Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigen

Lishan Ding; Yuepeng Yang; Mingsi Chi; Zijun Chen; Yaping Huang; Wenshan Ouyang; Weijian Li; Lei He; Ting Wei

doi:10.1371/journal.pone.0282221

. 2023 Apr 6;18(4):e0282221. doi: 10.1371/journal.pone.0282221

Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigen

Lishan Ding ^1,^#, Yuepeng Yang ^1,^#, Mingsi Chi ^1,^#, Zijun Chen ^1,^‡, Yaping Huang ^1,^‡, Wenshan Ouyang ^1,^‡, Weijian Li ¹, Lei He ^2,^*, Ting Wei ^3,^*

Editor: Alessandro Rizzo⁴

PMCID: PMC10079040 PMID: 37023015

Abstract

Objective

To investigate the diagnostic role of heart rate variability in breast cancer and its relationship with Carcinoembryonic antigen (CEA) in peripheral serum.

Methods

We reviewed the electronic medical records of patients who attended Zhujiang Hospital of Southern Medical University between October 2016 and May 2019. The patients were grouped based on breast cancer history and were divided into two groups: breast cancer group(n = 19) and control group(n = 18). All women were invited for risk factor screening, including 24-hour ambulatory ECG monitoring and blood biochemistry after admission. The difference and correlation between the breast cancer group and control group were performed by comparing the heart rate variability and serum CEA levels. Additionally, diagnostic efficacy analysis of breast cancer was calculated by combining heart rate variability and serum CEA.

Results

In total, 37 patients were eligible for analysis, with 19 and 18 patients in the breast cancer group and control groups, respectively. Women with breast cancer had a significantly lower level of total LF, awake TP, and awake LF, and a significantly higher level of serum CEA compared with women with no breast cancer. Total LF, awake TP, and awake LF were negatively correlated with the CEA index (P < 0.05). The receiver operating characteristic (ROC) curves indicated the highest area under the curve (AUC) scores and specificity of the combination of awake TP, awake LF, and serum CEA (P < 0.05), while sensitivity was highest for total LF, awake TP, and awake LF (P < 0.05).

Conclusions

Women with history of breast cancer had abnormalities in autonomic function. The combined analysis of heart rate variability and serum CEA analysis may have a predictive effect on the development of breast cancer and provide more basis for clinical diagnosis and treatment.

Introduction

Breast cancer is caused by a variety of carcinogens, leading to the uncontrolled proliferation of breast epithelial cells. According to the Global Cancer Update provided by Global Cancer Statistics 2020, there were 226,419 new confirmed cases of breast cancer and 684,996 new deaths. Breast cancer has surpassed lung cancer as the most common cancer in women and one of the highest incidences and mortality in women [1, 2]. Treatment options for breast cancer include targeted therapy, endocrine therapy, radiation therapy, surgery, and chemotherapy [3]. Clinically, the most suitable treatment for breast cancer patients is determined based on tumor subtype and cancer stage [4]. For example, novel treatment options, including targeted therapy and immunotherapy, have emerged in recent years for metastatic triple negative breast cancer [5–7]. Breast-conserving surgery is often used for patients with early-stage breast cancer, while mastectomy is considered the most effective method for patients with advanced breast cancer [8]. However, breast cancer patients often lose obvious symptoms in their early stages which are already in the intermediate and advanced stages, and miss the best time for treatment [9], with a low survival rate and easy recurrence [10–12].

Currently, common diagnostic methods for breast cancer include mammography, ultrasound scan, fine needle aspiration, and tumor marker testing (e.g., CA199, CEA, CA15-3, CA125) [13, 14]. Compared with other diagnostic methods, tumor marker testing is quick and easy, basically non-invasive and cost-effective in early cancer diagnosis leading to a better reflection of tumor development and the body’s response to the tumor. Carcinoembryonic antigen (CEA) is mainly used for clinical monitoring of colorectal cancer, gastric cancer, breast cancer, pancreatic cancer, hepatocellular carcinoma, lung cancer, and medullary thyroid cancer, which is of great value in the diagnosis, screening, and prognosis of tumors [15]. Recent studies have shown that preoperative CEA levels may provide useful for the identification and treatment of breast cancer [16]. Wu et al [17]. showed that serum CEA levels were elevated during breast cancer. And the European Tumor Markers Panel recommended CEA levels as an indicator for the assessment of prognosis, early detection of disease progression, and treatment monitoring in breast cancer patients [18]. However, the specificity of CEA for early diagnosis of breast cancer is relatively low. Therefore, we sought to combine other diagnostic methods to improve the efficacy.

Heart rate variability (HRV) refers to the change between each cardiac cycle, which originates from the autonomic regulation of the heart’s sinus node. It is considered to be an important indicator of autonomic function and action, reflecting the balance between the vagus and sympathetic nerves. Studies have found that patients with breast cancer have a higher risk of cardiovascular disease which presented a lower HRV, implying vagal dysfunction [19–21]. Karolina Majerova et al. [22] showed cardiac vagal modulation alterations in breast cancer survivors by measuring HRV, revealing a significant increase in sympathetic modulation in breast cancer patients relative to healthy volunteers. In addition, previous studies have shown that HRV analysis can help determine tumor staging, efficacy, prognosis, and autonomic function [23–25]. In Desmond G. Powe’s trial [26], beta-blocker therapy significantly reduced distant metastasis, cancer recurrence, and cancer-specific mortality in breast cancer patients, suggesting that sympathetic inhibition can inhibit breast cancer progression. To be concluded, breast cancer patients have impaired autonomic nervous system activity so early recognition is clinically significant to increase treatment chances and survival time. Therefore, HRV, as a non-invasive measure widely used in clinical practice to assess autonomic nervous system activity [27], may be a clinical tool for detecting early breast cancer.

In summary, it is insidious and lacks of effective screening methods to diagnose in the early stages of breast cancer, which seriously affects the life and health of women. Therefore, this study aimed to investigate the changes in heart rate variability and carcinoembryonic antigen in breast cancer patients and their role in the diagnosis of breast cancer, to provide a new adjunctive method for early diagnosis of breast cancer, and to improve the detection rate [28].

Materials and methods

The key elements of this study were to select qualified cancer patients by establishing exclusion criteria and select appropriate statistical methods to analyze the data according to the data characteristics. A large number of studies have shown that in addition to cancer, inflammation, cardiovascular disease, metabolic disease, dyslipidemia and other diseases affecting heart rate variability. Therefore, the patients in the cancer group were excluded from the above diseases that might interfere with heart rate variability in this study. Meanwhile, statistical methods were used to compare the various blood lipid indicators of patients in the cancer group and the control group of healthy patients (the difference was not statistically significant when P>0.05). The Ethics Committee of Zhujiang Hospital approved this study (NO.2022-KY-044).

Participants and procedures

The electronic medical records of patients diagnosed with breast cancer at Zhujiang Hospital of Southern Medical University from October 1, 2016 to May 1, 2019 were selected for retrospective analysis in this study.

Inclusion criteria: 1) patients with complete general clinical information; 2) meeting the diagnostic criteria for breast cancer lesions.

Exclusion criteria: 1) organic heart disease such as heart failure; 2) pre-existing palpitations, abnormal heart rate, etc.; 3) hyperthyroidism; 4) diabetes mellitus; 5) inflammation; 6) infection.

The study finally included 19 cases of breast cancer group, aged 40–78 years, with a mean age of (58.42±12.76) years; another 18 cases of healthy women, aged 32–71 years, with a mean age of (51.11±11.82) years, who were included in the voluntary test and had complete general clinical data at the same time, were selected as the control group. Data were compared between the two groups for baseline characteristics with P > 0.05, and the differences were not statistically significant or comparable.

Measures

The quantitative variables in this study were not grouped.

Biochemistry

Venous blood was collected from all subjects in a 12-h fasting state and the early morning of the following day, and the following venous blood parameters were measured using a Beckman CX5 automatic biochemistry analyzer: Alanine aminotransferase (ALT), aspartate aminotransferase (AST), aspartate aminotransferase/alanine aminotransferase ratio (AST/ALT), urea, total cholesterol (TC), total bilirubin (TBIL), total protein (TP), total calcium (Ca), globulin (GLO), triglycerides (TG), albumin (ALB), albumin/globulin ratio (ALB/GLO), direct bilirubin (DBIL), alkaline phosphatase (ALP), creatinine (Crea), glucose (Glu), indirect bilirubin (IBIL), carcinoembryonic antigen (CEA), and other items.

HRV

All subjects recorded test results using the domestic BIHONKOHDEN ambulatory ECG workstation recorder RAC-3012 and performed heart rate variability analysis of 24h ambulatory ECG using its analysis system. Recorded time domain metrics: 1) total standard deviation of normal sinus RR interval (SDNN); 2) the mean standard deviation of sinus RR interval every 5 minutes (SDNNin); 3) the root mean square of normal continuous sinus RR interval (rMSSD); 4) the percentage difference of adjacent RR interval > 50 ms (pNN50). Recorded frequency domain indicators: 1) total power (TP); 2) very low frequency power (VLF); 3) low frequency power (LF); 4) high frequency power (HF).

Statistical analysis

Data were analyzed using SPSS 26.0 statistical software, and data normality was tested by the Shapiro-Wilk method. For indicators conforming to the normal distribution, their intergroup comparisons were performed by independent sample t-test, expressed as mean ± standard deviation (x±s); for indicators not conforming to the normal distribution, their intergroup comparisons were performed by rank sum test, expressed as median (interquartile spacing), i.e., M (P25, P75). Spearman’s method was used for correlation analysis; binary logistic regression analysis was used for multifactorial analysis to assess the risk relationship in terms of odds ratio (OR) and 95% confidence interval (CI). Assessing collinearity between independent variables using collinearity diagnostics. The goodness of fit was tested by Hosmer-Lemeshow test. The receiver operating characteristic curve (ROC) was used to assess the diagnostic efficacy of CEA and HRV for breast cancer, and the critical value, sensitivity, and specificity were calculated. P < 0.05 was considered a statistically significant difference.

Results

All study data were obtained from 19 women with breast cancer and 18 women in the control group. Participants with missing data were not included in this study.

Participants selection

Using the methods described above, we identified 229 patients who were diagnosed with breast cancer (Fig 1). In total, 210 of 229 patients were excluded for incomplete data and specific diseases. In details, 179 patients were not available in HRV. Another 14 of 50 patients with HRV were excluded as they did not contained CEA. Seventeen patients were excluded because these patients were diagnosed diseases which may affect heart rate including organic heart disease such as heart failure (1), abnormal heart rate (3), hyperthyroidism (1), diabetes mellitus (1), inflammation or infection (11). Then we identified 562 people who did not suffer from cancer, inflammation, infection, heart failure, diabetes, hyperthyroidism, fever, leukemia, anemia control group during the same period. 343 people were not available in HRV. Another 168 men with HRV were excluded. Then 33 of 51 patients with HRV were excluded because they did not contained CEA. The study finally included 19 cases of breast cancer group and 18 cases of healthy women as the control group. The study finally included 19 cases of breast cancer group and 18 cases of healthy women as the control group.

Comparison of general information between the two groups

The age, weight, height, body mass index (BMI), total cholesterol (TC), total protein (TP), albumin (ALB), albumin/globulin (ALB/GLO), direct bilirubin (DBIL), and creatinine (Crea) indexes of the two groups conformed to the normal distribution. An independent samples t-test was conducted to investigate possible differences from baseline between the group’s statistics. There was no significant difference from baseline between the groups (P>0.05).

Basal metabolic rate (BMR% = systolic blood pressure–diastolic blood pressure + pulse count– 110), alanine aminotransferase (ALT), aspartate aminotransferase (AST), aspartate aminotransferase/alanine aminotransferase ratio (AST/ALT), urea, total bilirubin (TBIL), total calcium (Ca), globulin (GLO), triglyceride (TG), alkaline phosphatase (ALP), glucose (Glu), and indirect bilirubin (IBIL) indicators did not conform to the normal distribution. A rank sum test was conducted to verify possible differences from baseline between the group’s statistics. There was no significant difference from baseline between the groups (P>0.05) (S1 Table and Table 1).

Table 1. Comparison of general information between the two groups.

Variables	Control group	Breast cancer group	F /Z-value	P-value
Age^a(year)	51.11±11.82	58.42±12.76	0.561	0.080
Weight^a(kg)	51.81±8.86	57.03±7.84	0.339	0.066
Height^a(cm)	157.22±3.12	159.21±3.55	0.051	0.080
BMI^a(kg/m²)	20.97±3.54	22.54±3.31	0.091	0.170
BMR^b(%)	14.00(4.50,19.50)	18.00(10.00,25.00)	-1.234	0.217
ALT^b(IU/L)	11.50(9.00,16.25)	20.00(10.00,25.00)	-1.906	0.057
AST^b(IU/L)	16.50(13.75,19.25)	19.00(15.00,25.00)	-1.431	0.152
AST/ALT^b	1.40(1.08,1.58)	1.20(1.00,1.60)	-0.688	0.492
Urea^b(mmol/L)	4.57(4.06,5.26)	4.77(3.44,5.32)	-0.319	0.750
TC^a(mmol/L)	5.15±0.63	5.37±0.87	1.190	0.390
TBIL^b(μmol/L)	9.62(7.60,11.48)	8.90(7.40,10.90)	-0.851	0.395
TP^a(g/L)	71.36±4.99	68.35±7.91	1.820	0.179
Ca^b(mmol/L)	2.29(2.23,2.36)	2.31(2.26,2.45)	-0.639	0.523
GLO^b(g/L)	29.35(26.68,31.43)	27.70(23.20,29.10)	-1.520	0.129
TG^b(mmol/L)	0.99(0.75,1.47)	1.24(0.86,1.51)	-1.155	0.248
ALB^a(g/L)	42.62±3.37	10.72±4.51	1.045	0.158
ALB/GLO^a	1.47±0.20	1.50±0.22	0.179	0.691
DBIL^a(μmol/L)	4.33±2.00	4.50±1.30	0.953	0.764
ALP^b(IU/L)	60.50(52.50,69.25)	65.00(53.00,77.00)	-0.714	0.475
Crea^a(μmol/L)	62.65±11.96	64.41±19.20	1.102	0.742
Glu^b(mmol/L)	5.05(4.60,5.53)	5.00(4.53,6.40)	-0.030	0.976
IBIL^b(μmol/L)	5.55(3.63,6.83)	4.50(3.60,6.20)	-0.745	0.456

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^a All groups of this index followed a normal distribution and were expressed as $\bar{x} \pm s$ . Independent samples t-test was used for comparison between groups.

^b At least one of the groups of this indicator did not follow a normal distribution, denoted by M (P25, P75), and comparisons between groups were made using rank sum test.

Comparison of HRV and CEA indicators between the two groups

The total VLF, awake SDNN, and sleep VLF in both groups conformed to the normal distribution (P>0.05). An independent sample t-test was conducted to investigate possible differences between the group’s statistics. And there was no significant difference between the groups (P>0.05).

TP, total LF, total HF, total SDNN, total SDNNin, total rMSSD, total pNN50, awake TP, awake VLF, awake LF, awake HF, awake SDNNin, awake rMSSD, awake pNN50, sleep TP, sleep LF, sleep HF, sleep SDNN, sleep SDNNin, sleep rMSSD, sleep pNN50 HRV parameters and CEA indicators did not conform to the normal distribution. A rank sum test was conducted to investigate possible differences between the group’s statistics.

The comparison of HRV parameters between the control (n = 18) and breast cancer (n = 19) group was shown in the S2 Table and Fig 2. Total LF, awake total power, and awake LF HRV parameters and CEA indicators were significantly lower in the breast cancer group (283.90 (195.50, 554.50) vs 213.40 (80.00, 421.70), 1298.50 (1044.00, 2033.75) vs 816.00 (454.00, 1519.80), 298.50(214.75,612.00) vs 152.00(84.00,344.00), and 1.25(0.70,1.63) vs 2.70(1.60,5.20) P = 0.045, 0.033, 0.019, and <0.001, respectively). The differences of the remaining indicators were not statistically significant (P>0.05).

Multifactorial logistic regression analysis of breast cancer group

A logistic regression was performed to ascertain CEA and HRV parameters on the likelihood that participants have breast cancer. In multivariate analysis, CEA showed to be a significant risk factor of breast cancer, while there was insufficient evidence for an association between HRV parameters (indexed as Awake LF) and breast cancer (odds ratio (O.R.) and 95% confidence interval (95%CI): O.R. = 3.298, 95%CI: 1.156–9.413, p = 0.026) (Table 2).

Table 2. Logistic regression analysis of factors independently associated with breast cancer.

Variables	B-value	P-value	SE	OR	95%CI
CEA (ng/ml)	1.193	0.026	0.535	3.298	1.156–9.413
Awake LF (ms²)	-0.002	0.377	0.002	0.998	0.994–1.002

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The P-value of Hosmer-Lemeshow Test is 0.231 (P>0.05), representing the fitting is good (S3 Table).

The colinearity diagnosis found that there was a strong colinearity between HRV parameters, so only CEA and conscious LF were selected for binary logistic analysis (S4 Table).

Correlation analysis of HRV parameters and CEA

Spearman’s correlation analysis showed that total LF, awake TP, and awake LF were negatively correlated with the CEA index in both groups (P<0.05). Given that CEA has good diagnostic performance for breast cancer as a risk factor [29] and differences in HRV parameters between groups, CEA and HRV parameters may have a joint diagnostic effect on breast cancer (S5 Table and Fig 3).

Fig 3 — (A-C) Scatterplot of correlation analysis of CEA with (A)total LF (P = 0.018); (B)awake LF (P = 0.009); (C)awake TP (P = 0.009).

Analysis of the diagnostic efficacy of serum CEA and HRV parameters in breast cancer

ROC curve analysis showed that only AUC of awake TP, awake LF, and CEA are greater than 0.7. It can be used as a predictor of breast cancer, but the diagnostic efficacy is not high. We combined awake TP, awake LF, and CEA separately as a new combined diagnostic model, and then incorporated logistic regression models to derive predictive probabilities, and finally performed ROC curve analysis. The results showed that the combination of awake TP, awake LF and CEA had the largest AUC. Its AUC is 0.901 (p<0.001) with 73.7% sensitivity and 94.4% specificity, suggesting a high diagnostic value for breast cancer (Table 3 and Fig 4).

Table 3. Diagnostic efficacy of HRV parameters, CEA, and combined prediction for breast cancer.

Variables	Cut-off value	Sensitivity (%)	Specificity (%)	P-value	95%CI
Total LF (ms²)	158.85	0.944	0.474	0.045	0.522–0.864
Awake TP (ms²)	833.50	0.944	0.526	0.033	0.534–0.876
Awake LF (ms²)	183.30	0.944	0.579	0.019	0.556–0.895
CEA (ng/ml)	1.55	0.895	0.778	0.000	0.776–0.990
Joint prediction^a	-	0.737	0.944	0.000	0.803–0.998

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^a Joint prediction refers to combined awake TP, awake LF and CEA (S6 Table).

Fig 4 — Joint prediction, combined awake TP, awake LF and CEA; AUC, area under the ROC curve.

Discussion

Breast cancer occurred when breast epithelial cells undergo uncontrolled proliferation in response to multiple oncogenic factors. It is widely believed that the incidence of breast cancer has been increasing in recent years. Combining our results with earlier findings, we confirmed the close association of HRV and CEA with tumor development. Our results also showed that the combined analysis of heart rate variability and serum CEA may have clinical value in the early diagnosis and treatment of breast cancer.

Heart rate variability (HRV) is the difference between consecutive R-R intervals during normal heartbeats which also acts as a noninvasive index used to assess the activity of the autonomic nervous system. Studies have shown that HRV had been considered to be closely related to the assessment of disease, risk stratification, treatment outcome, and long-term prognosis of various cardiovascular diseases [30–32]. In recent years, an increasing number of researchers had also applied HRV in oncology. They considered it to be used as a reliable indicator to evaluate the prognosis of cancer patients [33]. And the reduction of heart rate variability in cancer patients was related to shorter survival time [34]. Therefore, our study focused on the relationship between breast cancer and HRV. We selected 19 breast cancer patients and 18 healthy individuals for comparison of 24h ambulatory ECG HRV indicators. The HRV indicators of total LF, awake TP, and awake LF were significantly lower in the breast cancer group than in the control group (P<0.05), where TP represents the level of autonomic nervous system activity and LF reflects the joint action of sympathetic and some parasympathetic nerves [35]. The results of this study suggested the presence of autonomic dysfunction in breast cancer patients, and similar results have been reported in other studies. Wu [36] showed that HRV was significantly lower in patients with advanced breast cancer and early breast cancer compared with patients with benign breast tumors. And there was a correlation between HRV and TNM stage of breast cancer. Patients with advanced breast cancer have lower HRV, autonomic dysfunction, and possibly poorer prognosis which embodied the help of HRV to construct an effective early diagnosis and clinical prognosis model for breast cancer. Liang [37] applied HRV to the assessment of changes in cardiac autonomic function in breast cancer patients treated with postoperative chemotherapy, reflecting that HRV had a certain guiding significance for the evaluation of cardiac damage in patients. Daniel et al [38] found significant changes in HRV of breast cancer patients and lower parasympathetic cardiac activity compared to the controls, which may be related to the fact that the autonomic nervous system played an important role in the development and progression of cancer [39]. In conclusion, the analysis of heart rate variability in breast cancer patients suggested the presence of autonomic dysfunction and showed significant changes in HRV indicators, which speculated from this result that HRV is of great value for the diagnosis of breast cancer.

Carcinoembryonic antigen (CEA) is a kind of cell membrane structural protein specific for human embryonic antigens secreted by mucosal epithelial cells [40, 41]. And it is also one of the most widely used serum tumor markers in the diagnosis and research of malignant tumors. In our study, we found that serum CEA was significantly higher in breast cancer patients than in healthy people, and that serum CEA had a low negative correlation with total LF, total awake power, and awake LF (p <0.05). CEA in the membranes of tumor cells differentiated from endodermal cells and involved in cell adhesion and regulation processes, so high CEA expression level may be related to a high potential for tumor cell migration and metastasis [41]. Compared with the control group, breast cancer patients showed a significant decrease in awake LF, indicating an increased sympathetic tone in breast cancer patients and some degree of autonomic dysfunction in the heart. Previously studies have found the interaction of the nervous system with the tumor microenvironment was a key regulator for cancer genesis and progression. The sympathetic and parasympathetic nerves in the tumor microenvironment usually regulated cancer development or metastasis through a neurotransmitter-dependent signaling cascade [42]. Additionally, peripheral nervous system activity as a stress response to low HRV indicators caused an elevated expression of noradrenaline levels in breast cancer patients [23]. And behavioral or physiological stressors could promote tumor growth and metastasis by activating tumor β-AR [43]. Sympathetic nerve endings could secrete or locally release stress hormones in the tumor microenvironment, which may directly affect tumor cells and promote their malignant properties. Specifically, norepinephrine and epinephrine could promote tumor cell proliferation, survival (anti-apoptosis), migration, invasion, epithelial-mesenchymal transition (EMT), and production of prostaglandins and matrix metalloproteinases (MMPs) in vitro. Breast cancer cells transferred to the brain received neurotransmitter activity-dependent neurotransmitter signals that triggered a receptor-mediated signaling cascade to induce inward currents in malignant cells, thus driving the development of breast cancer brain metastases [36, 43]. Moreover, the neurological impact of cancer was bidirectional as cancer may induce neurological remodeling and dysfunction. Tumors can secrete neuronal growth factors that increase sympathetic innervation of the tumor. So this created a feedforward cycle in which elevated tumor local noradrenaline levels under a stress-induced sympathetic activation state can promote cancer progression [43]. And since HRV was produced via the sympathetic and parasympathetic-related actions of the autonomic nervous system. Therefore, cancer may affect patient HRV by modulating sympathetic nerves. In summary, the nervous system may regulate the development or metastasis of cancer, while cancer may induce remodeling and dysfunction of the nervous system. Thus, as breast cancer progresses and cancer cells migrate and metastasize, patients have decreased HRV and increased levels of serum CEA, which is widely present in tumor cell membranes. However, in this study, the low negative correlation of awake LF may be related to the small sample size without excluding mental state, respiratory rate, drug use, and environmental factors.

In this study, binary logistic regression analysis showed that every 1 Hz decrease in awake LF, a protection factors for breast cancer, increased the risk of breast cancer by 2.0%;every 1 μg/L increase in serum CEA, a risk factors for breast cancer, increased the risk of breast cancer by 289.0%. However, total awake power, awake LF, and serum CEA alone only had certain accuracy in the diagnosis of breast cancer, while total LF had low accuracy and weak specificity in the diagnosis of breast cancer in ROC curve analysis. Earlier findings have shown a correlation between increased serum CEA levels and decreased [25, 44] HRV and tumor malignancy. The degree of tumor malignancy was directly proportional to the CEA level, while the CEA level in the early stage of cancer was not obvious [44, 45]; the reduction of HRV was more obvious in later stages of the disease [46]. In other words, separate analysis of serum CEA and HRV was not significant for the early diagnosis of breast cancer. Therefore, to assess this topic, we considered whether the combination of HRV and serum CEA can assist the clinical diagnosis of breast cancer at an early stage and improve its detection rate and the results represented that the diagnostic AUC of combining awake TP, awake LF, and serum CEA were 0.901 and the specificity was 0.944. For its high accuracy and specificity, we inferred that the combination of HRV and serum CEA can assist in the clinical diagnosis of breast cancer at an early stage and improve its detection rate.

Conclusions

In conclusion, HRV in breast cancer patients suggested abnormal autonomic function. Our study was prospective and we found that total LF, awake TP, and awake LF were negatively correlated with the CEA index (P < 0.05). And serum CEA was a risk factor for breast cancer. In addition, the combination of awake TP, awake LF, and serum CEA had high accuracy in the diagnosis of breast cancer. The combined analysis of heart rate variability and serum CEA may have a predictive effect on the development of breast cancer, and thus should be considered as important indicators for clinical diagnosis and treatment.

Limitations

In this study, we investigated the changes of HRV and carcinoembryonic antigen in breast cancer patients to provide new ideas for their diagnosis in breast cancer. By analyzing, we came to a clearer conclusion that the combination of HRV and serum CEA can assist in the clinical diagnosis of breast cancer at an early stage and improve its early detection rate, thus implementing early intervention and early treatment and reducing the chance of the disease developing to the middle and late stages. There is little research in this area.

However, there were still several limitations of this study. First, the subjects of this study were obtained from the data of 37 patients in Zhujiang Hospital, and the findings may only be applicable to a small sample size of Asian population which may not be sufficient to detect significant associations between cancer and these HRV indicators, leading to biased conclusions. So the expanded sample size to conduct a more reasonable study needed to be explored in future studies.

Arab et al [47] showed that patients with advanced breast cancer had lower levels of parasympathetic regulation, an imbalance of autonomic nerves, and may be at increased risk for cardiovascular disease compared to patients with early breast cancer. The stage of breast cancer can affect HRV levels to some extent, whereas the present study did not group the study population by breast cancer stage.

Another limitation of the study was that some breast cancer patients have undergone surgery and chemotherapy. However, factors including preoperative depression, anxiety [46] and cardiotoxicity caused by chemotherapeutic drugs [48] may affect HRV to varying degrees. These influences could not be excluded in this study due to the small sample size. Therefore, a large number of samples from clinical trials are still needed to reduce the interference of other factors with HRV.

Supporting information

S1 Data

(XLSX)

Click here for additional data file.^{(22.6KB, xlsx)}

S1 Table. Results of normality test.

In the grouping column, “1” represents the breast cancer group and “0” represents the control group.

(PDF)

Click here for additional data file.^{(244.9KB, pdf)}

S2 Table. Comparison of HRV and CEA indicators between the two groups.

^a All groups of this index followed a normal distribution and were expressed as $\bar{x} \pm s$ . Independent samples t-test was used for comparison between groups. ^b At least one of the groups of this indicator did not follow a normal distribution, denoted by M (P25, P75), and comparisons between groups were made using rank sum test.

(PDF)

Click here for additional data file.^{(171.9KB, pdf)}

S3 Table. The results of Hosmer-Lemeshow test.

(PDF)

Click here for additional data file.^{(130.6KB, pdf)}

S4 Table. Results of collinearity regression.

SE, standard error; D-W value, the indicators of Durbin-Watson test. -7.550E-5 represents -7.550×10⁻⁵.

(PDF)

Click here for additional data file.^{(149.6KB, pdf)}

S5 Table. Correlation between HRV and CEA.

(PDF)

Click here for additional data file.^{(225.1KB, pdf)}

S6 Table. Joint prediction of awake TP, awake LF and CEA.

(PDF)

Click here for additional data file.^{(139.5KB, pdf)}

Acknowledgments

We would like to thank the Zhujiang Hospital of Southern Medical University, Guangdong, China for providing the data.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The authors received no specific funding for this work.

References

1.Wei WQ, Zeng HM, Zheng RS, Zhang SW, An L, Chen R, et al. Cancer registration in China and its role in cancer prevention and control. Lancet Oncol, 2020. 21(7): p. e342–e349. doi: 10.1016/S1470-2045(20)30073-5 [DOI] [PubMed] [Google Scholar]
2.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 2021. 71(3): p. 209–249. doi: 10.3322/caac.21660 [DOI] [PubMed] [Google Scholar]
3.Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. Biol Res, 2017. 50(1): p. 33. doi: 10.1186/s40659-017-0140-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA, 2019. 321(3): p. 288–300. doi: 10.1001/jama.2018.19323 [DOI] [PubMed] [Google Scholar]
5.Rizzo A, Ricci AD, Lanotte L, Lombardi L, Federico AD, Brandi G, et al. Immune-based combinations for metastatic triple negative breast cancer in clinical trials: current knowledge and therapeutic prospects. Expert Opin Investig Drugs, 2022. 31(6): p. 557–565. doi: 10.1080/13543784.2022.2009456 [DOI] [PubMed] [Google Scholar]
6.Rizzo A, Ricci AD. Biomarkers for breast cancer immunotherapy: PD-L1, TILs, and beyond. Expert Opin Investig Drugs, 2022. 31(6): p. 549–555. doi: 10.1080/13543784.2022.2008354 [DOI] [PubMed] [Google Scholar]
7.Viscardi G, Tralongo AC, Massari F, Lambertini M, Mollica V, Rizzo A, et al. Comparative assessment of early mortality risk upon immune checkpoint inhibitors alone or in combination with other agents across solid malignancies: a systematic review and meta-analysis. Eur J Cancer, 2022. 177: p. 175–185. doi: 10.1016/j.ejca.2022.09.031 [DOI] [PubMed] [Google Scholar]
8.Trayes KP, Cokenakes SHE. Breast Cancer Treatment. Am Fam Physician, 2021. 104(2): p. 171–178. . [PubMed] [Google Scholar]
9.Wang NN. Research status and application prospect of combined detection of female tumor markers. Electr J Pract Gynecol Endocrinol. 2018;5(31):24,6. doi: 10.16484/j.cnki.issn2095-8803.2018.31.013 [DOI] [Google Scholar]
10.Gobbini E, Ezzalfani M, Dieras V, Bachelot T, Brain E, Debled M, et al. Time trends of overall survival among metastatic breast cancer patients in the real-life ESME cohort. Eur J Cancer, 2018. 96: p. 17–24. doi: 10.1016/j.ejca.2018.03.015 [DOI] [PubMed] [Google Scholar]
11.Cardoso F, Costa A, Norton L, Senkus E, Aapro M, André F, et al. ESO-ESMO 2nd international consensus guidelines for advanced breast cancer (ABC2). Breast, 2014. 23(5): p. 489–502. doi: 10.1016/j.breast.2014.08.009 [DOI] [PubMed] [Google Scholar]
12.Cardoso F, Costa A, Norton L, Senkus E, Aapro M, André F, et al. ESO-ESMO 2nd international consensus guidelines for advanced breast cancer (ABC2)dagger. Ann Oncol, 2014. 25(10): p. 1871–1888. doi: 10.1093/annonc/mdu385 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.He ZY, Chen Z, Tan MD, Elingarami S, Liu Y, Li TT, et al. A review on methods for diagnosis of breast cancer cells and tissues. Cell Prolif, 2020. 53(7): p. e12822. doi: 10.1111/cpr.12822 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Jafari SH, Saadatpour Z, Salmaninejad A, Momeni F, Mokhtari M, Nahand JS, et al. Breast cancer diagnosis: Imaging techniques and biochemical markers. J Cell Physiol, 2018. 233(7): p. 5200–5213. doi: 10.1002/jcp.26379 [DOI] [PubMed] [Google Scholar]
15.Tarighati E, Keivan H, Mahani H. A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med, 2022. doi: 10.1007/s10238-021-00781-1 [DOI] [PubMed] [Google Scholar]
16.Shao YB, Sun XF, He YN, Liu CJ, Liu H. Elevated Levels of Serum Tumor Markers CEA and CA15-3 Are Prognostic Parameters for Different Molecular Subtypes of Breast Cancer. PLoS One, 2015. 10(7): p. e0133830. doi: 10.1371/journal.pone.0133830 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Wu SG, He ZY, Zhou J, Sun JY, Li FY, Lin Q, et al. Serum levels of CEA and CA15-3 in different molecular subtypes and prognostic value in Chinese breast cancer. Breast, 2014. 23(1): p. 88–93. doi: 10.1016/j.breast.2013.11.003 [DOI] [PubMed] [Google Scholar]
18.Molina R, Barak V, Dalen AV, Duffy MJ, Einarsson R, Gion M, et al. Tumor markers in breast cancer- European Group on Tumor Markers recommendations. Tumour Biol, 2005. 26(6): p. 281–93. doi: 10.1159/000089260 [DOI] [PubMed] [Google Scholar]
19.Caro-Moran E, Fernández-Lao C, Galiano-Castillo N, Cantarero-Villanueva I, Arroyo-Morales M, Díaz-Rodríguez L. Heart Rate Variability in Breast Cancer Survivors After the First Year of Treatments: A Case-Controlled Study. Biol Res Nurs, 2016. 18(1): p. 43–9. doi: 10.1177/1099800414568100 [DOI] [PubMed] [Google Scholar]
20.Vigo C, Gatzemeier W, Sala R, Malacarne M, Santoro A, Pagani M, et al. Evidence of altered autonomic cardiac regulation in breast cancer survivors. J Cancer Surviv, 2015. 9(4): p. 699–706. doi: 10.1007/s11764-015-0445-z [DOI] [PubMed] [Google Scholar]
21.Wu S, Chen M, Wang JF, Shi B, Zhou YF. Association of Short-Term Heart Rate Variability With Breast Tumor Stage. Front Physiol, 2021. 12: p. 678428. doi: 10.3389/fphys.2021.678428 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Majerova K, Zvarik M, Ricon-Becker I, Hanalis-Miller T, Mikolaskova I, Bella V, et al. Increased sympathetic modulation in breast cancer survivors determined by measurement of heart rate variability. Sci Rep, 2022. 12(1): p. 14666. doi: 10.1038/s41598-022-18865-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Fagundes CP, Murray DM, Hwang BS, Gouin JP, Thayer JF, Sollers JJ 3rd, et al. Sympathetic and parasympathetic activity in cancer-related fatigue: more evidence for a physiological substrate in cancer survivors. Psychoneuroendocrinology, 2011. 36(8): p. 1137–47. doi: 10.1016/j.psyneuen.2011.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Mouton C, Ronson A, Razavi D, Delhaye F, Kupper N, Paesmans M, et al. The relationship between heart rate variability and time-course of carcinoembryonic antigen in colorectal cancer. Auton Neurosci, 2012. 166(1–2): p. 96–9. doi: 10.1016/j.autneu.2011.10.002 [DOI] [PubMed] [Google Scholar]
25.Chen C, Liu HY. Analysis of heart rate variability in patients with different stages of malignant tumors. J Fudan Univ Med Sci. 2009;36(4):413–6. [Google Scholar]
26.Powe DG, Voss MJ, Zänker KS, Habashy HO, Green AR, Ellis IO, et al., Beta-blocker drug therapy reduces secondary cancer formation in breast cancer and improves cancer specific survival. Oncotarget, 2010. 1(7): p. 628–38. doi: 10.18632/oncotarget.101009 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Sztajzel J. Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system. Swiss Med Wkly, 2004. 134(35–36): p. 514–22. doi: 10.4414/smw.2004.10321 [DOI] [PubMed] [Google Scholar]
28.Arab C, Dias DPM, Barbosa RTDA, Carvalho TDD, Valenti VE, Crocetta TB, et al. Heart rate variability measure in breast cancer patients and survivors: A systematic review. Psychoneuroendocrinology, 2016. 68: p. 57–68. doi: 10.1016/j.psyneuen.2016.02.018 [DOI] [PubMed] [Google Scholar]
29.Tang S, Zhou F, Sun YF, Wei LL, Zhu S, Yang RQ, et al. CEA in breast ductal secretions as a promising biomarker for the diagnosis of breast cancer: a systematic review and meta-analysis. Breast Cancer, 2016. 23(6): p. 813–819. doi: 10.1007/s12282-016-0680-9 [DOI] [PubMed] [Google Scholar]
30.Li CX, Ding XM, Zhu HJ. Research progress of heart rate variability analysis in cardiovascular diseases. Chin J Integr Med Cardio Cerebrovasc Disease. 2020;18(1):82–4. doi: 10.12102/j.issn.1672-1349.2020.01.018 [DOI] [Google Scholar]
31.Qu HL, Liu RL. Research progress of exercise therapy combined with heart rate variability analysis for prevention and treatment of cardiovascular diseases. Chin J Gerontol. 2009;29(24):3309–11. doi: 10.3969/j.issn.1005-9202.2009.24.080 [DOI] [Google Scholar]
32.Li JX, Zhang FJ, Liu X, Li XQ. Mechanism of heart rate variability and research advance in its relevance to cardiovascular diseases. J Pract Electrocardiol. 2021;30(4):293–6. doi: 10.13308/j.issn.2095-9354.2021.04.014 [DOI] [Google Scholar]
33.Yan RN, Li Y, Wang X, Chen Q. Clinical significance of heart rate variability in cancer patients. J Community Med. 2022;20(10):566–9. [Google Scholar]
34.Guo Y, Koshy S, Hui D, Palmer JL, Shin K, Bozkurt M, et al. Prognostic Value of Heart Rate Variability in Patients With Cancer. Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society, 2015. 32(6). doi: 10.1097/WNP.0000000000000210 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Xie YL, Ren J, Yu D, Yang HX, Liu J. Effects of 24-week tai chi exercise on heart rate variability in middle-aged and elderly people. Chin J Sport Med. 2011;30(9):842–4. [Google Scholar]
36.Wu S. Correlation analysis between short-range heart rate variability and TNM stage of breast cancer patients: Bengbu Med Coll. 2022.
37.Liang LP, Liang J, Liang HR. Clinical value of dynamic electrocardiogram in breast cancer patients undergoing postoperative chemotherapy. Shenzhen J Integr Tradit Chin Western Med. 2015;25(23):70–1. doi: 10.16458/j.cnki.1007-0893.2015.23.037 [DOI] [Google Scholar]
38.EscutiaReyes D, Garduño-García JDJ, Emilio-López-Chávez G, Gómez-Villanueva A, Pliego-Carrillo AC, Soto-Piña AE, et al. Differences in heart rate variability and body composition in breast cancer survivors and women without cancer. Scientific reports, 2021. 11(1). doi: 10.1038/s41598-021-93713-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Tibensky M, Mravec B. Role of the parasympathetic nervous system in cancer initiation and progression. Clinical & translational oncology: official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2020. doi: 10.1007/s12094-020-02465-w [DOI] [PubMed] [Google Scholar]
40.Wang J, Wang XX, Li GL, Hu J, Quan Y. Carcinoembryonic antigen level and its abnormal time and its correlation with tumor incidence in physical examination population. J Chin Gen Pract. 2011;14(27):3149–50. [Google Scholar]
41.Hu N, Liu L, Zeng Y, Jiao ZL, Jin S. Research progress on the relationship between serum carcinoembryonic antigen and lung cancer. Oncol Prog. 2020;18(6):547–8. doi: 10.11877/j.issn.1672-1535.2020.18.06.03 [DOI] [Google Scholar]
42.Michelle M, Borniger JC, D’Silva NJ, Deneen B, Dirks PB, Fattahi F, et al. Roadmap for the Emerging Field of Cancer Neuroscience. Cell, 2020. 181(2). doi: 10.1016/j.cell.2020.03.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Eckerling A, Ricon-Becker I, Sorski L, Sandbank E, Ben-Eliyahu S. Stress and cancer: mechanisms, significance and future directions. Nature reviews. Cancer, 2021. 21(12). doi: 10.1038/s41568-021-00395-5 [DOI] [PubMed] [Google Scholar]
44.Shi CY, Wang SX, Feng HQ. Advances in the relationship between tumor markers CA15-3, CEA and CA125 and breast cancer. Med Res Educ. 2012;29(3):49–52. [Google Scholar]
45.Li DY, Du B, Zheng YJ, Shen YH. Study on relationship between colonoscopy specimens, CEA combined with CA125 levels and colon cancer. China J Mod Med. 2019;29(1):113–6. doi: 10.3969/j.issn.1005-8982.2019.01.021 [DOI] [Google Scholar]
46.Farbood A, Sahmeddini MA, Bayat S, Karami N. The effect of preoperative depression and anxiety on heart rate variability in women with breast cancer. Breast cancer (Tokyo, Japan), 2020. 27(5). doi: 10.1007/s12282-020-01087-y [DOI] [PubMed] [Google Scholar]
47.Arab C, Vanderlei LCM, Paiva LDS, Fulghum KL, Fristachi CE, Nazario ACP, et al. Cardiac autonomic modulation impairments in advanced breast cancer patients. Clin Res Cardiol, 2018. 107(10): p. 924–936. doi: 10.1007/s00392-018-1264-9 [DOI] [PubMed] [Google Scholar]
48.Huang MG, Cao AH, Duan Y, Xue YF. Observation of heart rate variability in breast cancer patients before and after chemotherapy. South China Journal of Cardiovascular Diseases, 2011. 17(S1): 109. [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0282221.r001

Decision Letter 0

Alessandro Rizzo

20 Dec 2022

PONE-D-22-29053Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigenPLOS ONE

Dear Dr. He,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: No

Reviewer #4: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: No

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5. Review Comments to the Author

Reviewer #1: The study assesses a current, timely topic in breast cancer.

We recommend some changes:

- We believe this article is suitable for publication in the journal although major revisions are needed. The main strengths of this paper are that it addresses an interesting and very timely question and provides a clear answer, with some limitations. Certainly, the study is limited to an Asian population with a very small sample size, and authors should further express this point.

- Second, the study included a widely varied patient population from a chinese institute and the total number of patients analyzed was relatively small. Thus, the authors should better highlight the limitations of the current paper.

- The background of the changing scenario of medical treatment in breast cancer patients should be better discussed, and some recent papers regarding this topic should be included in the introduction section (PMID: 34802383; PMID: 36368251 ; PMID: 34793275), only for a matter of consistency. In fact, the introduction appears a but poor and more paragraphs and data are needed to introduce this topic.

Major changes are necessary.

Reviewer #2: Experimental and clinical studies have shown that the sympathetic nervous system (SNS) stimulates cancer progression and reduces the efficacy of oncological treatment. For the determination of SNS modulation, the non-invasive method of heart rate variability (HRV) is widely used. Research articles have been published addressing the clinical value of HRV in breast cancer patients since 1999. However, the small sample size and heterogeneity, the presence of confounders, and the observational study design are the limitations of those studies. The main findings from these studies included the prognostic value of HRV detection and it is revealing the SNS modulation in breast cancer survivors. Few studies have been reported about the value of HRV detection in the early diagnosis of breast cancer. Here, this study demonstrated that the combined HRV and serum CEA analysis might have a predictive effect on the development of breast cancer. However, a major revision of the study design and sample size should be awared before being accepted.

Major Issues

1. Although the idea of this research has novelty for the combination of HRV and CEA in early diagnosis of breast cancer, the sample size is too small which is not compelling for the conclusion. In that case, I would like to suggest you provide more solid data.

2. HRV could be influenced by several important factors in breast cancer survivors, eg. cardiotoxicity of chemotherapy and/or radiation therapy, surgery-induced fatigue, and stress. I suggest the author provide that information on enrolled breast cancer patients and discuss the potential influence on the results.

3. If you are trying to convince the aberrant HRV could be a new potential biomarker for diagnosis of breast cancer, the control group should include age-matched women with benign breast disease.

Minor Issues

1. Since HRV has been reported to be associated with the stage of cancer, they'd better provide information on breast cancer staging and timing of collecting the blood sample and measuring the HRV as well.

2. Please consider describing the limitations of your research in the discussion section. For example, a small sample size may cause bias in the conclusion but you are going to enroll more participants to study in the future.

3. Please simplify or precise the notes under all figures.

4. In the introduction section, please add more experimental and clinical evidence of HRV in breast cancer. For example, consider citing some associated literature.

1) Arab C, Dias DP, Barbosa RT, Carvalho TD, Valenti VE, Crocetta TB, Ferreira M, Abreu LC, Ferreira C. Heart rate variability measure in breast cancer patients and survivors: A systematic review. Psychoneuroendocrinology. 2016 Jun;68:57-68. doi: 10.1016/j.psyneuen.2016.02.018.

2) Majerova K, Zvarik M, Ricon-Becker I, Hanalis-Miller T, Mikolaskova I, Bella V, Mravec B, Hunakova L. Increased sympathetic modulation in breast cancer survivors determined by measurement of heart rate variability. Sci Rep. 2022 Aug 29;12(1):14666. doi: 10.1038/s41598-022-18865-7.

5. Please explain the reason why you did not choose other serum tumor markers, like CA153 instead of CEA.

6. Several sentences are tediously written and could be shortened.

7. I may suggest authors

8. The format of references should be corrected.

Reviewer #3: Sample size is not big enough in this study, to ensure the sample is sufficiently representative, the number of samples selected is modified to the needs of the statistical analysis. According to cross-sectional study, for age part, as SD set 11.82, the sample size for each group should be 42, as SD set 12.76, the sample size for each group should be 48.　Comparative studies, such as comparing experimental and control groups: 30 samples are required at least for each group (Gay, 1992).

For the missing data, it is over 20 percent. The control group is selected from 562 to 18, the breast cancer group is selected from 229 to 19 in the final. If too much original data is missing, not only will the statistical power be reduced, the standard error will become larger, and even the information of the data will be distorted or misleading; it will also make the correlation coefficient matrix or covariate matrix Estimates are biased, which leads to biases in the extraction of common factors, the resulting factors are different from the actual situation.

Reviewer #4: In "Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigen" Ding et al. investigated the role of heart rate variability (HRV) and serum carcinoembryonic antigen (CEA) in breast cancer prediction. Early diagnosis of breast cancer is critical and it's an interesting study to identify potential new biomarkers to identify breast cancer patients. However, there are a few major issues to be addressed before the manuscript is published in high impact journals.

Major:

1. I recommend the authors get editing help to improve the academic writing for this manuscript. I can appreciate the findings as it is, but I believe the manuscript can benefit from professional writing to reach its full potential.

2. The introduction lacks the literature and key references. A comprehensive introduction is essential for a paper that it bridges the gap between your readers and your own research. In this part, the manuscript is expected to guide the readers the key milestones in HRV, CEA and prediction of breast cancer, and also the rationale behind your hypothesis (the use of HRV and CEA can predict breast cancer at early stage.) The authors briefly introduced the background for HRV and CEA, however, their links to breast cancer are not clear.

3. Novelty of this work. To publish as an original article, the novelty of this work needs to be emphasized so the readers can fully appreciate the importance of your work.

4. The results section is not well organized. The authors need to state clearly the purpose of each experiment, the observations and conclusions. For example, line 174, the authors examined the correlation between HRV and CEA. The authors have already identified differences of HRV and CEA between patient to control groups. What questions do the authors would like to address by examining the relationship between HRV and CEA and what does the negative correlation between HRV and CEA mean in the context of breast cancer?

5. Joint prediction using combined markers have shown to improve the prediction performance. What computational models did the authors use to the markers (awake TP, awake LF and CEA) are not shown.

6. How to validate the findings in this manuscript? Have the authors consider independent studies as validation datasets?

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

**********

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PLoS One. 2023 Apr 6;18(4):e0282221. doi: 10.1371/journal.pone.0282221.r002

Author response to Decision Letter 0

29 Jan 2023

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigen” (ID: PONE-D-22-29053). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made the correction which we hope meet with approval. The revised portion is marked in the paper. The main corrections in the paper and the responses to the reviewer’s comments are as flowing:

Response to Journal Requirements: Thank you for your positive comments on our manuscript. We have responded to each of your points in the followings.

1.Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response1：We are very sorry for our incorrect writing. We have modified the style requirements of the manuscript according to the requirements of the PLOS ONE style template so that it meets the publication requirements of your journal.

2.Our staff editors have determined that your manuscript is likely within the scope of our Early Detection, Screening and Diagnosis of Cancer Call for Papers. This editorial initiative is headed by in-house PLOS editors. This Call for Papers aims to explore recent advances in the early detection of cancer and implications of these advances for patient survival. If you would like your manuscript to be considered for this collection, please let us know in your cover letter and we will ensure that your paper is treated as if you were responding to this call.

Response2：Thank the reviewer for the constructive comments and suggestions. We consider our manuscript is suitable for the scope of your Early Detection, Screening and Diagnosis of Cancer Call for Papers. We agree to include the manuscript in this collection.

Response to Reviewer 1: Thank you for your review of our paper. We have answered each of your points below.

1.The study assesses a current, timely topic in breast cancer. We believe this article is suitable for publication in the journal although major revisions are needed. The main strengths of this paper are that it addresses an interesting and very timely question and provides a clear answer, with some limitations.

Response 1：We thank the reviewer for his/her positive comments on our paper. As Reviewer suggested, we have noted those individually in the specific cases below.

2. The study is limited to an Asian population with a very small sample size, and authors should further express this point. The study included a widely varied patient population from a Chinese institute and the total number of patients analyzed was relatively small. Thus, the authors should better highlight the limitations of the current paper.

Response 2: Thanks for your kind suggestions, which is valuable for improving the accuracy of the manuscript. It is really true as Reviewer suggested that the study is limited to an Asian population with a very small sample size. So we have made modifications and further emphasized this limitation in the manuscript.

Because our hospital is located in Asia, the population of our study is primarily Asian.

As mentioned, our study population was drawn from a widely varied patient population from the hospital. In order to make the results reliable, we ensured the homogeneity of the enrolled cases through strict inclusion criteria and exclusion criteria as follows.

Inclusion criteria: 1) patients with complete general clinical information; 2) meeting the diagnostic criteria for breast cancer lesions.

Exclusion criteria: 1) organic heart disease such as heart failure; 2) pre-existing palpitations, abnormal heart rate, etc.; 3) hyperthyroidism; 4) diabetes mellitus; 5) inflammation; 6) infection.(p. 5-6, lines 103-106)

In addition, the number of patients with breast cancer who have a clinical examination such as a 24-hour ambulatory ECG is quite small, which makes it difficult to obtain a larger sample size by reviewing medical records.

However, it does not mean that our present study is unreliable. Like some literature, Karolina Majerova et al.[1] studied sympathetic nerve activity in breast cancer survivors by measurement of HRV. Their study sample consisted of four groups. The healthy women control group consisted of 21 randomly selected women. The group of patients with benign tumors consisted of 13 women. The group of patients with active breast cancer consisted of 20 women with newly diagnosed breast cancer, and the group of survivors consisted of 15 women. Despite their small number of subjects per group, their study was recognized by the journal through rigorous screening and inclusion criteria (IF:4.996).

In addition, we calculated the sample sizes with an actual power of 0.91153using the PASS (v. 15), a powerful sample size estimation software[2]. Notably, the power is the probability of rejecting a false null hypothesis. The parameters are shown in Response Fig 1A. The result showed that the sample size was 8 in each group (Response Fig 1B). Notably, patients with breast cancer who also had a 24-hour ambulatory ECG are indeed difficult to find clinically, so we are currently unable to expand the sample size further by reviewing medical records. However, the current sample size exceeds the number estimated by the PASS software.

Response Fig 1. The result of sample size calculation. (A) The parameters. (B) The result of calculation.

In summary, the small sample size of our study is indeed a limitation, but we believe that our strict inclusion and exclusion criteria will make our study comparable and reliable. In the future, we will continue to accumulate relevant clinical cases and make a higher quality and more reliable study based on this article.

3.The background of the changing scenario of medical treatment in breast cancer patients should be better discussed, and some recent papers regarding this topic should be included in the introduction section (PMID: 34802383; PMID: 36368251; PMID: 34793275), only for a matter of consistency. In fact, the introduction appears a but poor and more paragraphs and data are needed to introduce this topic.

Response 3: Thank you very much for your precious and constructive guidance on our foreword. We have rewritten the introduction and added background on the changing medical situation of breast cancer patients, citing the literature you provided. (p. 3-5, lines 44-88)

First, we reviewed the global cancer update provided by GLOBOCAN 2020 to add the latest incidence and mortality rates for breast cancer. We have added a discussion of the changing status of the current medical situation of breast cancer patients. In summary, breast cancer has become one of the most prevalent malignancies in women in terms of incidence and mortality. Patients with early stage breast cancer are asymptomatic. However, they are often in the middle to late stages when symptoms become apparent, making treatment difficult and prognosis poor. Therefore, we believe that it is important to explore the means of early diagnosis of breast cancer.

Secondly, we add the reasons why CEA has been chosen as an indicator for the early diagnosis of breast cancer and cite studies by others to support our claims. Firstly, among the methods of early diagnosis of breast cancer, CEA can better reflect the development of the tumour itself. Second, CEA, as a tumour marker, can be obtained by simply collecting venous blood, which has the characteristics of being rapid and easy to collect. Thirdly, monitoring CEA is basically harmless to human body and at the same time highly sensitive. Many studies in recent years have shown that preoperative CEA levels may provide useful information for the identification and treatment of breast cancer. The European Tumour Markers Panel recommends CEA levels as an indicator for the assessment of prognosis, early detection of disease progression and monitoring of treatment in breast cancer patients. We therefore believe that the use of CEA for the early diagnosis of breast cancer is possible. However, as the specificity of CEA for the early diagnosis of breast cancer is low, it is necessary to combine it with other diagnostic methods to improve the diagnostic efficacy.

Thus, combined with clinical experience, we found significant differences between HRV in breast cancer patients and controls, and by referring to the literature we found that HRV is widely used in clinical practice as a non-invasive measure to assess autonomic nervous system activity. The results of Karolina Majerova et al. showed that sympathetic regulation was significantly increased in breast cancer patients compared to healthy volunteers. The results suggest that sympathetic inhibition can inhibit the development of breast cancer. In summary, we conclude that HRV may be a clinical tool for detecting early breast cancer.

Therefore, the aim of this study was to investigate heart rate variability and carcinoembryonic antigen changes in breast cancer patients and their role in the diagnosis of breast cancer, to provide a new complementary method for the early diagnosis of breast cancer and to improve the detection rate.

Response to Reviewer 2: Thank you for your comments. Our answers to your points are as follows.

Major Issues

1.Although the idea of this research has novelty for the combination of HRV and CEA in early diagnosis of breast cancer, the sample size is too small which is not compelling for the conclusion. In that case, I would like to suggest you provide more solid data.

Response 1: Thank you very much indeed for your comments and your confirmation of the novelty of our manuscript.

As you mentioned, the small sample size is the limitation of our manuscript. I am sorry that the number of patients with breast cancer who have a clinical examination such as a 24-hour ambulatory ECG is quite small, which makes it difficult to obtain a larger sample size by reviewing medical records.

We have made modifications and further emphasized this limitation in the manuscript.

2.HRV could be influenced by several important factors in breast cancer survivors, eg. cardiotoxicity of chemotherapy and/or radiation therapy, surgery-induced fatigue, and stress. I suggest the author provide that information on enrolled breast cancer patients and discuss the potential influence on the results

Response2：Thank you very much indeed for your comments. We understand that the interference factors in HRV might exist as the reviewer stated. Information on radiotherapy, chemotherapy, surgery and staging of breast cancer patients is provided in the excel 'data'. In addition, we added a statement in our revised manuscript to reflect this point of the reviewer and will pay more attention on this question in our future studies.

Arab et al showed that patients with advanced breast cancer had lower levels of parasympathetic regulation, an imbalance of autonomic nerves, and may be at increased risk for cardiovascular disease compared to patients with early breast cancer. The stage of breast cancer can affect HRV levels to some extent, whereas the present study did not group the study population by breast cancer stage.

Another limitation of the study was that some breast cancer patients have undergone surgery and chemotherapy. However, factors including preoperative depression, anxiety and cardiotoxicity caused by chemotherapeutic drugs may affect HRV to varying degrees. These influences could not be excluded in this study due to the small sample size. Therefore, a large number of samples from clinical trials are still needed to reduce the interference of other factors with HRV. (p.18, lines 333-341).

3.If you are trying to convince the aberrant HRV could be a new potential biomarker for diagnosis of breast cancer, the control group should include age-matched women with benign breast disease.

Response3: We thank for the reviewer for pointing out this issue. We agree with this suggestion and have searched the articles on HRV and autonomic nervous system in patients with benign breast disease, but we disappointedly found that the relationship between them was not clear. In fact, this is an exciting future area of investigation for us. It is known that the later the stage of malignant tumor patients, the more obvious the reduction of heart rate variability[3]. This may suggest that patients with benign breast disease have less variation in heart rate. In addition, in our hospital, patients with benign breast disease received fewer samples for HRV analysis, which may increase the uncertainty of the results.

Minor Issues

1.Since HRV has been reported to be associated with the stage of cancer, they'd better provide information on breast cancer staging and timing of collecting the blood sample and measuring the HRV as well.

Response1: Thank you very much indeed for your comments. We had provided information on breast cancer staging and timing of collecting the blood sample and measuring the HRV in the excel 'data'. Of the subjects enrolled in the study, we selected the timing of the collection of blood samples and the measurement of HRV to be on essentially the same day. We have added as much information as possible on the staging of breast cancer. However, due to the small number of cases at each stage, we are unable to carry out a study on the relationship between CEA and HRV in patients with different stages of breast cancer at this time.

2.Please consider describing the limitations of your research in the discussion section. For example, a small sample size may cause bias in the conclusion but you are going to enroll more participants to study in the future

Response2: We greatly appreciate your valuable suggestions for the discussion section of our article. We understand that bias of a small sample size might exist as the reviewer stated. In the future, we will continue to accumulate relevant cases or recruit more participants for the study in order to improve the credibility and accuracy of the article. We have made modifications and further emphasized the limitation in the manuscript.

3.Please simplify or precise the notes under all figures.

Response3: Thank you very much for your guidance on the notes and details of our articles. We have precise the notes under all figures. We have simplified the notes under all the figures to make the article more concise and precise.

4.In the introduction section, please add more experimental and clinical evidence of HRV in breast cancer. For example, consider citing some associated literature. 1) Arab C, Dias DP, Barbosa RT, Carvalho TD, Valenti VE, Crocetta TB, Ferreira M, Abreu LC, Ferreira C. Heart rate variability measure in breast cancer patients and survivors: A systematic review. Psychoneuroendocrinology. 2016 Jun;68:57- 68. doi: 10.1016/j.psyneuen.2016.02.018. 2) Majerova K, Zvarik M, Ricon-Becker I, Hanalis-Miller T, Mikolaskova I, Bella V, Mravec B, Hunakova L. Increased sympathetic modulation in breast cancer survivors determined by measurement of heart rate variability. Sci Rep. 2022 Aug 29;12(1):14666. doi: 10.1038/s41598-022-18865-7.

Response4: Thank you very much for your precious and constructive guidance on our foreword. We have rewritten the introduction and added more experimental and clinical evidence of HRV in breast cancer, citing the literature you provided.

Heart rate variability (HRV) refers to the change between each cardiac cycle, which originates from the autonomic regulation of the heart's sinus node. It is considered to be an important indicator of autonomic function and action, reflecting the balance between the vagus and sympathetic nerves. Studies have found that patients with breast cancer have a higher risk of cardiovascular disease which presented a lower HRV, implying vagal dysfunction. Karolina Majerova et al. showed cardiac vagal modulation alterations in breast cancer survivors by measuring HRV, revealing a significant increase in sympathetic modulation in breast cancer patients relative to healthy volunteers. In addition, previous studies have shown that HRV analysis can help determine tumor staging, efficacy, prognosis, and autonomic function. In Desmond G. Powe's trial, beta-blocker therapy significantly reduced distant metastasis, cancer recurrence, and cancer-specific mortality in breast cancer patients, suggesting that sympathetic inhibition can inhibit breast cancer progression. To be concluded, breast cancer patients have impaired autonomic nervous system activity so early recognition is clinically significant to increase treatment chances and survival time. Therefore, HRV, as a non-invasive measure widely used in clinical practice to assess autonomic nervous system activity, may be a clinical tool for detecting early breast cancer.

5. Please explain the reason why you did not choose other serum tumor markers, like CA153 instead of CEA.

Response5：Thank you very much for pointing out this question. CA15-3 and CEA are considered to be the two most valuable tumour markers for the early diagnosis and efficacy of breast cancer due to their high expression and close association with breast carcinogenesis[4]. Wang et al.[5] found that when individual tumour markers were used to diagnose metastatic breast cancer, CEA had the highest sensitivity, relative to CA19-9, CA125, CA15-3 and tissue peptide-specific antigen (TPS). In addition, Li et al.[6] showed that elevated CA15-3 was associated with advanced histological grade and younger age (<35 years), whereas elevated CEA was associated with non-triple-negative tumour types and older age. According to statistics[7], the age of onset of breast cancer is high and the incidence of breast cancer in older women is high, and our study focuses on older patients with early stage breast cancer. In summary, we propose the following hypothesis: CEA may be more appropriate for diagnostic screening of early breast cancer.

6. Several sentences are tediously written and could be shortened.

Response6：Thank you very much for pointing out this question. We apologize for the long and tedious sentences in the article. Several sentences have been shortened and improved to respond to the reviewer’s comments. As the same time, we have had someone specializing in English to touch up the article. This deficiency has been corrected in the revised manuscript. Please see the revised manuscript.

7.I may suggest authors the format of references should be corrected.

Response7：Thank you very much for your valuable suggestions. We have made modifications to the format of the references.

Response to Reviewer 3: Thank you for your comments. Our answers to your points are as follows.

1.Sample size is not big enough in this study, to ensure the sample is sufficiently representative, the number of samples selected is modified to the needs of the statistical analysis. According to cross-sectional study, for age part, as SD set 11.82, the sample size for each group should be 42, as SD set 12.76, the sample size for each group should be 48. Comparative studies, such as comparing experimental and control groups: 30 samples are required at least for each group (Gay, 1992)

Response1：Thank you very much for your valuable suggestions. For normally distributed data, the Mean and Standard Deviation (SD) are generally used to describe concentrated trends and dispersion, while for non-normally distributed data, the Median and Interquartile Range (IQR) are commonly used to describe concentrated trends and dispersion. Dispersion. In our study, the key indicators of CEA and HRV were statistically analysed to be non-normally distributed and therefore standard deviation is not used to describe them.

We have made modifications and further emphasized this limitation in the manuscript.

However, it does not mean that our present study is unreliable. Like some literature, Karolina Majerova et al. studied sympathetic nerve activity in breast cancer survivors by measurement of HRV. Their study sample consisted of four groups. The healthy women control group consisted of 21 randomly selected women. The group of patients with benign tumors consisted of 13 women. The group of patients with active breast cancer consisted of 20 women with newly diagnosed breast cancer, and the group of survivors consisted of 15 women. Despite their small number of subjects per group, their study was recognized by the journal through rigorous screening and inclusion criteria (IF:4.996).

2.For the missing data, it is over 20 percent. The control group is selected from 562 to 18, the breast cancer group is selected from 229 to 19 in the final. If too much original data is missing, not only will the statistical power be reduced, the standard error will become larger, and even the information of the data will be distorted or misleading; it will also make the correlation coefficient matrix or covariate matrix Estimates are biased, which leads to biases in the extraction of common factors, the resulting factors are different from the actual situation.

Response2: Special thanks to you for your good comments. Data missing value does not exist in the independent variable. Our study only removed samples that did not meet the experimental conditions. In addition, as this index was not included in the examination program in the hospital until 2016, data before 2016 was missing, which may be similar to missing completely at random. Therefore, we adopt the direct elimination method to complete case analysis, which will not bias the evaluation of the results. We will be happy to edit the text further, based on helpful comments from the reviewers.

Response to Reviewer 4: Thank you for your comments. Our answers to your points are as follows.

Response1: Thank you for the constructive comments and suggestions that helped us to greatly improve the manuscript. We have had the article touched up by someone who specialises in English to ensure that it is grammatically correct. This deficiency has been corrected in the revised manuscript. Please see the revised manuscript.

Response2: Thank you very much for your precious and constructive guidance on our foreword. The introduction has been rewritten to include references to a number of clinical and experimental studies related to breast cancer, and to provide more detail on the link between HRV and CEA and breast cancer. Background information on breast cancer has also been added. (p. 3-5, lines 44-88)

First, we reviewed the global cancer update provided by GLOBOCAN 2020 to add the latest incidence and mortality rates for breast cancer. We have added a discussion of the changing status of the current medical situation of breast cancer patients. In summary, breast cancer has become one of the most prevalent malignancies in women in terms of incidence and mortality. Patients with early stage breast cancer are asymptomatic and by the time symptoms become apparent they are often in the middle to late stages, making treatment difficult and prognosis poor. Therefore, we believe that it is important to explore the means of early diagnosis of breast cancer.

3. Novelty of this work. To publish as an original article, the novelty of this work needs to be emphasized so the readers can fully appreciate the importance of your work.

Response3: Thank you very much indeed for your comments. We agree with this suggestion, so we have made modifications and further emphasized the novelty of this work in the manuscript.

Response4: Thank you very much indeed for your comments. We have added the significance of a negative correlation between HRV and CEA in the results section. (p.12, lines 211-216). In the Discussion section, we explain in detail the possible mechanism for the negative correlation between HRV and CEA in breast cancer patients, showing that as breast cancer progresses and cancer cells migrate and metastasize, patients have decreased HRV and increased levels of serum CEA, which is widely present in tumor cell membranes. (p.15-16, lines 265-298).

Spearman's correlation analysis showed that total LF, awake TP, and awake LF were negatively correlated with the CEA index in both groups (P<0.05). Given that CEA has good diagnostic performance for breast cancer as a risk factor and differences in HRV parameters between groups, CEA and HRV parameters may have a joint diagnostic effect on breast cancer. (p.12, lines 211-216).

In addition, we explain in the discussion section that the significant reduction in HRV in breast cancer patients in our experimental results suggests the presence of autonomic dysfunction, and speculate that HRV is of great value in the diagnosis of breast cancer. (p.14-15, lines 247-264). The results of the binary logistic regression analysis suggested that CEA was a risk factor for breast cancer, and the results of the ROC curve analysis suggested that the combination of CEA and HRV was of high value in the diagnosis of breast cancer. (p.16-17, lines 299-313).

5. Joint prediction using combined markers have shown to improve the prediction performance. What computational models did the authors use to the markers (awake TP, awake LF and CEA) are not shown.

Response5: Thanks for your kind suggestions, which is valuable for improving the accuracy of the manuscript. Considering the Reviewer’s suggestion, we have added our approach to deriving the joint prediction model in the article. (p.12, lines 221-223). The logistic regression equation calculates the probability of an outcome based on the values of awake TP, awake LF and CEA, which combine the diagnostic efficacy of awake TP, awake LF and CEA. This union is essentially the C-Statistics for computing logistic regression models. In addition, the data from the logistic regression equation are presented in Supplementary file 6.

We combined awake TP, awake LF, and CEA separately as a new combined diagnostic model, and then incorporated logistic regression models to derive predictive probabilities, and finally performed ROC curve analysis. (p.12, lines 221-223).

6. How to validate the findings in this manuscript? Have the authors consider independent studies as validation datasets?

Response6: We thank the reviewer for pointing out this issue. We fully agree with the reviewer that we indeed should have applied tests to validate the findings. The results require experiments with a larger sample size to verify their validity. In the future, we will focus more on studying the correlation of tumor markers with autonomic nerve and heart rate variation for further verification. And this is an interesting open issue, and we will continue to consider independent studies as validation datasets.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked with tracked revisions in revised paper. We appreciate for Editors/Reviewers’ warm work earnestly and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions.

References:

[1]. Majerova, K., et al., Increased sympathetic modulation in breast cancer survivors determined by measurement of heart rate variability. Sci Rep, 2022. 12(1): p. 14666.

[2]. Wang, Y.Y. and R.H. Sun, [Application of PASS in sample size estimation of non-inferiority, equivalence and superiority design in clinical trials]. Zhonghua Liu Xing Bing Xue Za Zhi, 2016. 37(5): p. 741-4.

[3]. Chen C, Liu HY. Analysis of heart rate variability in patients with different stages of malignant tumors. J Fudan Univ Med Sci. 2009;36(4):413-6

[4]. Tarighati, E., H. Keivan and H. Mahani, A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med, 2022.

[5]. Wang, W., et al., The diagnostic value of serum tumor markers CEA, CA19-9, CA125, CA15-3, and TPS in metastatic breast cancer. Clin Chim Acta, 2017. 470: p. 51-55.

[6]. Li, X., et al., Clinicopathological and Prognostic Significance of Cancer Antigen 15-3 and Carcinoembryonic Antigen in Breast Cancer: A Meta-Analysis including 12,993 Patients. Dis Markers, 2018. 2018: p. 9863092.

[7]. Kashyap, D., et al., Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures. Biomed Res Int, 2022. 2022: p. 9605439.

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PLoS One. doi: 10.1371/journal.pone.0282221.r003

Decision Letter 1

Alessandro Rizzo

10 Feb 2023

Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigen

PONE-D-22-29053R1

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PLoS One. doi: 10.1371/journal.pone.0282221.r004

Acceptance letter

Alessandro Rizzo

29 Mar 2023

PONE-D-22-29053R1

Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigen

Dear Dr. He:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data

(XLSX)

Click here for additional data file.^{(22.6KB, xlsx)}

S1 Table. Results of normality test.

In the grouping column, “1” represents the breast cancer group and “0” represents the control group.

(PDF)

Click here for additional data file.^{(244.9KB, pdf)}

S2 Table. Comparison of HRV and CEA indicators between the two groups.

(PDF)

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S3 Table. The results of Hosmer-Lemeshow test.

(PDF)

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S4 Table. Results of collinearity regression.

SE, standard error; D-W value, the indicators of Durbin-Watson test. -7.550E-5 represents -7.550×10⁻⁵.

(PDF)

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S5 Table. Correlation between HRV and CEA.

(PDF)

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S6 Table. Joint prediction of awake TP, awake LF and CEA.

(PDF)

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Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.

[pone.0282221.ref001] 1.Wei WQ, Zeng HM, Zheng RS, Zhang SW, An L, Chen R, et al. Cancer registration in China and its role in cancer prevention and control. Lancet Oncol, 2020. 21(7): p. e342–e349. doi: 10.1016/S1470-2045(20)30073-5 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref002] 2.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 2021. 71(3): p. 209–249. doi: 10.3322/caac.21660 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref003] 3.Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. Biol Res, 2017. 50(1): p. 33. doi: 10.1186/s40659-017-0140-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref004] 4.Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA, 2019. 321(3): p. 288–300. doi: 10.1001/jama.2018.19323 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref005] 5.Rizzo A, Ricci AD, Lanotte L, Lombardi L, Federico AD, Brandi G, et al. Immune-based combinations for metastatic triple negative breast cancer in clinical trials: current knowledge and therapeutic prospects. Expert Opin Investig Drugs, 2022. 31(6): p. 557–565. doi: 10.1080/13543784.2022.2009456 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref006] 6.Rizzo A, Ricci AD. Biomarkers for breast cancer immunotherapy: PD-L1, TILs, and beyond. Expert Opin Investig Drugs, 2022. 31(6): p. 549–555. doi: 10.1080/13543784.2022.2008354 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref007] 7.Viscardi G, Tralongo AC, Massari F, Lambertini M, Mollica V, Rizzo A, et al. Comparative assessment of early mortality risk upon immune checkpoint inhibitors alone or in combination with other agents across solid malignancies: a systematic review and meta-analysis. Eur J Cancer, 2022. 177: p. 175–185. doi: 10.1016/j.ejca.2022.09.031 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref008] 8.Trayes KP, Cokenakes SHE. Breast Cancer Treatment. Am Fam Physician, 2021. 104(2): p. 171–178. . [PubMed] [Google Scholar]

[pone.0282221.ref009] 9.Wang NN. Research status and application prospect of combined detection of female tumor markers. Electr J Pract Gynecol Endocrinol. 2018;5(31):24,6. doi: 10.16484/j.cnki.issn2095-8803.2018.31.013 [DOI] [Google Scholar]

[pone.0282221.ref010] 10.Gobbini E, Ezzalfani M, Dieras V, Bachelot T, Brain E, Debled M, et al. Time trends of overall survival among metastatic breast cancer patients in the real-life ESME cohort. Eur J Cancer, 2018. 96: p. 17–24. doi: 10.1016/j.ejca.2018.03.015 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref011] 11.Cardoso F, Costa A, Norton L, Senkus E, Aapro M, André F, et al. ESO-ESMO 2nd international consensus guidelines for advanced breast cancer (ABC2). Breast, 2014. 23(5): p. 489–502. doi: 10.1016/j.breast.2014.08.009 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref012] 12.Cardoso F, Costa A, Norton L, Senkus E, Aapro M, André F, et al. ESO-ESMO 2nd international consensus guidelines for advanced breast cancer (ABC2)dagger. Ann Oncol, 2014. 25(10): p. 1871–1888. doi: 10.1093/annonc/mdu385 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref013] 13.He ZY, Chen Z, Tan MD, Elingarami S, Liu Y, Li TT, et al. A review on methods for diagnosis of breast cancer cells and tissues. Cell Prolif, 2020. 53(7): p. e12822. doi: 10.1111/cpr.12822 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref014] 14.Jafari SH, Saadatpour Z, Salmaninejad A, Momeni F, Mokhtari M, Nahand JS, et al. Breast cancer diagnosis: Imaging techniques and biochemical markers. J Cell Physiol, 2018. 233(7): p. 5200–5213. doi: 10.1002/jcp.26379 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref015] 15.Tarighati E, Keivan H, Mahani H. A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med, 2022. doi: 10.1007/s10238-021-00781-1 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref016] 16.Shao YB, Sun XF, He YN, Liu CJ, Liu H. Elevated Levels of Serum Tumor Markers CEA and CA15-3 Are Prognostic Parameters for Different Molecular Subtypes of Breast Cancer. PLoS One, 2015. 10(7): p. e0133830. doi: 10.1371/journal.pone.0133830 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref017] 17.Wu SG, He ZY, Zhou J, Sun JY, Li FY, Lin Q, et al. Serum levels of CEA and CA15-3 in different molecular subtypes and prognostic value in Chinese breast cancer. Breast, 2014. 23(1): p. 88–93. doi: 10.1016/j.breast.2013.11.003 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref018] 18.Molina R, Barak V, Dalen AV, Duffy MJ, Einarsson R, Gion M, et al. Tumor markers in breast cancer- European Group on Tumor Markers recommendations. Tumour Biol, 2005. 26(6): p. 281–93. doi: 10.1159/000089260 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref019] 19.Caro-Moran E, Fernández-Lao C, Galiano-Castillo N, Cantarero-Villanueva I, Arroyo-Morales M, Díaz-Rodríguez L. Heart Rate Variability in Breast Cancer Survivors After the First Year of Treatments: A Case-Controlled Study. Biol Res Nurs, 2016. 18(1): p. 43–9. doi: 10.1177/1099800414568100 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref020] 20.Vigo C, Gatzemeier W, Sala R, Malacarne M, Santoro A, Pagani M, et al. Evidence of altered autonomic cardiac regulation in breast cancer survivors. J Cancer Surviv, 2015. 9(4): p. 699–706. doi: 10.1007/s11764-015-0445-z [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref021] 21.Wu S, Chen M, Wang JF, Shi B, Zhou YF. Association of Short-Term Heart Rate Variability With Breast Tumor Stage. Front Physiol, 2021. 12: p. 678428. doi: 10.3389/fphys.2021.678428 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref022] 22.Majerova K, Zvarik M, Ricon-Becker I, Hanalis-Miller T, Mikolaskova I, Bella V, et al. Increased sympathetic modulation in breast cancer survivors determined by measurement of heart rate variability. Sci Rep, 2022. 12(1): p. 14666. doi: 10.1038/s41598-022-18865-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref023] 23.Fagundes CP, Murray DM, Hwang BS, Gouin JP, Thayer JF, Sollers JJ 3rd, et al. Sympathetic and parasympathetic activity in cancer-related fatigue: more evidence for a physiological substrate in cancer survivors. Psychoneuroendocrinology, 2011. 36(8): p. 1137–47. doi: 10.1016/j.psyneuen.2011.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref024] 24.Mouton C, Ronson A, Razavi D, Delhaye F, Kupper N, Paesmans M, et al. The relationship between heart rate variability and time-course of carcinoembryonic antigen in colorectal cancer. Auton Neurosci, 2012. 166(1–2): p. 96–9. doi: 10.1016/j.autneu.2011.10.002 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref025] 25.Chen C, Liu HY. Analysis of heart rate variability in patients with different stages of malignant tumors. J Fudan Univ Med Sci. 2009;36(4):413–6. [Google Scholar]

[pone.0282221.ref026] 26.Powe DG, Voss MJ, Zänker KS, Habashy HO, Green AR, Ellis IO, et al., Beta-blocker drug therapy reduces secondary cancer formation in breast cancer and improves cancer specific survival. Oncotarget, 2010. 1(7): p. 628–38. doi: 10.18632/oncotarget.101009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref027] 27.Sztajzel J. Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system. Swiss Med Wkly, 2004. 134(35–36): p. 514–22. doi: 10.4414/smw.2004.10321 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref028] 28.Arab C, Dias DPM, Barbosa RTDA, Carvalho TDD, Valenti VE, Crocetta TB, et al. Heart rate variability measure in breast cancer patients and survivors: A systematic review. Psychoneuroendocrinology, 2016. 68: p. 57–68. doi: 10.1016/j.psyneuen.2016.02.018 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref029] 29.Tang S, Zhou F, Sun YF, Wei LL, Zhu S, Yang RQ, et al. CEA in breast ductal secretions as a promising biomarker for the diagnosis of breast cancer: a systematic review and meta-analysis. Breast Cancer, 2016. 23(6): p. 813–819. doi: 10.1007/s12282-016-0680-9 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref030] 30.Li CX, Ding XM, Zhu HJ. Research progress of heart rate variability analysis in cardiovascular diseases. Chin J Integr Med Cardio Cerebrovasc Disease. 2020;18(1):82–4. doi: 10.12102/j.issn.1672-1349.2020.01.018 [DOI] [Google Scholar]

[pone.0282221.ref031] 31.Qu HL, Liu RL. Research progress of exercise therapy combined with heart rate variability analysis for prevention and treatment of cardiovascular diseases. Chin J Gerontol. 2009;29(24):3309–11. doi: 10.3969/j.issn.1005-9202.2009.24.080 [DOI] [Google Scholar]

[pone.0282221.ref032] 32.Li JX, Zhang FJ, Liu X, Li XQ. Mechanism of heart rate variability and research advance in its relevance to cardiovascular diseases. J Pract Electrocardiol. 2021;30(4):293–6. doi: 10.13308/j.issn.2095-9354.2021.04.014 [DOI] [Google Scholar]

[pone.0282221.ref033] 33.Yan RN, Li Y, Wang X, Chen Q. Clinical significance of heart rate variability in cancer patients. J Community Med. 2022;20(10):566–9. [Google Scholar]

[pone.0282221.ref034] 34.Guo Y, Koshy S, Hui D, Palmer JL, Shin K, Bozkurt M, et al. Prognostic Value of Heart Rate Variability in Patients With Cancer. Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society, 2015. 32(6). doi: 10.1097/WNP.0000000000000210 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref035] 35.Xie YL, Ren J, Yu D, Yang HX, Liu J. Effects of 24-week tai chi exercise on heart rate variability in middle-aged and elderly people. Chin J Sport Med. 2011;30(9):842–4. [Google Scholar]

[pone.0282221.ref036] 36.Wu S. Correlation analysis between short-range heart rate variability and TNM stage of breast cancer patients: Bengbu Med Coll. 2022.

[pone.0282221.ref037] 37.Liang LP, Liang J, Liang HR. Clinical value of dynamic electrocardiogram in breast cancer patients undergoing postoperative chemotherapy. Shenzhen J Integr Tradit Chin Western Med. 2015;25(23):70–1. doi: 10.16458/j.cnki.1007-0893.2015.23.037 [DOI] [Google Scholar]

[pone.0282221.ref038] 38.EscutiaReyes D, Garduño-García JDJ, Emilio-López-Chávez G, Gómez-Villanueva A, Pliego-Carrillo AC, Soto-Piña AE, et al. Differences in heart rate variability and body composition in breast cancer survivors and women without cancer. Scientific reports, 2021. 11(1). doi: 10.1038/s41598-021-93713-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref039] 39.Tibensky M, Mravec B. Role of the parasympathetic nervous system in cancer initiation and progression. Clinical & translational oncology: official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2020. doi: 10.1007/s12094-020-02465-w [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref040] 40.Wang J, Wang XX, Li GL, Hu J, Quan Y. Carcinoembryonic antigen level and its abnormal time and its correlation with tumor incidence in physical examination population. J Chin Gen Pract. 2011;14(27):3149–50. [Google Scholar]

[pone.0282221.ref041] 41.Hu N, Liu L, Zeng Y, Jiao ZL, Jin S. Research progress on the relationship between serum carcinoembryonic antigen and lung cancer. Oncol Prog. 2020;18(6):547–8. doi: 10.11877/j.issn.1672-1535.2020.18.06.03 [DOI] [Google Scholar]

[pone.0282221.ref042] 42.Michelle M, Borniger JC, D’Silva NJ, Deneen B, Dirks PB, Fattahi F, et al. Roadmap for the Emerging Field of Cancer Neuroscience. Cell, 2020. 181(2). doi: 10.1016/j.cell.2020.03.034 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282221.ref043] 43.Eckerling A, Ricon-Becker I, Sorski L, Sandbank E, Ben-Eliyahu S. Stress and cancer: mechanisms, significance and future directions. Nature reviews. Cancer, 2021. 21(12). doi: 10.1038/s41568-021-00395-5 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref044] 44.Shi CY, Wang SX, Feng HQ. Advances in the relationship between tumor markers CA15-3, CEA and CA125 and breast cancer. Med Res Educ. 2012;29(3):49–52. [Google Scholar]

[pone.0282221.ref045] 45.Li DY, Du B, Zheng YJ, Shen YH. Study on relationship between colonoscopy specimens, CEA combined with CA125 levels and colon cancer. China J Mod Med. 2019;29(1):113–6. doi: 10.3969/j.issn.1005-8982.2019.01.021 [DOI] [Google Scholar]

[pone.0282221.ref046] 46.Farbood A, Sahmeddini MA, Bayat S, Karami N. The effect of preoperative depression and anxiety on heart rate variability in women with breast cancer. Breast cancer (Tokyo, Japan), 2020. 27(5). doi: 10.1007/s12282-020-01087-y [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref047] 47.Arab C, Vanderlei LCM, Paiva LDS, Fulghum KL, Fristachi CE, Nazario ACP, et al. Cardiac autonomic modulation impairments in advanced breast cancer patients. Clin Res Cardiol, 2018. 107(10): p. 924–936. doi: 10.1007/s00392-018-1264-9 [DOI] [PubMed] [Google Scholar]

[pone.0282221.ref048] 48.Huang MG, Cao AH, Duan Y, Xue YF. Observation of heart rate variability in breast cancer patients before and after chemotherapy. South China Journal of Cardiovascular Diseases, 2011. 17(S1): 109. [Google Scholar]

PERMALINK

Diagnostic role of heart rate variability in breast cancer and its relationship with peripheral serum carcinoembryonic antigen

Lishan Ding

Yuepeng Yang

Mingsi Chi

Zijun Chen

Yaping Huang

Wenshan Ouyang

Weijian Li

Lei He

Ting Wei

Roles

Abstract

Objective

Methods

Results

Conclusions

Introduction

Materials and methods

Participants and procedures

Measures

Biochemistry

HRV

Statistical analysis

Results

Participants selection

Fig 1. Inclusion and exclusion criteria for patient selection.

Comparison of general information between the two groups

Table 1. Comparison of general information between the two groups.

Comparison of HRV and CEA indicators between the two groups

Fig 2. Mean serum CEA, total LF, awake TP, and awake LF of the groups.

Multifactorial logistic regression analysis of breast cancer group

Table 2. Logistic regression analysis of factors independently associated with breast cancer.

Correlation analysis of HRV parameters and CEA

Fig 3. The association between CEA and total LF, awake TP, awake LF.

Analysis of the diagnostic efficacy of serum CEA and HRV parameters in breast cancer

Table 3. Diagnostic efficacy of HRV parameters, CEA, and combined prediction for breast cancer.

Fig 4. Receiver operating characteristic (ROC) curves for HRV parameters and CEA.

Discussion

Conclusions

Limitations

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Alessandro Rizzo

Roles

Author response to Decision Letter 0

Decision Letter 1

Alessandro Rizzo

Roles

Acceptance letter

Alessandro Rizzo

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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