Charting the blueprint: a bibliometric analysis reveals future strategies in lung cancer targeted therapy (2003–2025)

Introduction

Lung cancer remains among the most burdensome malignancies worldwide. In 2022, global cancer statistics reported approximately 2.5 million new cases and 1.8 million deaths, representing 12.4% and 18.7% of all cancers, respectively (1). Incidence and mortality rates are particularly high in regions such as East Asia, with a substantially greater burden among men (2). However, lung cancer incidence among women has risen rapidly in certain regions, in some cases approaching or surpassing that of men (3). Pathologically, lung cancer is primarily classified into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) (4), with adenocarcinoma representing the most prevalent subtype (5). Despite benefits from surgery, radiotherapy, and chemotherapy in selected patients, overall survival (OS) remains limited, with median survival under chemotherapy typically reported of 7–16 months (6), and the 5-year survival rate for advanced NSCLC generally below 10% (7). Treatment-related toxicity and complications further constrain therapeutic efficacy. The advent of molecular subtyping and identification of driver genes has markedly improved outcomes in specific patient populations. EGFR- and ALK-targeted inhibitors, for example, have demonstrated marked benefits in progression-free survival (PFS) and OS across multiple studies (8,9), Third-generation EGFR-tyrosine kinase inhibitors (TKIs) and next-generation ALK-TKIs have further enhanced efficacy, including better central nervous system control (10,11). Notably, the prevalence of driver mutations varies among populations; EGFR mutations are considerably more common in Asian cohorts (12), whereas KRAS G12C mutations are more frequent in Western populations (13). These findings underscore the pronounced molecular heterogeneity of lung cancer and its profound implications for therapeutic strategies. In this context, a systematic, quantitative, and visualized knowledge mapping of “targeted therapy” across all lung cancer subtypes can delineate the research landscape and evolving hotspots, providing critical guidance for future clinical and translational studies.

As molecularly targeted therapy for lung cancer has evolved, multiple driver genes have progressively acquired definitive clinical significance. Foundational targets such as EGFR and ALK have established first-line therapeutic status in landmark trials including FLAURA and ALEX, significantly improving PFS and central nervous system control (14,15). Subsequently, targeted inhibitors have been developed for less common but clinically meaningful alterations—such as ROS1, RET, NTRK, MET, KRAS G12C, HER2, and BRAF—with agents including crizotinib, selpercatinib, sotorasib, and trastuzumab deruxtecan, thereby expanding precision medicine to a broader patient population (16-19). In parallel, anti-angiogenic therapies have also demonstrated considerable clinical benefit. For instance, combining bevacizumab with chemotherapy has enhanced survival in patients with advanced or recurrent NSCLC (20), while VEGFR inhibitors have shown promise in prolonging PFS (21). Notably, in recent years, innovative therapeutic strategies have emerged, including antibody-drug conjugates (ADCs) (22), bispecific antibodies (BiAbs) (23) and circulating tumor DNA (ctDNA)-guided personalized therapy (24). Collectively, these advances are steering the field of lung cancer targeted therapy toward an era of greater precision and therapeutic diversity.

Despite rapid advances in targeted therapy for lung cancer, research in this field remains highly fragmented. Existing reviews and systematic evaluations typically focus on individual targets or therapeutic modalities—for example, the application of EGFR or ALK inhibitors (25,26), the clinical success of KRAS G12C inhibitors (27), or studies of rare alterations such as HER2 and MET (28). Although recent efforts have provided thematic overviews of combination strategies (29) and artificial intelligence–assisted diagnostics (30), a comprehensive synthesis covering the entire spectrum of targeted therapies is still lacking. Similarly, bibliometric studies to date have mainly concentrated on narrow subfields, including ALK-TKIs, KRAS mutations, and research trends in TKIs (31-33). By contrast, in areas such as immunotherapy, radiotherapy, and cancer screening, bibliometric approaches have been widely applied to reveal shifts in research priorities, disparities in national and institutional contributions, and projections of future directions (34,35). This highlights the absence of a systematic, panoramic, and quantitative overview in the highly dynamic domain of lung cancer targeted therapy.

As a systematic analytical approach, bibliometric analysis enables a structured examination of the evolution, knowledge structure, and thematic dynamics of a research field (36,37). In the context of lung cancer targeted therapy, where rapid scientific advances and expanding therapeutic targets have led to an increasingly fragmented literature, a comprehensive and integrative overview remains limited. More fundamentally, the diversification of targeted agents, therapeutic strategies, and clinical decision-making paradigms has emerged as a central challenge in lung cancer care, underscoring the need for integrative approaches capable of synthesizing increasingly heterogeneous bodies of evidence. In contrast to recent bibliometric studies that focus on individual molecular targets (e.g., EGFR or KRAS), the present study adopts a field-level perspective to characterize the integrated knowledge structure and thematic evolution of lung cancer targeted therapy as a whole. Accordingly, this study employs bibliometric methods and visualization techniques to address several key research questions in the field of lung cancer targeted therapy: (I) how the intellectual structure and thematic focus of targeted therapy research have evolved over time; (II) whether disparities exist between publication output, academic impact, and international collaboration across countries and institutions; and (III) which emerging targets, therapeutic strategies, and interdisciplinary directions are reshaping the current research landscape. Based on publications retrieved from the Web of Science Core Collection (WoSCC) and cross-validated using the Scopus database, this study aims to provide a data-driven and systematic overview of the knowledge domain of lung cancer targeted therapy. By integrating VOSviewer and CiteSpace for scientometric analysis and visualization, the findings are intended to offer structured evidence to support future basic research, clinical translation, and strategic academic decision-making. We present this article in accordance with the BIBLIO reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2521/rc).

Methods

Data sources and search strategy

The data for this study were retrieved from the WoSCC and the Scopus database to enable cross-validation and ensure the robustness of our findings. The citation indexes in WoSCC were limited to the Science Citation Index Expanded (SCIE) and the Social Sciences Citation Index (SSCI).

The search period spanned January 1, 2003, to September 4, 2025, and the search was performed on September 4, 2025. Search strategy was defined as follows: TS = (targeted therapy OR molecularly targeted therapy OR precision medicine OR personalized therapy OR individualized treatment) AND TS = (lung cancer* OR pulmonary neoplasms* OR bronchogenic carcinoma* OR pulmonary tumor* OR lung neoplasm* OR pulmonary cancer).

Inclusion and exclusion criteria

Inclusion criteria were: (I) studies directly related to lung cancer targeted therapy, published in English; (II) document types restricted to research articles (Article) or review papers (Review); (III) research focusing on driver gene mutations, including EGFR, ALK, ROS1, RET, MET, HER2, KRAS, BRAF, as well as emerging targeted therapy strategies such as anti-angiogenesis, ADCs, and BiAbs.

Exclusion criteria were: (I) studies unrelated to lung cancer or addressing non-targeted therapies (e.g., chemotherapy, radiotherapy, or immunotherapy alone); (II) non-peer-reviewed publications, including conference papers, editorials, or brief communications.

All retrieved records were exported in plain text format, including full records and references. Duplicate removal and format standardization were subsequently performed using CiteSpace, ensuring data accuracy and completeness. The analysis process is depicted in Figure 1.

Figure 1 Flowchart illustrating the research process.

Bibliometric analysis

This bibliometric study was conducted following established reporting guidelines for bibliometric research (e.g., PRISMA-Bib), ensuring methodological transparency and reproducibility. All steps, including data retrieval, cleaning, processing, analysis, and visualization, were performed systematically and are fully documented to allow independent replication. CiteSpace (v6.3.R4) and VOSviewer (v1.6.20) were employed to perform bibliometric and visualization analyses. CiteSpace was primarily used for keyword co-occurrence analysis, thematic clustering, dual-map overlay of journals, timeline and timezone mapping and burst detection to identify research hotspots and emerging trends. For each time slice, the top 50 nodes were selected, and Pathfinder and Pruning sliced networks algorithms were applied to simplify network structures for clarity and interpretability. Keywords appearing at least 30 times were included, with meaningless high-frequency terms closely related to the search query (e.g., “lung cancer”, “targeted therapy”) manually removed to enhance analytical validity. VOSviewer was employed to analyze co-authorship and co-citation networks across countries, institutions, authors, journals, and references, generating visual maps to elucidate collaborative relationships among various academic entities.

Statistical analysis

Descriptive statistical methods were used to analyze annual publication counts, total citation frequencies, and H-indices. Annual publication trends were visualized using Microsoft Excel 2021 and evaluated via linear regression model, applying a significance threshold of P<0.05. Total citation counts and H-indices were obtained directly from the Web of Science “Citation Report” function to ensure accuracy and reproducibility. All results are presented in tabular and graphical formats, providing a clear overview of targeted therapy research in lung cancer.

To validate the robustness of the primary findings derived from the WoSCC database, we independently repeated all core analyses (including annual publication trends, country/institution contributions, and keyword co-occurrence) on the Scopus dataset. A comparative analysis of the key outcomes from both sources was then conducted to assess consistency.

Results

Altogether, 2,125 publications on lung cancer targeted therapy were included, comprising 1,405 research articles and 720 review papers. These contributions originated from 64 countries and 2,934 research institutions, involving 11,456 authors. The publications appeared across 472 academic journals and collectively cited 54,368 references. Overall, the annual publication volume demonstrated a steady upward trend (Figure 2A), with linear regression confirming a significant increase in research output (R²=0.895, P<0.05). By the retrieval date, the included publications had accumulated a total of 64,149 citations, with an overall H-index of 110 (Figure 2B), indicating that research on lung cancer targeted therapy has steadily gained substantial academic influence over the past two decades.

Figure 2 Annual publication trends and citation analysis of targeted therapy in lung cancer (2003–2025). (A) Annual number of publications with linear regression trend line (R²=0.895, P<0.05). (B) Annual total citations and publication outputs. The cumulative total citations of all included publications reached 64,149, with an overall H-index of 110.

Country, institution and authors

Regarding the distribution by country, research in this field has contributions from 64 nations (Figure 3A). China leads with 888 publications (41.79%), exhibiting a pronounced upward trajectory over the past decade and markedly surpassing other countries since 2019 (Figure 3B), reflecting a clear dominance in research output. The United States ranks second with 653 publications (30.73%), leading in both total citations and average citations per paper (47.57), indicating the strongest academic influence (Table 1). European countries such as Italy, France, and Spain, although producing fewer publications, achieve an average citation count exceeding 50 per paper, demonstrating high research quality. In contrast, Asian countries like Japan and South Korea have steadily increased their publication output over the last decade, though their overall impact remains somewhat lower than that of Western nations.

Figure 3 National contributions in targeted therapy research for lung cancer (2003–2025). (A) International collaboration network among contributing countries. Node size reflects the number of publications, and link thickness indicates collaboration strength. (B) Annual publication trends of the top five countries (China, USA, Italy, Japan, and Germany) from 2015 to 2024.

At the institutional level, A total of 2,934 institutions have participated in lung cancer targeted therapy research (Figure 4). Network analysis revealed collaborative relationships among institutions, with leading U.S. cancer centers forming close partnerships with high-output Chinese universities. The top ten most productive institutions are listed in Table 2. Institutions from China and the U.S. dominate this field. Shanghai Jiao Tong University produced 53 papers, the highest among institutions, though its average citations per paper (8.70) are relatively modest. In comparison, U.S. institutions such as MD Anderson Cancer Center (43 publications, 114.98 citations/paper), Memorial Sloan Kettering Cancer Center (43 publications, 69.98 citations/paper), and Massachusetts General Hospital (42 publications, 89.98 citations/paper) not only maintain high publication numbers but also exert substantial academic influence.

Figure 4 Institutional collaboration network in lung cancer targeted therapy research (2003–2025). Each node represents an institution, and node size indicates publication volume. Colors denote different clusters of collaborating institutions. The thickness of the connecting lines reflects the strength of collaboration between institutions.

At the author level, A total of 11,456 authors have contributed to lung cancer targeted therapy research. According to Price’s Law (m = 0.749 × √n_max ≈ 3.82) (38), authors with ≥4 publications were designated as core contributors, forming a core collaboration network (Figure 5). This network illustrates that core authors have established transnational collaborations, although regional disparities persist. Among prolific authors (Table 3), Chinese scholars are predominant. Zhang Li leads with 26 publications, although the average citations per paper (22.58) are relatively low. Wu Yilong, with 20 publications and a total of 3,273 citations (163.65 per paper), demonstrates exceptional academic influence, representing a leading figure among Chinese researchers.

Figure 5 Core author collaboration network in lung cancer targeted therapy research (2003–2025). The map was generated by VOSviewer, where each node represents an author and node size indicates the number of publications. Edges reflect co-authorship links, with thickness corresponding to the collaboration strength. Colors represent different collaboration clusters, and the color gradient on the node border indicates the average publication year. Core authors were identified based on Price’s Law (m=0.749 × √nmax ≈ 3.82), defining authors with ≥4 publications as core contributors.

Internationally, scholars from North America and Europe have also made substantial contributions. Notably, David Ross Camidge (U.S., 13 publications, 135.08 citations/paper) and Robert C. Doebele (U.S., 13 publications, 103.15 citations/paper) maintain high influence. Other active contributors include Natasha B. Leighl (Canada, 16 publications, 42.38 citations/paper), Cesare Gridelli (Italy, 15 publications, 39.87 citations/paper), and Rafael Rosell (Spain, 14 publications, 52.43 citations/paper).

Overall, the research landscape is characterized by high output in Asia (China, Japan, South Korea) and strong academic influence in the United States and Europe. A more comprehensive list of top-contributing authors and institutions is provided in Appendix 1.

Journal

Table 4 shows the top ten productive journals, predominantly covering the fields of oncology and pulmonology. Among them, Frontiers in Oncology leads with 116 publications, though its average citations per paper are relatively modest (9.99). Lung Cancer and Clinical Lung Cancer are key journals in the domain, combining substantial publication output with notable citation impact. In contrast, the Journal of Thoracic Oncology, despite publishing only 45 articles, achieves an impressive average of 62.47 citations per paper and boasts a high impact factor (20.8), underscoring its significant academic influence.

Analysis of cited journals (Table 5) further indicates that authoritative clinical and basic research journals occupy central positions, with Journal of Clinical Oncology being the most frequently cited, with 12,828 citations, providing foundational theoretical and clinical guidance. Journal of Thoracic Oncology and New England Journal of Medicine follow with 6,860 and 6,557 citations, respectively. Additionally, high citation counts in prestigious journals such as Nature and Cancer Discovery reflect the growing interdisciplinary attention to lung cancer targeted therapy research.

Journals with higher publication volumes tend to be specialized oncology outlets, whereas those with the highest citations are concentrated in internationally authoritative medical and oncology journals. This discrepancy suggests that researchers often prioritize accessibility and subject alignment when selecting publication venues, while citations predominantly reflect high-quality evidence and recognized clinical guidelines.

Dual-map overlay analysis of journals (Figure 6) illustrates the interdisciplinary flow of knowledge within this research area. The findings indicate that the intellectual landscape of lung cancer targeted therapy research is primarily concentrated in medicine, clinical medicine, and molecular biology & genetics, forming two core knowledge pathways. The first (yellow path) flows from molecular biology and immunology journals to molecular biology and genetics journals (z=4.956, f=6,925), while the second (green path) flows from medical, healthcare, and clinical journals to molecular biology and genetics journals (z=5.074, f=7,081). These patterns demonstrate that molecular and genetic research provides critical support for the advancement of clinical targeted therapy, while clinical investigations continually drive deeper exploration of molecular mechanisms, reflecting the tight integration of basic research and clinical application.

Figure 6 Dual-map overlay of journals. The left side represents the disciplines of citing journals, while the right side shows the disciplines of cited journals. The colored lines indicate citation relationships, with the green and yellow paths highlighting the main knowledge flows in the field.

Reference

Co-citation analysis of references indicates that the top ten most-cited publications primarily focus on EGFR mutation discovery, pivotal clinical trials of EGFR inhibitors, and the identification of ALK fusion genes (Table 6). Notably, the IPASS study reported by Mok et al. in NEJM (455 citations) and the clinical trials by Maemondo and Shepherd established the efficacy of EGFR-TKIs in both early and later lines of therapy (39-41). The EURTAC study by Rosell et al. (Lancet Oncology, 358 citations) and the OPTIMAL study by Zhou et al. (283 citations) further validated efficacy across diverse populations (42,43).

At the molecular mechanism level, the studies by Lynch and Paez elucidated the direct relationship between activating EGFR mutations and gefitinib efficacy (44,45). The FLAURA study by Soria et al. in NEJM (291 citations) confirmed osimertinib as a first-line standard (15). Simultaneously, Soda et al.’s identification of the EML4-ALK fusion in Nature (238 citations) opened new avenues for ALK-targeted therapy (46).

These highly cited publications, appearing in top-tier journals such as NEJM, Lancet Oncology, Nature, and Science, collectively form the core evidence base for molecularly targeted therapy in lung cancer.

Key words

Keyword analysis further elucidates the central themes and evolutionary trends in lung cancer targeted therapy research (Figures 7-9). In the co-occurrence network (Figure 7), high-frequency keywords such as “EGFR mutation”, “tyrosine kinase inhibitor”, “first-line treatment”, and “chemotherapy” occupy central positions, reflecting their enduring core status. The emergence of newer keywords, including osimertinib and immunotherapy, indicates a continual expansion of research focus. Timeline analysis (Figure 8A) reveals that early research hotspots centered on EGFR mutations and phase III clinical trials, gradually expanding to ALK inhibitors, acquired resistance, and brain metastases. Recent frontier clusters include RET fusions, osimertinib, and gut microbiota. The timezone map (Figure 8B) further demonstrates the sequential shift of research themes over time, illustrating the progression of lung cancer targeted therapy from early chemotherapy- and EGFR-focused studies to a precision medicine stage characterized by multi-gene targets and immunotherapy. Burst keyword analysis corroborates this evolution: in the 2000s, bursts were predominantly associated with TKIs, monoclonal antibodies, and growth factor receptors, whereas recent bursts concentrate on case reports, gut microbiota, immunotherapy, and osimertinib, reflecting the latest research frontiers and emerging directions (Figure 9).

Figure 7 Keyword co-occurrence analysis of targeted therapy in lung cancer. The density visualization shows the distribution and intensity of keywords. Central hotspots such as EGFR mutations, 1st-line treatment, tyrosine kinase inhibitors, and chemotherapy dominate the field, with emerging terms like osimertinib and immunotherapy reflecting recent developments.

Figure 8 A timeline (A) and time zone (B) view of keywords for targeted therapy and lung cancer.

Figure 9 Burst keywords in articles related to targeted therapy and lung cancer. A blue line indicates the timeline, and the intervals in which bursts were found are indicated by red sections on the blue timeline, indicating the start year, the end year, and the burst duration.

Validation analysis using Scopus database

To validate the robustness of our primary findings, we replicated the core analyses using the Scopus database. The comparative results are presented below, focusing on publication trends, geographic/institutional contributions, and research hotspots.

The comparative annual publication trend is presented in Figure 10. As illustrated, the Scopus data closely mirrors the trajectory observed in WoS, with both databases exhibiting a consistent and substantial increase in publication output from 2003 to 2025. This parallel growth pattern, despite minor differences in absolute numbers, strongly confirms that the dynamic expansion of the lung cancer targeted therapy field is a robust and reproducible finding, not an artifact of a single database.

Figure 10 Comparative annual publication trend of lung cancer targeted therapy research from Web of Science and Scopus databases (2003–2025). The blue line represents data from the WoS Core Collection, while the red line represents data from the Scopus database. The close alignment of the trends confirms the robustness of the observed rapid growth and development in this research domain.

The concordance in the global research landscape was further confirmed by comparing the leading countries, institutions, and journals (Table 7). At the national level, a remarkable consistency was observed. China and the United States firmly held the top two positions in both databases, while Italy, Japan, and Germany also consistently ranked within the top five, underscoring a stable global contribution pattern.

While the ranking of specific institutions showed some variation—a common phenomenon due to differing collaborative networks and database indexing preferences—the landscape was demonstrably dominated by a consistent cohort of elite academic and clinical centers from both China (e.g., Shanghai Jiao Tong University, Sichuan University) and the United States (e.g., Memorial Sloan Kettering Cancer Center, MD Anderson Cancer Center). This affirms that the intellectual leadership in this field is concentrated within a well-defined group of high-output institutions from these two leading nations.

Similarly, journal analysis revealed a core set of high-impact outlets. While their relative ranks differed, journals such as Frontiers in Oncology, Lung Cancer, Cancers, and Clinical Lung Cancer consistently appeared at the forefront in both datasets, validating their central role in disseminating key findings in lung cancer targeted therapy.

A comparative analysis of the top 10 high-frequency keywords robustly validates the consistency of the core and evolving research architecture in lung cancer targeted therapy (Table 8). While the absolute ranking and specific term preference vary between Web of Science (WoS) and Scopus—a common phenomenon attributable to differential journal coverage and indexing—the underlying thematic convergence is unmistakable.

The WoS corpus initially shows a stronger imprint of early-phase clinical trial terminology (e.g., “open-label”, “gefitinib”, “erlotinib”). In contrast, Scopus immediately highlights broader disease and treatment categories (“non-small cell lung cancer”, “immunotherapy”). However, a profound alignment emerges upon examining the complete list. Foundational concepts such as “resistance”, “EGFR”, “ALK”, “tyrosine kinase inhibitors”, and “mutations” are powerfully represented in both datasets. The consistent presence of “osimertinib” in Scopus and “EGFR” in both lists signals a shared focus on next-generation therapeutics and driver genes.

This multi-database analysis confirms that the fundamental pillars of the field—defining the patient population (NSCLC), targeting key drivers (EGFR, ALK, mutations), overcoming resistance, and evolving through generations of TKIs—are not artifacts of a single database but are the authentic, reproducible intellectual core of the discipline.

Discussion

This study conducted a systematic bibliometric analysis of 2,125 publications on lung cancer targeted therapy from 2003 to 2025, delineating the developmental landscape and research dynamics of the field. The findings reveal a steady increase in overall publication volume, reflecting sustained scholarly interest. China leads in publication output, whereas the United States maintains dominance in total citations and academic influence. Certain European countries, despite modest output, demonstrate high per-article impact. Research capacity is concentrated in top-tier centers within a limited number of countries, particularly China and the U.S., while international collaboration networks reveal notable imbalances. Core journals and highly cited works form the essential academic foundation that drives progress in this domain. Keyword and burst-word analyses further highlight the evolution of research hotspots, shifting from classical driver mutations such as EGFR and ALK to multi-target strategies, resistance mechanisms, and combination therapy approaches, with precision medicine increasingly gaining prominence in recent years.

Crucially, the robustness of these primary findings—spanning publication trends, global contributions, and thematic evolution—was rigorously confirmed through an independent cross-validation analysis using the Scopus database, underscoring that the intellectual structure mapped in this study is reliable and not an artifact of a single data source.

Research hotspots

In the keyword and burst-word analysis, EGFR and ALK consistently occupy central, high-frequency positions, closely associated with burst terms such as TKI and phase III trial, underscoring their foundational roles in lung cancer targeted therapy. EGFR-targeted therapy was initially pioneered by first-generation TKIs (gefitinib and erlotinib), establishing the paradigm of genotype-guided treatment (47). Later, next-generation inhibitors, such as dacomitinib, showed improved PFS compared with first-generation agents in the ARCHER 1050 study (14.7 vs. 9.2 months), further optimizing therapeutic strategies (48). A pivotal paradigm shift occurred with the advent of third-generation EGFR-TKI osimertinib, with the FLAURA trial confirming substantial prolongation of PFS (18.9 vs. 10.2 months) and OS (38.6 vs. 31.8 months) in treatment-naïve patients with advanced EGFR-mutant NSCLC (49), alongside notable control of central nervous (CNS) metastases (50), establishing it as the current first-line standard (51). Similarly, therapeutic strategies for ALK-positive patients has evolved from crizotinib to next-generation inhibitors. The ALEX study demonstrated that alectinib markedly extended mPFS (34.8 vs. 10.9 months) (52), and significantly improved CNS control (53) and health-related quality of life (54), outperforming crizotinib in both efficacy and safety (14), thereby prompting comprehensive updates in clinical practice. Notably, resistance remains a pervasive challenge for both EGFR- and ALK-targeted therapies. EGFR T790M and C797S mutations (55), along with ALK kinase domain alterations and bypass pathway activation (56), underscore the limitations of single-agent targeted therapy and provide a compelling rationale for the exploration of new targets and combination strategies.

The discovery of the EML4-ALK fusion gene marked the onset of the multi-target era in lung cancer targeted therapy following EGFR, being identified in approximately 3–7% of NSCLC cases (57), and catalyzing the rapid development of ALK inhibitors. Successive generations of ALK inhibitors have since emerged. Brigatinib demonstrated a substantial extension of mPFS (24.0 vs. 11.1 months) in the ALTA-1L study (58), while lorlatinib in the CROWN trial exhibited superior CNS control and coverage of resistance mutations (59), thereby continuously refining therapeutic strategies. Concurrently, other low-frequency driver genes have attracted increasing research attention. Patients harboring HER2 mutations achieved an objective response rate (ORR) of 55% and a median PFS of 8.2 months with trastuzumab deruxtecan in the DESTINY-Lung01 study (16); RET fusions reached ORRs of 64–85% in the LIBRETTO-001 trial, with notable intracranial efficacy (60); first-generation TRK inhibitors elicited complete responses in selected patients and demonstrated robust CNS penetration (61); furthermore, BRAF V600E mutations have been incorporated into the precision therapy spectrum, exhibiting pronounced antitumor activity (62). Collectively, these findings illustrate the expansion of precision medicine to encompass “long-tail” patient populations. Meanwhile, the burst keyword “gene copy number” reflects the field’s evolving focus from point mutations to copy number and structural variations. MET amplification and METex14 skipping mutations have been validated as actionable events, with MET inhibitors demonstrating clear ORR and PFS benefits in both METex14 and high-copy-number cohorts, highlighting copy number and splicing aberrations as critical therapeutic targets (63). The burst keyword “never smokers” emphasizes epidemiological trends, revealing that EGFR and ALK driver mutations are disproportionately prevalent among non-smokers (with EGFR occurring in 30–50% of non-smoking cohorts in some studies) (64). This pattern has facilitated the integration of molecular testing with clinical stratification, further advancing personalized treatment approaches.

The burst keywords such as “monoclonal antibody”, “chemotherapy-naïve”, and “previously treated patients” highlights the growing importance of combination therapy. First-generation TKIs, exemplified by erlotinib, were initially investigated alongside anti-angiogenic agents. The JO25567 study demonstrated that erlotinib plus bevacizumab significantly prolonged median PFS (16.0 vs. 9.7 months) (65), laying the foundation for subsequent trials. Phase III NEJ026 and RELAY studies further confirmed the efficacy of this approach, showing substantial PFS improvements in chemotherapy-naïve EGFR-mutant patients (NEJ026: 16.9 vs. 13.3 months; RELAY: 19.4 vs. 12.4 months), with benefits observed across both 19del and L858R subgroups (66), albeit associated with increased grade ≥3 adverse events (72–88%) (67,68). In previously treated or resistant patients, the BeTa and West Japan 8715L studies did not demonstrate OS or PFS advantages, indicating that the efficacy of combination therapy depends on treatment line and resistance status (69,70). Regarding chemotherapy combinations, NEJ009 and FLAURA-2 confirmed that TKIs combined with platinum/pemetrexed regimens could prolong both PFS and OS, yielding particularly pronounced benefits in patients with brain metastases. However, careful management of myelosuppressive toxicity is required, suggesting that this approach is most suitable for patients needing rapid disease control or with CNS involvement (71). These findings underscore the foundational role of first-generation EGFR-TKIs, such as erlotinib, in combination strategies and provide a direct reference for studies involving next-generation TKIs, including osimertinib.

With osimertinib established as a first-line therapy, research emphasis has progressively shifted toward elucidating resistance mechanisms and exploring combination strategies. Key resistance mutations emerging post-osimertinib, including C797S, L718Q, and M766Q, have been increasingly characterized (72-74), prompting investigations into reversible EGFR inhibitors, pan-HER inhibitors (75) and novel combination approaches targeting WEE1 (76) and PDK1 (77), thereby offering avenues for precision interventions after resistance. Concurrently, immunotherapy has become a critical area of exploration. The IMpower150 trial demonstrated improvements in ORR and disease control rate (DCR) among TKI-failed patients, particularly in the PD-L1 ≥1% subgroup (78); however, trials including KEYNOTE-789, CheckMate-722, and ORIENT-31 reported limited overall efficacy and increased adverse events, with pulmonary toxicity being a notable concern (79-81). Notably, the gut microbiome has emerged as an influential research direction, modulating immune checkpoint inhibitor (ICI) efficacy via short-chain fatty acid–mediated immune regulation (82). High abundances of Akkermansia and Bifidobacterium correlate with enhanced immune responses (83,84), suggesting potential interventions through dietary modulation (85) and antibiotic exposure management (86). Additionally, complex clinical cases continue to push research boundaries, including next-generation sequencing (NGS)-guided personalized therapy for patients with leptomeningeal metastases (87) and remarkable responses to pembrolizumab in bone metastases (88). These developments delineate the trajectory of future NSCLC research: evolving from single-target therapies toward resistance mechanism elucidation, combination regimens, multi-omics microenvironment analyses, and increasingly sophisticated personalized precision medicine.

Future directions

The future directions discussed below are derived from bibliometric indicators identified in this study, including keyword burst detection, temporal co-occurrence trends, and citation clustering, rather than from an independent narrative literature review.

The keywords such as “deep learning”, “landscape”, and “precision oncology” reflects a paradigm shift in lung cancer research, transitioning from a single-target focus to a data- and systems-driven approach. Deep learning and radiomics models have shown considerable potential in noninvasively predicting driver mutations, including EGFR, ALK, and KRAS, with multicenter studies reporting AUC values of 0.78–0.95. These approaches enhance molecular subtype screening efficiency and provide supportive guidance for early diagnosis and personalized therapy (89-91). Simultaneously, real-world evidence (RWE) offers a valuable complement to clinical trials. For example, osimertinib has been linked to higher cardiotoxicity risks in broader patient populations, while resistance mechanisms to ALK inhibitors demonstrate greater heterogeneity, highlighting the need for precision medicine assessments that extend beyond narrowly selected trial cohorts to encompass diverse clinical contexts (92). Moreover, multi-omics integrative studies have revealed the genomic instability and immunosuppressive microenvironment characteristic of NSCLC brain metastases (93,94), whereas single-cell transcriptomics has clarified the relationship between distinct tumor immune microenvironment (TIME) subtypes and immunotherapy responses (95). Exosome-based models combining metabolomics and proteomics further enable prediction of distant metastatic risk (96). These developments are shaping the evolving research landscape of lung cancer, advancing the field from a target-driven paradigm toward systemic integration, and laying the groundwork for precision stratification and individualized interventions. Within this context, resistance stratification and novel drug exploration have emerged as central pillars of precision-guided therapy.

Although EGFR- and ALK-targeted therapies have significantly improved patient outcomes, acquired resistance remains pervasive, particularly with rare EGFR alterations such as C797S mutations following osimertinib treatment and exon 20 insertions (97). The integration of ctDNA and liquid biopsy technologies has enabled dynamic monitoring of resistance mechanisms and patient stratification, thereby facilitating the development of personalized therapeutic strategies (98). In the domain of novel drug development, ADCs have demonstrated notable promise. These include TROP2-targeted agents (Sacituzumab govitecan, Dato-DXd), HER3-targeted compounds (Patritumab deruxtecan, EGFR-HER3 bispecific BL-B01D1), and MET-targeted therapies (Telisotuzumab vedotin), all showing efficacy signals in their respective patient populations (22,99,100). Concurrently, PROTAC (proteolysis-targeting chimeras) technology, by inducing targeted protein degradation, has transcended conventional inhibition approaches; for example, ABTAC (antibody-based proteolysis-targeting chimeras) exhibits superior activity compared with monotherapy in both EGFR-sensitive and resistant cell lines (101). Clinical trial designs are also becoming increasingly adaptive. Basket trials enable rapid evaluation of drugs across multiple cancer types or rare mutations (102), whereas umbrella trials allow simultaneous assessment of multiple targets within a single cancer type (60). Collectively, post-resistance NSCLC management is advancing toward finely tuned precision interventions under a framework integrating ctDNA-guided stratification, novel mechanistic therapeutics, and innovative trial designs.

Terms such as “immune microenvironment”, “oral microbiota”, and “potential bacterial biomarkers” have shown a pronounced upward trajectory over the past three years, indicating that immune milieu and microbiome factors are emerging as pivotal extensions in lung cancer targeted therapy research. Unlike traditional stratification based solely on driver gene mutations (e.g., EGFR, ALK, MET), future directions are shifting toward integrating TIME characteristics with microbiome-associated indicators for patient classification, thereby enabling more precise combinatorial treatment strategies. For instance, specific microbial compositions correlate with CD8+ T cell infiltration within the tumor microenvironment (TME), factors that may influence the differential efficacy of TKIs or ALK inhibitors when paired with ICIs (103,104). In terms of trial design, emerging Bayesian adaptive designs allow mid-study adjustments of patient stratification based on dynamic changes in ctDNA, tumor mutational burden (TMB), or microbiome profiles, thereby optimizing targeted-immunotherapy regimens in real time (105). Multi-stage enrichment designs further enhance efficiency by preselecting subpopulations sensitive to particular biomarker combinations before validation, increasing trial yield and the probability of positive outcomes (106). Moreover, platform trial frameworks are progressively expanding; beyond classical targets such as EGFR, ALK, and BRAF, they now incorporate TME features and microbiome dynamics into continuous monitoring, facilitating biomarker-guided interventions across tumor types, as exemplified by umbrella trials initially developed in gastric cancer (107). These trends underscore that the future of lung cancer targeted therapy is evolving beyond single-molecule targeting toward an integrative stratification model encompassing driver genes, immune microenvironment, microbiome factors, and dynamic liquid biopsy.

It should be noted that several recently emerging targets, such as KRAS G12D-specific inhibitors, NRG-related signaling, and FGFR alterations, have only entered translational and clinical investigation in recent years. Due to their limited publication volume and short citation history, these topics have not yet formed stable bibliometric clusters within the analyzed time frame but are expected to gain prominence as the literature matures. Future precision interventions are increasingly shifting toward an integrated “rare mutation-epigenetic regulation-data platform” paradigm. Low-frequency driver genes, including RET, HER2 exon 20, NTRK, and BRAF V600E, have progressed from isolated case reports and early-phase trials to multi-center validation. RET inhibitors demonstrated high ORRs and notable intracranial activity in the LIBRETTO-001 and ARROW studies, while HER2-targeted ADCs (T-DxD) exhibited clear efficacy in the DESTINY-Lung series, highlighting that “long-tail” patient populations can also benefit (108,109). Epigenetic interventions are emerging as promising avenues post-resistance. Alterations in DNA methylation, HDAC regulation, or the ARID1A pathway show potential when combined with novel mechanism-based agents such as WEE1 inhibitors or PROTAC/ABTAC strategies (110). Concurrently, case reports are increasingly converted into systematically verifiable evidence through NGS and liquid biopsy, allowing rare mutations or atypical resistance mechanisms to rapidly enter basket or umbrella trials, while liquid biopsy captures spatiotemporal heterogeneity, such as EGFR→SCLC transformation (111). The key to this translational process lies in multi-center data standardization and platformization, leveraging ctDNA frameworks and RWE integration to accelerate the feedback loop from individual discoveries to population-level evidence. This trend aligns with the emergence of new keywords in our analysis, such as “rare mutations” and “epigenetic regulation”.

Global research landscape and academic contributions

Bibliometric analysis revealed pronounced regional disparities in lung cancer targeted therapy research. Over the past decade, China has experienced a rapid increase in publication output, ranking among the leading contributors worldwide. However, citation-based indicators, including average citations per article and international co-authorship link strength, remain comparatively lower, suggesting a mismatch between research quantity and academic influence. These patterns, reflected in citation distributions and collaboration networks, indicate that research outputs are predominantly concentrated in medium- to low-impact journals and are characterized by relatively limited international collaboration. Such features are consistent with the uneven development of research infrastructure and clinical trial capacity, particularly in central and western regions, where large-scale trial networks and data-sharing mechanisms remain less established (112,113). In contrast, the United States demonstrates sustained advantages across multiple bibliometric dimensions, including high publication output, strong citation impact, and dense international collaboration networks. These characteristics correspond to its well-established funding systems and integrated clinical trial platforms, which have supported landmark studies such as FLAURA, ARCHER 1050, and PROFILE 1014, thereby shaping standard-of-care pathways for EGFR-, ALK-, and HER2-targeted therapies (114,115). Europe, while producing fewer publications overall, exhibits consistently high per-article citation impact. Multinational trials such as EURTAC and TAILOR, led or co-led by institutions in Italy and Spain, exemplify the role of coordinated multicenter collaboration in generating influential clinical evidence.

Collectively, these bibliometric patterns indicate that global differences in academic impact are not solely attributable to publication volume but are closely associated with structural features of research ecosystems, including collaboration intensity, trial organization, and evidence-generating capacity. The United States maintains leadership through integrated research and trial infrastructures, Europe achieves high impact through rigorously designed multicenter studies, and China occupies a transitional position, characterized by high productivity but comparatively lower citation influence, as reflected in current collaboration and citation networks. At the same time, analysis of highly cited studies and collaboration patterns highlights the growing importance of shared data resources and platform-based research. International databases such as TCGA, ICGC, and AACR Project GENIE, together with basket and umbrella trials including LCMC and DESTINY-Lung04, constitute key structural components of the contemporary research landscape. The increasing reliance on such collaborative frameworks is evident in both Western and Asian research networks and represents a measurable feature of recent publication and co-authorship trends, rather than a purely conceptual expectation.

Significance and practical value

By applying bibliometric analysis, this study provides a structured and data-driven characterization of the intellectual evolution and research configuration of lung cancer targeted therapy. Beyond descriptive publication statistics, this study emphasizes relational and temporal analyses, including collaboration networks, thematic shifts, and knowledge structures, which extend beyond the scope of conventional database searches. Rather than focusing on individual targets or therapeutic modalities, the analysis delineates how research priorities have shifted over time—from early emphasis on classical driver genes such as EGFR and ALK to emerging themes including HER2, KRAS, resistance mechanisms, the immune microenvironment, and the microbiome—based on keyword co-occurrence, temporal clustering, and citation patterns. Importantly, the findings offer an evidence-based perspective on how scientific attention and research intensity are distributed across targets and strategies, thereby helping researchers contextualize emerging topics within the broader knowledge structure of the field. For clinical and translational research, this mapping clarifies which areas have accumulated sustained evidence and which remain in earlier, rapidly evolving phases, providing a rational basis for prioritizing future investigations rather than prescriptive clinical recommendations. At the global level, bibliometric indicators further reveal a pronounced imbalance between research productivity and academic influence across regions. The coexistence of high publication volume with relatively limited citation impact and international collaboration—particularly in China—highlights structural gaps in trial organization and cross-border research integration. These observations, derived directly from collaboration networks and citation metrics, offer objective reference points for research administrators and funding agencies when evaluating capacity building and international cooperation strategies.

Limitations and future directions

This study systematically delineated the research hotspots and landscape of lung cancer targeted therapy through bibliometric analysis. To enhance the robustness of our findings, we addressed a common limitation of single-database bibliometric studies by incorporating a cross-validation analysis using the Scopus database. Despite this strengthening of the core analysis, several considerations remain. Keyword clustering and co-occurrence analyses are powerful for mapping trends but cannot fully reflect the depth of clinical nuance or underlying molecular mechanisms. Although our literature search did not preselect histological subtypes, the resulting dataset predominantly reflects NSCLC research, as SCLC-targeted therapy publications are currently limited and did not form stable bibliometric clusters within the analyzed timeframe. Therefore, the findings primarily pertain to NSCLC, and future bibliometric studies focusing specifically on emerging SCLC targets, such as tarlatamab, would be valuable as the literature in this area grows. Finally, bibliometric findings primarily offer a “quantitative mapping” and lack direct correspondence with experimental or clinical data.

Looking forward, the evolution of research in lung cancer targeted therapy underscores the need for methodological integration rather than reliance on single analytical perspectives. In this study, cross-database validation using both the WoSCC and Scopus demonstrated that major trends—such as thematic shifts, core knowledge clusters, and collaboration patterns—were highly consistent across data sources, supporting the robustness of the bibliometric findings. This result suggests that multi-database integration is not merely a technical refinement but a necessary step to reduce database-specific bias in future bibliometric research. Beyond database coverage, the results also highlight the inherent limitations of bibliometric mapping. While keyword co-occurrence, citation networks, and burst detection effectively capture the structural evolution of research topics, they do not directly address biological mechanisms or clinical efficacy. Therefore, the value of bibliometric analysis lies in its ability to contextualize and prioritize research directions, which should subsequently be interpreted alongside evidence from clinical trials, real-world data, and multi-omics studies rather than used in isolation for clinical decision-making.

Conclusions

This study systematically examined research on lung cancer targeted therapy using bibliometric analysis, providing a data-driven overview of publication trends and knowledge structures. The cross-validation with an independent Scopus dataset solidifies the reliability of these findings, ensuring that they reflect the authentic research landscape rather than artifacts of a single database. Findings indicate a clear temporal transition in research focus, from early concentration on classical driver mutations such as EGFR and ALK to increasing attention toward emerging targets, resistance mechanisms, and integrative domains including the immune microenvironment and microbiome. At the global level, bibliometric indicators reveal a persistent imbalance between research productivity, citation impact, and international collaboration across regions, reflecting structural differences in research ecosystems rather than differences in topic selection alone. These results indicate that lung cancer targeted therapy research is undergoing a shift toward greater complexity and multidimensional integration. By transforming a large and heterogeneous literature into an interpretable knowledge framework, bibliometric analysis serves as a complementary tool for understanding how scientific priorities evolve and where future research efforts may be most effectively directed.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2521/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2521/prf

Funding: This work was supported by the Suqian Science and Technology Bureau (grant No. SY202221).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2521/coif). Z.C. was supported by the Suqian Science and Technology Bureau (grant No. SY202221). The other authors have no conflicts of interest to declare.

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

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

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