Cluster 4 (yellow): monoclonal antibodies

The fourth and more focused cluster (in yellow) is defined by keywords related to monoclonal antibodies, including “antibody daratumumab”, “monotherapy”, “cd38”, and “phase-2”. The distinct formation of this cluster signals the emergence of immunotherapy, particularly anti‑CD38 therapy, as exemplified by daratumumab and isatuximab, as a specialized and prominent research theme in the resistance landscape [39, 40]. Beyond anti‑CD38‑directed strategies, elotuzumab, a humanized monoclonal antibody targeting signaling lymphocyte activation molecular family 7 (SLAMF7), has also demonstrated significant benefit in lenalidomide‑resistant MM [41]. Together, these findings underscore the increasingly critical role of monoclonal antibodies in enhancing therapeutic efficacy for resistant disease. The separation of this cluster from the broader combination‑therapy cluster (Cluster 1) may reflect that research on monoclonal antibodies has developed a relatively distinct knowledge structure and investigation paradigm, one that frequently examines their application both as monotherapies and as components of novel combinations specifically designed for the treatment‑resistant setting.

Temporal dynamics of research focus

The keyword burst analysis delineates distinct phases of research focus in MM and lenalidomide resistance, reflecting evolving scientific priorities over the past two decades. The initial phase (2005–2010) centered on foundational therapeutic strategies, as indicated by the strong bursts of keywords such as “thalidomide” and “lenalidomide,” marking the establishment of first-generation IMiDs as core treatments [42]. The concurrent emergence of “combination therapy” as a burst keyword during this period reveals a shift in research focus from validating monotherapy toward exploring drug combinations, paving the way for the subsequent integration of novel agents, such as anti‑CD38 antibodies [43].

The transitional period (2010–2020) was characterized by a refinement of treatment protocols. The prominence of keywords like “thalidomide plus dexamethasone” and “stem cell transplantation” reflects the consolidation of a standard treatment framework encompassing induction therapy, autologous stem‑cell transplantation, and maintenance [44]. Additionally, the keyword “pomalidomide” emerged strongly, signaling the development of a second‑generation IMiD specifically designed to overcome lenalidomide resistance, which became a standard option for relapsed/refractory MM, especially in combination with low‑dose dexamethasone [45, 46]. This evolution illustrates how research priorities shifted from demonstrating efficacy to addressing resistance mechanisms.

The most recent period (2020–2025) has been defined by two major trends evident in the burst keywords. First, the strong appearance of “ixazomib” in keyword trends highlights the growing research focus on integrating next‑generation oral agents—noted for their favorable safety profile and dosing convenience—as alternative or complementary strategies in the management of lenalidomide-resistant settings [47]. Real‑world evidence, though initially from a French cohort, has further supported the safety and efficacy of ixazomib‑based regimens [48]. Second, the bursts of “multicenter” and “phase 3” point to a growing emphasis on large‑scale, collaborative trials aimed at forming evidence‑based consensus for managing lenalidomide‑resistant disease. For example, the APOLLO trial provides a representative clinical example corresponding to the burst terms “phase 3”, “open label”, and “survival outcome”, illustrating how recent studies have increasingly emphasized multicenter trial designs and survival endpoints in lenalidomide-refractory settings [49]. Looking forward, T-cell–redirecting bispecific antibodies, such as teclistamab and elranatamab, represent lenalidomide-independent therapeutic strategies because their activity is mediated through immune-cell engagement rather than direct modulation of the CRBN–IKZF axis. Teclistamab targets BCMA and CD3 and has shown activity in relapsed/refractory MM [26], while elranatamab, another BCMA–CD3 bispecific antibody, demonstrated clinically meaningful activity in the phase 2 MagnetisMM-3 trial [50]. Together, these keyword dynamics reflect a shift toward immune-focused combinations and innovative effector mechanisms, opening new avenues for overcoming resistance and improving long-term outcomes.

Strengths and limitations

The bibliometric analysis presents several strengths, including its comprehensive scope, offering an extensive overview of the research landscape on MM and lenalidomide resistance. It highlights global publication trends, key contributors, and emerging research clusters, providing valuable insights into the evolving focus within the field. Additionally, the detailed methodology, utilizing tools like CiteSpace, VOSviewer, and bibliometrix, strengthens the study by offering objective, data-driven analysis of research patterns, international collaborations, and thematic developments. However, several methodological limitations should be acknowledged. First, this study relied exclusively on the WoSCC database and included only English-language articles. Therefore, relevant studies indexed in PubMed/MEDLINE, Scopus, Embase, regional databases, or published in non-English languages may have been missed, which may have led to underrepresentation of research from certain countries or language regions. Second, although the search strategy was intentionally broad to capture the literature surrounding lenalidomide resistance within the wider RRMM context, the inclusion of terms such as “relapse”, “refractory”, “progression”, and “treatment failure” may have reduced search specificity. Consequently, the dataset may include studies of lenalidomide use in RRMM that did not directly investigate lenalidomide-specific resistance mechanisms or strictly defined lenalidomide-refractory disease. Although a subset of retrieved records was manually checked for topical relevance, no formal validation against an external gold-standard set of known key publications was performed. Therefore, some relevant studies may have been missed, and some broadly RRMM-focused studies may have been included. Third, bibliometric indicators such as citation counts, H-index, G-index, M-index, and total link strength are citation- and network-dependent measures. These metrics can be influenced by publication year, journal visibility, collaborative network size, self-citation, field-specific citation practices, and industry-sponsored research activity. As a result, they should be interpreted as descriptive indicators within the retrieved dataset rather than direct measures of scientific quality, clinical importance, or causal influence. In particular, the H-index obtained from WoSCC primarily reflects cumulative career-wide citation impact and may not fully represent an author’s field-specific contribution to lenalidomide resistance in MM. Fourth, the included literature spans a spectrum from mechanistic resistance studies to clinical refractory investigations and broader RRMM outcome research. Although lenalidomide-refractory disease is commonly defined according to International Myeloma Working Group (IMWG) criteria as disease progression while receiving lenalidomide or within 60 days after the last dose, this operational definition was not consistently applied across all included publications. This heterogeneity should be considered when interpreting the bibliometric findings.

Finally, the presence of pharmaceutical companies, such as Johnson & Johnson and Bristol-Myers Squibb, among highly productive institutions may reflect substantial industry involvement in this therapeutic area. However, industry-sponsored research may also shape publication patterns and research priorities. In addition, keyword clustering is sensitive to co-occurrence thresholds and clustering algorithms; therefore, smaller clusters, such as the monoclonal antibody cluster, may partly reflect methodological compression or fragmentation rather than the full clinical importance of that topic.

Conclusion

This bibliometric analysis provides a comprehensive overview of the scientific landscape of MM and lenalidomide resistance research from 2005 to 2025, elucidating evolving trends in productivity, collaboration, and thematic focus. The identification of four major research clusters—combination therapies, cellular mechanisms and apoptosis pathways, proteasome inhibitors, and monoclonal antibodies—offers a structured framework for understanding the multifaceted strategies employed to combat resistance. Looking ahead, key priorities for future research should include: (1) developing next-generation oral proteasome inhibitors and targeted monoclonal antibodies capable of overcoming lenalidomide resistance; (2) designing rational combination regimens that concurrently target critical resistance pathways, such as STAT3 and NF-κB signaling; and (3) establishing evidence-based treatment consensus to optimize patient outcomes and prognosis.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

(I) Conception and design: Lingjuan Huang, and Ruihua Li (II) Administrative support: Lingjuan Huang (III) Data analysis and interpretation: Zhuanghui Hao, Jinqian Dai (IV) Manuscript writing: Ruihua Li, Xue Zhao, Yani Gao and Qin Gao (V) Final approval of manuscript: All authors.

Funding

This study was supported by the Innovation Capability Support Program for Medical Research Projects of Xi’an Science and Technology Bureau (Grant No. 23YXYJ0123) and the Hospital level Fund of the First Affiliated Hospital of Xi’an Medical University (Grant No. XYYFY-2023-08).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.