The occurrence of lymph node metastasis (LNM) is a significant factor contributing to poor prognosis in patients with CRC. Accurate preoperative assessment of LNM not only dominates the preoperative treatment plan, surgical approach, and extent of intraoperative lymph node dissection but also has an important impact on the postoperative treatment adjustment. Currently, the clinical examinations commonly lack of specificity, objectivity, and/or sensitivity, and some are invasive, which makes widespread screening for preoperative LNM in CRC challenging. Therefore, there is a critical need to develop a new tool that can objectively and accurately predict preoperative LNM. Such a tool would greatly benefit patients with CRC by providing more precise diagnostic information, facilitating better treatment decisions, and ultimately improving patient outcomes.

The systemic inflammatory response (SIR), including markers, such as NLR and LMR [18, 19], plays a crucial role throughout different stages of tumorigenesis, progression, invasion, and metastasis. Owing to the advantages of objectivity, accessibility, and affordability, an increasing number of studies have focused on the role of SIR in tumorigenesis and development in recent years [20]. Previous studies have highlighted that increased NLR correlates with poorer prognosis in patients with CRC [21], while elevated NLR was typically caused by increased neutrophils and/or decreased lymphocytes. Strong infiltration of neutrophils within tumors can lead to immunosuppression, excessive proliferation of tumor cells, and promote angiogenesis, thus promoting tumor metastasis [18, 20]. In contrast, lymphocytes, act as host cell-mediated immune substitutes, play a role in inhibiting tumor cell proliferation and metastasis [22]. Consequently, the NLR is significant in determining patients’ prognosis. In a retrospective study, Khan et al.[23] found that high preoperative NLR levels were positively correlated with pathologically confirmed LNM in patients after surgery, suggesting that NLR ’s potential utility as a marker for lymph node involvement in patients with CRC. In this study, univariate analysis demonstrated that NLR was significantly higher in the LNM group than in the control group, and the results of subsequent multivariate analysis similarly confirmed NLR as an independent risk factor for LNM in patients with CRC (P < 0.05). Therefore, our findings support NLR could be used as predictor for LNM in CRC.

Monocytes play a crucial role in the progression of malignant tumors. Elevated levels of peripheral blood monocytes are associated with an increased tumor burden, which can lead to a higher likelihood of tumor spread, metastasis, and deterioration. Therefore, tumors are more likely to deteriorate and metastasis when lymphocytes are also decreased [22]. The results of this study also suggest that the LMR may serve as an independent risk factor for LNM in patients with CRC.

In recent years, numerous studies have focused on investigating the prognostic impact of FAR on malignant tumors. An et al. [24] identified elevated FAR was a risk factor for preoperative LNM in patients with cervical squamous cell carcinoma. Zhang et al. [25] highlight that the prechemotherapy FAR was a reliable indicator for predicting the efficacy and prognosis of chemotherapy in patients with metastatic CRC. Additional studies have confirmed that in patients with CRC liver metastasis and hepatectomy, the overall survival (OS) and disease-free survival (DFS) rates of patients with a high preoperative FAR index were significantly lower than those of the control group, indicating that the preoperative FAR index is an independent predictor of OS and DFS in patients with CRC liver metastasis and hepatectomy [26]. Paik et al. [27] also corroborated that high preoperative FAR was an independent risk factor for LNM in CRC. Nonetheless, given the retrospective nature of these studies, which limits the strength of the evidence, further research is required to definitively elucidate the relationship between FAR and LNM in CRC.

Researches have demonstrated that smoking elevates the risk of CRC [28]. Botteri, et al. [29] through an analysis and synthesis of data from 188 original studies, concluded that the risk of CRC is positively associated with smoking. Furthermore, another study confirmed that nicotine present in tobacco could enhance lymphatic metastasis in human esophageal cancer by downregulating OTUD3, which in turn inhibit the expression of vascular endothelial growth factor-C mRNA [30]. Despite these findings, there is limited research specifically investigating the relationship between smoking and lymph node metastasis (LNM) in CRC. In this study, univariate analysis revealed a statistically significant difference in smoking history between the LNM group and the control group. Additionally, multivariate analysis also confirmed that smoking history was an independent risk factor for LNM in CRC. The precise mechanism by which smoking influences LNM in CRC remains to be elucidated.

At present, FOB is mostly utilized for the early screening of CRC, but its use in prognostication being relatively uncommon [31]. This study is the first to include FOB as a potential risk factor for lymph node metastasis (LNM) in CRC. Interestingly, univariate analysis revealed a statistically significant difference in FOB results between the two groups. It was also shown in the multivariate analysis that it could serve as an independent risk factor for LNM in CRC. However, given this study was retrospective and involves a small sample size, the credibility of this conclusion may be limited. Further studies are needed to verify the relationship between FOB and LNM in CRC.

Previous studies on risk factors have primarily conducted univariate analysis to screen variables followed by multivariate analysis. This approach is extremely inefficient for studies with a large number of variables, as it often leads to issues such as multicollinearity and model overfitting. Therefore, to address these challenges, our study applied Lasso regression and cross-validation, techniques commonly used in high-throughput screening, to identify potential risk factors for lymph node metastasis (LNM) in colorectal cancer (CRC) from inflammatory indicators. This method minimized the impact of multicollinearity among variables in the final model and resulted in a predictive model with superior fitting performance [32, 33]. Furthermore, since logistic regression equation is complex and difficult to generalize in clinical practice, we translated the complex logistic regression equation into a more intuitive nomogram. So that each independent risk factor is represented by a certain weight (or score), which can be read directly on the “points” scale, allowing the calculation of a total score for each patient. By referencing the “total points” scale, clinicians can determine the probability of preoperative LNM on the “LNM predictive probability” scale. This facilitates the formulation of a lymph node dissection plan for high-risk patients, potentially improving their prognosis and survival, and making the model convenient for clinical use. In the meantime, this study also supported that the model possesses good clinical utility using DCA analysis, indicating that patients could benefit from its application. However, this study also had some limitations: first, being a retrospective study, the results may carry inherent biases. Second, the sample size was relatively small, necessitating further studies to validate the model in the future.