Breast cancer (BC) is the most frequently diagnosed cancer and is associated with one of the highest mortality rates among female malignant tumors (1). It is estimated that 287,000 new cases of BC will be diagnosed in 2023 (2). The current treatment modalities for BC encompass surgery, chemotherapy, hormonal therapy, and radiation therapy (3, 4). Despite remarkable advancements in early detection and therapeutic approaches, BC patients have poor prognoses (5, 6).

Although the traditional TNM staging system of the American Joint Committee on Cancer (AJCC) is extensively used for predicting prognosis and guiding clinical management, its ability to accurately identify patients at high risk of cancer-related mortality may be limited (7, 8). This limitation arises from the inherent heterogeneity observed in BC, where patients sharing the same stage can exhibit diverse clinical outcomes (9, 10). Patients with BC at the same stage may have different outcomes due to the heterogeneity of the disease (11). Consequently, there exists a critical need to identify reliable and easily applicable predictive models that can complement the TNM staging system and offer more precise predictions of individual patient outcomes.

Extensive Research consistently demonstrates the link between systemic inflammation and poor prognosis in cancer patients (12, 13). Chronic inflammation drives tumor progression, angiogenesis, and metastasis while suppressing the immune response (14, 15). In parallel, the significance of nutritional status in cancer patients’ survival has been widely recognized (16, 17). Cancer-associated malnutrition weakens immune function and triggers inflammation, worsening treatment outcomes (18). These findings underscore the importance of considering both systemic inflammation and nutritional status in the management and prognosis of cancer patients.

Recent studies have shed light on the prevalence of cancer-associated systemic inflammation and malnutrition in the majority of patients with malignancy, including those with BC (14, 19, 20). These factors have been closely linked to tumor progression and have been shown to have a detrimental impact on patient’s clinical outcomes (12, 21, 22). Various inflammation-based and nutritional markers, such as neutrophil (NEU), lymphocyte (LYM), platelet count (PLT), serum ferritin (SF) and lymphocyte-to-monocyte ratio (LMR), as well as prognostic nutritional index hemoglobin (HGB), albumin (ALB), transferrin (TRF), albumin to globulin (AGR), and prealbumin (PA), have been identified as promising clinical prognostic predictors for various cancers due to their simplicity and cost-effectiveness (23–29). However, it is worth noting that many nomogram studies in the literature often fail to consider the assessment of hematological markers encompassing both inflammation and nutritional biomarkers in conjunction with tumor characteristics (30–32). There are two studies of prognostic scoring systems for patients with BC, published by Hua X, et al. (33) and Jiang C, et al. (34). The scoring system developed from those studies provides useful tools for clinicians and researchers to predict the prognostic value of BC patients. However, two of the studies simply classified patients as early-stage or underwent neoadjuvant chemotherapy BC patients, their clinical application is restricted to a subset of patients, not all females with BC treated at their center. Meanwhile, both were developed using limited sample sizes. Furthermore, these models have not received any independent validation, most likely because of the small sample size. In addition, most current studies have primarily focused on the combination of one or a few inflammatory and nutritional parameters with clinicopathological factors, without incorporating more accurate variables such as TNM staging, subtype, and tumor size (35–37). These factors are of great importance in the treatment of BC. Therefore, the assessment of hematological markers including inflammation and nutritional biomarkers, could be of great importance in revealing the survival of patients with BC, which most nomogram studies did not mention.

In this study, our objective was to develop an inexpensive, trustworthy, and more accurate prognostic model by simultaneously combining inflammation and nutritional biomarkers collected from a substantial cohort of nearly 3000 patients diagnosed with BC. By incorporating these diverse factors, we aimed to enhance the accuracy and reliability of prognosis analysis in BC patients.

In the present study, we constructed and confirmed a nomogram model that combines easily accessible inflammation and nutritional factors, and clinicopathological variables to predict OS and DFS in BC patients. The nomogram presented superior predictive accuracy, discriminative ability, and clinical usefulness compared to the traditional TNM stage system. Clinicians can utilize this nomogram to guide treatment decisions, monitor disease progression, and provide personalized patient care.

A growing body of research has demonstrated that inflammation and nutritional status play important roles in both tumor development and patient prognosis (39, 40). Inflammatory responses play a key role in the tumor microenvironment in regulating tumor growth, metastasis and treatment resistance (12). Nutritional status directly affects the immune function, metabolic status and physiological regulation of the patient, resulting in their resistance to tumor and therapeutic response (41). This study found that inflammatory (LYM, PLT) and nutritional (HGB, AGR, PA) indicators were strongly associated with BC prognosis. Watanabe J et al. and Kazuhiro Araki et al. discovered that elevated LYM levels are associated with an improved response to chemotherapies in metastatic BC patients (42, 43). Sung Min Ko et al. reported that LYM was a strong predictor of DFS in BC patients (44), which is consistent with our results. LYM plays a crucial role in anti-tumor immunity by inducing tumor cell apoptosis and is one of the key factors in immune surveillance and immune editing (45, 46).They combat tumors through multiple mechanisms, including the activation of cytotoxic T cells and natural killer (NK) cells, which directly target and eliminate tumor cells. Additionally, lymphocytes secrete cytokines such as interferon-gamma (IFN-γ and tumor necrosis factor-alpha (TNF-α which enhance the anti-tumor activity of other immune cells. These cytokines modulate the tumor microenvironment by promoting an immune-activating milieu and inhibiting tumor growth and metastasis (47, 48). Some studies have also shown that a low LYM may be the cause of inadequate immune response and the result of low survival rates in many types of cancer. This immune deficiency may lead to increased tumor proliferation and metastasis and reduced response to therapeutic interventions (49). Therefore, the immune response to breast tumors could vary depending on the composition of lymphocytes, which ultimately affects prognosis. Further research is needed on the mechanism of peripheral lymphocytes affecting BC. Discoveries have uncovered that PLTs function in inflammatory diseases and malignant tumors (50, 51). PLTs are closely associated with tumor cells and play a crucial role in the key stage of cancer metastasis. PLT regulates immune responses in the tumor microenvironment by secreting a variety of cytokines and growth factors (e.g., transforming growth factor-β, TGF-β). TGF-β not only inhibits the proliferation and function of lymphocytes, weakening the body’s immune surveillance and immune editing ability but also promotes the growth and invasion of tumor cells, thus inhibiting the anti-tumor activity of immune cells (52). Through the release of cytokines, tumor cells activate platelets, promoting the extravasation and spread of cancer cells and negatively correlates with survival prognosis (53, 54). Notably, in our study, platelets were an independent prognostic factor of BC, which was also confirmed by the study of Liefaard, M. C. et al. and Graziano, V. et al (55, 56). Changes in LYM and platelets PLT, as indicators of inflammation, not only directly affect the immune response, but also indirectly influence tumor progression by affecting the nutritional status. Our research indicates that HGB is a potential prognostic indicator for BC. A study by Michael Henke et al. showed that HGB concentration affects the prognosis of patients with early BC, which corresponds to our results (57). Several investigations indicated anemia and HGB play a pivotal role in malignant progression (58, 59). An important factor contributing to tumor hypoxia is the reduced oxygen transport capacity in the blood resulting from tumor-related and/or treatment-related anemia, which is a frequent complication seen in cancer patients (59, 60). HGB is an important prognostic indicator of nutritional status and hypoxia in cancer patients. Hence, it is essential to elucidate the underlying mechanisms of HGB that affect BC. In cancer patients, changes in the AGR are closely related to prognosis (61). Cancer-related chronic inflammation and malnutrition typically lead to a decrease in AGR, primarily by reducing albumin levels and increasing globulin levels. A low AGR usually indicates poor prognosis, higher recurrence rates, and shorter survival times (62). In the present study, few studies have systematically examined the connection between BC and AGR (63, 64). Basem N. Azab has shown that pretreatment AGR is an independent, significant predictor of long-term mortality in BC patients (65). Additionally, this research demonstrates that AGR is an independent predictive factor for BC. PA, also known as transthyretin, is a thyroid hormone transport protein synthesized by the liver and partially degraded by the kidneys; its primary function is thyroxine transport. Serum PA concentrations less than 10 mg/dL are associated with malnutrition (66). PA is more sensitive to acute changes in protein balance and reacts to nutrition (67, 68). Numerous studies indicate that PA is a helpful single parameter for assessing protein-energy malnutrition, including postoperative outcomes and recurrence of non-small cell lung cancer (69, 70). Our research also revealed the prognostic significance of PA in BC. We have determined a comprehensive and systematic assessment, combining inflammation and nutritional blood markers, to evaluate their impact on the prognosis of BC.

Our nomogram demonstrated a significant improvement in predicting OS and DFS of BC patients compared to the TNM stage system. The model was further validated in an independent external cohort, confirming its reliability and reproducibility. Currently, several prognostic models are accessible for the clinical assessment of BC patients (71). Jeongmin Lee et al. and Xuanyi Wang et al. both developed a prognostic model based on radiomics to predict the DFS in BC patients, achieving a C-index of 0.63 and 0.82, respectively (72, 73). There are also nomograms based on molecular testing, gene expression profiling, and RNA sequencing data that can provide accurate predictions for BC patients. For instance, Jie Sun et al. created a model to predict BC risk in BRCA gene carriers, but in an empirical investigation, their C index was only 0.711 (74). MammaPrint test on 70 genes proved useful for early-stage BC treatment decisions (75), the C-index of the model for predicting OS was 0.614 (76). Liu Z. et al. developed a nomogram composed of 7-lncRNA signatures associated with immune invasion and tumor mutation burden in BC (77). However, molecular testing, gene expression profiling, and RNA sequencing data were not included in our model due to their requirement for highly specialized testing facilities, which entail high costs and necessitate skilled personnel for operation, thereby limiting their applicability. Our nomogram achieved a C-index of 0.820 for OS and 0.76 for DFS in the training cohort, which is relatively high compared to the analyses described above. Notably, in our study, the acquisition of hematological indicators is generally non-invasive, simpler, and more cost-effective, making them suitable for dynamic monitoring. These indicators can reflect changes in the patient’s condition and provide comprehensive information on systemic status, including inflammatory responses, immune function, and nutritional status. In contrast, radiomics may involve the use of radiation or contrast agents, posing certain risks and discomfort. Moreover, radiomics can only provide structural information for specific sites and typically requires longer intervals between repeated assessments. Our prediction models can be cheaper, more accurate, and simpler to use in primary hospitals compared to these models. We developed a nomogram to predict 3-year, 5-year, and 7-year OS and DFS for BC patients in both training and validation cohorts. This tool aids clinicians in estimating individual survival probabilities with greater precision. Our nomogram demonstrates better prognostic accuracy and clinical utility. This prognostic model can be of great clinical value for patient management, risk stratification, therapeutic options, and postoperative monitoring strategies.

Despite the excellent discrimination ability of our nomogram, our research has its limitations. First, as with any retrospective study analysis, there is a potential risk of selection bias. Second, despite the use of an independent external validation cohort in this study, further research involving a multi-center prospective study with a larger dataset is warranted. Third, due to the limitations of our database, we are unable to incorporate genetic variables such as BRCA1/2 and P53 into our current model. We plan to explore and collect additional genetic and molecular marker data in future research to improve predictive accuracy and provide deeper insights into the biological complexity of BC.

Our study successfully developed a predictive nomogram for OS and DFS in BC patients by incorporating inflammation, nutritional factors, and pathologic, which showed greater precision than the conventional TNM staging system. The nomogram in this study was validated using independent cohorts from different institutions. Independent validation of the model with a C-index greater than 0.7 indicates that our model exhibits good performance in terms of robustness and reliability. The nomogram is a straightforward, low-cost, and useful tool that can assist clinicians with choosing therapies and patient counseling. Further analysis and validation studies are warranted to refine and improve the nomogram, taking into account the limitations mentioned above, and to establish its usefulness in clinical practice.

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

The studies involving humans were approved by Guangxi Medical University Cancer Hospital Ethical Review Committee; Guangxi Zhuang Autonomous Region Maternal and Child Health Care Hospital Ethical Review Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

CW: Writing – original draft, Investigation, Methodology, Software. HA: Software, Writing – original draft, Data curation, Formal analysis, Validation. DM: Methodology, Supervision, Visualization, Writing – original draft. PW: Formal analysis, Resources, Writing – original draft, Data curation. LW: Formal analysis, Resources, Writing – original draft. ZL: Writing – original draft, Validation. PL: Validation, Writing – original draft. TH: Writing – original draft, Visualization, Writing – review & editing. ML: Writing – original draft, Writing – review & editing, Conceptualization.