Survival benefit of concurrent beta-blocker use in cancer patients treated with radiotherapy: A systematic review and meta-analysis

Abstract

Background:

Both preclinical and retrospective studies have implicated β-adrenergic signaling in cancer progression, leading to interest in β-blockers (BBs) as adjunctive anticancer agents. There is evidence that they may enhance the sensitivity of cancer cells to radiotherapy and other conventional therapies. We conducted a systematic review and meta-analysis to explore the radiosensitizing effects of BBs.

Methods:

PubMed, the Cochrane Library, Embase, Web of Science, the China National Knowledge Infrastructure, the China biology medicine database, Wan fang Data, and the VIP database were searched for articles in both English and Chinese from the establishment of the databases to September 8, 2025, to identify studies comparing outcomes according to beta-blocker use (yes vs no) in patients with solid tumors treated with radiotherapy. The primary endpoint was overall Survival (OS), Secondary objectives included 1, 2, 5-Year Survival, disease-free survival, distant metastasis-free survival, locoregional progression-free survival and progression-free survival (PFS).

Results:

8 studies (3165 patients), including 7 retrospective cohort studies and 1 randomized controlled trial, were analyzed. The most common cancer was non-small cell lung cancer (n = 5). BB use was associated with significantly improved OS (hazard ratio [HR] 0.73, 95% confidence interval: 0.64–0.83). Benefits were also observed for disease-free survival (HR 0.69) and Distant Metastasis-Free Survival (HR 0.62), indicating reduced recurrence and metastasis.

Conclusion:

In this meta-analysis, the use of BBs during or around radiotherapy may be associated with longer OS in cancer patients, suggesting that it may enhance the efficacy of radiotherapy and provide a survival benefit. Alternatively, the effect of β-blockers on survival may shift from a nonspecific effect to an intriguing cancer-specific effect over time. Beta-blockers are an intriguing option to explore in prospective studies of patients with solid tumors undergoing radiotherapy.

1. Introduction

Cancer is a major global public health problem, and radiotherapy is one of the cornerstones means of solid tumor treatment, with about half of patients expected to receive radiotherapy after a cancer diagnosis.^[1,2] The aim is to achieve Locoregional Control (LRC) by destroying the DNA of tumor cells and ultimately improve Overall Survival (OS).^[3,4] However, the efficacy of radiotherapy varies significantly among different patients. Radioresistance and local recurrence after treatment are still the main causes of treatment failure.^[5]

In order to overcome radioresistance and improve the therapeutic ratio of radiotherapy, researchers have been exploring radiosensitizers or therapeutic modulation strategies.^[6] These strategies include combination chemotherapy, targeted therapy, and immunotherapy.^[7,8] In recent years, repurposing of approved non-anticancer drugs has received much attention as a cost-effective sensitization strategy with known safety.

In preclinical studies, excessive activation of the β-adrenergic signaling pathway is considered to be one of the causes of tumor resistance to treatment.^[9,10] Studies have shown that activation of this pathway can help tumor cells resist radiotherapy-induced cell death.^[11] Radiation causes damage to tumor cells and activates DNA repair mechanisms. β-adrenergic receptor blockers can inhibit the DNA repair ability of tumor cells, reduce non-homologous end joining and homologous recombination repair, thereby reducing the radiation resistance of tumor cells.^[12] β-adrenergicreceptors may show potential value in radiotherapy.^[13] In addition, β-blockers reversed catecholamine-induced immunosuppression, enhanced CD8 + T cell infiltration, and enhanced radiation-induced abnormal effects^[13,14]; It can also normalize blood vessels by inhibiting vascular endothelial growth factorand improve tumor hypoxia, thereby improving the efficacy of oxygen-dependent radiotherapy.^[15]

In addition, Chaudhary et al reported that β-adrenergic receptor blockers improved tumor metastasis and OS in addition to improving the sensitivity of tumor cells to radiotherapy.^[16] Multiple retrospective clinical studies have investigated whether concomitant use of β-blockers is associated with improved LRC, DMFS, disease-free survival (DFS) and OS in cancer patients receiving radiotherapy (e.g., breast cancer, head and neck cancer, lung cancer, etc).^[17,18]

Current preclinical studies and Meta-analysis suggest that β-blockers may indirectly affect cancer treatment through cardioprotective or immunomodulatory mechanisms.^[19–21] At present, the research of radiotherapy combined with β-blockers in the treatment of tumors has made some progress, but the research of β-blockers combined with radiotherapy is limited to a single cancer, and the conclusions are contradictory due to the small sample size and the heterogeneity of endpoint indicators. The characteristics of microenvironment (such as immune infiltration and β-adrenoceptor expression) of different cancers may significantly affect the therapeutic effect, and the conclusions of a single cancer cannot guide the clinical application. Therefore, meta-analysis of whole cancer, integration of multi-cancer data, and systematic evaluation of the effect of β-blockers combined with radiotherapy on cancer can provide more universal evidence-based basis for clinical treatment.

2. Materials and methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were followed as closely as possible for this systematic review and meta-analysis. The protocol for this systematic review was registered on PROSPERO with the registration number CRD420261411579.

2.1. The inclusion criteria were as follows

1. Patients diagnosed with malignant tumors;

2. The therapeutic regimen involves a combination of radiotherapy and beta-blockers;

3. Experimental or observational studies;

4. Outcome measures including OS, LRC, DMFS and DFS.

2.2. The exclusion criteria encompassed

1. Duplicate publications, review articles, animal studies, case reports, etc;

2. Incomplete outcomes or statistical inconsistencies.

The initial screening of articles was conducted independently by 2 researchers according to predefined criteria, with any discrepancies adjudicated by a third researcher. Following the screening process, 8 articles met the inclusion criteria.

2.3. Search strategy and study selection

As of August 2025, a comprehensive search was conducted across 8 databases, including PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, China Biology Medicine Database, Wan fang Data, and VIP Database. The search terms are: “Neoplasm” or “Tumor” or “Cancer” or “Malignant Neoplasm” and “Radiotherapies” or “Radiation Therapy” or “Radiation Treatment” and “Adrenergic beta Antagonists” or “Adrenergic beta Receptor Blockaders” or “beta Adrenergic Blocking Agents” or “beta Adrenergic Blocker” or “Propranolol” or “Alprenolol” or “Atenolol” or “metoprolol.” As only published statistical data were utilized, ethical approval was deemed unnecessary for this systematic review. The entire step-by-step processes of study design, clinical study scrutiny, and data collection procedures are described in a flow chart (Fig. 1).

Figure 1.

Flow diagram of the study selection process for the meta-analysis.

2.4. Data extraction and quality assessment

The assessment was independently conducted by two investigators (WX and XY). Any disagreement that arose during the quality assessment of the evidence was discussed with a third investigator (FZ) to reach a consensus. Data from each study were extracted by two of the three authors (WX, XY and FZ). Disagreements were resolved through consensus among the 3 authors. We extracted patient characteristics (gender, age and country), treatment regimens and outcomes (including OS, LRC, DMFS and DFS) from each study. For each included study, we extracted detailed beta-blocker exposure information, including treatment duration, prescribed dosage, medication adherence, and receptor selectivity (β1-selective or non-selective agents). Two of the 3 authors (WX, XY) independently assessed the quality of 7 retrospective cohort studies included in the review using the risk of bias in non-randomized studies – of interventions^[22]bias risk tool (Table 1), and the weighted Cohen’s kappa coefficient was used to measure consistency.^[23] Besides, Cochrane Collaboration’s Risk of Bias^[23] tool for randomized controlled trials (RCT) was applied to assess the quality of the included RCTs.^[23] This tool evaluates domains such as Random sequence generation, Allocation concealment, Blinding of participants and personnel, Blinding of outcome assessment, incomplete outcome data, Selective reporting, and other bias. Each domain was judged as low risk, unclear risk, or high risk of bias. Disagreements were discussed with the third investigator after 2 reviewers independently assessed the quality of the included RCT. Across all included studies, beta-blocker exposure was assessed prior to or during radiotherapy and treated as a baseline variable rather than a time-varying variable, with exposure status fixed at treatment initiation.

Table 1.

Characteristics of studies enrolled.

Study yr	Study type	Phase	Country	Intervention	Comparator	Dosage details		Total population(I/C)	Male n (%) (I/C)	Median age (range)	Types of beta-blockers	Pathological type
						Intervention	Comparator
Lee CM 2025[25]	RCS	Any stage	Canada	RT + beta blocker	RT	Beta blocker + RT (6600Gy)	RT (6600Gy)	212 (106/106)	NR	NR	NR	Oropharyngeal squamous cell carcinoma
Wang HM 2013[17]	RCS	I-IIIB	America-China	RT ± Chemo + beta blocker	RT ± Chemo	RT:5 fx/week, 60–87.4 Gy/GyE to ≥ 95% PTV, Treatment: IC then RT (6%); IC then CRT (35%); CRT (49%); RT alone (10%), Taking beta-blockers (metoprolol, atenolol) during RT	RT:5 fx/week, 60–87.4 Gy/GyE to ≥ 95% PTV, Treatment: IC then RT (6%); IC then CRT (35%); CRT (49%); RT alone (10%)	722 (155/567)	86 (55.5)/312(55)	65(34-59)	Metoprolol, atenolol	Adenocarcinoma (n = 255), squamous cell carcinoma (n = 268), and non-small cell lung cancer, not otherwise specified (NOS) (n = 227)
Chaudhary KR 2019[16]	RCS	IIIA	America	NCRT + beta blocker + surgery	NCRT + Surgery	NCRT: IC (carboplatin or cisplatin doublet), CR (41.4–66.6 Gy), Taking beta-blockers during the course of treatment	NCRT: IC (carboplatin or cisplatin doublet), CR (41.4–66.6 Gy)	77 (16/61)	10 (62.5)/38(62.3)	65(41–79)	Propranolol	Adenocarcinoma (n = 37), squamous cell carcinoma (n = 19), and non-small cell lung cancer, not otherwise specified (NOS) (n = 21)
Farrugia MK 2020[26]	RCS	T3N0-1	America	CCRT + beta blocker	CCRT	CCRT (Chemo: carboplatin and paclitaxel, RT: VMAT 50.4 Gy), beta-blockers (metoprolol, carvedilol, atenolol)	CCRT (Chemo: carboplatin and paclitaxel, RT: VMAT 50.4 Gy)	291 (81/210)	NR	NR	Metoprolol (56.8%), carvedilol (23.5%), atenolol (14.8%), and other (4.9%)	Adenocarcinoma of the esophagus (n = 245), Squamous cell carcinoma (n = 46)
Liang XY 2025[12]	RCS	IIIA-IIIB	China	RT + beta blocker	RT	RT (60-70 Gy, CF (1.8-2 Gy/fx), technique: IMRT or 3D-CRT) + beta blocker	RT (60-70 Gy, CF (1.8-2 Gy/fx), technique: IMRT or 3D-CRT)	750 (278/472)	81 (29.1)/170(36)	NR	NR	Squamous cell lung cancer (n = 353), non-squamous (n = 397)
Chen Y 2022[29]	RCT	Locally advanced	China	RT + Chemo + beta blocker	RT + Chemo	IMRT (45 Gy, 1.8 Gy/fx, 5 fx/week) + Chemo (oxaliplatin, tigio) + propranolol radiotherapy	IMRT (45 Gy, 1.8 Gy/fx, 5 fx/week) + Chemo (oxaliplatin, tigio)	74 (37/37)	24 (64.9)/21(56.8)	NR	Propranolol	Highly differentiated/moderately differentiated/poorly differentiated/signet ring gastric carcinoma(n = 8/17/9/3)
Wang TC 2016[27]	RCS	I-III	China	RT + beta blocker	RT	RT(>60Gy) + beta blocker	RT(>60Gy)	197 (86/111)	NR	64(38–81)	Metoprolol (n = 53), Atenolol (n = 25), Bisoprolol (n = 1), Propranolol (n = 6), Sotalol (n = 1)	Non-small cell lung cancer
Wang H 2012[28]	RCS	I-IIIB	America	RT ± Chemo + beta blocker	RT ± Chemo	RT (Median RT dose: 64 Gy) ± Chemo + beta blocker	RT ± Chemo	842 (204/638)	NR	NR	Metoprolol (n = 176), propranolol (n = 11), carvedilol (n = 17)	Non-small cell lung cancer

C = comparator, CCRT = concurrent radiochemotherapy, CF = conventional fractionation, Chemo = chemotherapy, CR = concurrent radiation, CRT = concurrent chemoradiotherapy, I = intervention, IC = induction chemotherapy, IMRT/3D-CRT = intensity modulated radiotherapy/three dimensional-conformal radiation therapy, NR = not reported, RCS = retrospective cohort study, RT = radiotherapy, VMAT = volumetric modulated arc therapy.

2.5. Statistical analysis

All meta-analyses were performed using Cochrane RevMan version 5.4 and Stata (version 16).

For time-to-event outcomes (e.g., survival outcomes), results were reported as pooled hazard ratios (HRs) with 95% confidence intervals (CIs) (95% CIs). For fixed-time binary outcomes, results were reported as pooled odds ratios (ORs) with 95% CIs. HR was preferred for time-to-event endpoints because it appropriately accounts for censoring and the timing of events, while OR was used for binary outcomes assessed at a fixed time point. The results were reported as pooled ORs with 95% confidence intervals (95% CIs). We used Cochran’s Q test and I² statistics to evaluate the heterogeneity of all the studies. Given the expected clinical and methodological heterogeneity across studies, the random-effects model (DerSimonian–Laird) was used as the default approach for all meta-analyses. If the heterogeneity was low (I² < 50%), the fixed effects model was adopted. For moderate heterogeneity (50% ≤ I² ≤ 75%), the fixed effects model was still used, but a sensitivity analysis was conducted to assess the robustness of the results. If the heterogeneity was high (I² > 75%), the random effects model was adopted to account for the significant variation between studies. Only adjusted effect estimates (adjusted HRs or ORs) from the most fully adjusted multivariable model of each included study were used for pooling to minimize confounding. Potential publication bias was assessed using funnel plots, Egger’s test, and Begg’s test. Potential confounding by indication, differences in performance status, and immortal time bias in the original studies were identified and addressed in the qualitative synthesis via appropriate statistical adjustment, including multivariate regression and propensity score matching. All P-values were 2, and statistical significance was set at P < .05.

2.6. Certainty of evidence

The certainty of evidence for key clinical outcomes was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations approach.^[24] Evidence certainty was rated as high, moderate, low, or very low based on 5 domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias.

2.7. Overall survival (OS)

The certainty of evidence was rated as moderate. No heterogeneity was observed (I² = 0%), but downgrading was applied due to the observational design of included studies and potential confounding by indication.

2.8. Disease-free survival (DFS)

The certainty of evidence was rated as moderate with no heterogeneity (I² = 0%). The rating was limited by the retrospective nature of primary studies and potential immortal time bias.

2.9. Distant metastasis-free survival (DMFS)

The certainty of evidence was rated as moderate due to no observed heterogeneity (I² = 0%), but downgraded for confounding by indication and variable baseline characteristics across cohorts.

2.10. Local recurrence–free survival (LRPFS)

The certainty of evidence was rated as moderate with no heterogeneity (I² = 0%). Limitations included incomplete outcome reporting and potential performance status bias in some studies.

2.11. Progression-free survival (PFS)

The certainty of evidence was rated as moderate with low to moderate heterogeneity (I² = 40.9%). Downgrading was applied due to the small number of included studies and incomplete long-term outcome data.

2.12. Safety and adverse events

Safety data pertaining to beta-blocker administration were systematically extracted from all included studies. Most investigations were not primarily designed for safety evaluation, and only one of the 8 studies documented specific adverse events, while safety reporting was substantially incomplete in the remaining studies. In the included RCT (Chen et al., 2022), the incidence of grade III–IV treatment-related adverse events, including gastrointestinal reactions, myelosuppression, and oral mucositis, was comparable between the beta-blocker and control groups, although two adverse events potentially associated with beta-blocker dose escalation were identified. Known mild-to-moderate adverse effects of beta-blockers include bradycardia, hypotension, fatigue, and dizziness; however, no consistent or significant increase in beta-blocker–related severe adverse events was detected across studies. Given the markedly limited and incomplete safety data, the safety and tolerability of beta-blockers during radiotherapy cannot be reliably determined. Therefore, clinical recommendations should be conservative, restricted to carefully selected populations, and interpreted with caution until more comprehensive safety evidence is available.

3. Results

3.1. Characteristics of studies

Of the 8 included studies, all were retrospective cohort studies. Except for one study conducted in Canada ^[25] 3 studies were conducted in China; 3 additional studies were conducted in the United States; the remaining one is in China and the United States.^[17]

A total of 3165 patients with malignant tumors were included in the study: 2202 patients received radiotherapy without β-blockers, and 963 patients received radiotherapy and β-blockers. Beta-blockers included metoprolol, which was used mainly in 4 studies,^[17,26–28] propranolol in 2 studies,^[16,29] and β-blockers that were not mentioned in the remaining 2 studies.^[12,25] Study type, β-blocker type, intervention strategy, radiotherapy details, and tumor stage are shown in the Table 1

3.2. Quality assessment

This tool covers 7 domains: bias due to confounding; bias in participant selection; bias in exposure measurement; bias due to misclassification of exposure during follow-up; bias dueto missing data; bias in outcome measurement; and bias in selection of reported results. In this approach, if a study is judged to be at low risk of bias for all domains, it is considered to have a low risk of bias; If it is judged to be at low or moderate risk of bias for all domains, it has a moderate risk of bias; If the study is judged to be at serious risk of bias in at least 1 domain, but not at critical risk of bias in any domain, it is regarded as having a serious risk of bias; If the study is judged to be at critical risk of bias in at least 1 domain, it has a critical risk of bias; If there is no clear indication that the study is at serious or critical risk of bias and there is a lack of information in one or more key domains of bias, a judgement of “No information” is required for this study. Evaluation was performed by 2 investigators independently (WX and XY). Any discordance during the assessment of the quality of evidence was discussed with a third investigator (YG) to reach consensus. The weighted kappa scores varied across the 7 domains of bias assessed by risk of bias in non-randomized studies – of interventions^[22] (Table 2). Table 2 shows that there was high inter-rater agreement for risk of bias assessments (kappa coefficient between 0.588 and 1.00 across domains). All studies but one were open label with primary outcome of OS. Given that the included studies were not primarily designed to assess adverse events, and only one out of the 8 studies reported certain adverse reactions (such as gastrointestinal reactions and bone marrow suppression), while the collection and description of adverse event information in the remaining studies were notably inadequate, we conclude that all studies exhibit a high risk of bias in terms of incomplete outcome data and selective outcome reporting. Besides, the results of the risk of bias assessment for the included RCTs demonstrated that all studies were rated as having a low risk of bias in the domains of random sequence generation, incomplete outcome data, and selective reporting. In contrast, all studies were rated as unclear risk of bias for allocation concealment, blinding of participants and personnel, blinding of outcome assessment and other bias. Detailed risk of bias assessments for the included RCT are presented in Supplementary Figure S12, Supplemental Digital Content 1 (risk of bias summary) and Supplementary Figure S13, Supplemental Digital Content 2 (risk of bias assessment graph).

Table 2.

Risk of bias for the 8 included publications (2013–2025) from 8 studies, based on ROBINS-I tool (low, moderate, serious, critical).

Author, yr, location	Type of bias
	Bias due to confounding	Bias in selection of participants into the study	Bias in classification of in low terventions	Bias due to deviations from intended interventions	Bias due to missing data	Bias in measurement of outcomes	Bias in selection of the reported result	Overall rating
Lee CM, 2025, Canada	Moderate	Moderate	low	low	low	Serious	Serious	Serious
Wang HM, 2013, America-China	low	low	low	low	Moderate	Serious	Serious	Serious
Chaudhary KR,2019, America	Moderate	low	low	low	low	Serious	Serious	Serious
Farrugia MK, 2020, America	Moderate	low	low	low	low	Serious	Serious	Serious
Liang XY, 2025, China	Moderate	low	low	low	low	Moderate	Serious	Serious
Wang TC, 2016, China	low	low	low	low	low	Serious	Serious	Serious
Wang H, 2012, America	Moderate	low	low	Moderate	low	Serious	Serious	Serious
Kappa	0.588	1	1	1	1	1	1	1

3.3. Overall survival (OS)

Based on data from 6 studies, the use of beta-blockers in cancer patients undergoing radiotherapy was significantly associated with improved OS (adjusted HR 0.73; 95% CI: 0.64–0.83; P < .00001) (Fig. 2). Egger’s test indicated no evidence of publication bias, and no significant heterogeneity was observed in this analysis (I² = 0%, P = .64); thus, a fixed-effects model was used. (Figure S1, Supplemental Digital Content 3)

Figure 2.

Forest plot for the association between β-blocker use and overall survival (OS) in patients receiving radiotherapy. CI = confidence interval.

Specifically, studies such as Farrugia MK 2020 (adjusted HR= 0.55, 95% CI: 0.38–0.80), Wang H 2012 (adjusted HR = 0.75, 95%CI: 0.60–0.94), and Wang HM 2013 (adjusted HR = 0.76, 95% CI: 0.61–0.95) all demonstrated a significant improvement in OS due to beta-blocker therapy. The other studies also showed a consistent protective trend. As shown in Figure 3, the survival benefit of beta-blockers was consistent across different β-receptor selectivity types.

Figure 3.

Subgroup analysis of the association between β-blocker use and overall survival by β-receptor selectivity. CI = confidence interval.

3.4. 1-year survival

As an exploratory analysis, we evaluated the association between beta-blockers and the 1-year OS rate, including 4 studies. The pooled results from the random‑effects model showed a statistically significant improvement in 1-year OS in the beta-blocker group (adjusted OR = 2.39, 95% CI: 1.00–5.696, P < .0001), as shown in Figure 4. However, there was a high degree of heterogeneity among the included studies (I² = 86.9%, P < .0001), indicating significant inconsistency in the effect sizes.

Figure 4.

Forest plot for β-Blockers and 1-year OS in radiotherapy-treated patients. CI = confidence interval.

To explore the source of heterogeneity, we conducted subgroup analyses by cancer type. The results showed that there was no statistically significant difference in effect size between the oropharyngeal squamous cell carcinoma and non-small cell lung cancer (NSCLC) subgroups (P = .4598) (Fig. 5), indicating that cancer type could not explain the high heterogeneity observed. Subsequently, a sensitivity analysis determined that the Liang XY 2025 study was the main source of heterogeneity (Figure S2, Supplemental Digital Content 4). After excluding this study, the heterogeneity significantly decreased (I² = 29.2%), and the combined effect size was no longer significant (adjusted OR = 1.44, P = .240) (Figure S3, Supplemental Digital Content 5). These findings should be interpreted cautiously given the high heterogeneity and dependence on a single influential study.

Figure 5.

Forest plot for subgroup analysis of 1-year overall survival by cancer type (OPSCC vs NSCLC). CI = confidence interval, NSCLC = non-small cell lung cancer, OR = odds ratio, OPSCC = oropharyngeal squamous cell carcinoma.

The Egger test results are shown in Figure S4, Supplemental Digital Content 6. There is a blank on the left side of the funnel plot. To objectively quantify and correct for potential publication bias, we applied the non-parametric “trim-and-fill method” to these 3 studies (Figure S5, Supplemental Digital Content 7). The results were not statistically significant both before and after.

3.5. 2-year survival

As an exploratory analysis, the initial meta-analysis indicated that beta-blockers significantly improved 2-year OS (adjusted OR = 2.15, P < .0001), but there was a high degree of heterogeneity (I² = 82.0%, P = .0038) (Fig. 6). Subgroup analysis showed that the type of cancer was not the main cause of heterogeneity (Fig. 7). After conducting a sensitivity analysis and excluding the Liang XY 2025 study, the heterogeneity was significantly reduced, and the combined effect size was no longer statistically significant (adjusted OR = 1.74, P = .287) (Figure S6, S7, Supplemental Digital Content 8). These results should be interpreted cautiously due to high heterogeneity.

Figure 6.

Forest plot for β-blockers and 2-year OS in radiotherapy-treated patients. CI = confidence interval, OR = odds ratio, OS = overall survival.

Figure 7.

Forest plot for subgroup analysis of 2-year overall survival by cancer type (OPSCC vs NSCLC). CI = confidence interval, NSCLC = non-small cell lung cancer, OR = odds ratio, OPSCC = oropharyngeal squamous cell carcinoma.

3.6. 5-year survival

For the 5-year OS rate, only 2 studies were available, with extreme between-study heterogeneity (I² = 96.7%, P < .0001, Fig. 8). Given the small number of studies and very high heterogeneity, meta-analysis was not performed, and a narrative synthesis was conducted instead. We conducted subgroup analyses by cancer type, and the results showed significant differences in the effect of beta-blockers (test for subgroup differences, P < .0001, Fig. 9). In patients with oropharyngeal squamous cell carcinoma, beta-blockers were significantly associated with a substantial increase in 5-year survival rate (adjusted OR = 13.62, 95% CI: 7.02 - 26.41). However, no significant benefit was observed in NSCLC patients (adjusted OR = 1.12, 95% CI: 0.62 - 2.01). Given this extreme heterogeneity, the overall combined effect size calculated using the random effects model is not meaningful (adjusted OR = 3.88, 95% CI: 0.00–30944319.00, P = .474, Figure S8, Supplemental Digital Content 10). Overgeneralized inferences are not warranted for this endpoint.

Figure 8.

Forest plot for β-blockers and 5-year OS in radiotherapy-treated patients. CI = confidence interval, OR = odds ratio, OS = overall survival.

Figure 9.

Forest plot for subgroup analysis of 5-year overall survival by cancer type (OPSCC vs NSCLC). CI = confidence interval, NSCLC = non-small cell lung cancer, OR = odds ratio, OPSCC = oropharyngeal squamous cell carcinoma.

3.7. Disease-free survival (DFS)

To evaluate the impact of beta-blockers on the DFS of cancer patients undergoing radiotherapy, a fixed-effect meta-analysis was conducted. The pooled results showed a statistically significant benefit (adjusted HR = 0.69, 95% CI: 0.59–0.80; P < .0001, Fig. 10), and the heterogeneity among the included studies was negligible (I² = 0.0%, Tau² = 0, P = .8831); thus, a fixed-effects model was used. Three studies included patients with the same type of cancer, namely NSCLC. The leave-one-out sensitivity analysis confirmed the robustness of the overall results, and excluding any single study did not significantly change the point estimates or the significance of the results, with the adjusted HR ranging from 0.67 to 0.71, and still being statistically significant in most cases (Figure S9, Supplemental Digital Content 11).

Figure 10.

Forest plot for DFS in radiotherapy-treated patients. CI = confidence interval, DFS= disease-free survival, HR = hazard ratio.

3.8. Distant metastasis-free survival (DMFS)

The meta-analysis of fixed-effect models for survival without distant metastasis included a total of 3 studies. The pooled results showed that beta-blockers significantly improved DMFS (adjusted HR = 0.62, 95% CI: 0.52–0.75, P < .0001, Fig, 11). There was extremely low heterogeneity among the studies (I² = 0.0%, P = .8764); thus, a fixed‑effects model was used. Leave-one-out sensitivity analysis indicated that this result was robust, and after eliminating any single study one by one, the pooled adjusted HR remained at 0.62 (95% CI: 0.54–0.72, P = .0050, Figure S10, Supplemental Digital Content 12).

Figure 11.

Forest plot for DMFS in radiotherapy-treated patients. CI = confidence interval, DMFS = distant metastasis-free survival, HR = hazard ratio.

3.9. Locoregional progression-free survival (LRPFS)

The meta-analysis on local progression-free survival only included 2 studies. The results showed a non-significant trend of improved LRPFS with the addition of beta-blockers (adjusted HR = 0.89, 95% CI: 0.72–1.10, P = .2869, Fig. 12). There was no heterogeneity between the 2 studies (I² = 0.0%); thus, a fixed‑effects model was used.

Figure 12.

Forest plot for LRPFS in radiotherapy-treated patients. CI = confidence interval, LRPFS = locoregional progression-free survival, HR = hazard ratio.

3.10. Progression-free survival (PFS)

As an exploratory analysis, this meta-analysis of PFS included 2 studies. Using a random-effects model, the pooled results demonstrated that the addition of beta-blockers significantly improved PFS, reducing the risk of disease progression or death by 48% (Fig. 13). There was moderate heterogeneity between the 2 studies (I² = 40.9%), and we used the random effects model with an adjusted HR of 0.52; however, the 95% CI was extremely wide (0.04–7.52), and the result was not statistically significant (P = .1972). The prediction interval indicated that the true effect of future studies might range from adjusted HR = 0.01 to 19.12 (Figure S11, Supplemental Digital Content 13). High uncertainty precludes strong or generalized conclusions.

Figure 13.

Forest plot for PFS in radiotherapy-treated patients. CI = confidence interval, PFS = progression-free survival, HR = hazard ratio.

3.11. Safety and biomarker evidence from a randomized trial

In the included RCT (Chen et al., 2022^[29]), compared with the control group, although there was no statistically significant difference in the incidence of grade III-IV treatment-related adverse reactions such as gastrointestinal reactions, myelosuppression, and oral mucositis when adding beta-blockers, the study also reported 2 adverse events related to the increased dose of beta-blockers; in terms of efficacy, this study showed that adding propranolol could significantly reduce the levels of tumor markers (CEA, CA50, CA125, CA242) in patients, and the difference between the groups was statistically significant (P < .05).

4. Discussion

Beta-blockers, also known as beta-adrenergic blockers, are a class of drugs that antagonize beta-adrenergic receptors, blocking the binding and action of norepinephrine and epinephrine. These drugs reduce blood pressure and heart rate by decreasing cardiac output. Although initially developed for the treatment of cardiovascular conditions, beta-blockers have demonstrated additional pharmacological properties, including potential anti-cancer effects. Emerging evidence suggests that they may enhance the sensitivity of cancer cells to chemotherapy and other conventional therapies, as well as help overcome drug resistance.^[30–33]

Previously, preclinical and epidemiological studies have shown that beta-blockers can inhibit cancer progression and improve the prognosis of certain malignant tumors (such as breast cancer, ovarian cancer, and prostate cancer.^[31,34–36]) Although there is currently a lack of prospective research evidence to support the routine use of beta-blockers in patients with solid tumors receiving radiotherapy, several preclinical experiments have suggested their potential efficacy as radiosensitizers.^[37–39] This study, by summarizing the data of 8 studies involving 3165 patients with solid tumors, provides the first important evidence-based basis for beta-blockers as a potential sensitizing agent in radiotherapy and their ability to significantly improve the OS rate of patients. This finding is consistent with the preclinical research results of Liao et al.^[38] and Rossi et al.,^[37] revealing the clinical application prospects of it as an adjuvant drug for radiotherapy.

However, the survival benefits of beta-blockers show complex temporal and cancer-type dependence. In terms of short-term endpoints, their improvement effect on 1-year OS weakened after excluding a specific study^[12] and lost significance, suggesting that the result is unstable and may be influenced by partial data missing and high heterogeneity among studies. The subgroup analysis indicates that the type of cancer is not the main source of heterogeneity. It is more likely to result from the patient’s baseline characteristics (such as tumor stage, comorbidities), the type and dosage of beta-blockers, the timing of medication administration, and the differences in radiotherapy regimens. In the long term, the benefits of beta-blockers show significant cancer type-specificity. For instance, a significant improvement in 5-year OS was observed in oropharyngeal cancer, while no such effect was seen in NSCLC. In oropharyngeal cancer, a large proportion of patients are human papillomavirus-positive. Some studies^[40] suggest that the benefits of beta-blockers may interact with human papillomavirus-driven oncogenic pathways. This pattern indicates that the mechanism of action may shift from an initial nonspecific stress regulation to highly dependent on the specific tumor microenvironment over time. Furthermore, in patients with NSCLC, this therapy has shown significant benefits in terms of DFS and DMFS, indicating that inhibiting tumor recurrence and distant metastasis may be a key approach to improving prognosis, which is consistent with the research results in breast cancer.^[41–43] Beta-blockers also show a trend of improvement in LRPFS, but this did not reach statistical significance.

The above heterogeneity has prompted us to delve deeper into the key factors influencing the therapeutic effect, among which the medication pattern and drug selectivity are particularly important. Barron et al.^[41] found that long-term use of propranolol (a nonselective beta-blocker) was associated with a lower proportion of T4 stage tumors, and no significant effect was observed for atenolol (a selective beta1 blocker), suggesting that the core role of the therapeutic effect may stem from the antagonism of beta2 receptors. Multiple preclinical studies also support the distinct effects of nonselective beta-blockers and beta1-selective drugs on cancer cells.^[44–46] However, the meta-analysis of this study revealed that the patient group classified as using selective β1 receptor blockers also showed significant improvements in survival. Given that the types of some drugs included in the study were unclear and there were no prospective randomized controlled studies directly comparing different selective drugs, the current evidence is not sufficient to clearly distinguish the relative efficacy of nonselective and selective β1 receptor blockers. The core role of the β2 receptor as suggested by preclinical studies still needs to be ultimately verified through rigorous and clearly defined prospective clinical studies that classify drugs.

This study has several limitations. First, the included studies varied in the detailed documentation of beta-blocker treatment duration, dosage, medication adherence, and receptor selectivity, which may contribute to heterogeneity across the synthesized results. Second, potential confounding by indication, differences in performance status, and immortal time bias were present in the original observational studies, and these biases were addressed via multivariate adjustment, propensity score matching, or standardized exposure definitions across cohorts in the qualitative synthesis. Third, for some studies, the specific types of beta-blockers, dosages, and timing of administration were incomplete, which limited more in-depth subgroup analyses. Fourth, the heterogeneity among the included studies was significant, and its exact source needs to be clarified. Fifth, the data on secondary endpoints such as PFS was limited, which reduced the robustness of the related conclusions.

Finally, the limited and inconsistent reporting of safety data across included studies restricts a comprehensive evaluation of beta-blocker tolerability, and the certainty of evidence for most outcomes was rated as moderate due to the observational design of the primary studies.

Nevertheless, the results of this study are highly enlightening. Future research should focus on clarifying the specific types and dosing guidelines of beta-blockers, and conduct prospective clinical trials in specific cancer types (such as oropharyngeal cancer) and specific treatment targets (such as preventing metastasis), in order to precisely define their radiosensitizing value.

5. Conclusion

This meta-analysis indicates that in patients with solid tumors who receive radiotherapy, the combination of beta-blockers may be associated with an improvement in OS. This represents the primary and most robust finding of the present study. Such benefits appear to be specific to certain cancer types and time periods, with more apparent long-term effects in oropharyngeal cancer, which may be linked to reduced tumor recurrence and distant metastasis. Current evidence suggests that the positive effects of nonselective beta-blockers (such as propranolol) may be related to their antagonism of beta2 receptors. However, due to the unclear specific types of drugs selectivity in some studies, it remains difficult to definitively distinguish the efficacy differences of different selective beta-blockers at present. Results for secondary endpoints and time-point-specific analyses were less reliable. Ultimately, the potential radiosensitizing role of beta-blockers, - along with their optimal administration regimen and target population- requires further validation in prospective clinical trials.

Acknowledgments

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author contributions

Conceptualization: Wentong Xu, Xue Yang, Xinyu Lu.

Formal analysis: Wentong Xu, Xinyu Lu, Fengjuan Zhou.

Methodology: Wentong Xu, Yong Xin, Xue Yang, Yilong Guo, Dehong Yu, Fengjuan Zhou.

Validation: Wentong Xu, Fengjuan Zhou.

Supervision: Yong Xin, Yilong Guo, Dehong Yu, Fengjuan Zhou.

Data curation: Xinyu Lu.

Funding acquisition: Fengjuan Zhou.

Writing – original draft: Wentong Xu, Xue Yang.

Writing – review & editing: Wentong Xu, Yong Xin, Mengyang Wu, Hengchang Chen, Yilong Guo, Dehong Yu, Fengjuan Zhou.