The prognostic predictive value of monocyte to high-density lipoprotein cholesterol ratio in HR+/HER− breast cancer patients

Introduction

Breast cancer is now the biggest threat to women’s health worldwide, ranking first in new cancer diagnoses and second in cancer-related deaths. Data from the American Cancer Society indicate that the most common cancer diagnosed in women in 2024 was breast cancer, accounting for 32% of all cases (1). Breast cancer can be stratified into multiple molecular subtypes according to the expression status of hormone receptors (HRs) [estrogen receptor (ER) and progesterone receptor (PR)] and human epidermal growth factor receptor 2 (HER2), each with distinct clinicopathological features and therapeutic implications (2). Accounting for approximately 70% of all breast cancer cases, the HR+/HER2− subtype is characterized by positive HRs (ER and/or PR) status and negative HER2 expression (3). Compared with HER2+ and triple negative breast cancer (TNBC), HR+/HER2− breast cancer patients generally have a better prognosis owing to their sensitivity to anti-hormone therapy. Among HR+/HER2− breast cancer patients, approximately half of the recurrences occurs after 5 years from the initial diagnosis (4). Although the recurrence rate of HR+/HER2− breast cancer is relatively stable, recurrence can still recur for up to 20 years from initial diagnosis (5). In order to reduce the rate of late recurrence, it is necessary to improve the management of HR+/HER2− breast cancer patients.

The monocyte to high-density lipoprotein cholesterol ratio (MHR) obtained by dividing monocyte count by high-density lipoprotein cholesterol (HDLC) has recently been identified as a novel biomarker representing the equilibrium between pro-inflammatory monocytes and anti-inflammatory high-density lipoprotein (HDL) (6,7). Several studies have reported that elevated MHR is correlated with worse clinical outcomes in multiple cancer types including gastric cancer, papillary thyroid cancer, colorectal cancer, hepatocellular cancer (6-9). These findings indicate that MHR, as a cost-effective and straightforward biomarker, has the potential utility in breast cancer risk stratification. Beyond MHR, several other systemic inflammation biomarkers, namely the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and systemic immune inflammation index (SII) have demonstrated independent prognostic value in patients with breast cancer (10,11).

Numerous prediction models have been constructed to predict survival outcomes in HR+/HER2− breast cancer based on factors such as age, menopausal status, tumor size, lymph node status, histopathologic grade, adjuvant therapy, and genetic background (12-14). Despite these findings, MHR has yet to be combined with clinicopathological variables for prognostic prediction in HR+/HER2− breast cancer patients. Accordingly, the present study seeks to construct a prognostic nomogram for patients with HR+/HER2− breast cancer that integrates clinicopathological factors together with molecular variables, specifically MHR and other systemic inflammation biomarkers. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0507/rc).

Methods

Patient population and data processing

Data for the training and validation groups were obtained from 310 patients with HR+/HER2− breast cancer who fulfilled the inclusion criteria and received diagnosis and treatment at Wenzhou Central Hospital from 2012 to 2017. Inclusion criteria: (I) female sex; (II) age ≥18 years; (III) histologically confirmed non-metastatic invasive HR+/HER2− breast cancer; (IV) complete clinical and laboratory data; and (V) available tumor tissue samples. Exclusion criteria: (I) inflammatory breast cancer; (II) prior history of malignancy; (III) pre-existing hematological or autoimmune disorders; (IV) recent active inflammatory disease; and (V) history of significant organ dysfunction (including acute/chronic hepatic or renal impairment and severe cardiopulmonary disease). The criteria for HR+/HER2− breast cancer classification required ER and/or PR positive status, with reference to immunohistochemical staining positive cells ≥1%, and HER2 negative status. HER2 overexpression was considered as HER2 positive. HER2 negative or low expression were considered as HER2 negative. Fluorescence in situ hybridization (FISH) was performed to determine HER2 amplification status in patients with HER2 expression between the two above statuses. A positive FISH result was classified as HER2-positive, whereas a negative FISH result was classified as HER2-negative. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Wenzhou Central Hospital (No. L2025-11-006). All enrolled patients provided their written informed consent prior to participation. Ultimately, 310 patients met the inclusion criteria and were enrolled in the study. Clinicopathologic data including age at diagnosis, menstrual status, bodyweight, height, surgery type, histopathological type, pathology grade, Ki-67 index, tumor size, lymph node metastasis (LNM) and multiple tumor lesions was collected. Routine blood examination was conducted 2 weeks prior to surgery in order to obtain laboratory data. All patients received postoperative adjuvant treatment following the standard protocols outlined in the Chinese clinical practice guidelines applicable at the time of diagnosis.

Follow-up and endpoints

The study’s endpoints comprised disease-free survival (DFS) and overall survival (OS). DFS was calculated from the date of diagnosis to the date of first occurrence of local or regional recurrence, distant metastasis, contralateral breast cancer, death (including non-cancer death), or last follow-up (October 2025), whichever occurred first. OS was measured from the date of diagnosis to the date of death from any cause or last follow-up (October 2025).

Statistical analysis

The statistical analysis was conducted with SPSS Statistics 26.0 software (IBM Corporation, Armonk, NY, USA). The Pearson Chi-squared test was used to compare the rates between groups. To select independent prognostic factors, we employed the log-rank test, Cox univariate analysis, and the Cox regression model. Univariate predictors were selected by log-rank test and Cox univariate analysis (P<0.05). Variables with P<0.05 from the univariate analysis were then entered into a Cox risk regression model with backward elimination to identify independent prognostic factors (P<0.05) that would be included in the final nomogram. Survival curves for distinct variables were generated using the Kaplan-Meier estimates and were compared using log-rank test. All P values were two-sided, and P<0.05 was considered statistically significant. To address multicollinearity between inflammatory markers, we calculated variance inflation factors (VIFs). R 3.5.3 software (www.r-project.org/) was used to develop nomogram. X-tile 3.6.1 software (Yale University, CT, USA) was used to determine cutoff values of variables with the total group. Then, these derived cutoff values were locked and subsequently applied to the separate training and validation groups to avoid overfitting. Internal validation was undertaken with a validation group. To assess the performance of the nomogram, we evaluated both its predictive accuracy and discriminative ability using the concordance index (C-index), the area under the curve (AUC), and calibration curves. The predictive performance of our nomogram was compared head-to-head with that of the eighth American Joint Committee on Cancer (AJCC) staging system using the overall dataset.

Results

Study population characteristics

The current study enrolled 310 patients with primary HR+/HER2− breast cancer. All 310 patients were randomly assigned to training and validation groups using R software, with 66% allocated to the training group (seed set to 81). The median follow-up duration for the 310 patients was 120 months, with a range of 18 to 165 months. During the follow-up period, 62 recurrence and 28 death cases occurred. DFS and OS rates were 80.0% and 91.0%, respectively. The adjuvant treatments received and the clinicopathological characteristics of patients in the training and validation groups were shown in Table 1. Forty recurrence and 19 death cases occurred in the training group. By contrast, 22 recurrence and 9 death cases occurred in the validation group. No statistically significant difference was observed between the training and validation groups with respect to clinicopathological characteristics.

DFS-related prognostic factors

Univariate analysis conducted in the training group showed that age, tumor size, LNM, Ki-67 index, NLR and MHR were relevant prognostic factors for DFS. Thus, the six factors were incorporated into the multivariate model. Finally, age, tumor size, LNM, NLR, and MHR were shown to be independent prognostic factors for DFS in the final Cox multivariate regression model (Table 2). The VIFs of NLR, PLR, SII and MHR was 3.268, 2.107, 5.034 and 1.006 respectively indicating no significant collinearity among inflammatory markers.

Development of a prediction model for DFS

The R software was used to incorporate these five independent prognostic factors into a nomogram for the prediction of DFS (Figure 1). Different subtype of these five independent prognostic factors was assigned a score. The total score of each patient was obtained by adding scores of five variables incorporated into the nomogram. The total score allowed us to predicted 5- and 8-year DFS for each individual.

Figure 1 Nomogram for predicting DFS in HR+/HER2− breast cancer. DFS, disease-free survival; MHR, monocyte to high density lipoprotein cholesterol ratio; NLR, neutrophil to lymphocyte count ratio.

Evaluation of the prediction model

In the training group, our nomogram for predicting DFS achieved a C-index of 0.781 [95% confidence interval (CI): 0.704–0.858]. In the validation group, findings showed that the C-index of DFS was 0.733 (95% CI: 0.627–0.839). Calibration curves showed great consistency between the actual and predicted probability of 5-, and 8-year DFS in both training and validation groups (Figure 2).

Figure 2 Calibration curves for prediction of DFS in patients with HR+/HER2− breast cancer in training group at (A) 5 years and (B) 8 years and in validation group at (C) 5 years and (D) 8 years. Predicted probability is plotted on x-axis and actual survival is plotted on y-axis. DFS, disease-free survival.

Comparison with the 8th AJCC staging system

The C-index was 0.754 (95% CI: 0.690–0.818) for our nomogram and was 0.681 (95% CI: 0.612–0.750; P<0.001) for the 8th AJCC tumor, node, metastasis (TNM) staging system. For predicting 5- and 8-year DFS, the AUCs were 0.811 and 0.779 for our nomogram, and 0.742 and 0.674 for the 8th AJCC TNM staging system (Figure 3). The above data indicate that our nomogram outperformed the traditional TNM staging system in terms of predictive accuracy and discriminative ability.

Figure 3 The 5 years (A) and 8 years (B) ROC curves for disease-free survival. AUC, area under the curve; ROC, receiver operating characteristic; TNM, tumor, node, metastasis.

Nomogram performance in risk stratification

We employed X-tile software to determine the cutoff values for the total DFS score, thereby grouping patients in the total, training, and validation sets into three risk groups. As shown in Figure 4, survival outcomes of different risk groups showed significant differences.

Figure 4 Risk group stratification in the total, training and validation group. DFS curves of patients in the total group (A), training group (B) and validation group (C). DFS, disease-free survival.

Discussion

Breast cancer is now the biggest threat to women’s health worldwide, ranking first in new cancer diagnoses and second in cancer-related deaths. Based on the data investigated by American Cancer Society in 2024, about 313,510 breast cancer cases and 42,780 deaths due to breast cancer would be diagnosed in 2024 (1). Approximately 70% of patients with breast cancer are diagnosed as HR +/HER2−. Patients with HR+/HER2− breast cancer have a better prognosis than those with the other two subtypes (15). Although the recurrence rate of HR+/HER2− breast cancer is relatively stable, recurrence can still occur for up to 20 years from initial diagnosis (5). Moreover, a group of highly heterogeneous breast cancers are included in HR+/HER2− breast cancer, and there is significant variability in the clinical outcomes among patients (16). Thus, it is essential to classify HR+/HER2− breast cancer patients based on individual characteristics in order to predict outcomes and support clinicians in tailoring personalized treatment approaches. Previous studies have identified age, tumor size, LNM, and NLR as independent prognostic factors for breast cancer, which aligns with the results of the current study (14,17-19). Moreover, this study finds MHR as an independent prognostic factor for breast cancer for the first time.

Systemic inflammatory indicators have been proven to be reliable and easily performed prognostic indicators in various cancers, including breast cancer (14,20,21). Numerous evidences have proven the important role of inflammation in cancer development, progression, metastasis and treatment resistance (22). Theoretically, the activation of systemic inflammation can be reflected by the changes in circulating white blood cells. According to the number of circulating inflammatory cells, some combined indices including NLR, PLR, SII are calculated as simple parameters to reflect systemic inflammation.

MHR is a novel systemic inflammation marker and has been proven to be associated with worse prognosis in several malignancies including gastric cancer, papillary thyroid cancer, colorectal cancer, hepatocellular cancer (6-9). In accordance with other systemic inflammation markers such as NLR, PLR and SII, MHR can be easily derived from routine blood tests commonly conducted for cancer patients in clinical practice. The current study demonstrated an association between elevated MHR levels and unfavorable prognosis in HR+/HER2− breast cancer patients. Unlike other inflammatory indicators such as NLR or PLR, MHR reflects not only systemic inflammation but also abnormal lipid metabolism since monocytes serve as an inflammatory marker and HDLC is a blood lipid indicator. The rationale for MHR as a prognostic indicator is biologically supported by the important functions of HDL and circulating monocytes in tumor biology and systemic inflammation. As essential cells in immune system, monocytes play an important role in the inflammatory response. Differentiation of monocytes into tumor-associated macrophages drives angiogenesis and immune evasion (8,23). For the contrary, HDL plays an important role in reverse cholesterol transport and possesses antioxidative as well as anti-inflammatory properties (24). Hyperlipidemia has been demonstrated to impair arterial intimal function. Low density lipoprotein (LDL) enters the intima and undergoes oxidative modification, leading to intimal damage. In response, damaged vascular endothelial cells express adhesion molecules, which enable monocytes to bind, migrate to the subendothelial space, and mature into macrophages. Oxidized low density lipoprotein cholesterol (LDLC) subsequently promotes the release of pro-inflammatory cytokines and induces chronic inflammation, thereby facilitating the development and progression of breast cancer (7). HDL mediates reverse cholesterol transport and exerts antioxidative and anti-inflammatory effects. On the one hand, low or damaged HDL mediates low cholesterol efflux which is associated with increased monocyte counts, thereby contributing to the advancement of chronic inflammation (25). On the other hand, the progression of oxidative stress and inflammatory response can be effectively inhibited by HDL (26). Through inhibition of p38c and phosphoinositol 3 kinase activation, HDL is able to downregulate tissue factor expression in monocytes (27). HDL additionally reduces F-actin expression, thereby inhibiting monocyte aggregation and adhesion to the vascular endothelium, and also modulates monocyte activation, proliferation, and differentiation (28), ultimately exerts antioxidative and anti-inflammatory effect.

It has been reported that elevated NLR is associated with poor prognosis in patients with breast cancer (19). The current study confirmed the finding. While the precise molecular mechanisms remain to be fully clarified, prior studies have shown that neutrophils promote tumor progression through multiple mechanisms, such as driving the cell cycle progression of circulating tumor cells in the bloodstream and increasing their metastatic potential (29). It has been reported that neutrophils release pro-inflammatory factors including IL-6, IL-8, IL-1β and TNF-α, thus triggering the process of cancer cell migration and invasion (30). Furthermore, the cytotoxic activity of immune cells, including lymphocytes, natural killer cells and T cells, which would counteract the anti-tumor immune response can be inhibited by neutrophils (10). By contrast, the important roles of lymphocytes in preventing tumor growth and in immunity have been established by previous studies (31). Lymphocytopenia reflects impaired anti-tumor immunity, which in turn facilitates a microenvironment conducive to tumor cell expansion (32).

The protein Ki-67, which is encoded by the MKI67 gene, is expressed during the G1, S, G2, and M cell cycle stages (33). Ki-67 index can be assessed by immunohistochemistry and reflect the proliferative activity of breast cancer (33). Previous studies showed that Ki-67 index can predict the risk of recurrence in breast cancer patients and can assess their response to systemic therapeutic strategies (14,34). Our study found Ki-67 as a prognostic factor during univariate analysis. However, it was excluded during the final Cox multivariate analysis. This may due to the short follow-up period and limited sample capacity of our study.

Postoperative adjuvant chemotherapy represents an important and effective therapeutic approach for breast cancer. In high-risk breast cancer associated with poor prognosis including TNBC, HER2+ breast cancer, as well as tumors with larger size or greater lymph node involvement, adjuvant chemotherapy has been shown to significantly improve both DFS and OS. In contrast, its benefit remains debated in patients with HR+/HER2− early-stage breast cancer. Current clinical guidelines generally recommend that patients with HR+/HER2− early breast cancer undergo testing with Oncotype DX or MammaPrint to assess the necessity of postoperative adjuvant chemotherapy (35,36). However, the widespread adoption of such genomic testing is constrained by its high cost and limited accessibility, which remain challenges in both developing and developed countries (37). For the above reason, developing a simple clinical prediction model to forecast the prognosis of HR+/HER2− breast cancer and to inform decisions regarding postoperative adjuvant chemotherapy is of considerable importance. Our nomogram can effectively stratify patients into low-, medium-, and high-risk groups with markedly distinct prognoses across the three groups. This stratification helps identify patients who may be exempt from postoperative chemotherapy, thereby further guiding treatment planning.

The nomogram created in this study revealed remarkable predictive accuracy and effective discriminative ability. The DFS C-index was 0.781 (95% CI: 0.704–0.858) in the training cohort, compared to 0.733 (95% CI: 0.627–0.839) in the validation cohort. Calibration curves showed great consistency between the actual and predicted probability of 5-, and 8-year DFS in both groups. Besides, our nomogram showed superior predictive performance than the 8th AJCC TNM staging system in predicting DFS according to a higher C-index and larger AUCs. In addition, the nomogram created in the present study effectively stratified patients into distinct risk groups in the total, training, and validation populations. To the best of our knowledge, this study represents the sole investigation incorporating MHR into clinicopathological variables for prognosis prediction in HR+/HER2− breast cancer.

In addition to the aforementioned advantages, the current study has some limitations. First, given the retrospective nature of this study, our findings are limited to hypothesis generation. Accordingly, a prospective study is necessary to confirm the results. Second, because of the short follow-up period, there was only few death cases. Therefore, despite tumor size and LNM, no indicator was screened out as independent prognostic factor for OS in this study. As a result, OS was not predicted in our study. Third, the nomogram constructed in the current study is based on Chinese HR+/HER2− breast cancer patients, and it is not yet clear whether it can be applied to the western patient population. Fourth, the model of the current study was only internally validated using a training/validation split from the same cohort. Therefore, our results should be considered preliminary. Independent external validation is necessary before clinical application, and we are actively seeking multi-center datasets to address this in future work. Fifth, the sample capacity was limited. Sixth, While the locked cutoff validation mitigates over optimism, the thresholds reported herein remain exploratory. The reproducibility of these specific numeric cutoff values requires prospective confirmation in external multi-institutional cohorts. Further efforts on prospective and multi-center data collection with a longer follow-up period are needed to improve our nomogram.

Conclusions

We developed and validated a novel nomogram with good calibration for predicting DFS in non-metastatic Chinese HR+/HER− breast cancer patients. This nomogram represented the first integration of MHR into clinicopathological variables for predicting prognosis among breast cancer patients. The prognostic nomogram provides relatively accurate prediction of prognosis in HR+/HER− breast cancer patients and will aid clinicians in customizing individualized treatment approaches.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0507/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0507/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0507/prf

Funding: This study was supported by the Wenzhou Science and Technology Bureau (No. Y2023910).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0507/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Wenzhou Central Hospital (No. L2025-11-006). All enrolled patients provided their written informed consent prior to participation.

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

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