Translational progress and bottlenecks in neurotransmitter-cancer research [2000–2025]: an integrated bibliometric and interventional trial analysis

Introduction

Neurotransmitter signaling has emerged as an important frontier in cancer biology, challenging the long-standing view that neurotransmitters are confined to neuronal communication. A growing body of evidence demonstrates that classical neurotransmitters—including acetylcholine, dopamine, serotonin, and norepinephrine—exert broad regulatory effects on tumor initiation, progression, and response to therapy (1-3). These molecules influence fundamental malignant processes such as cell proliferation, apoptosis, invasion, metastasis, and angiogenesis through receptor-mediated signaling pathways expressed on tumor cells (4,5). Beyond direct effects on cancer cells, neurotransmitters also play a critical role in shaping the tumor microenvironment by modulating immune cell infiltration, stromal remodeling, and metabolic reprogramming (6,7).

A conceptual turning point in this field was the identification of the cholinergic anti-inflammatory pathway by Pavlov and Tracey, which demonstrated that neural signals can directly regulate immune responses and systemic inflammation (8). This discovery provided a mechanistic framework linking the nervous system to immune regulation and has since been recognized as highly relevant to cancer immunology. Subsequent studies revealed that many tumor types express functional neurotransmitter receptors, enabling malignant cells to respond to signals originating from peripheral and autonomic nerves (9). Moreover, tumors can actively promote neural infiltration through neurotropic signaling, establishing bidirectional neuro-tumor interactions that facilitate tumor growth, invasion, and metastasis (10). Together, these findings have reframed neurotransmitters as integral components of the tumor ecosystem rather than passive bystanders.

The therapeutic implications of these insights have attracted increasing attention. Preclinical and epidemiological studies suggest that pharmacologic modulation of neurotransmitter pathways may influence cancer outcomes, particularly when combined with standard treatments (11,12). For example, β-adrenergic receptor antagonists have been associated with reduced tumor progression and improved survival in several cancer types, while dopamine modulators and serotonergic agents have demonstrated context-dependent antitumor and immunomodulatory effects. The clinical relevance of neurotransmitter signaling in oncology has been further substantiated by epidemiological observations linking psychological stress, which elevates catecholamine levels, with accelerated tumor growth and poor prognosis across multiple malignancies (13). Preclinical models have mechanistically connected chronic stress-induced adrenergic signaling with enhanced metastatic potential, immune suppression, and therapeutic resistance (14). Conversely, pharmacological or surgical interruption of neural pathways has shown promise in inhibiting tumor progression in experimental settings (15).

Despite this rapid expansion, neurotransmitter-cancer research remains highly heterogeneous and fragmented across neuroscience, oncology, immunology, and pharmacology. Neurotransmitter effects vary substantially by cancer types, receptor subtypes, cellular context, and neuro-tumor interactions are inherently bidirectional and dynamic. These complexities complicate interpretation of individual studies and hinder efficient translation from mechanistic discovery to clinical application (9). Although numerous preclinical investigations have established plausible therapeutic targets, it remains unclear how effectively this growing body of knowledge has been integrated into clinical trial design and execution.

To date, no comprehensive analysis has systematically mapped the intellectual structure of neurotransmitter-cancer research while simultaneously evaluating its translational progression into clinical testing. Key questions remain unanswered: Which countries, institutions, and research themes have driven advances in this field; how research priorities have evolved over time; and to what extent preclinical discoveries have translated into interventional oncology trials. Addressing these gaps is essential for identifying both successful translational pathways and persistent bottlenecks.

In this study, we conducted an integrated bibliometric analysis of 2,823 publications published between 2000 and 2025 alongside a systematic assessment of 76 interventional clinical trials registered in ClinicalTrials.gov. Using network analysis, keyword co-occurrence, and thematic evolution mapping, we characterized the field’s knowledge base, emerging research fronts, and collaboration patterns. By linking bibliometric indicators with clinical trial characteristics, including drug class distribution, combination strategies, and geographic patterns, we quantified the translational gap between discovery and clinical implementation. Through this combined approach, our study aims to clarify the developmental trajectory of neurotransmitter-cancer research and to provide an evidence-based perspective on future directions for neurotransmitter-targeted precision oncology. We present this article in accordance with the BIBLIO reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0089/rc).

Methods

Data source and systematic search strategy

All bibliometric data were obtained from the Web of Science Core Collection (WoSCC), a widely used database that provides standardized citation indexing and comprehensive coverage of peer-reviewed biomedical literature. A topic-based search was conducted within the TS field to capture publications addressing neurotransmitter-related signaling in oncologic contexts. The search covered the period from 1 January 2000 to 30 September 2025. Only original articles and review articles published in English were included to ensure consistency and comparability of bibliographic records.

The search strategy combined neurotransmitter-related terms with cancer-related terms using Boolean operators, as follows:

TS = (neurotransmitter OR neurotransmission OR “neurotransmitter signaling” OR “neural signaling*”) AND TS = (cancer* OR carcino* OR tumor* OR tumour* OR malign* OR neoplasm*)

Data filtering and quality assurance

The initial search yielded 2,910 records. After excluding non-research document types and non-English publications, a total of 2,823 records were retained for analysis (Figure 1A). To ensure data accuracy and thematic relevance, a quality control procedure was implemented. To ensure independence, the two reviewers (M.Z. and W.S.) conducted all screening and data extraction tasks separately, working from a standardized Excel-based screening form and evaluating each record independently without influencing each other’s decisions during the initial screening phase. Two independent reviewers manually screened all highly cited publications (defined as >100 citations, n=147) together with a random 5% sample of the remaining records (n=134).

Figure 1 Literature screening flowchart and bibliometric overview. (A) PRISMA-style flowchart showing screening process from initial 2,910 publications to final 2,823 publications after exclusions. (B) Summary statistics panel showing timespan [2000:2025], sources [1,151], authors [15,471], single-authored documents [143], keywords [6,876], references [191,468], documents [2,823], annual growth rate (4.33%), international co-authorship (24.51%), co-authors per document (6.08), document average age (9.88), and average citations per document (59.01).

During screening, bibliographic information, document type, and topical relevance to neurotransmitter signaling in cancer were verified. Discrepancies were resolved in a joint adjudication session chaired by the senior author (G.G.), using pre-specified resolution criteria defined in the study protocol. Inter-reviewer agreement was high, with a Cohen’s κ coefficient of 0.89, indicating excellent consistency.

Identification of neurotransmitter-targeted clinical trials

To assess translational progress from basic research to clinical application, interventional oncology trials involving neurotransmitter-targeted agents were identified through ClinicalTrials.gov (accessed September 2025). Searches combined cancer-related conditions with predefined classes of neuroactive drugs, including β-adrenergic antagonists, selective serotonin reuptake inhibitors (SSRIs), and dopamine modulators.

Eligible studies were defined as interventional trials enrolling adult patients with confirmed malignant diagnoses, initiated between 2000 and 2025, and including at least one pre-specified endpoint related to tumor response, progression-free survival, overall survival, disease-free survival, or mechanistic tumor biomarker assessment. Trials designed exclusively for cardioprotection against chemotherapy-induced cardiotoxicity, symptom management, or non-malignant conditions were excluded irrespective of the neuroactive drug class employed, as these endpoints do not constitute cancer-related efficacy evaluation. Two reviewers independently screened all retrieved records and extracted key trial characteristics, including intervention type, cancer indication, study phase, enrollment size, combination strategy, geographic location, and sponsorship. Trials were further categorized as monotherapy or combination therapy based on study design.

Bibliometric and network analysis

Bibliometric analyses were conducted using an integrated workflow based on R (v4.1.0), CiteSpace, and VOSviewer. Descriptive indicators, temporal publication trends, and network parameters were calculated using the bibliometrix and igraph packages. CiteSpace was employed for co-citation analysis, citation burst detection, and temporal knowledge mapping, whereas VOSviewer was used to construct and visualize collaboration networks and keyword co-occurrence patterns.

Network structure was assessed using standard centrality measures, including degree, betweenness, and closeness centrality. Clustering quality was evaluated using modularity (Q), with values greater than 0.3 considered indicative of meaningful community structure. To reduce the influence of nonspecific oncology terms, keyword relevance was further weighted using a term frequency-inverse document frequency (TF-IDF) approach, allowing domain-specific signals to be distinguished from generic background terms.

Integration of bibliometric and clinical trial data

To characterize translational alignment, publications were classified into basic mechanistic, preclinical, or clinical research categories based on keyword profiling and abstract review. Studies spanning multiple translational stages were assigned to more than one category where appropriate. Clinical trial records were cross-referenced with the bibliometric dataset using National Clinical Trial (NCT) identifiers, trial titles, and investigator information to identify associated publications. Temporal relationships between publication activity, citation bursts, and trial initiation were examined to estimate the lag between scientific discovery and clinical translation. In addition, geographic concordance between national research output and clinical trial activity was assessed to evaluate the relationship between scientific productivity and translational implementation.

Statistical analysis and sensitivity validation

Temporal publication trends were modeled using polynomial regression, with model performance assessed by the coefficient of determination (R²) and Akaike Information Criterion. Network robustness was evaluated by comparison with 1,000 Erdős-Rényi random networks matched for size and density. Sensitivity analyses using alternative search strategies yielded more than 95% concordance in major thematic structures. To further assess temporal stability, analyses were repeated across three predefined periods (2000–2010, 2011–2016, and 2017–2025). These analyses consistently identified β-adrenergic, serotonergic, and dopaminergic signaling as core themes, with a pronounced increase in immunotherapy-related studies during the most recent period.

Results

Clinical trial landscape and translational progression

To evaluate the translational progression of neurotransmitter-cancer research from preclinical discovery to clinical application, we systematically analyzed interventional clinical trials registered in ClinicalTrials.gov that evaluated neurotransmitter-targeted agents in oncology. A total of 76 interventional trials conducted over a 25-year period were included in the primary analysis, identified through structured searches combining cancer-related conditions with predefined drug classes including β-adrenergic antagonists (propranolol, carvedilol, metoprolol), SSRIs (fluoxetine, sertraline, paroxetine, citalopram, escitalopram), and dopamine modulators (cabergoline, bromocriptine). A complete list of included trials is provided in Table S1.

Temporal trends and translational acceleration

Analysis of trial initiation dates revealed three distinct temporal phases. An initial exploratory phase from 2000 to 2010 included 12 trials (15.8% of the total), followed by an early validation phase from 2011 to 2017 with 28 trials (36.8%). A marked increase in trial activity was observed from 2018 onwards, during which 36 trials (47.4%) were initiated. Notably, the period from 2020 to 2025 alone accounted for 48 trials, indicating a pronounced acceleration of clinical testing in recent years. This temporal pattern closely paralleled trends observed in the bibliometric analysis.

Drug class distribution and clinical development stage

Across all 76 included trials, β-adrenergic antagonists represented the largest drug class, accounting for 25 trials (33%). Propranolol was the most frequently investigated agent (20 trials), followed by carvedilol (4 trials) and metoprolol (1 trial). β-blockers exhibited the highest level of clinical maturity, with the majority of phase III trials occurring in this class. SSRIs were evaluated in 17 trials (22%). In contrast to β-blockers, SSRI studies focused on direct oncologic efficacy were predominantly early-phase, with 11 trials (64.7%) conducted in phase I or II. SSRI trial activity targeting tumor efficacy has expanded since 2021, consistent with growing mechanistic evidence for serotonin pathway involvement in antitumor immunity. Dopamine modulators comprised 10 trials (13%), predominantly targeting pituitary adenomas and prolactinomas. Histone deacetylase (HDAC) inhibitors (12 trials, 16%) were concentrated in hematologic malignancies including peripheral T-cell lymphoma and multiple myeloma, and in pediatric brain tumors (Table 1).

With respect to trial design, the 76 included trials were heterogeneous in design and enrollment scale, based on data extracted from trial registry records (ClinicalTrials.gov and equivalent registries). Randomized controlled designs were predominantly employed in phase III trials, whereas single-arm or open-label designs were more common in phase I/II trials. Planned enrollment figures ranged widely across drug classes and phases, with larger cohorts concentrated among phase III β-blocker trials and smaller cohorts in early-phase SSRI and dopamine modulator studies. These design characteristics reflect the varying stages of translational maturity across drug classes and should be considered when interpreting the strength of available evidence.

Cancer type distribution and combination strategies

Across the 76 included trials, cancer type distribution differed markedly by drug class. Breast cancer was the most frequent single indication (10 trials, 13%), predominantly in the β-blocker and SSRI classes. Pituitary/neuroendocrine tumors (9 trials, 12%) accounted for nearly all dopamine modulator trials. Brain tumors (8 trials, 11%) and lymphoma (8 trials, 11%) were the dominant indications for HDAC inhibitors, followed by leukemia/myelodysplastic syndrome (MDS) (6 trials, 8%). Melanoma (5 trials, 7%) and head-and-neck cancer (4 trials, 5%) were concentrated in the β-blocker and SSRI classes, respectively. Lung, colorectal, gastric, and pancreatic cancers each accounted for 3–4 trials (4–5%), distributed across multiple drug classes.

An evolution in combination strategy was observed over time. Among the 76 included trials, 8 (11%) investigated neurotransmitter-targeted drugs in combination with immune checkpoint inhibitors (ICIs), all initiated after 2018. This shift was concentrated in the β-blocker class (6 ICI combination trials, 24% of all β-blocker trials), with two additional ICI combinations identified in the antipsychotic and mood stabilizer categories. HDAC inhibitors, despite their substantial representation, were predominantly evaluated as single-agent or conventional-chemotherapy combinations, consistent with their historical development in hematologic oncology. For SSRIs, in which 18 of the 23 trials initiated since 2021 (78.3%) employed anti-programmed cell death protein 1 (PD-1) or anti-programmed death ligand 1 (PD-L1) agents.

Publication overview and growth trends

A total of 2,823 publications related to neurotransmitter signaling in cancer were retrieved from the WoSCC between 2000 and 2025, including 1,941 original articles (69%) and 882 review articles (31%). These publications involved 15,471 authors from 66 countries and were published across 1,151 journals. The mean number of authors per paper was 6.08, the international co-authorship rate was 24.5%, and the mean citation count per publication was 59.0 (Figure 1B).

Annual publication output followed a three-phase trajectory (Figure 2A). From 2000 to 2010, the number of publications increased steadily from 72 to 102 per year. Between 2011 and 2019, annual output fluctuated between 84 and 117 publications. A pronounced acceleration occurred after 2020, with publication counts rising from 135 to 208 papers by 2025. Overall, 1,437 publications (approximately 51% of the total) were published during the last 5 years of the study period. Polynomial regression analysis demonstrated a strong fit for cumulative publication growth (y = 0.0246x³ − 0.7414x² + 9.1853x + 45.314; R²=0.9465), with the cumulative curve indicating an inflection point after 2015 consistent with a transition from linear to accelerated growth (Figure 2B,2C).

Figure 2 Publication trends and geographic distribution. (A) Bar chart showing annual publication counts from 2000–2025 with cumulative trend line, displaying growth from 72 publications in 2000 to 208 in 2025; inset pie chart illustrates the proportion of original articles (69%) versus reviews (31%) among the total 2,823 records. (B) Cubic polynomial regression curve (R2=0.9465) fitted to annual publication numbers with 95% confidence interval (model-predicted). (C) Logistic regression validation confirming a highly significant temporal growth trend (z=18.29, P<0.001). (D) Horizontal stacked bar chart of the top 10 contributing countries ranked by total publication count; dark blue segments indicate SCP and light blue segments indicate MCP, with the percentage of MCP shown in parentheses. (E) Vertical bar chart of the top 10 institutions by publication count, led by the University of California System, the Egyptian Knowledge Bank, and the University of Texas System. CI, confidence interval; MCP, multi-country publication; SCP, single-country publication.

Global research distribution and international collaboration

Research output originated from 66 countries, with production concentrated in a limited number of regions (Figure 2D). The United States ranked first with 741 publications (26.2%), followed by China with 511 publications (18.1%) and Germany with 162 publications (5.7%). Together, these three countries accounted for approximately half of the total output.

Patterns of international collaboration varied substantially. Multiple-country publication (MCP) rates were highest in France (40.9%), the United Kingdom (39.3%), and Germany (34.0%), whereas Japan (8.6%) and China (14.7%) exhibited lower MCPs rates. The United States demonstrated a moderate MCP rate of 19.3%. Network visualization revealed a dense collaboration axis between North America and Europe, with expanding connections to Asia-Pacific regions (Figure S1).

Citation impact did not strictly correspond to publication volume. Although the United States accumulated the highest total citation count [71,503], higher mean citations per article were observed for the United Kingdom (104.6), the Netherlands (101.2), and Germany (86.0). Institutional-level analysis showed similar concentration, with the top 10 institutions contributing 774 publications (27.4%). The University of California System ranked first (174 publications), followed by the Egyptian Knowledge Bank (114 publications) and the University of Texas system (88 publications) (Figure 2E).

Translational gap analysis

Integration of bibliometric and clinical trial datasets revealed a substantial gap between mechanistic research output and clinical translation. Of the 2,823 publications analyzed, 353 studies (12.5%) involved clinical investigations, whereas only 76 unique interventional clinical trials were included in the primary analysis. The majority of publications focused on mechanistic discovery (1,847 publications, 65.4%) or preclinical validation (623 publications, 22.1%).

Clinical trial was geographically concentrated in regions with high research productivity. The United States accounted for the largest share of trials, followed by China and European countries, a pattern broadly consistent with overall publication output. Although this distribution broadly mirrored publication output, international collaboration was less frequent in clinical trials than in publications (14.2% vs. 24.5%).

Among the 76 included trials, 43 (57%) had been completed by September 2025, and 24 of these (56%) had reported results. Of the trials reporting results, the majority met their primary endpoints. Trials reporting positive outcomes accumulated higher citation counts (mean 47.3 citations) than neutral or negative trials (mean 20.6 citations).

Research hotspots and thematic structure

Keyword co-occurrence analysis generated a network comprising 542 nodes and 1,279 links, with an overall density of 0.0087 (Figure 3A). The term “Expression” occupied a central position, connecting receptor regulation, intracellular signaling pathways, and tumor microenvironment-related topics. Prominent thematic clusters included cancer, activation, tumor necrosis factor, and oxidative stress, with dopaminergic and serotonergic signaling serving as major organizing frameworks.

Figure 3 Keyword analysis. (A) Network diagram with “expression” as the central node connected to various research terms. (B) Timeline showing top 25 keywords with strongest citation bursts represented as horizontal bars across years. (C) Cluster map showing different colored thematic regions including immune system, marine environment, and human health. (D) Timeline network showing keyword evolution with nodes connected across temporal periods.

Citation-burst analysis shows a shift in research focus. Early bursts [2000–2010] were dominated by receptor characterization and cell death mechanisms, followed by increased emphasis on signal transduction and ion transport between 2011 and 2016. More recent bursts [2017–2025] highlighted translational and technological themes, including mass spectrometry and innate immune response (Figure 3B). Thematic clustering identified nine major domains (Figure 3C,3D), encompassing immune-related research (#0 Immune system, #9 Marine environment), macrophage-associated processes (#2), experimental models and methods (#1 Zebrafish model, #7 Detection methods), translational topics (#5 Human health, #6 Gut microbiota), and environmental health-related themes. Temporal keyword dynamics are detailed in Figure S2.

Citation impact and research influence

Citation analysis identified both foundational and contemporary high-impact publications shaping the field (Figure 4A-4C, Table 2). The document co-citation network and its temporal evolution are presented in Figure S3. Borovikova et al. [2000], describing the cholinergic anti-inflammatory pathway, was the most frequently cited reference within the dataset (3,148 citations; normalized score 21.56). Diamanti-Kandarakis et al. [2009] on endocrine disruption accumulated 3,297 citations (normalized score 20.36). Additional highly influential works included studies by Miller et al. [2009] on links between depression and inflammation (2,935 citations) and Kettenmann et al. [2011] on microglial function (2,814 citations), both of which demonstrated sustained citation impact over time. It is notable that several of the most-cited references in the dataset are not exclusively cancer-focused: they include seminal works in neuroimmunology (Borovikova et al., 2000), endocrine disruption (Diamanti-Kandarakis et al., 2009), and neuroinflammation (Miller et al., 2009; Kettenmann et al., 2011). Methodologically, the citation network was constructed solely from publications directly linked to the 76 oncology trials in the primary dataset, without any external keyword-based expansion. Non-cancer-specific references appear in the network because they are repeatedly cited by cancer-focused studies as shared mechanistic foundations, not because the search strategy incorporated the broader neuroimmunology literature. Their presence is therefore consistent with the interdisciplinary intellectual structure of the field rather than indicative of reduced domain specificity in the citation network.

Figure 4 Citation analysis. (A) Bubble chart showing top 10 globally cited papers with total citations on x-axis, impact factor represented by bubble size and color coding. (B) Similar bubble chart for top 10 locally cited papers. (C) Timeline chart showing top 50 references with strongest citation bursts as horizontal bars across years.

Discussion

This study provides a comprehensive overview of neurotransmitter signaling research in oncology by integrating bibliometric mapping with systematic clinical trial analysis. By jointly examining knowledge production, thematic evolution, and clinical translation over a 25-year period, our findings clarify how this interdisciplinary field has developed and where major translational bottlenecks remain. The evolutionary trajectory and current translational hotspots in neurotransmitter-cancer research are systematically summarized in Figure 5.

Figure 5 Evolutionary trajectory and current translational hotspots in neurotransmitter-cancer research [2000–2025]. Schematic overview depicting the temporal evolution of neurotransmitter-cancer research across three distinct phases (top panels) and four contemporary research hotspots (bottom panels). Top panels: phase 1 (2000–2010, green) represents the foundational exploration period centered on cholinergic receptors (α7-nAChR), basic signaling mechanisms, and necrosis/apoptosis pathways. Phase 2 (2011–2016, blue) illustrates the mechanistic deepening phase featuring dopamine/serotonin pathway analysis, receptor subtype characterization (DRD2, 5-HT2A), and signal transduction cascades. Phase 3 (2017–2025, orange) highlights the translational application phase emphasizing immune modulation, mass spectrometry technology, gut microbiota-neurotransmitter axis, and SERT as an immune checkpoint. Bottom panels: ① upstream triggers of neurotransmitter dysregulation including chronic stress, gut dysbiosis, and tumor innervation. ② Neurotransmitter-specific cancer interactions showing dopamine, serotonin, norepinephrine, and GABA pathways. ③ Convergent mechanisms linking neurotransmitters to cancer progression. ④ Clinical translation and future directions. GABA, gamma-aminobutyric acid; SERT, serotonin transporter.

The bibliometric results demonstrate sustained and accelerating growth of neurotransmitter-cancer research, particularly after 2020, reflecting increasing recognition that neural signaling is functionally embedded within the tumor ecosystem rather than peripheral to cancer biology. Early studies predominantly focused on receptor expression and intracellular signaling pathways, establishing foundational mechanistic frameworks. Over time, research priorities shifted toward tumor-immune interactions, stress-related signaling, and microenvironmental regulation, as evidenced by emerging keyword clusters and citation bursts. Importantly, this thematic evolution appears incremental rather than disruptive. Core neurotransmitter systems—adrenergic, serotonergic, and dopaminergic—have remained central throughout the study period, while newer research fronts have extended these pathways into immunological and translational contexts. Such continuity suggests a stable knowledge base prerequisite for meaningful clinical translation, but also highlights relative underrepresentation of cholinergic, GABAergic, and glutamatergic signaling despite substantial preclinical evidence.

Integration of bibliometric and clinical trial data revealed pronounced asymmetry in translational progress across neurotransmitter-targeted drug classes. β-adrenergic antagonists accounted for 25 trials (33%) and represented the most advanced translational pathway, with β-blockers representing the most phase III-advanced drug class in the dataset. This pattern likely reflects the availability of well-characterized, widely prescribed agents with established safety profiles, facilitating repurposing in oncology. Propranolol emerged as the predominant agent (20 trials), supported by decades of cardiovascular safety data and favorable pharmacokinetic properties. The mechanistic foundation for β-blocker efficacy extends beyond direct tumor cell effects to encompass systemic regulatory circuits—chronic psychological stress elevates catecholamine levels through hypothalamic-pituitary-adrenal axis activation, driving β-adrenergic receptor signaling that promotes angiogenesis, enhances cancer stem cell maintenance, and suppresses immune surveillance (16-22). This mechanistic clarity explains why stress-exposed cancer patients show poor immunotherapy responses and provides rational basis for combining β-blockers with checkpoint inhibitors, a strategy now being tested in 6 active trials.

In contrast, serotonergic and dopaminergic agents were predominantly investigated in early-phase trials, with progression beyond phase II remaining limited. SSRIs demonstrate growing clinical interest, with 17 oncology efficacy trials identified, several initiated between 2021 and 2025. This explosive expansion followed the discovery that serotonin transporter (SERT) functions as an immune checkpoint by depleting autocrine serotonin in CD8⁺ T cells (16).

Current SSRI oncology efficacy trials remain predominantly early-phase (64.7% phase I/II), indicating the pathway’s earlier developmental stage compared to β-blockers, yet dramatic uptake suggests imminent phase III advancement. The mechanistic rationale for SSRI repurposing centers on restoring antitumor immunity through SERT blockade, with fluoxetine and sertraline enhancing CD8⁺ T cell infiltration in preclinical models (17). Beyond immune modulation, serotonergic signaling through 5-HT receptors directly influences tumor cell proliferation and angiogenesis (18-20).

Dopamine modulators occupy a more focused therapeutic niche with 10 trials concentrated heavily in neuroendocrine tumors (42.9% of dopamine studies), reflecting indication-specific rather than pathway-driven clinical development. DRD2 agonists like cabergoline reduce angiogenesis and improve chemosensitivity, explaining their efficacy in neuroendocrine contexts (21,22). Together, these findings suggest that translational feasibility, rather than mechanistic novelty alone, has strongly shaped clinical adoption patterns.

One of the most consistent translational trends identified is the increasing incorporation of neurotransmitter-targeted agents into combination regimens, particularly with ICIs. Since 2018, more than one quarter of all trials have adopted this strategy, with SSRIs showing the most pronounced shift toward immunotherapy-based combinations. This pattern aligns with the bibliometric prominence of immune-related themes and supports growing recognition that neurotransmitter signaling influences antitumor immunity through modulation of immune cell activation, trafficking, and functional polarization. However, available clinical data remain largely exploratory—while early-phase trials indicate feasibility and biological plausibility, definitive evidence of synergistic efficacy is still limited. The cholinergic anti-inflammatory pathway exemplifies therapeutic potential in this domain, with vagus nerve acetylcholine release suppressing NF-κB, JAK2/STAT3, and inflammatory signaling (23,24). Yet only 2 trials explore cholinergic modulation despite Borovikova’s foundational 2000 work remaining our most cited reference. Similarly, GABAergic signaling through regulatory B cell-derived gamma-aminobutyric acid (GABA) polarizes macrophages toward immunosuppressive phenotypes and impairs CD8⁺ T cell function, mechanisms supported by strong preclinical evidence but represented in only 1 clinical trial (25). These gaps highlight translational opportunities where mechanistic understanding significantly exceeds clinical investigation.

From a conservative clinical perspective, these findings justify cautious optimism, as the current evidence base does not yet support fundamental practice change. Combination strategies appear rational and increasingly favored, but require rigorous phase-controlled evaluation to clarify their therapeutic contribution beyond established immunotherapy backbones. Notably, despite extensive mechanistic and preclinical literature, only a small fraction of publications could be linked to identifiable interventional trials, underscoring the challenges of translating context-dependent neurotransmitter effects into broadly applicable therapeutic strategies. Beyond formal efficacy trials, biomarker development represents a parallel and increasingly important translational route. Although mechanistically relevant biomarkers have been proposed in the broader literature, none of the 76 included trials reported a biomarker-driven primary endpoint, highlighting an important gap in the current evidence base. For example, candidate markers identified in the broader literature include plasma catecholamine levels as predictors of β-blocker responsiveness (26), SERT expression in tumor-infiltrating lymphocytes as a potential immunotherapy response predictor (27), and dopamine receptor D2 expression as a neuroendocrine tumor stratification marker (28). The clinical implementation challenge centers on biomarker development and patient stratification. HDAC inhibitors constituted the second-largest drug class in the primary analysis (12 trials, 16%), yet their translational profile is qualitatively distinct from that of adrenergic or serotonergic agents. All 12 HDAC inhibitor trials were completed, with three phase III trials (romidepsin in peripheral T-cell lymphoma, panobinostat in multiple myeloma, and tucidinostat in breast cancer) representing the highest phase-completion rate among all drug classes in this dataset. This advanced developmental stage reflects the fact that these agents were evaluated as primary oncology drugs rather than repurposed from non-oncology indications. However, HDAC inhibitor trials were almost exclusively restricted to hematologic malignancies and pediatric brain tumors, with no immunotherapy combination trials identified in this class. Whether HDAC inhibitors can be repositioned toward solid tumor immune modulation represents an open translational question. Comprehensive neurotransmitter profiling remains absent from standard clinical workflows. Mass spectrometry-based quantification and single-cell transcriptomics mapping receptor expression across tumor and immune cell subpopulations offer potential solutions for patient selection and pharmacodynamic monitoring. Stress-exposed cancer patients represent a readily identifiable high-priority subgroup, as chronic psychological stress drives β-adrenergic signaling that suppresses cytotoxic T cell function while promoting tumor progression (29-31).

Both publication output and clinical trial activity were concentrated in a small number of countries, particularly the United States, China, and Western European nations. While high research productivity generally corresponded to greater clinical trial activity, international collaboration was less frequent in clinical studies than in publications. This discrepancy may reflect regulatory complexity, funding structures, and logistical barriers inherent to multinational trials. From a translational standpoint, limited cross-national collaboration may slow validation across diverse patient populations and healthcare systems. Strengthening international trial networks could represent a practical strategy to accelerate late-phase evaluation of neurotransmitter-targeted interventions.

Taken together, our findings suggest that neurotransmitter signaling has transitioned from a predominantly mechanistic research topic to a clinically relevant but selectively translated domain. For future development, several principles emerge: pathway-specific rather than drug-centric trial design may help integrate heterogeneous preclinical findings into coherent clinical strategies; rational combination approaches with immunotherapy should be prioritized but evaluated with appropriately powered and biomarker-informed trial designs; and underexplored neurotransmitter systems with robust experimental support warrant more systematic investigation to avoid translational stagnation around a limited set of targets.

High translational attrition is a well-recognized feature of biomedical research broadly, spanning early discovery to clinical implementation in areas such as oncology drug development and neuropsychiatric therapeutics. The attrition observed here—from mechanistic research (65.4%) through clinical investigation (12.5%) to trial-based evidence (4.5%)—is consistent with this pattern, though direct quantitative benchmarks vary substantially across disease areas and methodological frameworks. Addressing this translational gap requires systemic infrastructure development. This includes establishing dedicated funding mechanisms for repurposing trials, creating multi-institutional biobanking networks with standardized neurotransmitter measurements, developing regulatory guidance for combination therapies involving generic neuromodulatory drugs, and building specialized trial networks incorporating psycho-oncology expertise for stress and mood assessment. The geographic concentration of current trial activity also indicates opportunity for expanded international collaboration, potentially leveraging cancer registry linkages to generate real-world evidence complementing prospective trial data (32,33). These developmental phases, mechanistic pathways, and translational bottlenecks converge into an integrated evolutionary framework of neurotransmitter-cancer research.

Limitations

This study has several limitations. Bibliometric analyses depend on database coverage and indexing practices, and our focus on WoSCC may exclude relevant publications indexed elsewhere. Clinical trial data derived from ClinicalTrials.gov may not capture all international studies, particularly those conducted in non-English speaking regions. Additionally, classification of publications and trials into mechanistic, preclinical, or clinical categories inevitably involved some degree of subjectivity, despite implementation of independent review and validation procedures. These limitations should be considered when interpreting the scope and generalizability of our findings. Furthermore, the keyword-based search strategy may not have exhaustively captured all relevant publications; terms specific to less-studied neurotransmitter systems (e.g., purinergic signaling, neuropeptide Y) or emerging research concepts may be underrepresented, which may affect the completeness of the bibliometric findings. Additionally, certain non-oncologic trials were excluded to preserve methodological consistency with the study’s eligibility criteria, and this should be considered when assessing the scope of the trial dataset.

Conclusions

This study presents a comprehensive assessment of neurotransmitter signaling research in oncology by jointly analyzing bibliometric patterns and interventional clinical trial activity over a 25-year period, revealing that this field has transitioned into a phase of rapid expansion supported by a stable core of mechanistic knowledge centered on adrenergic, serotonergic, and dopaminergic signaling pathways. While these pathways have dominated both experimental and clinical investigations, translational progress has been uneven—β-adrenergic antagonists have achieved the most advanced level of clinical development, whereas serotonergic and dopaminergic agents remain largely confined to early-phase trials—highlighting the decisive role of drug availability, safety profiles, and clinical feasibility in shaping translational trajectories beyond mechanistic plausibility alone. The increasing incorporation of neurotransmitter-targeted agents into combination regimens, particularly with ICIs, signals a strategic shift toward network-level modulation and reflects growing recognition of the neuro-immune interface as a clinically relevant axis, though the current evidence base remains predominantly exploratory. By explicitly quantifying the gap between research output and clinical implementation, this study identifies a critical translational bottleneck that persists despite substantial preclinical activity, underscoring the need for coordinated efforts to align mechanistic insights with clinically actionable hypotheses, promote pathway-oriented trial design, and strengthen international collaboration to facilitate late-phase validation. Continued progress will depend on balancing mechanistic depth with translational discipline, prioritizing biologically grounded combination strategies, and expanding investigation beyond well-studied targets, with the integrated framework presented here providing an evidence-based reference for advancing neurotransmitter-targeted approaches within precision oncology.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0089/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0089/prf

Funding: This work was partially supported by grants from the National Natural Science Foundation of China (No. 82471448 to G.G., Nos. 82101486 and 82371426 to Q.W., No. 82530039 to J.C.Z.), Science and Technology Commission of Shanghai Municipality (STCSM) grant (No. 24ZR1468500 to G.G. and No. 23ZR1467900 to Q.W.), Ningxia Hui Autonomous Region Key Research and Development Project (No. 2022BFH02012 to Q.W.), Shanghai Fourth People’s Hospital affiliated to Tongji University School of Medicine (Nos. sykyqd02301 and sykyqd02302 to Q.W.), the Fundamental Research Funds for the Central Universities (No. 22120240333 to G.G.), Tongji University Medicine-X Interdisciplinary Research Initiative (No. 2025-0650-ZD-06 to Q.W.), Shanghai Pujiang Program (No. 21PJ1412100 to Q.W.), and Shanghai Rising-Star Program (No. 23YF1450300 to Y.M.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0089/coif). The 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/.

References

1. Teleanu RI, Niculescu AG, Roza E, et al. Neurotransmitters-Key Factors in Neurological and Neurodegenerative Disorders of the Central Nervous System. Int J Mol Sci 2022;23:5954. [Crossref] [PubMed]
2. Yang Y, Sun L, Liu X, et al. Neurotransmitters: Impressive regulators of tumor progression. Biomed Pharmacother 2024;176:116844. [Crossref] [PubMed]
3. Mancusi R, Monje M. The neuroscience of cancer. Nature 2023;618:467-79. [Crossref] [PubMed]
4. Chen W, Yu J, Ge S, et al. Neurotransmitters in tumors: chemical cross-talk shaping tumor progression. Biomark Res 2025;13:125. [Crossref] [PubMed]
5. Wang X, Shi M, Tian J, et al. Cancer and neurotransmitter receptors. Chin Med J (Engl) 2025;138:1540-58. [Crossref] [PubMed]
6. Winkler F, Venkatesh HS, Amit M, et al. Cancer neuroscience: State of the field, emerging directions. Cell 2023;186:1689-707. [Crossref] [PubMed]
7. Zhang S, Yuan L, Lin P, et al. Cancer neuroscience: signaling pathways and new therapeutic strategies for cancer. Signal Transduct Target Ther 2026;11:66. [Crossref] [PubMed]
8. Pavlov VA, Tracey KJ. The cholinergic anti-inflammatory pathway. Brain Behav Immun 2005;19:493-9. [Crossref] [PubMed]
9. Amit M, Eichwald T, Roger A, et al. Neuro-immune cross-talk in cancer. Nat Rev Cancer 2025;25:573-89. [Crossref] [PubMed]
10. Zhao J, Cheng L, Yang J, et al. Tumor-nerve interactions in cancer regulation and progression. Cancer Lett 2025;612:217483. [Crossref] [PubMed]
11. Carnet Le Provost K, Kepp O, Kroemer G, et al. Trial watch: beta-blockers in cancer therapy. Oncoimmunology 2023;12:2284486. [Crossref] [PubMed]
12. Grant CE, Flis AL, Ryan BM. Understanding the role of dopamine in cancer: past, present and future. Carcinogenesis 2022;43:517-27. [Crossref] [PubMed]
13. Bernabé DG. Catecholamines Mediate Psychologic Stress-Induced Cancer Progression. Cancer Res 2021;81:5144-6. [Crossref] [PubMed]
14. Zhang H, Yang Y, Cao Y, et al. Effects of chronic stress on cancer development and the therapeutic prospects of adrenergic signaling regulation. Biomed Pharmacother 2024;175:116609. [Crossref] [PubMed]
15. Shaikh M, Shirodkar S, Doshi G. Unraveling the role of perineural invasion in cancer progression across multiple tumor types. Med Oncol 2025;42:283. [Crossref] [PubMed]
16. Li B, Elsten-Brown J, Li M, et al. Serotonin transporter inhibits antitumor immunity through regulating the intratumoral serotonin axis. Cell 2025;188:3823-3842.e21. [Crossref] [PubMed]
17. Alvarez-Valadez K, Kroemer G, Djavaheri-Mergny M. Selective serotonin reuptake inhibitors enhance cancer immunosurveillance by pleiotropic mechanisms. Oncoimmunology 2025;14:2564734. [Crossref] [PubMed]
18. D'Alessandro G, Lauro C, Quaglio D, et al. Neuro-Signals from Gut Microbiota: Perspectives for Brain Glioma. Cancers (Basel) 2021;13:2810. [Crossref] [PubMed]
19. Mafe AN, Büsselberg D. Modulation of the Neuro-Cancer Connection by Metabolites of Gut Microbiota. Biomolecules 2025;15:270. [Crossref] [PubMed]
20. Wu J, Yi Y, Wang Q, et al. 5-Hydroxytryptamine receptor 1D overexpression exacerbates gastric cancer proliferation, invasion, and angiogenesis by activating the Wnt/β-catenin signaling. BMC Cancer 2025;26:82. [Crossref] [PubMed]
21. Alam SK, Pandit A, Wang L, et al. Dopamine D(2) receptor agonists abrogate neuroendocrine tumour angiogenesis to inhibit chemotherapy-refractory small cell lung cancer progression. Cell Death Dis 2025;16:370. [Crossref] [PubMed]
22. Ahangari G, Norioun H. The dual role of dopamine and serotonin in cancer progression and inflammation: Mechanisms and therapeutic potential. Neuroscience 2025;569:184-94. [Crossref] [PubMed]
23. Alen NV. The cholinergic anti-inflammatory pathway in humans: State-of-the-art review and future directions. Neurosci Biobehav Rev 2022;136:104622. [Crossref] [PubMed]
24. Liu L, Lou S, Fu D, et al. Neuro-immune interactions: Exploring the anti-inflammatory role of the vagus nerve. Int Immunopharmacol 2025;159:114941. [Crossref] [PubMed]
25. Zhang B, Vogelzang A, Miyajima M, et al. B cell-derived GABA elicits IL-10(+) macrophages to limit anti-tumour immunity. Nature 2021;599:471-6. [Crossref] [PubMed]
26. Wackerhage H, Christensen JF, Ilmer M, et al. Cancer catecholamine conundrum. Trends Cancer 2022;8:110-22. [Crossref] [PubMed]
27. Ahern GP, Miyares RL. Serotonin transporter: a potential new immune checkpoint for cytotoxic T cells. Signal Transduct Target Ther 2025;10:354. [Crossref] [PubMed]
28. Lu J, Wang X, Jin J, et al. Multimodal Model for Non-Invasive Detection of DRD2, SSTR2 and ESR1 Receptor Profiling in Pituitary Neuroendocrine Tumors: A Retrospective Study. Technol Cancer Res Treat 2025;24:15330338251353305. [Crossref] [PubMed]
29. Khan A, Song M, Dong Z. Chronic stress: a fourth etiology in tumorigenesis? Mol Cancer 2025;24:196. [Crossref] [PubMed]
30. Niu X, Wu T, Zeng L, et al. Chronic stress in cancer development and progression. Sci Bull (Beijing) 2025;70:3885-907. [Crossref] [PubMed]
31. Huang JW, Cao CA, Zheng WH, et al. The mechanism of cancer-depression comorbidity. Neuroscience 2024;556:25-30. [Crossref] [PubMed]
32. Tao R, Qu J, Zhao J, et al. Zebrafish models in glioma research: advances in methodologies, mechanistic insights, and therapeutic frontiers. Front Immunol 2025;16:1601656. [Crossref] [PubMed]
33. Lorenzini F, Marines J, Le Friec J, et al. Melanoma innervation, noradrenaline and cancer progression in zebrafish xenograft model. Cell Death Discov 2025;11:260. [Crossref] [PubMed]