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Open Access

Peer-reviewed

Research Article

Association between Systemic Immune-Inflammation Index and female breast cancer based on NHANES data (2001–2018): A cross-sectional study

  • Juan Xiong ,

    Contributed equally to this work with: Juan Xiong, Deju Zhang

    Roles Conceptualization, Data curation, Methodology, Software, Validation, Visualization, Writing – original draft

    Affiliations Department of Family Medicine, The Second People’s Hospital of Deyang, Deyang, Sichuan, China, Graduate Collaborative Training Base of Shenzhen Second People’s Hospital, Heng Yang, Medical School, University of South China, Hengyang, Hunan, China, BioBank, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China

    ⨯
  • Deju Zhang ,

    Contributed equally to this work with: Juan Xiong, Deju Zhang

    Roles Data curation, Writing – review & editing

    Affiliations Graduate Collaborative Training Base of Shenzhen Second People’s Hospital, Heng Yang, Medical School, University of South China, Hengyang, Hunan, China, BioBank, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China

    ⨯
  • Ying Yuan,

    Roles Investigation

    Affiliation BioBank, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China

    ⨯
  • Chuntao Quan,

    Roles Investigation

    Affiliation BioBank, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China

    ⨯
  • Ni Xie

    Roles Funding acquisition, Writing – review & editing

    xn100@szu.edu.cn

    Affiliations Department of Family Medicine, The Second People’s Hospital of Deyang, Deyang, Sichuan, China, Graduate Collaborative Training Base of Shenzhen Second People’s Hospital, Heng Yang, Medical School, University of South China, Hengyang, Hunan, China, BioBank, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China

    ⨯

Abstract

Worldwide cancer statistics have shown that breast cancer dominates female cancer incidence and remains a leading cause of death. The Systemic Immune-Inflammation Index (SII) is a new prognostic indicator of systemic inflammation used to assess systemic immune-inflammatory response levels in the human body. It is associated with the prognosis of various diseases, such as malignant tumors, cardiovascular diseases, and autoimmune diseases. Although SII offers valuable information for diagnosing and predicting the risk of female breast cancer (FBC), the association between SII and FBC has not yet been analyzed. Therefore, the relationship between SII and FBC was investigated in this study. Multivariate logistic regression, model fit assessment using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), and smoothing curve fitting were applied to examine the correlation between SII and FBC using data from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Then the stability of their association was further examined using subgroup analysis and interaction tests among populations. Results showed a positive correlation between SII and FBC in 17,044 participants with age ≥ 20 years. In the fully adjusted model, every 100-unit increase in SII was accompanied by a 3% increased odds of FBC prevalence [OR = 1.03 (95% CI: 1.01, 1.05)]. Individuals in the highest quartile of SII exhibited 44% increased odds of FBC prevalence than those in the lowest quartile [OR = 1.44 (95% CI: 1.11, 1.88)]. Model fitness assessment using AIC and BIC criteria demonstrated that multivariable-adjusted models exhibited better fit compared to unadjusted models for both continuous and categorical SII specifications. Receiver Operating Characteristic (ROC) curve analysis demonstrated that SII exhibited excellent diagnostic capability for breast cancer, with the area under the ROC curve (AUC) of 0.816 (95% CI: 0.801–0.831), comparable to NLR (AUC = 0.816) and neutrophil counts (AUC = 0.815). In disease-specific performance comparison, SII’s predictive ability for breast cancer (AUC = 0.816) was slightly superior to that for hypertension (AUC = 0.799), with the difference being statistically significant (P = 0.0407). Our findings confirmed that SII was a promising biomarker associated with FBC prevalence, and it may provide valuable insights into early screening and personalized treatment strategies.

Citation: Xiong J, Zhang D, Yuan Y, Quan C, Xie N (2025) Association between Systemic Immune-Inflammation Index and female breast cancer based on NHANES data (2001–2018): A cross-sectional study. PLoS One 20(9): e0330571. https://doi.org/10.1371/journal.pone.0330571

Editor: Marcus Tolentino Silva, University of Brasilia: Universidade de Brasilia, BRAZIL

Received: June 21, 2024; Accepted: August 2, 2025; Published: September 4, 2025

Copyright: © 2025 Xiong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

Funding: This research was funded by grants from the National Natural Science Foundation of China (No. 82172356) and the Natural Science Foundation of Shenzhen, China (JCYJ20230807115124048, JCYJ20230807115112024). These are all government funds, and the government had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The grant recipients, in order, are Ni Xie , Ying Yuan , and Chuntao Quan. Their roles have been indicated in the "Authors contributions" section of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Breast cancer is the most common malignant tumor and the main cause of cancer-related deaths in women around the world [1]. In 2020, approximately 2.3 million new cases of breast cancer and 685,000 deaths of breast cancer were recorded globally and these two parameters have continued to rise over the past decades [2]. The primary treatment modalities for breast cancer involve surgery, radiotherapy, and systemic treatments such as chemotherapy, endocrine therapy, targeted therapy, or a combination of these approaches [3,4]. Despite decades of advances in breast cancer treatment, the therapeutic outcomes remain far from satisfactory, with many patients still facing poor prognoses and treatment resistance. The current treatment strategies often prove inadequate, particularly for advanced-stage or aggressive subtypes of breast cancer, leading to high mortality rates and treatment failures [5–7]. Thus, there is an urgent need to identify novel, accurate, and cost-effective early detection methods to address both the clinical limitations and economic burden imposed on patients in current breast cancer management.

Biomarkers are biological molecules that serve as indicators for physiological and pathological processes. In cancer diagnostics, these molecular signatures are crucial for disease detection, progression monitoring, and treatment response evaluation [8–10]. Several established serum biomarkers, including Cancer Antigen 15–3 (CA15–3) and Carcinoembryonic Antigen (CEA), have shown clinical significance in breast cancer management [11]. However, these conventional markers exhibit limited sensitivity and specificity for early screening, primarily serving as monitoring tools for disease progression and treatment efficacy. There is growing evidence showing that chronic inflammation may contribute to breast cancer development [12]. Elevated levels of inflammatory markers, such as Tumor Necrosis Factor-α (TNF-α), Interleukin-6 (IL-6), and neutrophils, have been observed in HR-positive breast cancer patients, suggesting their potential role as diagnostic indicators [13]. The systemic immune-inflammation index (SII) is an index incorporating platelet, neutrophil, and lymphocyte counts, and has been applied to estimate the pre-treatment balance between inflammatory factors and immune status in people with cancer [14,15]. Notably, researchers found that SII was associated with cancer progression. In 2024, Pang et al. reported that a low baseline SII was associated with better survival outcomes in HER2-positive metastatic breast cancer patients, manifested as prolonged survival time and decreased malignant progression [16]. Additionally, it was proven to be a promising tool for predicting the prognosis of patients with endometrial cancer [17], colorectal cancer [18], bladder cancer [19], and non-small cell lung cancer [20]. Therefore, there is growing interest in the role of SII in cancer prognosis, and it may outperform the neutrophil-to-lymphocyte ratio/platelet-to-lymphocyte ratio (NLR/PLR) as a predictor of breast cancer [21]. To explore the relationship between SII and breast cancer prevalence in the general population, we utilized large-scale population data from 2001–2018 NHANES. Additionally, we employed both Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as model selection criteria to assess the goodness of fit, while also utilizing Receiver Operating Characteristic (ROC) curve analysis to determine the area under the ROC curve (AUC), sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of SII as a diagnostic biomarker for breast cancer, thereby providing evidence for its potential clinical application in early breast cancer screening.

Materials and methods

Study design

NHANES collects information on the health and nutritional conditions of the noninstitutionalized United States civil population. Our study included data from 91,351 participants during the 9 cycles (2001–2018), among which 74,307 participants were excluded due to missing data, and 17,044 participants (503 breast cancer female participants and 16,541 normal females) were finally involved. Fig 1 shows the full process of the sample exclusion process. All participants gave written informed consent when recruited and the NCHS Research Ethics Review Board approved the study methodology, and no external ethic approval was required for the study to be conducted. The detailed experimental design and data can be obtained from the database (https://www.cdc.gov/nchs/nhanes/; accessed on 12 February 2024).

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Fig 1. Flow chart of participants selection.

NHANES, National Health and Nutrition Examination Survey.

https://doi.org/10.1371/journal.pone.0330571.g001

Calculation and assessment of SII

The SII is calculated using the following equation: SII = (P * N)/ L, where P, N, and L represent peripheral platelet count, neutrophil count, and lymphocyte count respectively.

Diagnosis and assessment of breast cancer

In this research, diagnoses of cancer were self-reported through medical questionnaires on medical conditions. All the participants were asked to self-report whether or not they had cancer, based on their visits to a doctor or other health professional. Those who answered “No cancer” were classified as control people, while those answering “YES” were asked to specify their cancer type. Those who answered “breast cancer” were assigned to the breast cancer participants category.

Study covariates

Based on related studies of current literature [22,23], age, race/ethnicity, educational level, marital status, body mass index (BMI), hypertension status, diabetes status, reproductive health conditions (including age at menarche, pregnancy, and oral contraceptive use), and smoking history were considered major potential confounding factors. Race/ethnicity involves Non-Hispanic White, Non-Hispanic Black, Mexican American, and Other Race. Education level includes below high school, high school, and college or above. Marital status was classified as both sexual partner and asexual partner. BMI measurements were conducted at the mobile examination center. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2). After excluding extreme values, it was categorized into normal weight (< 25 kg/m2), overweight (25–30 kg/m2), and obesity (≥ 30 kg/m2). Hypertension status was classified as having or not having hypertension. Diabetes status was divided into two categories: no and diabetes. Smoking history was classified as never (lifetime <100 cigarettes), former (lifetime ≥ 100 cigarettes but currently quit), and current (lifetime ≥ 100 cigarettes and currently smoking). Finally, the model also included reproductive health status, which included age at menarche (<12 years or ≥ 12 years), ever been in a pregnancy (No or Yes), and oral contraceptive use (No or Yes).

Statistical analysis

All statistical analyses were conducted using NHANES sampling in accordance with the Centers for Disease Control and Prevention (CDC) guidelines to illustrate the complex multistage survey design. Continuous variables were presented as mean ± standard deviation (SD), whereas categorical variables were expressed as proportions. The chi-squared test was applied to evaluate differences between female breast cancer (FBC) patients and normal females for categorical variables, while the Student’s t-test was used for continuous variables within the covariates. Logistic regression was used to calculate the covariate-adjusted odds ratio (OR) for the association between SII and FBC. After the translation of the SII score from a continuous variable to a categorical variable (quartile), a trend test was conducted to evaluate the trend of linear association between SII and breast cancer. In model I, no covariates were adjusted. Model II was adjusted for age, race/ethnicity, education level, and marital status. Model III was adjusted for the covariates in model II as well as BMI, hypertension status, diabetes status, ever been pregnant, oral contraceptive use, age at menarche, and smoking history. Model fit was assessed using AIC and BIC, where lower values indicate better model fit. ROC curve analysis was performed to evaluate the diagnostic performance of SII, NLR, and neutrophil counts for breast cancer. AUC with 95% CI was calculated to assess discriminative ability. At these thresholds, sensitivity, specificity, accuracy, precision, PPV and NPV were calculated for each marker. We also compared the diagnostic performance of SII between breast cancer and hypertension using DeLong’s test. Subgroup analysis was performed to evaluate the correlations between SII and breast cancer in participants of different ages, race/ethnicity, education level, marital status, BMI, hypertension status, and diabetes status, and interaction tests were employed to explore the consistency of the correlations between subgroups. A smoothing curve fitting clearly presents the relationship between SII and breast cancer. All analyses were performed with R (version 4.2) and Empowerstats (version 2.0) and DecisionLinnc (version 1.0). P < 0.05 was considered statistically significant.

Results

Baseline characteristics

In this study, a total of 17,044 participants were included, with a mean ± SD age of 48.73 ± 18.09 years, consisting of 2.95% FBC participants. Among them, non-Hispanic white individuals, non-Hispanic black individuals, Mexican Americans, and other ethnicities comprised 43.82%, 19.52%, 17.71%, and 18.95%, respectively. Further, the FBC patients were significantly older than normal individuals (P < 0.001), with a majority of Non-Hispanic White (P < 0.001) (Table 1). Additionally, Compared to healthy control, breast cancer patients were more likely to have no sexual partners (48.91% vs. 43.36%, P = 0.014), ever been pregnant (91.25% vs. 84.96%, P < 0.001), without oral contraceptive use (44.93% vs. 33.67%, P < 0.001), former smoker (29.42% vs. 18.17%, P < 0.001), with a history of hypertension (56.06% vs. 31.82%, P < 0.001), and with a history of diabetes (20.87% vs. 10.40%, P < 0.001), a lower platelet counts [245.88 ± 62.73 vs. 265.35 ± 68.68, P = 0.012], a lower lymphocyte counts [1.88 ± 0.69 vs. 2.19 ± 0.87, P < 0.001], and a higher SII value [631.10 ± 447.79 vs. 574.79 ± 359.16, P = 0.011]. Nevertheless, no significant differences were observed in education level, age at menarche, BMI, and neutrophil counts.

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Table 1. Characteristics among female population ≥20 years of age from NHANES 2001–2018 (n = 17044).

https://doi.org/10.1371/journal.pone.0330571.t001

Association between SII and FBC

As the effect per unit of SII for FBC was small, we examined the linear correlation between SII and breast cancer per 100 units. The results in Table 2 revealed a statistically significant positive association between SII and FBC in the crude model [OR=1.03 (95% CI: 1.01, 1.05)] and partially adjusted model [OR=1.03 (95% CI: 1.01, 1.05)]. After adjusting for all covariates, the FBC prevalence odds rose by 3% for every 100-unit increase in SII [OR=1.03 (95% CI: 1.01, 1.05)]. When the SII was divided into quartiles, their association was still significant (P < 0.01). Individuals in the highest quartile of SII showed 44% increased odds of FBC prevalence [OR = 1.44 (95% CI: 1.11, 1.88)] than those in the lowest quartile of SII. Furthermore, a further supported finding was obtained in the smoothed curve fitting (Fig 2). We evaluated model fitness of two SII specifications using AIC and BIC criteria. Results demonstrated improved model fit for multivariable-adjusted models compared to unadjusted models across all SII specifications. Specifically, in the continuous SII/100 analysis, Model I showed AIC and BIC values of 4529.22 and 4544.70 respectively. After adjusting for demographic characteristics (Model II), these values significantly decreased to 3867.60 and 3937.29. Further adjustment for clinical features (Model III) yielded a slight reduction in AIC (3864.77) but a marginal increase in BIC (3996.41), potentially reflecting the trade-off between model complexity and goodness of fit. The categorical SII exhibited similar trends, indicating the robustness of our findings (Table 3).

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Table 2. Associations between systemic immune-inflammation index and breast cancer.

https://doi.org/10.1371/journal.pone.0330571.t002

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Table 3. Model comparison using AIC and BIC criteria across different SII specifications.

https://doi.org/10.1371/journal.pone.0330571.t003

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Fig 2. The association between SII and FBC.

The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Age, race/ethnicity, education level, marital status, BMI, hypertension status, diabetes status, ever been pregnant, oral contraceptive use, age at menarche, and smoking history were adjusted. Abbreviation: SII = Systemic Immune-Inflammation Index.

https://doi.org/10.1371/journal.pone.0330571.g002

Diagnostic utility and disease specificity of SII

To evaluate the diagnostic utility of SII as a biomarker for breast cancer, we compared it with other inflammatory markers (Table 4, S1 Fig, Fig 3). SII, NLR, and neutrophil counts demonstrated comparable discriminative ability with AUC values of 0.816, 0.816, and 0.815, respectively. In the sensitivity-specificity analysis, SII exhibited the highest sensitivity (0.839) with moderate specificity (0.648), neutrophil counts showed the highest specificity (0.774) with lower sensitivity (0.714), and NLR demonstrated intermediate values for both parameters (sensitivity 0.728, specificity 0.761). Regarding predictive values, SII showed the highest negative predictive value (NPV = 0.993) but lowest positive predictive value (PPV = 0.068) among all parameters.

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Table 4. Performance comparison of three inflammatory markers (SII, NLR, and neutrophil counts) as predictive biomarkers for breast cancer.

https://doi.org/10.1371/journal.pone.0330571.t004

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Fig 3. ROC curves evaluating the predictive value of SII for breast cancer.

Age and race/ethnicity were adjusted. SII = Systemic Immune-inflammation Index.

https://doi.org/10.1371/journal.pone.0330571.g003

We also compared SII’s performance between breast cancer and hypertension using ROC curve analysis (Table 5, S2 Fig). SII demonstrated similar overall discriminative ability for both conditions (AUC = 0.816 for breast cancer; AUC = 0.799 for hypertension). For breast cancer, SII showed higher sensitivity (0.839) and lower specificity (0.648) compared to that of hypertension (sensitivity 0.749, specificity 0.724). The predictive values also differed between conditions, with SII for breast cancer showing higher NPV (0.993) but lower PPV (0.068) compared to hypertension (NPV = 0.857, PPV = 0.567).

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Table 5. Comparison of ROC curve analysis of SII as a predictive biomarker for hypertension and breast cancer.

https://doi.org/10.1371/journal.pone.0330571.t005

Subgroup analyses

To examine whether the above association was consistent across the general population and to determine any potential variation in population settings, subgroup analyses, and interaction tests were performed and stratified by age, race/ethnicity, education level, marital status, BMI, hypertension, and diabetes (Table 6). Notably, the former exhibited that the correlation between SII and the occurrence of FBC was stable. This association was not significantly affected by age, race/ethnicity, education level, marital status, BMI, hypertension, or diabetes status (all P for interaction > 0.05).

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Table 6. Subgroup analysis of the association between SII/100 and breast cancer.

https://doi.org/10.1371/journal.pone.0330571.t006

Discussion

Our findings indicated that a positive correlation between elevated SII levels and the occurrence of FBC and this association was consistent across different population settings.

This study is highly innovative because it is the first to explore the association between SII and FBC using data from the 2001–2018 NHANES database of US women aged ≥ 20 years. Previous studies have investigated the relationship between preoperative SII and oncological outcomes in patients with FBC using different epidemiological methods and target populations [21,24,25]. A retrospective cohort study of 1489 Chinese patients with primary breast cancer demonstrated that SII may eclipse NLR and PLR as a predictor of breast cancer, especially in the HR-positive subtype [21]. Specifically, we also found that the specificity of the follow-up SII for detecting cancer recurrence was greater than 98.0% at a cut-off of 900. SII-high patients are positively correlated with worse chemotherapy responses and a greater possibility of relapse [21]. Another retrospective research showed that SII predicts prognosis and neoadjuvant chemotherapy efficacy in patients with Human Epidermal Growth Factor Receptor 2 (HER2) positive breast cancer [25]. The complete response group had lower SII than the non-complete response group (587.43 ± 175.97 vs. 821.82 ± 231.58; P = 0.000) [25]. Furthermore, higher preoperative SII was reported as a predictive factor for axillary lymph node metastases in FBC patients [26]. A study with 160 TNBC patients in China suggests that SII may have an independent prognostic significance in triple-negative breast cancer (TNBC) patients. Further investigation is required to identify independent prognostic factors for SII in TNBC, including the use of larger sample sizes, multi-center, and prospective studies [27]. Tang et al. reported that both skeletal muscle index (SMI) and SII were found to independently predict the prognosis of patients with lymph node-positive breast cancer. The combination of SMI and SII may prove to be a more effective prognostic factor than either alone but should be further researched in a larger study [28]. Another meta-analysis involving 2,724 subjects stated that higher SII represented poor overall survival (OS) in cancer patients. Still, the association with disease-free survival/progression-free survival (DFS/PFS) was only significant for breast cancer and ovarian cancer [29].

While SII is indeed a valuable inflammatory indicator, numerous classic markers of inflammation are already in use in clinical practice. A population-based study of 259,435 female participants in the UK Biobank found that markers of inflammation, including C-reactive protein (CRP), monocyte-to-HDL-c ratio (MHR), CRP-to-albumin Ratio (CAR), CRP-to-lymphocyte Ratio (CLR), and neutrophil-to-HDL-c ratio (NHR) were related to FBC [12]. In a study comparing serum levels of inflammatory cytokines between patients with ductal carcinoma and healthy controls, it was found that IL-6 and interleukin-8 (IL-8) content were markedly elevated in the breast cancer group [30,31]. Additionally, TNF-α content was markedly greater in stage III carcinoma patients compared to controls (P < 0.01) and was positively correlated to lymph node metastasis (P < 0.01) [31]. These findings suggest that these inflammatory cytokines may play a crucial role in the progression and metastasis of breast cancer. Prior research indicated that CRP was generally elevated in breast cancer patients, suggesting its crucial role in the pathophysiology of inflammation in patients with metastatic breast cancer, which is associated with higher mortality [32,33]. A recent retrospective analysis demonstrated that CAR served as an independent prognostic indicator in patients with luminal B subtype breast cancer. The study revealed significantly improved survival outcomes in patients with low CAR levels – both in terms of disease-free survival (DFS) and OS compared to those with high CAR (p < 0.05) [34]. Siersbæk et al. revealed that IL6/STAT3 signaling was decoupled from ER in breast cancer. IL-6 activated STAT3-driven metastasis by co-opting ER-FOXA1-STAT3 enhancers metastasis, suggesting that targeting IL6/STAT3 may have clinical potential in ER+ breast cancer [35]. Kim et al. found that TNF-α promoted tumor progression and invasiveness by regulating tumor remodeling through the induction of matrix metalloproteinas (MMPs), stimulating breast cancer cell activity, and activating JNK and NF-κB pathways [36]. A special subtype of breast cancer was inflammatory breast cancer, which was characterized by negative hormone receptor status and amplification of the HER2 gene. The pathogenesis and progression of IBC largely depended on the tumor microenvironment, featuring an abundance of macrophages, monocytes, and lymphocytes. The tumor and microenvironment cells were well-characterized at the molecular level, demonstrating the major contribution of inflammatory pathways in inflammatory breast cancer tumor biology [37].

In addition to breast cancer, our research findings demonstrated that SII also exhibited significant associations with prostate cancer and non-melanoma skin cancer. In the prostate cancer study, for every 100-unit increase in SII, the prevalence of prostate cancer increased by 2%. Notably, when comparing the Q4 (highest quartile) group with the reference group (Q1), the prevalence of prostate cancer was significantly elevated, with an OR of 1.91 (95% CI: 1.51–2.42, P < 0.001). This association persisted in Model III, with an OR of 1.57 (95% CI: 1.20–2.04, P = 0.0008) (S1 Table). Regarding non-melanoma skin cancer, in Model I without covariate adjustment, SII showed a modest association (continuous SII/100 OR=1.02, 95% CI: 1.00–1.03, P = 0.0105), with the Q4 group demonstrating an increased prevalence relative to the Q1 group [OR=1.46 (95% CI: 1.16–1.84, P = 0.0013)] (S2 Table). These findings demonstrated a correlation that aligned with the growing evidence from hazard ratio (HR) analyses, indicating that SII served as an important prognostic factor across various cancer types. In hepatocellular carcinoma (HCC), the meta-analysis included 10 retrospective studies with 2796 patients, demonstrating that elevated pretreatment SII was significantly associated with poor OS (HR = 1.54, P < 0.001) and earlier time to recurrence (HR = 1.77, P < 0.001) [38]. Another study revealed that both SII and alpha-fetoprotein(AFP) were effective predictors of post-interventional recurrence and metastasis, with AUC values of 0.797 and 0.839 respectively, and their combined diagnostic accuracy was notably higher (AUC = 0.910) [39]. Patients with high SII levels demonstrated significantly poorer survival outcomes compared to those with low SII levels. This study inspires us to combine SII with other breast cancer indicators in the next step to improve the predictive accuracy for breast cancer. Across multiple large-scale clinical studies in various cancers, SII proved to be a valuable prognostic marker. Meta-analyses demonstrated that elevated SII was closely associated with poor prognosis, a trend consistently observed across different tumor types. In a meta-analysis of 4,236 gastric cancer patients, high pretreatment SII levels were significantly associated with shortened overall survival (OS) [HR = 1.40 (95% CI: 1.08–1.81)] [40], while in early gastric cancer, SII exhibited excellent predictive capability through ROC curve analysis [AUC = 0.637 (95% CI: 0.551–0.723)] [41]. Similarly, a study of 2,267 non-small cell lung cancer (NSCLC) patients revealed that elevated SII independently predicted poorer OS [HR = 1.52 (95% CI: 1.15–2.00)] [42]. This predictive pattern was also validated in bladder cancer, where analysis of 7,087 patients showed high SII as an independent predictor of worse OS [HR = 1.22 (95% CI: 1.04–1.44)] [19]. These consistent findings across different tumor types emphasized SII’s potential as a universal inflammatory prognostic marker, establishing a solid foundation for its clinical application in multiple cancers. In comparison to traditional inflammatory factors, SII correlates better with inflammatory status preferably and has exhibited more robust prognostic value across multiple types of research [18,43,44]. For instance, Huang et al investigated the predictive accuracy of SII, NLR, NMLR, PLR, and CAR for postoperative survival in colorectal cancer patients. Their analysis showed that SII was one of the most effective and accurate predictors among these markers [21,45]. Our comparative analysis of inflammatory markers revealed that while SII, NLR, and neutrophil counts showed similar overall discriminative ability (as measured by AUC values), they demonstrated distinct sensitivity-specificity profiles. This distinction has important clinical implications. The superior sensitivity of SII compared to both NLR and neutrophil counts positions it as a potentially valuable screening tool for breast cancer. Besides, SII is more suitable for initial screening and exclusion diagnosis of breast cancer compared to other indicators, especially in clinical scenarios where reducing missed diagnoses is critical, as its high sensitivity and high NPV can more effectively capture potential breast cancer patients. The finding aligns with Watson et al. who reported that systemic inflammatory markers with high sensitivity could serve as effective first-line screening tools in cancer detection [46].

In addition to inflammatory markers like SII, recent years have witnessed significant advancements in next-generation sequencing (NGS) technology and long non-coding RNAs (lncRNAs) for breast cancer diagnosis, offering complementary value to existing methods [47]. Circulating lncRNA RP11-445H22.4 has shown specificity (74%) in breast cancer diagnosis. NGS can identify specific genetic alterations driving carcinogenesis, including mutations and gene fusions. NGS provides more detailed molecular information but at higher costs and requires specialized laboratory support [48]. SII, as a cost-effective and simple inflammatory marker, demonstrates sensitivity and comprehensiveness, offering new possibilities for tumor diagnosis. By reflecting the complex interactions between tumors and the immune system, SII provides crucial clinical assessment insights. The research trend points towards further integration with NGS and lncRNA molecular technologies, enabling comprehensive analysis of tumor molecular characteristics, significantly improving diagnostic accuracy and providing precise guidance for personalized treatment decisions.

Recent studies have demonstrated significant interactions between SII and various biomarkers, suggesting complex regulatory networks in systemic inflammation. Wei et al. revealed that SII showed a significant positive correlation with female estradiol levels and was identified by the XGBoost model as one of the key indicators affecting sex hormone levels [49]. Higher levels of estradiol had been associated with increased breast cancer incidence, which explained why elevated SII values indicated higher breast cancer prevalence [50]. Notably, loss of tumor protein p53 (p53) triggered systemic inflammation through promoting WNT ligand secretion and subsequent IL-1β production by tumor-associated macrophages, leading to increased circulating neutrophils. This systemic inflammatory response is reflected by elevated SII values, representing a process driven by genetic alterations in tumor cells (such as p53 loss) [51]. In summary, these findings collectively demonstrated that SII interacted with diverse biological processes from hormonal regulation to cancer progression, providing new insights into the complex relationships between systemic inflammation, endocrine function, and tumor development.

Furthermore, our comparison of SII’s performance between breast cancer and hypertension provides novel insights into its disease-specific characteristics. SII demonstrated high sensitivity and NPV in breast cancer detection. This differential performance across different diseases illustrates the disease-specific characteristics of SII as a diagnostic tool. However, it is essential to emphasize that SII cannot substitute for mammography and breast imaging-reporting and data System (BI-RADS). It may serve as an initial screening tool, where positive findings necessitate further confirmation through mammography and BI-RADS to establish a definitive diagnosis of breast cancer [52]. Implementation of SII as a complementary screening parameter demonstrates optimal utility, particularly in resource-limited settings. From a scientific perspective, the observed association between elevated SII and breast cancer risk provides new insights into the role of systemic inflammation in breast cancer development [53]. From a clinical practice viewpoint, as a diagnostic biomarker, SII exhibits significant methodological advantages, such as minimally invasive acquisition, cost-effectiveness, and accessibility via routine hematological parameters without ionizing radiation exposure [54]. We proposed a tiered clinical implementation framework where baseline SII measurements were incorporated into routine health check-ups, with regular monitoring implemented for individuals with elevated risk. SII could contribute to individualized screening schedules based on specific risk profiles, which could lead to more efficient resource utilization while maintaining screening effectiveness. Future research priorities should focus on prospective validation of these proposed applications, development of standardized protocols, assessment of optimal monitoring frequencies, and evaluation of implementation cost-effectiveness.

Our study has multiple strengths. First, this study has a representative sample and a large sample size. Second, we adjusted for confounding factors, such as socio-demography, reproductive health conditions, diabetes, and blood pressure to produce more reliable results. Finally, this research is innovative due to the lack of exploration of the correlation between SII levels and FBC. However, our findings should be interpreted with caution owing to some limitations. Firstly, the self-reported nature of the NHANES questionnaire limits its ability to provide detailed information on the pathological stages and subtypes of breast cancer among the participants.Secondly, the cross-sectional study design prevents us from building causal relationships. Hence, prospective studies involving more participants are needed to elucidate causality. Thirdly, although some potential covariates were accounted for in our study, the effect of confounding factors still cannot be completely ruled out. Fourthly, we advocate for more detailed subgroup analyses (particularly with stratification by smoking status and alcohol consumption patterns) to examine SII’s role in diverse populations, which would help establish population-specific SII reference ranges and enhance its clinical utility. Lastly, as NHANES participants were exclusively from the United States, the predictive accuracy of our findings may vary when applied to non-U.S. populations due to differences in demographic characteristics, lifestyle factors, and environmental exposures across geographic regions. Future studies should validate these findings in diverse populations across different countries through large-scale prospective cohort studies (n ≥ 10,000) and randomized controlled trials to establish broader external validity and applicability.

Conclusion

Our study confirmed a positive correlation between elevated SII levels and the occurrence of breast cancer. Nevertheless, further large-scale prospective studies are necessary to confirm the findings as well as establish a causal relationship between SII and FBC.

Supporting information

S1 Fig. ROC curves comparing the predictive performance of different inflammatory markers for breast cancer prevalence.

Age and race/ethnicity were adjusted.

https://doi.org/10.1371/journal.pone.0330571.s001

(TIF)

S2 Fig. ROC curve analysis evaluating the diagnostic performance of predictive models for breast cancer and hypertension.

Age and race/ethnicity were adjusted.

https://doi.org/10.1371/journal.pone.0330571.s002

(TIF)

S1 Table. Associations between systemic immune-inflammation index and prostate cancer.

Model I: no covariates were adjusted. Model II: age and race/ethnicity were adjusted. Model III: age, race/ethnicity, education level, marital status, BMI, hypertension status, diabetes status, and smoking history were adjusted. Abbreviation: SII, systemic immune-inflammation index, Q means quartile; OR, odds ratio; 95% CI, 95% confidence interval.

https://doi.org/10.1371/journal.pone.0330571.s003

(DOCX)

S2 Table. Associations between systemic immune-inflammation index and non-melanoma skin cancer.

Model I: no covariates were adjusted. Model II: age and race/ethnicity were adjusted. Model III: age, race/ethnicity, education level, marital status, BMI, hypertension status, diabetes status, and smoking history were adjusted. Abbreviation: SII, systemic immune-inflammation index, Q means quartile; OR, odds ratio; 95% CI, 95% confidence interval.

https://doi.org/10.1371/journal.pone.0330571.s004

(DOCX)

S1 File. Original Data.

Raw experimental data supporting all analyses presented in this study.

https://doi.org/10.1371/journal.pone.0330571.s005

(XLSX)

References

  1. 1. 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):209–49. pmid:33538338
  2. 2. Zhang D, Yuan Y, Xiong J, Zeng Q, Gan Y, Jiang K, et al. Anti-breast cancer effects of dairy protein active peptides, dairy products, and dairy protein-based nanoparticles. Front Pharmacol. 2024;15:1486264. pmid:39605907
  3. 3. Burstein HJ, Curigliano G, Thürlimann B, Weber WP, Poortmans P, Regan MM, et al. Customizing local and systemic therapies for women with early breast cancer: the St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021. Ann Oncol. 2021;32(10):1216–35. pmid:34242744
  4. 4. Zhang D, Yuan Y, Zeng Q, Xiong J, Gan Y, Jiang K, et al. Plant protein-derived anti-breast cancer peptides: sources, therapeutic approaches, mechanisms, and nanoparticle design. Front Pharmacol. 2025;15:1468977. pmid:39898323
  5. 5. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. pmid:36633525
  6. 6. Hu M, Shao B, Ran R, Li H. Prognostic factors for patients with metastatic breast cancer: a literature review. Transl Cancer Res. 2021;10(4):1644–55. pmid:35116490
  7. 7. Trapani D, Ginsburg O, Fadelu T, Lin NU, Hassett M, Ilbawi AM, et al. Global challenges and policy solutions in breast cancer control. Cancer Treat Rev. 2022;104:102339. pmid:35074727
  8. 8. Seijo LM, Peled N, Ajona D, Boeri M, Field JK, Sozzi G, et al. Biomarkers in lung cancer screening: achievements, promises, and challenges. J Thorac Oncol. 2019;14(3):343–57. pmid:30529598
  9. 9. Smith LM, Mahoney DW, Bamlet WR, Yu F, Liu S, Goggins MG, et al. Early detection of pancreatic cancer: study design and analytical considerations in biomarker discovery and early phase validation studies. Pancreatology. 2024;24(8):1265–79. pmid:39516175
  10. 10. Dakal TC, Dhakar R, Beura A, Moar K, Maurya PK, Sharma NK, et al. Emerging methods and techniques for cancer biomarker discovery. Pathol Res Pract. 2024;262:155567. pmid:39232287
  11. 11. Tarighati E, Keivan H, Mahani H. A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med. 2023;23(1):1–16. pmid:35031885
  12. 12. Zhang Y, Song M, Yang Z, Huang X, Lin Y, Yang H. Healthy lifestyles, systemic inflammation and breast cancer risk: a mediation analysis. BMC Cancer. 2024;24(1):208. pmid:38360584
  13. 13. Bouras E, Karhunen V, Gill D, Huang J, Haycock PC, Gunter MJ, et al. Circulating inflammatory cytokines and risk of five cancers: a Mendelian randomization analysis. BMC Med. 2022;20(1):3. pmid:35012533
  14. 14. Zhong J-H, Huang D-H, Chen Z-Y. Prognostic role of systemic immune-inflammation index in solid tumors: a systematic review and meta-analysis. Oncotarget. 2017;8(43):75381–8. pmid:29088873
  15. 15. Nøst TH, Alcala K, Urbarova I, Byrne KS, Guida F, Sandanger TM, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol. 2021;36(8):841–8. pmid:34036468
  16. 16. Pang J, Ding N, Liu X, He X, Zhou W, Xie H, et al. Prognostic value of the baseline Systemic Immune-Inflammation Index in HER2-positive metastatic breast cancer: exploratory analysis of two prospective trials. Ann Surg Oncol. 2025;32(2):750–9. pmid:39565489
  17. 17. Huang Y, Chen Y, Zhu Y, Wu Q, Yao C, Xia H, et al. Postoperative Systemic Immune-Inflammation Index (SII): a superior prognostic factor of endometrial cancer. Front Surg. 2021;8:704235. pmid:34746222
  18. 18. Chen J-H, Zhai E-T, Yuan Y-J, Wu K-M, Xu J-B, Peng J-J, et al. Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol. 2017;23(34):6261–72. pmid:28974892
  19. 19. Li J, Cao D, Huang Y, Xiong Q, Tan D, Liu L, et al. The prognostic and clinicopathological significance of systemic immune-inflammation index in bladder cancer. Front Immunol. 2022;13:865643. pmid:35572533
  20. 20. Liu J, Li S, Zhang S, Liu Y, Ma L, Zhu J, et al. Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio can predict clinical outcomes in patients with metastatic non-small-cell lung cancer treated with nivolumab. J Clin Lab Anal. 2019;33(8):e22964. pmid:31282096
  21. 21. Zhou Y, Guo X, Shen L, Liu K, Sun Q, Wang Y, et al. Predictive significance of systemic immune-inflammation index in patients with breast cancer: a retrospective cohort study. Onco Targets Ther. 2023;16:939–60. pmid:38021447
  22. 22. Rigi S, Mousavi SM, Benisi-Kohansal S, Azadbakht L, Esmaillzadeh A. The association between plant-based dietary patterns and risk of breast cancer: a case-control study. Sci Rep. 2021;11(1):3391. pmid:33564027
  23. 23. McEligot AJ, Poynor V, Sharma R, Panangadan A. Logistic LASSO regression for dietary intakes and breast cancer. Nutrients. 2020;12(9):2652. pmid:32878103
  24. 24. Zhang Y, Sun Y, Zhang Q. Prognostic value of the systemic immune-inflammation index in patients with breast cancer: a meta-analysis. Cancer Cell Int. 2020;20:224. pmid:32528232
  25. 25. Gu Q, Zhao J, Liu Y, Chen H, Wang L. Association between the systemic immune-inflammation index and the efficacy of neoadjuvant chemotherapy, prognosis in HER2 positive breast cancer-a retrospective cohort study. Gland Surg. 2023;12(5):609–18. pmid:37284715
  26. 26. Tong L, Wang S, Zhang R, Wu Y, Xu D, Chen L. High levels of SII and PIV are the risk factors of axillary lymph node metastases in breast cancer: a retrospective study. Int J Gen Med. 2023;16:2211–8. pmid:37287504
  27. 27. Liu J, Shi Z, Bai Y, Liu L, Cheng K. Prognostic significance of systemic immune-inflammation index in triple-negative breast cancer. Cancer Manag Res. 2019;11:4471–80. pmid:31191009
  28. 28. Tang R, Deng J-P, Zhang L, Zhang W-W, Sun J-Y, Chi F, et al. Prognostic significance of the skeletal muscle index and systemic inflammatory index in patients with lymph node-positive breast cancer after radical mastectomy. BMC Cancer. 2022;22(1):234. pmid:35241010
  29. 29. Ji Y, Wang H. Prognostic prediction of systemic immune-inflammation index for patients with gynecological and breast cancers: a meta-analysis. World J Surg Oncol. 2020;18(1):197. pmid:32767977
  30. 30. Habanjar O, Bingula R, Decombat C, Diab-Assaf M, Caldefie-Chezet F, Delort L. Crosstalk of inflammatory cytokines within the breast tumor microenvironment. Int J Mol Sci. 2023;24(4):4002. pmid:36835413
  31. 31. Ma Y, Ren Y, Dai Z-J, Wu C-J, Ji Y-H, Xu J. IL-6, IL-8 and TNF-α levels correlate with disease stage in breast cancer patients. Adv Clin Exp Med. 2017;26(3):421–6. pmid:28791816
  32. 32. Busch EL, Whitsel EA, Kroenke CH, Yang YC. Social relationships, inflammation markers, and breast cancer incidence in the Women’s Health Initiative. Breast. 2018;39:63–9. pmid:29614476
  33. 33. Mikkelsen MK, Lindblom NAF, Dyhl-Polk A, Juhl CB, Johansen JS, Nielsen D. Systematic review and meta-analysis of C-reactive protein as a biomarker in breast cancer. Crit Rev Clin Lab Sci. 2022;59(7):480–500. pmid:35403550
  34. 34. Liu X, Guo X, Zhang Z. Preoperative Serum Hypersensitive-c-Reactive-Protein (Hs-CRP) to albumin ratio predicts survival in patients with luminal b subtype breast cancer. Onco Targets Ther. 2021;14:4137–48. pmid:34276217
  35. 35. Siersbæk R, Scabia V, Nagarajan S, Chernukhin I, Papachristou EK, Broome R, et al. IL6/STAT3 signaling hijacks estrogen receptor α enhancers to drive breast cancer metastasis. Cancer Cell. 2020;38(3):412-423.e9. pmid:32679107
  36. 36. Kim S, Choi JH, Kim JB, Nam SJ, Yang J-H, Kim J-H, et al. Berberine suppresses TNF-alpha-induced MMP-9 and cell invasion through inhibition of AP-1 activity in MDA-MB-231 human breast cancer cells. Molecules. 2008;13(12):2975–85. pmid:19052522
  37. 37. Di Bonito M, Cantile M, Botti G. Pathological and molecular characteristics of inflammatory breast cancer. Transl Cancer Res. 2019;8(Suppl 5):S449–56. pmid:35117122
  38. 38. Wang B, Huang Y, Lin T. Prognostic impact of elevated pre-treatment systemic immune-inflammation index (SII) in hepatocellular carcinoma: a meta-analysis. Medicine (Baltimore). 2020;99(1):e18571. pmid:31895801
  39. 39. Deng Y, Chen Z, He Q, Wu B, Su T, Mao C, et al. Clinical value of systemic immunoinflammatory index in predicting recurrence and metastasis in patients with primary liver cancer. BMC Gastroenterol. 2025;25(1):169. pmid:40082749
  40. 40. Qiu Y, Zhang Z, Chen Y. Prognostic value of pretreatment systemic immune-inflammation index in gastric cancer: a meta-analysis. Front Oncol. 2021;11:537140. pmid:33777726
  41. 41. Jing Y, Ren M, Li X, Sun X, Xiao Y, Xue J, et al. The Effect of Systemic Immune-Inflammatory Index (SII) and Prognostic Nutritional Index (PNI) in early gastric cancer. J Inflamm Res. 2024;17:10273–87. pmid:39654858
  42. 42. Zhou Y, Dai M, Zhang Z. Prognostic significance of the Systemic Immune-Inflammation Index (SII) in patients with small cell lung cancer: a meta-analysis. Front Oncol. 2022;12:814727. pmid:35186750
  43. 43. Huang H, Liu Q, Zhu L, Zhang Y, Lu X, Wu Y, et al. Prognostic value of preoperative systemic immune-inflammation index in patients with cervical cancer. Sci Rep. 2019;9(1):3284. pmid:30824727
  44. 44. Meng L, Yang Y, Hu X, Zhang R, Li X. Prognostic value of the pretreatment systemic immune-inflammation index in patients with prostate cancer: a systematic review and meta-analysis. J Transl Med. 2023;21(1):79. pmid:36739407
  45. 45. Zhou Z-Q, Pang S, Yu X-C, Xue Q, Jiang H-Y, Liang X-J, et al. Predictive values of postoperative and dynamic changes of inflammation indexes in survival of patients with resected colorectal cancer. Curr Med Sci. 2018;38(5):798–808. pmid:30341514
  46. 46. Watson J, Salisbury C, Banks J, Whiting P, Hamilton W. Predictive value of inflammatory markers for cancer diagnosis in primary care: a prospective cohort study using electronic health records. Br J Cancer. 2019;120(11):1045–51. pmid:31015558
  47. 47. Lu C, Wei D, Zhang Y, Wang P, Zhang W. Long non-coding RNAs as potential diagnostic and prognostic biomarkers in breast cancer: progress and prospects. Front Oncol. 2021;11:710538. pmid:34527584
  48. 48. Xu N, Chen F, Wang F, Lu X, Wang X, Lv M, et al. Clinical significance of high expression of circulating serum lncRNA RP11-445H22.4 in breast cancer patients: a Chinese population-based study. Tumour Biol. 2015;36(10):7659–65. pmid:25929808
  49. 49. Gao Z, Liu K. Association between systemic immunity-inflammation index and sex hormones in children and adolescents aged 6-19. Front Endocrinol (Lausanne). 2024;15:1355738. pmid:38938517
  50. 50. Nounu A, Kar SP, Relton CL, Richmond RC. Sex steroid hormones and risk of breast cancer: a two-sample Mendelian randomization study. Breast Cancer Res. 2022;24(1):66. pmid:36209141
  51. 51. Wellenstein MD, Coffelt SB, Duits DEM, van Miltenburg MH, Slagter M, de Rink I, et al. Loss of p53 triggers WNT-dependent systemic inflammation to drive breast cancer metastasis. Nature. 2019;572(7770):538–42. pmid:31367040
  52. 52. Lee SH, Yi A, Jang M-J, Chang JM, Cho N, Moon WK. Supplemental screening breast US in women with negative mammographic findings: effect of routine axillary scanning. Radiology. 2018;286(3):830–7. pmid:29083988
  53. 53. Liu X, Yan G, Pang J, Xiao Z, Xie H. The Systemic Immune-Inflammation Index is a predictor of chemotherapy sensitivity and disease-free survival in patients with hormone receptor-positive human epidermal growth factor receptor 2-negative breast cancer. Clin Breast Cancer. 2025;25(2):e190–5. pmid:39448293
  54. 54. Zhu J, Wang D, Liu C, Huang R, Gao F, Feng X, et al. Development and validation of a new prognostic immune-inflammatory-nutritional score for predicting outcomes after curative resection for intrahepatic cholangiocarcinoma: A multicenter study. Front Immunol. 2023;14:1165510. pmid:37063918