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Vagal nerve activity and cancer prognosis: a systematic review and meta-analysis

BMC Cancer volume 25, Article number: 579 (2025) Cite this article

Abstract

Background

The prognostic significance of vagal nerve (VN) activity, as measured by heart rate variability (HRV) in cancer patients remains a subject of debate. The aim of this meta-analysis was to evaluate the association between various HRV parameters and cancer prognosis.

Methods

We conducted an extensive search of the PubMed, Embase, Cochrane, and Web of Science databases and compared the overall survival (OS) of cancer patients with high and low HRV. The data type was unadjusted hazard ratio (HR). Random or fixed-effects models were used to calculate the pooled HR along with the 95% Confidence Interval (CI). We used funnel plot analysis to evaluate potential publication bias.

Results

A total of 11 cohort studies were included with 2539 participants. The methodological quality of the included studies is generally high. Compared with low standard deviation of normal-to-normal intervals (SDNN) group, higher SDNN was a protective factor for OS in patients with cancer (I2 = 66%, HR = 0.59, 95% CI: 0.46–0.75, P < 0.0001). Compared with low root mean square of successive differences (RMSSD) group. The prognostic value of RMSSD did not reach statistical significance (I2 = 0%, HR = 0.85, 95% CI: 0.70–1.03, P = 0.11). Among the frequency domain indicators, higher high-frequency power HRV (HF-HRV) and low-frequency power HRV (LF-HRV) were associated with significantly longer overall survival compared to the low HF-HRV and LF-HRV groups (I2 = 6%, HR = 0.59, 95% CI: 0.43–0.80, P = 0.006 and I2 = 74%, HR = 0.45, 95% CI: 0.22–0.93, P = 0.03). In the nonlinear indicators, higher maximal diagonal line length (Lmax), mean diagonal line length (Lmean), percent of recurrence (REC), and determinism (DET) were associated with poorer tumor OS. The funnel plot shows that there is no publication bias in the study.

Conclusions

The findings of this study demonstrate that HRV parameters, particularly SDNN, HF-HRV, and nonlinear indices, exhibit predictive value for prognosis in cancer. Furthermore, it can be inferred that elevated VN activity may predict prolonged survival outcomes. However, these findings should be interpreted with caution due to the heterogeneity observed across included studies. Future research should prioritize prospective studies with standardized measurement protocols to validate these associations.

Peer Review reports

Introduction

The vagus nerve (VN) is a major component of the parasympathetic nervous system and the most widely distributed cranial nerve, regulating multiple systems including the cardiovascular, respiratory, and digestive systems [1,2,3]. Several cohort studies in humans and experimental studies in animals support that an intact VN may have a protective effect in various cancer. For example, vagotomy increases the risk of colorectal cancer [4,5,6], gastric cancer [7, 8], prostate cancer [9], breast cancer [10, 11] and pancreatic tumorigenesis [12]. Furthermore, VN stimulation may also inhibit tumor metastasis or occurrence [13,14,15] and regulate immune function and [16, 17].

Heart rate variability (HRV) refers to the variation in consecutive heartbeats and is primarily influenced by the cardiac VN, it is considered an indirect and non-invasive method for assessing vagal tone [18]. HRV metrics can be calculated using three different methods: time-domain analysis, frequency-domain analysis, and nonlinear dynamic analysis [19]. The time domain parameter includes standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD); The main frequency domain parameter includes high-frequency power HRV (HF-HRV) and low-frequency power HRV (LF-HRV); Nonlinear parameters include the mean diagonal line length (Lmean), maximal diagonal line length (Lmax), percent of recurrence (REC), determinism (DET) and Shannon entropy (ShanEn) [20].

HRV has been identified as a predictor of adverse outcomes in patients with conditions such as myocardial infarction [21], congestive heart failure [22], and renal failure [23]. However, it’s predictive value regarding survival outcomes in cancer patients remains controversial. Although previous systematic reviews and meta-analyses has suggested that higher HRV might be associated with longer cancer survival [24, 25], subsequent studies have presented contradictory findings. McGovern et al. found that SDNN and RMSSD may have limited prognostic value for survival in patients undergoing potentially curative surgery for colorectal cancer [26]. Strous et al. also found no significant correlation between the linear parameters and overall survival (OS) [27]. Compared to traditional linear parameters, nonlinear parameters can more effectively characterize the complexity of cardiac regulatory mechanisms in cancer patients [28]. Therefore, a meta-analysis is warranted to systematically and comprehensively evaluate the prognostic value of different HRV parameters in cancer patients.

Materials and methods

Data sources and search strategy

This study was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Collaboration [29], and followed the PICOS principle (Supplementary Table 1). This meta-analysis has been registered in the International Prospective Register of Systematic Reviews (PROSPERO)(crd.york.ac.uk/PROSPERO/display_record.php? RecordID = 568076).

A comprehensive search was conducted across the PubMed, Embase, Cochrane, and Web of Science databases using both subject headings and free-text terms (Heart rate variability, HRV, cancer, carcinoma, survival, outcome, prognosis) for a structured literature review. More details on the retrieval strategy are shown in the supplementary materials (Supplementary Table 2). The last search was conducted on August 20, 2024.

Study selection

Inclusion criteria:

  1. (1)

    Cancer patients with higher and lower HRV (Both linear and nonlinear parameters) were compared in the study.

  2. (2)

    OS was included in the study endpoints.

  3. (3)

    Reported or extractable unadjusted or available hazard ratio (HR) value for the univariate analysis.

Exclusion criteria:

  1. (1)

    Full text was not available.

  2. (2)

    Patients with heart disease or related medication use were excluded from the study.

  3. (3)

    Case reports, review articles, editorials, and letters were excluded. If the same data were published multiple times, the most recent or highest quality publication was selected.

Data extraction

Two authors (W.B.H. and H.Z.L) independently extracted study characteristics, methodological quality, and HR using a pre-specified form, followed by cross-checking to ensure consistency, when inconsistencies arose between the two researchers during the screening or extraction process, they first re-examined the original literature and exchanged opinions to achieve consensus. If the initial discussion did not resolve the disagreement, a third researcher (L.J) was consulted to review and arbitrate the matter, with the final decision based on this arbitration. When HR were presented as figures, the required HR values were extracted using the software Engauge Digitizer (version 12.1).

Exposure definition

The different HRV parameters have distinct meanings and cannot be used interchangeably [30]. Therefore, we included the commonly used HRV parameters: time -domain parameter (SDNN, RMSSD), frequency -domain parameter (HF, LF) and Nonlinear parameters (Lmean, Lmax, REC, DET, ShanEn).

Risk-of-bias assessment

The quality of the included studies was assessed by the Newcastle‒Ottawa Scale (NOS) [31], which were classified into three levels: low (7 or more), moderate (4–6), and high risk (3 or less) of bias. The final results were visualized using R software 4.1.3 (R, Foundation, Vienna, Austria).

Preplanned subgroup analyses

To address potential heterogeneity arising from methodological and clinical variations in HRV assessment, we prespecified the following subgroup analyses based on factors known to modulate HRV metrics, SDNN < 100 ms indicates mild decrease in heart rate variability, SDNN < 50 ms indicates significant decrease in heart rate variability, and SDNN < 20 ms indicates severe abnormality [32].

  1. (1)

    Cancer types (respiratory system cancers, digestive system cancers and other).

  2. (2)

    Cut-off value of SDNN (SDNN < 20 ms, 20 ms ≤ SDNN ≤ 50 ms and SDNN > 50 ms).

  3. (3)

    Population (European and American, Asian).

Statistical analysis

The results of the meta-analysis were obtained using RevMan Manager (version 5.3, The Cochrane Collaboration), with both fixed-effects and random-effects models applied. Sensitivity analysis was conducted utilizing STATA statistical software (version 18, STATA Corp). Visualization was performed using R software 4.1.3, with the following R packages for plotting: grid (version 4.2.2), forestploter (version 0.2.3), pheatmap (version 1.0.12).

A meta-analysis was conducted using HR values for OS. HR data were presented as dichotomous variables, providing a point estimate and a 95% CI for each effect size. P < 0.05 was considered statistically significant.

Heterogeneity was evaluated by the Chi-squared (Chi2) test with significance set at a p-value of 0.10, and I2 statistic was used to quantify heterogeneity. We considered I² < 50% as indicative of low heterogeneity, allowing for the use of a fixed-effects model in the meta-analysis. For I² > 50%, which suggest high heterogeneity, a random-effects model was applied. Galbraith radial plot was constructed to systematically investigate potential sources of heterogeneity.

Sensitivity analysis

Stable results enhance credibility, whereas significant changes require caution in interpretation and drawing conclusions [33]. Therefore, in this study, a sensitivity analysis was performed by excluding each study individually.

Analysis of publication bias

Publication bias refers to the tendency for research results to be published or not published based on the nature and direction of the findings [34]. In this study, a funnel plot was used to assess publication bias.

Results

Study selection

A flowchart of the literature search, screening, and eligibility assessment process is shown in Fig. 1. We identified a total of 1208 potentially relevant studies, of which 237 were duplicates. We screened 971 records based on titles and abstracts, excluding 943 studies for the following reasons: irrelevant topics (n = 733), review articles, commentaries, or editorials (n = 28), systematic reviews or meta-analyses (n = 170), and animal studies (n = 12). Full texts of the remaining 28 studies were assessed, and 17 studies were excluded: full text not available (n = 2), data published in duplicate (n = 8), no report or inability to extract HR from figures, or lack of univariate HR results (n = 7). Finally, 11 studies were included in the analysis [26, 27, 35,36,37,38,39,40,41,42,43].

Fig. 1
figure 1

PRISMA flow chart. Abbreviation: HR, hazard ratio

Full size image

Characteristics of the included studies

Tables 1 and 2 provide detailed descriptions of the characteristics of the studies included in the meta-analysis and the relevant HRV parameters. We included a total of 11 cohort studies published between 2013 and 2023, comprising 2 prospective cohort studies and 9 retrospective cohort studies, involving 2539 patients in total. The smallest cohort consisted of 39 participants, while the largest included 651 participants. The studies included cases of lung cancer, colorectal cancer, pancreatic cancer, liver cancer, and cervical cancer. Two studies measured EGC for more than 5 min, while only three studies had measurements shorter than 5 min.

We assessed publication bias in the included studies using the Newcastle-Ottawa Scale (NOS) and found that 2 studies scored 9 points, 5 studies scored 8 points, and 4 studies scored 7 points, indicating an overall high quality of the literature (Supplementary Fig. 1).

Qualitative symmetry was observed in the funnel plots shown in Supplementary Fig. 3, which indicated no significant publication bias.

Table 1 Characteristics of the studies included in the meta-analysis
Full size table
Table 2 HRV-related parameters of the studies included in this meta-analysis
Full size table

Meta-analyses

1. Time domain parameters

Higher SDNN VS. lower SDNN

In this analysis, we included 10 studies, with a total I2 of 66% and an HR of 0.59 (95% CI: 0.46–0.75; P < 0.0001), revealed that OS was significantly longer in the higher SDNN group than in the lower SDNN group (Fig. 2). To assess the impact of individual studies on the overall effect size, we conducted a sensitivity analysis by sequentially excluding each study and re-running the meta-analysis. The point estimates of the combined effect sizes consistently fell within the 95% CI of the overall effect size, indicating that the removal of any single study had minimal impact on the overall results, thereby demonstrating the stability of our study (Supplementary Fig. 2).

Fig. 2
figure 2

Effect of different HRV parameters on tumor prognosis. using HR for OS as raw data. Abbreviation: CI, confidence interval; HR, hazard ratio

Full size image
1. Higher RMSSD VS. lower RMSSD

A total of 5 studies were included in this analysis, including Lung cancer (n = 2), colorectal cancer (n = 2), and hepatocellular carcinoma (n = 1), with a total I2 of 0% and an HR of 0.85 (95% CI: 0.70–1.03; P = 0.11), revealed that OS was non-significantly longer in the higher RMSSD group than in the lower RMSSD group (Fig. 2).

2. Frequence domain parameters

Higher LF VS. lower LF

The results of the higher LF versus lower LF analysis are shown in Fig. 2, we included 2 studies, covering lung cancer and hepatocellular carcinoma. The results were statistically significant (I² = 74%, HR = 0.45, 95% CI: 0.22–0.93; P = 0.03).

Higher HF VS. lower HF

Two studies were included in the analysis of higher HF versus lower HF, with a total I2 of 6% and an HR of 0.59 (95% CI: 0.43–0.80; P = 0.006), and the results were statistically significant (Fig. 2).

3. Nonlinear parameters

This analysis results are shown in Fig. 2, we included 2 studies. Since there was no heterogeneity between the studies, a fixed-effects model was used to combine the effect sizes. The combined effect estimates for L max, L mean, REC, DET and ShanEn were (HR = 2.56, 95% CI: 0.52–4.29; P = 0.0004), (HR = 1.81, 95% CI: 1.04–3.14; P = 0.04), (HR = 1.79, 95% CI: 1.07-3.00; P = 0.03), (HR = 1.95, 95% CI: 1.18–3.21; P = 0.0009), and (HR = 0.56, 95% CI: 0.32–0.99; P = 0.05).

Discussion

Main finding

This meta-analysis focused on the relationship of different parameters of HRV and with the prognosis of patients with cancer. Our study suggests that among time-domain parameters, the relationship between SDNN and tumor OS varies by tumor type. For digestive system and other cancers, higher SDNN is a protective factor for OS. Notably, this association is not supported for respiratory system tumors. However, all three studies included in this research focused on metastatic lung cancer. SDNN appears to be more susceptible to respiratory influences, such as respiratory sinus arrhythmia (RSA) [44, 45]. The respiratory pressure in advanced lung cancer may introduce errors in SDNN measurements, thereby affecting its prognostic value. Furthermore, De Couck et al. indicated that SDNN significantly predicts survival time in cancer patients under 65 years old, independent of confounding factors, but this was not observed in patients over 65 [36]. This suggests that the prognostic value of SDNN may be modulated by age.

Fig. 3
figure 3

Subgroup analysis of the impact of higher SDNN and lower SDNN on tumor prognosis, using HR for OS as raw data. Abbreviations: CI, confidence interval; HR, hazard ratio

Full size image

Failed to identify significant contributions of predefined covariates to the observed heterogeneity (I² = 66%) (Fig. 3). we constructed a Galbraith radial plot and identified two studies that fell outside the 95% confidence interval (Fig. 4). Notably, the cut-off value of SDNN (110 ms) in the study by Ciurea et al. was derived from comparisons with healthy controls and utilized 24-hour Holter monitoring for HRV assessment, differing from the 5-minute short-term ECG protocol recommended by the European Society of Cardiology [46]. McGovern et al. focused on patients with stage I-III colorectal cancer and found no association between time-domain parameters and tumor prognosis [26]. The majority of studies demonstrating a significant association between SDNN and cancer survival outcomes have predominantly involved patients with locally advanced or metastatic disease [35, 40, 47]. This discrepancy suggests that SDNN thresholds may serve as a potential confounding factor. Furthermore, this divergence may reflect underlying pathophysiological differences in autonomic regulation between localized and systemic cancers, emphasizing the need for stage-specific HRV interpretation frameworks.

Fig. 4
figure 4

Galbraith radial plot of higher SDNN and lower SDNN on tumor prognosis

Full size image

RMSSD is the most specific estimator for the cardiac vagal tone due to its lower susceptibility to respiratory frequency influences [48]. Our results demonstrated RMSSD did not demonstrate statistically significant prognostic value for OS in cancer patients. (P = 0.11). However, Wu et al. identified through multivariate analysis that RMSSD showed significant correlation with OS in ES-SCLC patients after adjusting for KPS scores, suggesting potential masking effects of KPS on RMSSD’s true association [43]. Conversely, Strous et al. found that even after adjusting for potential confounding factors, there was no significant association between RMSSD and cancer prognosis, which may be attributed to the lower proportion of advanced stage tumors in their cohort [27]. In the future, the prognostic value of RMSSD needs to be validated under standardized conditions.

Higher HF in patients with recurrent or metastatic breast cancer is closely related to their longer overall survival [49]. Our meta-analysis further supports this. In contrast, while elevated LF showed a survival benefit, heterogeneity (I² = 74%) likely stems from differences in Cancer types, Wu et al. included advanced NSCLC [43], whereas Ciurea et al. analyzed HCC at diagnosis [39]. Similarly, Petrescu et al. reported that frequency domain indicators showed no significant association with median survival in pancreatic cancer (PC) patients, which may be attributed to the extensive sympathetic innervation of the pancreas [50, 51]. This suggests that the predictive value of frequency domain parameters may be influenced by tumor type.

Heart rate regulation exhibits inherent nonlinear complexity, nonlinear parameters can more effectively characterize the complexity of cardiac regulatory mechanisms in cancer patients [52]. Our analysis identified elevated Lmax, Lmean, REC, and DET as predictors of worse prognosis. Notably, Lmax remained an independent prognostic marker in lung cancer patients with brain metastasis even after multivariate adjustment [41]. These findings underscore the prognostic value of nonlinear HRV parameters in oncology, while necessitating further investigation to elucidate their precise pathophysiological mechanisms underlying cancer prognosis.

Mechanisms

The predictive value of HRV for cancer Prognosis can be attributed to the “protective” role of VN. Accumulating evidence has demonstrated that an intact VN exerts protective effects against tumor progression (Table 3). It may regulate tumor progression through mechanisms such as suppressing inflammation, modulating immune and maintaining the balance between the sympathetic and parasympathetic.

Table 3 Evidence for a role of the vagus nerve in cancer development
Full size table
  1. 1)

    Suppress inflammation Chronic inflammation and tumor progression go hand in hand. VN may suppress oxidative stress and “reflexively” mitigate inflammation through the cholinergic anti-inflammatory pathway (CAP), thereby potentially improving tumor prognosis [53, 54]. VN can locally exert anti-inflammatory effects by reducing the production of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α), through acetylcholine (ACh) and its receptor, α7 nicotinic acetylcholine receptor (α7nAChR) [55,56,57]. Additionally, the VN can modulate cardiac vagal tone and TNF-α [58, 59].

  2. 2)

    Modulating immunity The VN is an important conduit for bidirectional communication between the brain and the immune system, and may potentially regulate immune suppression to modulate tumor progression [60, 61]. Activation of the VN has been shown to inhibit the production of TNF-α [62, 63], whereas vagotomy leads to an increase in TNF-α levels in immune cells, such as macrophages and neutrophils. Antonica et al. demonstrated that sectioning the right VN reduces the number of lymphocytes released from the thymus, whereas electrical stimulation of the VN induces a temporary increase in their release through experiments on rats [65]. Additionally, the cholinergic system on immune cells is likely to play a role in regulating immune responses [65].

  3. 3)

    Regulating the balance of the sympathetic and parasympathetic VN and the sympathetic nervous system maintain a dynamic balance, working together to preserve homeostasis in the body [66, 67]. Excessive sympathetic nervous activity can promote angiogenesis through norepinephrine and accelerate tumor progression [68, 69]. Numerous studies have shown that VN stimulation can attenuate the sympathetic stress response and inhibit tumor growth, which may be associated with a reduction in plasma TNF-α levels [70,71,72].

Furthermore, VN can also influence tumor progression by modulating components of the tumor microenvironment, such as gut microbiota and stem cells [62, 73, 74]. However, the complexity of VN and its extensive branching make it challenging to determine its exact role in tumor progression. Reijmen et al. indicates that taVNS alone or in combination with radiotherapy fails to suppress tumor growth [75]. VN may promote the occurrence and metastasis of prostate cancer and colon cancer through acetylcholine (ACh) [76, 77]. Magnon et al. indicates that patients with tumors characterized by parasympathetic nerve fiber density exhibit a significantly increased propensity for biochemical recurrence and metastatic dissemination [78]. Therefore, some experts have suggested that the VN may slow down tumor development at the systemic level, while may promote tumor development at the local level, in advanced or metastatic cancer, VN activity may exhibit its protective effect [16, 79]. Further research is needed to explore the actual role and mechanisms of the VN in general cancer and its metastatic stage.

Implications for future research

Future research should delve into the specific role of VNS in cancer therapy, including understanding the exact mechanism by which HRV changes are associated with cancer progression, effects on different tumor types and stages, thereby providing evidence-based support for the clinical application of VN modulation techniques in oncology. In clinical practice, it is recommended to incorporate HRV monitoring into routine cancer management, focus on the predictive value of nonlinear parameters for prognosis. Given the circadian rhythm of HRV (RMSSD and SDNN peak around 6 a.m.), it is advisable to standardize monitoring to the morning [80, 81]. HRV monitoring strategies should be dynamically adjusted based on cancer stage and treatment objectives, continuous monitoring during the treatment phase to detect early autonomic dysfunction, where reduced HRV may indicate chemotherapy-related cardiotoxicity risk in breast cancer patients [82]. Integrating HRV with multimodal data (e.g. CRP, IL-6, KPS) is recommended to enhance prognostic evaluation. Gidron et al. demonstrated that a new vagal neuroimmunomodulation (NIM) index, defined as the ratio of RMSSD to CRP, represents a novel independent prognostic biomarker for lethal cancers [83]. Moreover, Studies suggest that the LF/HF ratio may serve as a potential biomarker for psychological interventions targeting depressive states in cancer patients [84, 85]. Convenient and cost-effective monitoring can be achieved through 5-minute short-term HRV assessments combined with wearable devices (chest straps or wristbands), with HRV parameters seamlessly integrated into electronic medical record systems for real-time anomaly alerts (SDNN < 20ms).

Strengths and limitations

Compared to prior meta-analyses [24], the present study demonstrates several methodological and conceptual advancements. Firstly, we expanded the sample size from 1,286 to 2,539 participants, enabling a comprehensive analysis of diverse HRV parameters (including time-domain, frequency-domain, and nonlinear indices), in relation to oncological outcomes. Systematic subgroup analyses were conducted according to tumor type, cut-off values of SDNN and populations to enhance clinical specificity. Secondly, this investigation was prospectively registered in the PROSPERO registry prior to data synthesis. To optimize measurement validity, stringent exclusion criteria were implemented to control for confounding factors affecting HRV assessments, particularly pre-existing cardiovascular comorbidities and pharmacotherapies with autonomic effects. Finally, we discussed the dualistic role of vagal nerve signaling in tumorigenesis, and discussed its implications for future research and clinical strategies. Unavoidably, this study still has some limitations. First, this is a meta-analysis based on cohort studies, and inherent bias may be present. Second, the included studies exhibit potential heterogeneity in terms of tumor types, stages, and SDNN cut-off values. The SDNN values are significantly influenced by ECG acquisition methods, as well as the duration and technique of HRV measurement. Third, due to the insufficient number of included studies, subgroup analyses for the frequency-domain and nonlinear HRV parameters were not conducted.

Conclusion

The findings of this study demonstrate that HRV parameters, particularly SDNN, HF-HRV, and nonlinear parameters, exhibit predictive value for prognosis in cancer. Furthermore, it can be inferred that elevated VN activity may predict prolonged survival outcomes. However, these findings should be interpreted with caution due to the heterogeneity observed across included studies. Future research should prioritize prospective studies with standardized measurement protocols to validate these associations.

Data availability

All the research data and materials used in this study were obtained from pub-licly available sources. The following sources were used: PubMed, EMBASE, the Cochrane Library, and Web of Science.

Abbreviations

HRV:

Heart rate variability

VN:

Vagus nerve

SDNN:

Standard deviation of all normal-to-normal RR intervals

RMSSD:

Root mean square standard deviations of RR intervals

HF-HRV:

High-frequency power HRV

LF-HRV:

Low-frequency power HRV

OS:

Overall survival

Lmax:

Maximal diagonal line length

Lmean:

Mean diagonal line length

REC:

Percent of recurrence

DET:

Determinism

ShanEn:

Shannon entropy

NOS:

Newcastle-Ottawa Scale

HR:

Hazard ratio

CI:

Confidence interval

RSA:

Respiratory sinus arrhythmia

ACh:

Acetylcholine

CAP:

Cholinergic anti-inflammatory pathway

TNF-α:

Tumor necrosis factor-alpha

VNS:

Vagus nerve stimulation

taVNS:

Transcutaneous auricular vagus nerve stimulation

NIM:

Neuroimmunomodulation

References

  1. Maki A, Kono H, Gupta M, et al. Predictive power of biomarkers of oxidative stress and inflammation in patients with hepatitis C virus-associated hepatocellular carcinoma. Ann Surg Oncol. 2007;14(3):1182–90.

    Article  PubMed  Google Scholar 

  2. Browning KN, Verheijden S, Boeckxstaens GE. The vagus nerve in appetite regulation, mood, and intestinal inflammation. Gastroenterology. 2017;152(4):730–44.

    Article  PubMed  Google Scholar 

  3. Huang Y, Zhao C, Su X. Neuroimmune regulation of lung infection and inflammation. QJM. 2019;112(7):483–7.

    Article  CAS  PubMed  Google Scholar 

  4. Nelson RL, Briley S, Vaz OP. The effect of vagotomy and pyloroplasty on colorectal tumor induction in the rat. J Surg Oncol. 1992;51(4):281–6.

    Article  CAS  PubMed  Google Scholar 

  5. Mullan FJ, Wilson HK, Majury CW. Bile acids and the increased risk of colorectal tumours after truncal vagotomy. Br J Surg. 1990;77(10):1085–90.

    Article  CAS  PubMed  Google Scholar 

  6. Sammi SR, Rawat JK, Raghav N, et al. Galantamine attenuates N,N-dimethyl hydrazine induced neoplastic colon damage by inhibiting acetylcholinesterase and bimodal regulation of nicotinic cholinergic neurotransmission. Eur J Pharmacol. 2018;818:174–83.

    Article  CAS  PubMed  Google Scholar 

  7. Toftgaard C. Gastric cancer after peptic ulcer surgery. A historic prospective cohort investigation. Ann Surg. 1989;210(2):159–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rabben HL, Zhao CM, Hayakawa Y, et al. Vagotomy and gastric tumorigenesis. Curr Neuropharmacol. 2016;14(8):967–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ahsberg K, Olsson H, von Staël C. Increased mortality in prostate carcinoma and smoking-related disease after parietal cell vagotomy: a long-term follow-up study. Scand J Gastroenterol. 2009;44(8):947–51.

    Article  PubMed  Google Scholar 

  10. Erin N, Akdas Barkan G, Harms JF, et al. Vagotomy enhances experimental metastases of 4THMpc breast cancer cells and alters substance P level. Regul Pept. 2008;151(1–3):35–42.

    Article  CAS  PubMed  Google Scholar 

  11. Erin N, Barkan GA, Clawson GA. Vagus nerve regulates breast cancer metastasis to the adrenal gland. Anticancer Res. 2013;33(9):3675-82. PMID: 24023295.

  12. Renz BW, Tanaka T, Sunagawa M, et al. Cholinergic signaling via muscarinic receptors directly and indirectly suppresses pancreatic tumorigenesis and cancer stemness. Cancer Discov. 2018;8(11):1458–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Erin N, Duymus O, Oztürk S, et al. Activation of vagus nerve by semapimod alters substance P levels and decreases breast cancer metastasis. Regul Pept. 2012;179(1–3):101–8.

    Article  CAS  PubMed  Google Scholar 

  14. Hiramoto T, Yoshihara K, Asano Y, et al. Protective role of the hepatic vagus nerve against liver metastasis in mice. Neuroimmunomodulation. 2017;24(6):341–7.

    Article  CAS  PubMed  Google Scholar 

  15. Rawat JK, Roy S, Singh M, et al. Transcutaneous vagus nerve stimulation regulates the cholinergic Anti-inflammatory pathway to counteract 1, 2-Dimethylhydrazine induced colon carcinogenesis in Albino Wistar rats. Front Pharmacol. 2019;10:353.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kurata-Sato I, Mughrabi IT, Rana M. Vagus nerve stimulation modulates distinct acetylcholine receptors on B cells and limits the germinal center response. Sci Adv. 2024;10(17):eadn3760.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dubeykovskaya Z, Si Y, Chen X, et al. Neural innervation stimulates Splenic TFF2 to arrest myeloid cell expansion and cancer. Nat Commun. 2016;7:10517.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Stein PK, Pu Y. Heart rate variability, sleep and sleep disorders. Sleep Med Rev. 2012;16(1):47–66.

    Article  PubMed  Google Scholar 

  19. Tiwari R, Kumar R, Malik S. Analysis of heart rate variability and implication of different factors on heart rate variability. Curr Cardiol Rev. 2021;17(5):e160721189770.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Thayer JF, Ahs F, Fredrikson M. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev. 2012;36(2):747–56.

    Article  PubMed  Google Scholar 

  21. Dyab R, Zuccarella-Hackl C, Princip M, et al. Role of heart rate variability in the association between myocardial infarction severity and Post-Myocardial infarction distress. Life (Basel). 2023;13(12):2266.

    PubMed  Google Scholar 

  22. Chattipakorn N, Incharoen T, Kanlop N. Heart rate variability in myocardial infarction and heart failure. Int J Cardiol. 2007;120(3):289–96.

    Article  PubMed  Google Scholar 

  23. Ranpuria R, Hall M, Chan CT. Heart rate variability (HRV) in kidney failure: measurement and consequences of reduced HRV. Nephrol Dial Transpl. 2008;23(2):444–9.

    Article  Google Scholar 

  24. Zhou X, Ma Z, Zhang L. Heart rate variability in the prediction of survival in patients with cancer: A systematic review and meta-analysis. J Psychosom Res. 2016;89:20–5.

    Article  PubMed  Google Scholar 

  25. Kloter E, Barrueto K, Klein SD. Heart rate variability as a prognostic factor for cancer Survival - A systematic review. Front Physiol. 2018;9:623.

    Article  PubMed  PubMed Central  Google Scholar 

  26. McGovern J, Leadbitter S, Miller G. The relationship between heart rate variability and TNM stage, co-morbidity, systemic inflammation and survival in patients with primary operable colorectal cancer. Sci Rep. 2023;13(1):8157.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Strous MTA, Daniels AM, Zimmermann FM. Is pre-operative heart rate variability a prognostic indicator for overall survival and cancer recurrence in patients with primary colorectal cancer? PLoS ONE. 2020;15(8):e0237244.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shi B, Wang L, Yan C, et al. Nonlinear heart rate variability biomarkers for gastric cancer severity: A pilot study. Sci Rep. 2019;9(1):13833.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Rohr M, Tarvainen M, Miri S. An extensive quantitative analysis of the effects of errors in beat-to-beat intervals on all commonly used HRV parameters. Sci Rep. 2024;14(1):2498.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wells GA, Shea BJ, O’Connell D et al. The newcastle–ottawa scale (NOS) for assessing the quality of non-randomized studies in meta-analysis. (2000).

  32. Larsson CE, Cabassut V, Peretout P, et al. Assessment of the objective effect of virtual reality for preoperative anxiety in interventional cardiology. Am J Cardiol. 2023;205:207–13.

    Article  PubMed  Google Scholar 

  33. Chap. 10: Analysing data and undertaking meta-analyses. Available at https://training.cochrane.org/handbook/current/chapter-10.Accessed January 18, 2023.

  34. Chap. 7: Considering bias and conflicts of interest among the included studies. Available at https://training.cochrane.org/handbook/current/chapter-07. Accessed January 18, 2023.

  35. Wang YM, Wu HT, Huang EY. Heart rate variability is associated with survival in patients with brain metastasis: a preliminary report. Biomed Res Int. 2013;2013:503421.

    Article  PubMed  PubMed Central  Google Scholar 

  36. De Couck M, Maréchal R, Moorthamers S. Vagal nerve activity predicts overall survival in metastatic pancreatic cancer, mediated by inflammation. Cancer Epidemiol. 2016;40:47–51.

    Article  PubMed  Google Scholar 

  37. Kim K, Chae J, Lee S. The role of heart rate variability in advanced non-small-cell lung cancer patients. J Palliat Care. 2015;31(2):103–8.

    Article  PubMed  Google Scholar 

  38. Guo Y, Koshy S, Hui D. Prognostic value of heart rate variability in patients with cancer. J Clin Neurophysiol. 2015;32(6):516–20.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ciurea AM, Gheonea DI, Schenker M. The prognostic correlation of heart rate variability at diagnosis with survival of patients with hepatocellular carcinoma. Diagnostics (Basel). 2021;11(5):890.

    Article  CAS  PubMed  Google Scholar 

  40. Cherifi F, Lefevre Arbogast S, Font J. The promising prognostic value of vagal nerve activity at the initial management of ovarian cancer. Front Oncol. 2022;12:1049970.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Li G, Wu S, Zhao H. Non-invasive prognostic biomarker of lung cancer patients with brain metastases: recurrence quantification analysis of heart rate variability. Front Physiol. 2022;13:987835.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wu S, Guan W, Zhao H. Prognostic role of short-term heart rate variability and deceleration/acceleration capacities of heart rate in extensive-stage small cell lung cancer. Front Physiol. 2023;14:1277383.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wu S, Li G, Chen M. Association of heartbeat complexity with survival in advanced non-small cell lung cancer patients. Front Neurosci. 2023;17:1113225.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kiviniemi AM, Breskovic T, Uglesic L. Heart rate variability during static and dynamic breath-hold dives in elite divers. Auton Neurosci. 2012;169(2):95–101.

    Article  PubMed  Google Scholar 

  45. Sevoz-Couche C, Laborde S. Heart rate variability and slow-paced breathing: when coherence Meets resonance. Neurosci Biobehav Rev. 2022;135:104576.

    Article  PubMed  Google Scholar 

  46. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation. 1996;93:1043–65.

    Article  Google Scholar 

  47. Chiang JK, Kuo TB, Fu CH. Predicting 7-day survival using heart rate variability in hospice patients with non-lung cancers. PLoS ONE. 2013;8(7):e69482.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fpubh.2017.00258.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Giese-Davis J, Wilhelm FH, Tamagawa R, et al. Higher vagal activity as related to survival in patients with advanced breast cancer: an analysis of autonomic dysregulation. Psychosom Med. 2015;77(4):346–55.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Petrescu M, Udriștoiu I, Militaru F, et al. The assessment of heart rate variability in patients with pancreatic cancer. Curr Health Sci J. 2023 Apr-Jun;49(2):172–8.

  51. Guo K, Ma Q, Li J, et al. Interaction of the sympathetic nerve with pancreatic cancer cells promotes perineural invasion through the activation of STAT3 signaling. Mol Cancer Ther. 2013;12(3):264–73.

    Article  CAS  PubMed  Google Scholar 

  52. Vaillancourt DE, Newell KM. Changing complexity in human behavior and physiology through aging and disease. Neurobiol Aging 2002 Jan-Feb;23(1):1–11.

  53. Reijmen E, Vannucci L, De Couck M. Therapeutic potential of the vagus nerve in cancer. Immunol Lett. 2018;202:38–43.

    Article  CAS  PubMed  Google Scholar 

  54. Martelli D, Mckinley MJ, Mcallen RM. The cholinergic anti-inflammatory path-way: a critical review. Auton Neurosci. 2014;182:65–9.

    Article  CAS  PubMed  Google Scholar 

  55. Olofsson PS, Rosas-Ballina M, Levine YA. Rethinking inflammation: neural circuits in the regulation of immunity. Immunol Rev. 2012;248(1):188–204.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Tracey KJ. The inflammatory reflex. Nature. 2002 Dec 19–26;420(6917):853–9.

  57. Ricon-Becker I, Fogel E, Cole SW. Tone it down: vagal nerve activity is associated with pro-inflammatory and anti-viral factors in breast cancer - An exploratory study. Compr Psychoneuroendocrinol. 2021;7:100057.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Brock C, Brock B, Aziz Q et al. Transcutaneous cervical vagal nerve stimulation modulates cardiac vagal tone and tumor necrosis factor-alpha. Neurogastroenterol Motil. 2017;29(5).

  59. Partecke LI, Käding A, Trung DN, et al. Subdiaphragmatic vagotomy promotes tumor growth and reduces survival via TNFα in a murine pancreatic cancer model. Oncotarget. 2017;8(14):22501–12.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Irwin MR, Cole SW. Reciprocal regulation of the neural and innate immune systems. Nat Rev Immunol. 2011;11(9):625–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. De Couck M, Maréchal R, Moorthamers S, et al. Vagal nerve activity predicts overall survival in metastatic pancreatic cancer, mediated by inflammation. Cancer Epidemiol. 2016;40:47–51.

    Article  PubMed  Google Scholar 

  62. Hutchings C, Phillips JA, Djamgoz MBA. Nerve input to tumours: pathophysiological consequences of a dynamic relationship. Biochim Biophys Acta Rev Cancer. 2020;1874(2):188411.

    Article  CAS  PubMed  Google Scholar 

  63. Rosas-Ballina M, Olofsson PS, Ochani M. Acetylcholine-synthesizing T cells relay neural signals in a vagus nerve circuit. Science. 2011;334(6052):98–101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Antonica A, Ayroldi E, Magni F, et al. Lymphocyte traffic changes induced by monolateral vagal denervation in mouse thymus and peripheral lymphoid organs. J Neuroimmunol. 1996;64(2):115–22.

    Article  CAS  PubMed  Google Scholar 

  65. Fujii T, Mashimo M, Moriwaki Y. Expression and function of the cholinergic system in immune cells. Front Immunol. 2017;8:1085.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Wehrwein EA, Orer HS, Barman SM. Overview of the anatomy, physiology, and Pharmacology of the autonomic nervous system. Compr Physiol. 2016;6(3):1239–78.

    Article  PubMed  Google Scholar 

  67. Schiller Y. The anatomy and physiology of the sympathetic innervation to the upper limbs. Clin Auton Res. 2003;13(Suppl 1):I2–5.

    PubMed  Google Scholar 

  68. Xia Y, Wei Y, Li ZY. Catecholamines contribute to the neovascularization of lung cancer via tumor-associated macrophages. Brain Behav Immun. 2019;81:111–21.

    Article  CAS  PubMed  Google Scholar 

  69. Silverman DA, Martinez VK, Dougherty PM. Cancer-Associated neurogenesis and Nerve-Cancer Cross-talk. Cancer Res. 2021;81(6):1431–40.

    Article  CAS  PubMed  Google Scholar 

  70. Saku K, Kishi T, Sakamoto K. Afferent vagal nerve stimulation resets baroreflex neural Arc and inhibits sympathetic nerve activity. Physiol Rep. 2014;2(9):e12136.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Kamiya A, Hiyama T, Fujimura A, et al. Sympathetic and parasympathetic innervation in cancer: therapeutic implications. Clin Auton Res. 2021;31(2):165–78.

    Article  PubMed  Google Scholar 

  72. Tibensky M, Mravec B. Role of the parasympathetic nervous system in cancer initiation and progression. Clin Transl Oncol. 2021;23(4):669–81.

    Article  CAS  PubMed  Google Scholar 

  73. Lei C, Sun R, Xu G. Enteric VIP-producing neurons maintain gut microbiota homeostasis through regulating epithelium fucosylation. Cell Host Microbe. 2022;30(10):1417–e14348.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Serafini MA, Paz AH, Nunes NS. Cholinergic Immunomodulation in inflammatory bowel diseases. Brain Behav Immun Health. 2021;19:100401.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Reijmen E, De Mey S, Van Damme H, et al. Transcutaneous vagal nerve stimulation alone or in combination with radiotherapy stimulates lung tumor infiltrating lymphocytes but fails to suppress tumor growth. Front Immunol. 2021;12:772555.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Zhou H, Shi B, Jia Y. Expression and significance of autonomic nerves and Α9 nicotinic acetylcholine receptor in colorectal cancer. Mol Med Rep. 2018;17(6):8423–31.

    CAS  PubMed  Google Scholar 

  77. Raufman JP, Samimi R, Shah N. Genetic ablation of M3 muscarinic receptors attenuates murine colon epithelial cell proliferation and neoplasia. Cancer Res. 2008;68(10):3573–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Magnon C, Hall SJ, Lin J. Autonomic nerve development contributes to prostate cancer progression. Science. 2013;341(6142):1236361.

    Article  PubMed  Google Scholar 

  79. Kamiya A, Hiyama T, Fujimura A. Sympathetic and parasympathetic innervation in cancer: therapeutic implications. Clin Auton Res. 2021;31(2):165–78.

    Article  PubMed  Google Scholar 

  80. Zhou L, Zhang Z, Nice E, et al. Circadian rhythms and cancers: the intrinsic links and therapeutic potentials. J Hematol Oncol. 2022;15(1):21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Heathers JA. Everything Hertz: methodological issues in short-term frequency-domain HRV. Front Physiol. 2014;5:177.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Luna-Alcala S, Espejel-Guzmán A, Lerma C, et al. Heart rate variability-based prediction of early cardiotoxicity in breast-cancer patients treated with anthracyclines and trastuzumab. Cardiooncology. 2024;10(1):32.

    PubMed  PubMed Central  Google Scholar 

  83. Gidron Y, De Couck M, Schallier D, et al. The relationship between a new biomarker of vagal neuroimmunomodulation and survival in two fatal cancers. J Immunol Res. 2018;2018:4874193.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Zhang Q, Li W, Yu S et al. Heart rate variability and cytokines are involved in anxiety in breast cancer patients: A cross-sectional study. Clin Breast Cancer 2024 Nov 28:S1526-8209(24)00322-7.

  85. Kim EH, Park JH, Lee SM, et al. Preoperative depressed mood and perioperative heart rate variability in patients with hepatic cancer. J Clin Anesth. 2016;35:332–8.

    Article  PubMed  Google Scholar 

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Funding

This work was supported by the following funds: (1) National Natural Science Foundation of China (No. 82405359); (2) Chongqing Technology Innovation and Application Development Sichuan Chongqing Science and Technology Innovation Cooperation Plan Project (No. CSTB2024TIAD-CYKJCXX0037).

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Authors and Affiliations

  1. TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Wen-Bo Huang, Heng-zhou Lai, Jing Long, Qiong Ma, Xi Fu, Feng-Ming You & Chong Xiao

  2. Institute of Oncology, Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Xi Fu, Feng-Ming You & Chong Xiao

  3. Oncology Teaching and Research Office of Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Feng-Ming You & Chong Xiao

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  1. Wen-Bo Huang
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Contributions

Conceptualization: H.W.B, L.H.Z. Design of the work: H.W.B, L.H.Z. Data Curation: LJ., MQ. Formal analysis: H.W.B, LJ. Investigation: LJ., FX. Supervision: LJ., FX., Y.F.M, XC. Writing original draft: H.W.B, L.H.Z. Writing-review and editing: all authors.

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Huang, WB., Lai, Hz., Long, J. et al. Vagal nerve activity and cancer prognosis: a systematic review and meta-analysis. BMC Cancer 25, 579 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-13956-w

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BMC Cancer

ISSN: 1471-2407

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