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Preoperative inflammatory markers and tumor markers in predicting lymphatic metastasis and postoperative complications in colorectal cancer: a retrospective study

BMC Surgery volume 25, Article number: 71 (2025) Cite this article

Abstract

Objective

To analyze the impact of preoperative inflammatory markers and tumor markers on lymphatic metastasis and postoperative complications in colorectal cancer patients, and explore their predictive value for these outcomes. Furthermore, based on the preoperative inflammatory and tumor marker indicators with significant effects, predictive models for the risk of lymphatic metastasis and the incidence of postoperative complications will be constructed.

Methods

This study retrospectively analyzed the clinical data of CRC patients who underwent surgical treatment at Shanxi Bethune Hospital between January 2021 and June 2024. Preoperative inflammatory markers and tumor markers were compared between the lymph node-positive and lymph node-negative groups. Variables were selected using Lasso regression, and independent factors influencing lymph node metastasis were identified through multivariate logistic regression analysis. Based on these results, a Nomogram prediction model was constructed, and its accuracy was evaluated using a calibration curve. The discriminatory ability of the model was assessed with the ROC curve, and its clinical applicability was analyzed using the DCA curve. Similarly, for predicting postoperative complications, Pearson correlation analysis was used to examine the relationships between preoperative inflammatory markers, tumor markers, and complications. ROC curves were employed to calculate the AUC and optimal cutoff values for each marker. Kaplan-Meier (KM) curves were used to analyze the impact of these markers on DFS. Independent factors were identified through univariate and multivariate logistic regression analyses, and a Nomogram model was constructed and validated.

Results

A total of 196 patients were included in the study. The NLR, PLR, FAR, CEA, CA199, and CA724 levels were significantly elevated in the lymph node metastasis group (P < 0.05). Lasso regression identified smoking history, NLR, FAR, and CA724 as non-zero coefficient variables. Multivariate logistic regression further confirmed smoking history (HR = 4.20), NLR (HR = 2.52), FAR (HR = 1.18), and CA724 (HR = 1.32) as independent predictors of lymph node metastasis (P < 0.05). The Nomogram prediction model constructed based on these results showed high prediction accuracy, with a ROC curve AUC of 0.880, indicating excellent discriminatory ability. The DCA decision curve demonstrated good clinical applicability. In postoperative complication prediction, Pearson correlation analysis revealed a positive correlation between NLR, PLR, FAR, CA199, and CA724 with complication rates (P < 0.05), with correlation coefficients of 0.24, 0.34, 0.16, 0.19, and 0.19, respectively, with PLR showing the strongest correlation. ROC curve analysis showed that the AUCs for NLR, PLR, LMR, FAR, and CAR were 0.633, 0.675, 0.467, 0.580, and 0.559, with optimal cutoff values of 4.29, 261.71, 3.39, 18.20, and 11.26, respectively. The AUCs for CEA, CA199, and CA724 were 0.567, 0.612, and 0.609, with optimal cutoff values of 11.87, 10.27, and 6.85. KM curve analysis showed that higher levels of NLR, FAR, CAR, CEA, CA199, and CA724 were associated with poorer DFS. Univariate and multivariate logistic regression further confirmed NLR (HR = 1.53) and CA724 (HR = 1.11) as independent predictors of complications (P < 0.05). The calibration curve indicated high prediction accuracy, with a ROC curve AUC of 0.729, demonstrating excellent discriminatory ability, and the DCA decision curve showed good clinical applicability.

Conclusion

Preoperative inflammatory markers and tumor markers have a significant impact on the occurrence of lymphatic metastasis and postoperative complications in colorectal cancer patients, demonstrating certain clinical value in predicting lymphatic metastasis and postoperative complications. The predictive models developed in this study provide a reference for personalized diagnosis and treatment, but their practical application needs to be further validated through large-scale clinical studies.

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Background

Colorectal cancer (CRC) is a common and fatal malignant tumor, ranking third in global incidence and second in mortality, posing a serious threat to patients’ life and health [1, 2]. The treatment of CRC includes surgery, preoperative and postoperative radiotherapy and chemotherapy, targeted therapy, and immunotherapy. However, for resectable tumors, radical surgery remains the main treatment, while radiotherapy and chemotherapy are typically adjunctive therapies [3, 4]. Despite some therapeutic success, postoperative complications and tumor metastasis remain significant factors affecting patient recovery and long-term survival [5]. Inflammatory responses are the natural reactions of the immune system to bodily injury or stimuli, involving the aggregation of immune cells and the release of inflammatory factors. Tumor cells, as persistent sources of stimulation, can trigger both systemic and local inflammatory responses [6]. Studies have shown that inflammation plays a critical role in tumorigenesis, progression, and survival prognosis, especially the neutrophil-to-lymphocyte ratio (NLR), which has been proven to be associated with the prognosis of various malignancies, including colorectal cancer, prostate cancer, and bladder cancer [7, 8]. In 2011, scholar Hanahan listed inflammation as one of the ten hallmarks of cancer, highlighting its key role in cancer biology. Inflammation influences cancer progression through mechanisms such as promoting tumor initiation, increasing tumor cell proliferation, metastasis, and immune evasion. Inflammatory-related cytokines like TNF-α, IL-6, and CRP can promote tumor growth, angiogenesis, and enhance metastatic potential, making inflammation an important therapeutic target in cancer treatment [9, 10]. Accurate preoperative evaluation of lymph node metastasis is crucial for surgical planning, determining the extent of lymph node dissection, and adjusting postoperative treatment strategies. Existing prediction models mainly rely on imaging examinations, medical history, and tumor differentiation, with fewer applications of inflammatory markers. Furthermore, they often neglect multicollinearity and model overfitting, which leads to lower model reliability [11,12,13]. Traditional prediction models typically rely on clinical and pathological factors, such as tumor size, staging, and grading. However, by integrating inflammatory biomarkers, these markers can better reflect the tumor microenvironment and immune response, providing a more comprehensive prognosis for clinical practice. Research has shown that inflammatory markers are closely associated with lymph node metastasis, and compared to other markers, inflammatory indicators are more accessible, cost-effective, and suitable for various healthcare institutions [14]. The incidence of postoperative complications is a key factor affecting patient recovery and prognosis. Accurately predicting complications helps in formulating individualized treatment plans and postoperative interventions, significantly reducing the incidence of complications and improving patient prognosis. Therefore, this study aims to analyze the impact of preoperative inflammatory markers and tumor markers on lymph node metastasis and postoperative complications in colorectal cancer patients, assess the value of these markers in predicting CRC-related outcomes, and construct corresponding prediction models to provide scientific evidence for clinical practice.

Methods

Patients

Patients diagnosed with colorectal cancer (CRC) for the first time at Shanxi Bethune Hospital between January 2021 and June 2023 were identified through the hospital’s medical record system. All patients underwent standard surgical tumor resection and lymph node dissection. CRC diagnosis was confirmed by preoperative endoscopic biopsy pathology and postoperative surgical specimen examination.

Preoperative management included routine monitoring of blood glucose and blood pressure, along with necessary auxiliary tests such as electrocardiograms, cardiac ultrasound, chest and abdominal CT scans. Rectal cancer patients routinely had pelvic MRI. Patients with underlying conditions underwent consultations with relevant departments to exclude surgical contraindications. Preoperative preparation involved a liquid diet, oral anti-inflammatory bowel medication for three days before surgery, laxatives one day before surgery, and morning enema on the day of surgery.

During surgery, all patients underwent curative resection for CRC, with laparoscopic or open surgery performed according to the patient’s condition. The type of radical surgery was chosen based on tumor location, and some patients had preventive stoma formation. All surgeries were conducted by the same senior surgical team to ensure R0 resection standards.

Postoperatively, all patients received symptomatic supportive treatments, including fasting, total parenteral nutrition, infection prevention, correction of electrolyte imbalances, and routine wound care, with additional examinations and treatments based on changes in their condition. Postoperative lymph node metastasis was assessed based on pathological examination results, and postoperative complications were recorded and analyzed. A one-year follow-up period was conducted. The specific inclusion and exclusion criteria are as follows:

Inclusion criteria: No prior antitumor treatment or supportive therapy such as leukocyte or platelet elevation before surgery; Postoperative pathological examination confirmed no residual tumor cells at the surgical margin; Complete clinical data, including preoperative assessment, intraoperative records, and postoperative follow-up information.

Exclusion criteria: Severe hematological diseases before surgery (such as leukemia, thrombocytopenic purpura) or active infections; Severe heart, lung, or other vital organ dysfunctions or immunological diseases; Other malignant tumors in different locations; Emergency surgery due to complications such as obstruction, perforation, or bleeding, and patients who did not undergo bowel preparation; Palliative surgery performed; Tumor with distant metastasis (such as in the liver, lungs, etc.); Incomplete or missing key clinical data.

Two independent researchers (HM W and YZ W) carefully screened the cases retrieved in the initial search, strictly following the inclusion and exclusion criteria to remove cases that did not meet the study’s focus. Any disagreements during the selection process were resolved through in-depth discussion, reaching a consensus, or seeking the opinion of a third independent researcher (HY L) for systematic resolution. This study has been approved by the Ethics Committee of Shanxi Medical University.

Data extraction

Data extraction was independently performed by two researchers (HM W and YZ W). Any disagreements during the extraction process were resolved through comprehensive discussion, and if necessary, the opinion of a third author (HY L) was sought.

Basic characteristics of the included cases

The extracted data included: Gender; Age; Tumor Length; BMI; Tumor Location; T Stage; Presence or absence of lymph node metastasis; Smoking status; Alcohol consumption status; Presence or absence of Hypertension; Presence or absence of Coronary Heart Disease; Presence or absence of diabetes; CA199 Level; CA724 Level.

Preoperative inflammatory markers of the included cases

The inflammatory markers from the last preoperative blood test included: neutrophil count, lymphocyte count, platelet count, monocyte count, fibrinogen, albumin, and C-reactive protein (CRP). Based on current research reports [15,16,17], systemic inflammatory response indicators that could be potential predictors for CRC lymph node metastasis and postoperative complications were further calculated, including: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), fibrinogen-to-albumin ratio (FAR), and C-reactive protein-to-albumin ratio (CAR).

Postoperative complications of the included cases

The extracted data included: Anastomotic leak; Infection; Bleeding; Intestinal obstruction; Ascites; Pneumonia; Thrombosis; Mortality status.

Research process

This study explores the impact of preoperative inflammatory markers and tumor biomarkers on lymph node metastasis and postoperative complications in CRC patients, respectively, and constructs prediction models for evaluation.

Lymph node metastasis

First, the differences in preoperative indicators between the lymph node-positive and lymph node-negative groups were compared to preliminarily investigate the relationship between preoperative factors and lymph node metastasis. Based on this, statistical methods were used to identify preoperative indicators that independently influence lymph node metastasis. Then, a model was constructed to predict the risk of lymph node metastasis based on the selected independent factors, and the model’s predictive performance was internally validated and assessed.

Postoperative complications

For potential predictors of postoperative complications in CRC, the correlation between preoperative inflammatory markers and tumor biomarkers with postoperative complications was first analyzed. Subsequently, key preoperative inflammatory markers and tumor biomarkers were divided into high and low groups, and survival analysis was used to explore their relationship with short-term prognosis, to identify the specific impact of these markers on CRC prognosis. Based on this, further selection of preoperative indicators independently influencing postoperative complications was conducted, and a model to predict the risk of postoperative complications was constructed. The model’s predictive performance and clinical utility were comprehensively assessed through internal validation and clinical benefit evaluation.

Statistical methods

Data processing and statistical analysis were performed using R software (V4.0.3). Categorical data were presented as the number of cases (n) and percentages (%). The choice of statistical test for group comparisons was based on the theoretical frequency (T): when T ≥ 5, the Chi-square test was used; when T < 5, the continuity-corrected Chi-square test or Fisher’s exact test was applied. Continuous data were presented as mean ± standard deviation (mean ± SD), and group comparisons were conducted using the independent samples t-test.

Correlation analysis was performed using Pearson correlation to assess the linear relationship between variables. Survival analysis was conducted using the Kaplan-Meier (KM) method to plot survival curves, and the Log-rank test was used to compare survival differences between groups. The optimal cutoff value was determined by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The best cutoff value was determined by the Youden Index, calculated as: Youden Index = (Sensitivity + Specificity) − 1.

To identify preoperative indicators independently influencing lymph node metastasis, Lasso regression with 10-fold cross-validation was first performed, using lambda.1se as the penalty parameter to select variables with non-zero regression coefficients. The selected variables were then included in multivariate logistic regression analysis. A P-value < 0.05 was considered statistically significant, indicating that the variable independently influenced lymph node metastasis. For identifying preoperative indicators independently influencing postoperative complications, univariate logistic regression analysis was first performed, and variables with P < 0.05 were included in multivariate logistic regression. Independent factors influencing postoperative complications were confirmed (P < 0.05). To avoid multicollinearity affecting the multivariate regression model, variance inflation factors (VIF) were calculated for the candidate variables before constructing the logistic regression model. If VIF > 10, it indicated severe collinearity, and the model was further optimized by eliminating or aggregating variables.

Based on the above analysis results, nomogram models for predicting lymph node metastasis and postoperative complication risks were constructed. Internal validation was performed using the Bootstrap method with 1000 resampling iterations to calculate the stability and reliability of the model. Calibration curves were plotted to assess the consistency between the predicted values and actual observations, and ROC curves were used to evaluate the model’s discriminatory ability. Furthermore, decision curve analysis (DCA) was performed to assess the clinical applicability and benefits of the model.

Results

Predictive value of lymph node metastasis

Through a systematic search of the Shanxi Bethune Hospital case system, a total of 196 colorectal cancer (CRC) patients were included, with the screening process shown in Fig. 1. Based on the presence or absence of lymph node metastasis, the patients were divided into a non-lymph node metastasis group (108 patients) and a lymph node metastasis group (88 patients). In the preliminary analysis of the relationship between the patients’ preoperative history and lymph node metastasis, we found that the lymph node metastasis group had a higher number of smokers. Further analysis of preoperative inflammatory markers and their relationship with lymph node metastasis revealed that the NLR, PLR, and FAR were significantly elevated in the lymph node metastasis group, while no significant differences were found between the two groups for LMR and CAR (P > 0.05). Finally, we analyzed the relationship between preoperative tumor biomarkers and lymph node metastasis, finding that the CEA, CA199, and CA724 levels were significantly higher in the lymph node metastasis group (P < 0.001). The detailed results are shown in Table 1.

Fig. 1
figure 1

Case Screening Flowchart

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Table 1 Basic characteristics of included patients
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Due to the large number of independent variables, only univariate analysis was conducted, which makes it difficult to reduce issues such as multicollinearity between variables and model overfitting. Therefore, this study utilized LASSO regression and 10-fold cross-validation (Fig. 2A, B) to achieve more efficient variable selection and obtain a model with the fewest variables and the best fit. The non-zero coefficient variables selected were: smoking history, NLR, FAR, and CA724. Further multivariate logistic regression analysis confirmed that smoking history, NLR, FAR, and CA724 were independent predictive factors for lymph node metastasis (Fig. 2C). Based on the above results, we further created a Nomogram to visualize the model. The calibration curve showed good accuracy for the model, with the ROC curve area under the curve (AUC) being 0.880, indicating good discrimination. On this basis, we also plotted the DCA decision curve, where the model curve does not approach the two reference lines (All and None), indicating that the model has potential clinical value (Fig. 3).

Fig. 2
figure 2

Analysis of factors affecting lymph node metastasis. A: Lasso regression coefficient selection; B: Lasso regression variable trajectory; C: Multivariate logistic regression

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Fig. 3
figure 3

Predictive model for lymph node metastasis. A: Nomogram model; B: Calibration curve; C: ROC curve; D: DCA decision curve

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Predictive value of postoperative complications

We also collected information on the occurrence of complications in 196 CRC patients, as detailed in Table 2. Among these 196 patients, 32 patients experienced anastomotic leak, 45 patients had infections, 17 patients experienced bleeding, 26 patients had intestinal obstruction, 22 patients developed ascites, 13 patients had pneumonia, 11 patients had thrombosis, 28 patients had postoperative recurrence, and 2 patients died. Overall, 62 patients experienced postoperative complications, resulting in a postoperative complication rate of 31.63%.

Table 2 Incidence of postoperative complications
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We performed Pearson correlation analysis on postoperative complications (Fig. 4). The results showed that the overall complication rate was positively correlated with NLR, PLR, FAR, CA199, and CA724 (P < 0.05), with correlation coefficients of 0.24, 0.34, 0.16, 0.19, and 0.19, respectively, with PLR showing the strongest correlation. Specifically, anastomotic leak, infection, bleeding, intestinal obstruction, ascites, thrombosis, and death were most strongly correlated with PLR, with correlation coefficients of 0.47, 0.46, 0.29, 0.22, 0.30, 0.39, and 0.27, respectively. Pneumonia had the strongest correlation with NLR (correlation coefficient = 0.33), while recurrence had the strongest correlation with CEA (correlation coefficient = 0.28).

Fig. 4
figure 4

Correlation analysis of postoperative complications

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We plotted ROC curves for preoperative inflammatory markers and tumor markers based on the overall incidence of postoperative complications. The optimal cutoff values were calculated using the Youden index derived from the ROC curves. The ROC curve results for inflammatory markers showed that the AUC for NLR, PLR, LMR, FAR, and CAR were 0.633, 0.675, 0.467, 0.580, and 0.559, respectively. The optimal cutoff values were 4.29, 261.71, 3.39, 18.20, and 11.26. The ROC curve results for tumor markers showed that the AUC for CEA, CA199, and CA724 were 0.567, 0.612, and 0.609, with optimal cutoff values of 11.87, 10.27, and 6.85, respectively (Fig. 5).

Fig. 5
figure 5

ROC curves of inflammatory markers and tumor markers

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Based on the optimal cutoff values, we grouped the preoperative inflammatory markers and tumor markers into high and low categories, and further investigated the impact of these markers on DFS using KM survival analysis (Fig. 6). When analyzing the impact of inflammatory markers on DFS, we found that higher levels of NLR, FAR, and CAR were associated with worse DFS (P < 0.001). However, no significant impact on DFS was observed when analyzing NLR and LMR. When further analyzing the effect of tumor markers on DFS, we found that higher levels of CEA, CA199, and CA724 were associated with worse DFS (P < 0.005).

Fig. 6
figure 6

KM survival analysis of DFS

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We performed univariate and multivariate logistic regression on the factors influencing the overall postoperative complication rate. The analysis revealed that NLR, PLR, FAR, CA724, and CA199 are factors influencing postoperative complications, with NLR and CA724 identified as independent predictive markers (P < 0.05) (Fig. 7). Since the Nomogram model constructed with only NLR and CA724 had limited discriminative ability and predictive accuracy due to the small number of included variables, we incorporated all factors associated with postoperative complications into the prediction model and constructed a Nomogram. The calibration curve showed that the model had good accuracy, and the area under the ROC curve was 0.729, indicating good discrimination. Based on this, we further plotted the DCA decision curve, and the model curve did not approach the two reference lines (All and None), suggesting that it has practical value (Fig. 8).

Fig. 7
figure 7

Analysis of factors influencing postoperative complications

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Fig. 8
figure 8

Predictive model for postoperative complications. A: Nomogram model; B: Calibration curve; C: ROC curve; D: DCA decision curve

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Discussion

Globally, the incidence and mortality rates of colorectal cancer (CRC) have been increasing, with incidence ranking third and mortality ranking second. As early as 1863, Rodolph Virchow first linked inflammation with cancer [18]. In 2011, the renowned scholar Hanahan et al. [19] described the ten hallmarks of cancer, with inflammation identified as the seventh hallmark, emphasizing its relationship with cancer initiation and progression. As lifestyle changes and environmental factors evolve, chronic inflammation induced by various factors significantly increases the risk of cancer development [20]. There is a reciprocal interaction between inflammation and tumor development. Whether it is chronic inflammation leading to tumor formation, the development of precancerous lesions, or the progression of cancer, they all exhibit an inflammatory microenvironment that supports tumor growth [21]. The tumor inflammatory microenvironment is a complex network formed by tumor cells and surrounding immune-inflammatory cells, stromal cells, extracellular matrix, and microvessels. In peripheral blood, inflammatory responses are mainly indicated by changes in white blood cells, neutrophils, monocytes, lymphocytes, platelets, C-reactive protein, albumin, and other markers [22–23]. This study statistically analyzed the inflammatory markers and tumor markers in the peripheral blood of patients, exploring the correlation between preoperative inflammatory markers and lymph node metastasis and postoperative complications in colorectal cancer patients.

With advancements in diagnostic and therapeutic techniques, the prognosis of CRC patients has significantly improved. However, lymph node metastasis remains a major factor affecting prognosis. Previous studies have shown a close correlation between preoperative inflammatory markers and lymph node metastasis. A study by Zhang et al. [24], through a retrospective analysis of 904 gastric cancer patients, found that the levels of NLR and PLR were significantly correlated with lymph node metastasis. Similarly, a study by Catal et al. [25], analyzing 171 colon cancer patients, found that platelet count and PLR values were significantly associated with lymph node metastasis, and could serve as indicators for predicting lymph node metastasis. Based on these studies on the relationship between lymph node metastasis, it is of significant clinical value for formulating personalized surgical plans and determining the extent of lymph node dissection.

In this study, we found that NLR, PLR, FAR, CEA, CA199, and CA724 were significantly elevated in the lymph node metastasis group. Further univariate and multivariate logistic regression analyses revealed that NLR, FAR, CA199, and CA724 were independent predictors of lymph node metastasis. NLR reflects changes in the body’s inflammatory response and immune status, promoting tumor cell proliferation and metastasis. Specifically, the increase in neutrophils and platelets can promote the lymph node metastasis process by secreting growth factors, enhancing tumor cell angiogenesis, and facilitating immune escape mechanisms [26–27]. FAR reflects the systemic inflammatory state, where a low value indicates a poor immune status, making it easier for tumor cells to metastasize and grow. A high FAR value usually signifies enhanced systemic inflammation, which further exacerbates the spread of tumor cells and lymph node metastasis [28–29]. Furthermore, CA199 and CA724, as tumor markers, can promote the metastatic potential of tumor cells. CA199 is typically associated with tumor invasiveness and metastasis. It can facilitate tumor cell growth, angiogenesis, and immune escape, helping tumor cells enter the bloodstream and spread to other organs via the lymphatic or vascular system. CA724, a carbohydrate antigen associated with colorectal cancer, has been shown to be elevated in association with lymph node metastasis, tumor cell adhesion, and migration, further promoting the metastatic capacity of tumors [30,31,32,33]. These markers, working through various mechanisms, together serve as important tools for predicting lymph node metastasis in CRC.

The treatment of CRC includes surgery, preoperative and postoperative chemotherapy and radiotherapy, targeted therapy, and immunotherapy. However, for resectable tumors, radical surgical resection remains the primary treatment modality. Radiotherapy and chemotherapy serve as adjuvant therapies and are often administered through a multidisciplinary team (MDT) collaboration approach [34,35,36]. Although surgical treatment has made significant progress in improving patient prognosis, postoperative complications remain one of the key factors affecting postoperative recovery and survival. Multiple studies have shown that inflammatory markers in cancer patients are closely related to prognosis and can serve as effective predictors of outcomes [37,38,39].

This study found that the incidence of postoperative complications was positively correlated with various inflammatory markers, including NLR, PLR, FAR, CAR, CA199, and CA724 (P < 0.05). Among these, PLR had the strongest correlation with the incidence of complications (correlation coefficient = 0.35). Further analysis showed that Anastomotic leak, Infection, Bleeding, Intestinal Obstruction, Ascites, Thrombosis, and Death were most strongly correlated with PLR. PLR reflects the body’s immune and inflammatory status, and a higher PLR may be associated with increased platelets and reduced lymphocytes, suggesting a systemic inflammatory response, which is closely related to the occurrence of postoperative complications. Pneumonia had the strongest correlation with NLR (correlation coefficient = 0.33). A higher NLR usually indicates an increase in neutrophils, which is often associated with acute inflammation and infection. Therefore, NLR may be a potential biomarker for predicting the occurrence of pneumonia. Recurrence was most strongly correlated with CEA (correlation coefficient = 0.28), which is consistent with the role of CEA as an important tumor marker in colorectal cancer. CEA levels are typically used to monitor cancer recurrence and treatment response, so higher CEA levels may be associated with an increased risk of cancer recurrence, highlighting the importance of CEA in postoperative monitoring of colorectal cancer.

We further categorized the related indicators into high and low groups and analyzed their impact on short-term prognosis using KM survival curves. The results showed that when the levels of PLR, FAR, CAR, and CA199 were higher, patients had worse short-term prognosis (P < 0.05). Further logistic regression analysis revealed that NLR and CA724 are independent predictive markers for postoperative complications (P < 0.05). These findings suggest that inflammation-related markers, such as NLR and PLR, play a significant role in predicting postoperative complications. By monitoring these indicators, clinicians can identify high-risk patients and implement personalized treatment strategies, effectively reducing complication rates and improving short-term prognosis.

This study preliminarily explored the impact of preoperative inflammatory markers and tumor biomarkers on lymphatic metastasis and postoperative complications in colorectal cancer (CRC) patients, as well as their potential value in prognostic prediction. However, this study also has certain limitations. First, the clinical applicability of the predictive models constructed in this study requires further exploration. Although inflammatory markers and tumor biomarkers demonstrated good predictive ability in the study population, patient groups from different regions or with different types may affect the predictive performance of these models. Therefore, validation in a broader clinical setting is necessary. Secondly, this study only included preoperative inflammatory markers and tumor biomarkers, without further investigation into whether other potential variables, such as genomic data, molecular biomarkers, or imaging data, could further enhance the accuracy of the predictive model. Genomic data and other molecular biomarkers are increasingly valued for their predictive potential in oncology, and incorporating such data could help improve the accuracy and personalized predictive capability of the model [40,41,42]. Therefore, future prospective studies with larger and more diverse populations are needed to validate these results, with the aim of providing more precise predictive models and treatment decision-making tools for clinical practice.

Conclusion

Preoperative inflammatory markers and tumor biomarkers have a significant impact on the occurrence of lymph node metastasis and postoperative complications in colorectal cancer (CRC) patients. These markers show clinical value in predicting lymph node metastasis and postoperative complications. The risk prediction model constructed based on inflammatory markers and tumor biomarkers demonstrates good predictive performance and clinical applicability. By applying this model, early identification of the risk of preoperative lymph node metastasis and postoperative complications can be achieved, enabling the development of more personalized and optimized treatment plans to improve patient prognosis.

Data availability

All data is contained within the manuscript. The datasets used and analyzed during the current study available from the corresponding author on reasonable request.

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Funding

This study was funded by “Key Research and Development (R&D) Projects of Shanxi Province” (2021XM22) and “Fundamental Research Program of Shanxi Province” (202103021224346) and Shanxi Province “136 Revitalization Medical Project Construction Funds” (2019XY005).

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Authors and Affiliations

  1. Department of General Surgery, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital, Shanxi, China

    Huiming Wu, Min Deng, Zhensheng Zhai, Dingwen Xue & Huiyu Li

  2. Department of General Surgery, Second Hospital of Shanxi Medical University, Taiyuan, China

    Yize Wang

  3. Department of Breast Surgery, Afiliated Cancer Hospital of Shanxi Medical University, Shanxi Provincial Cancer Hospital, Taiyuan, China

    Fei Luo

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  1. Huiming Wu
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Contributions

Huiming Wu and Yize Wang (Co-first Author): Conceptualization, Methodology, Data base and literature search, Writing—Original draft preparation, Writing—Reviewing and Editing, Final approval of manuscript. Min Deng: Literature search, Data curation, Writing—Reviewing and Editing, Final approval of manuscript. Zhensheng Zhai and Dingwen Xue: Literature search, Data curation, Writing—Reviewing and Editing, Final approval of manuscript. Fei Luo and Huiyu Li: Writing—Reviewing and Editing, Supervision, Funding, Final approval of manuscript.

Corresponding authors

Correspondence to Fei Luo or Huiyu Li.

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The requirement for consent to participate was waived by the Ethics Committee of Shanxi Medical University. This study was reviewed and approved by the Ethics Committee of Shanxi Medical University (Approval No. 2021GLL134).

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Wu, H., Wang, Y., Deng, M. et al. Preoperative inflammatory markers and tumor markers in predicting lymphatic metastasis and postoperative complications in colorectal cancer: a retrospective study. BMC Surg 25, 71 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12893-025-02795-y

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BMC Surgery

ISSN: 1471-2482

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