Abstract
Objective
This study aims to investigate the relationship between immune cell levels, tumor markers, and metastatic colorectal cancer (mCRC), evaluate their predictive efficacy, and construct a predictive model.
Method
Retrospectively selected patients diagnosed with colorectal cancer (CRC) through pathological examination at Shanxi Cancer Hospital between January 2016 and December 2018. T-tests and chi-square tests were used to identify clinical and pathological characteristics associated with mCRC. Evaluate predictive performance using the ROC curve. A risk factor scoring system is constructed based on immune cell levels and tumor markers, categorizing patients into low-risk and high-risk groups according to their scores. Single-factor and multi-factor logistic regression analyses were employed to identify independent predictors and construct a nomogram.
Results
A total of 270 patients with CRC were included, including 45 cases of mCRC. Statistical analysis results indicate that peripheral blood laboratory indicators associated with mCRC include immune cell levels (Th, Tc, Th/Tc) and tumor markers (CEA, CA199, CA242, CA724, CA50). The risk factor scoring system revealed that the incidence of distant metastasis was significantly higher in the high-risk group than in the low-risk group (P < 0.001). Furthermore, logistic regression analysis results indicate that the risk factor score is an independent predictor of distant metastasis in CRC.
Conclusion
A risk factor scoring system based on immune cell levels (Th, Tc, Th/Tc) and tumor markers (CEA, CA199, CA242, CA724, CA50) effectively predicts the occurrence of mCRC. This approach holds promise as a non-invasive tool for dynamic monitoring of distant metastasis risk in clinical practice.
Keywords: Colorectal cancer, Immune cell levels, Tumor markers, Distant metastasis
Introduction
Colorectal cancer (CRC) is the fourth most fatal worldwide, accounting for 10% of all cancer diagnoses and cancer-related deaths globally each year [1]. Its epidemiological characteristics are manifested in a significantly higher incidence rate among males than females, with developed countries exhibiting a 3–4 times higher incidence rate than developing countries [2]. Additionally, the high incidence of CRC is primarily associated with unhealthy dietary habits and lifestyles such as obesity and alcohol consumption [3]. Due to its large population, China ranks first globally in both new CRC cases and CRC-related deaths, posing a serious threat to public health [4]. Among these, metastatic colorectal cancer (mCRC) carries a poor prognosis and is the primary cause of high mortality rates among colorectal cancer patients. The five-year survival rate for mCRC is approximately 14%, significantly lower than that for early-stage CRC [5]. Research indicates that treatment outcomes for mCRC are associated with the primary tumor site and molecular subtype [6]. In recent years, beyond traditional chemotherapy, the advancement of immunotherapy and targeted therapy has provided more personalized treatment options for patients with mCRC [7].
Currently, clinical assessment of CRC metastasis risk primarily relies on pathological TNM staging [8], Lymphatic vessel invasion [9], and key differential genes [10]. However, these methods suffer from insufficient sensitivity, poor reproducibility, or limited predictive value, failing to meet the demands of individualized, precision-based treatment. Therefore, identifying earlier and more comprehensive biological predictive markers holds significant clinical importance.
Extensive literature indicates that the immune microenvironment plays a crucial role in CRC, particularly the imbalance of immune cells, which has been demonstrated to be closely associated with tumor progression and metastasis. For example, cytotoxic T cells (CTLs、Tc cells) [11], helper T cells (Th cells) [12], and NK cells are all key effector cells that suppress distant metastasis of CRC. At the same time, literature reports indicate that tumor markers are widely used in the auxiliary diagnosis and prognosis assessment of cancer. For example, carbohydrate antigen 199 (CA199), carbohydrate antigen 242 (CA242), carbohydrate antigen 724 (CA724), and carcinoembryonic antigen (CEA) are key serum biomarkers for gastric cancer diagnosis. When these four markers are tested together, the rate of missed and misdiagnosed cases can be significantly reduced, substantially improving the accuracy of gastric cancer diagnosis [13].However, most current studies focus solely on individual indicators, with very few investigations exploring the prediction of mCRC occurrence through integrating immune cell levels and serum tumor markers. Therefore, this study aims to further explore the efficacy of combining immune cell levels and tumor markers in predicting mCRC and to construct a predictive model for mCRC.
Materials and methods
Research subjects
This study included 270 patients with CRC who underwent pathological diagnosis at Shanxi Cancer Hospital from January 2016 to December 2018. Collect peripheral blood samples from all CRC patients and record their gender, age, smoking and alcohol consumption status, hypertension, diabetes, body mass index, tumor histological grading, immune cell levels, tumor markers, blood routine examination, and fecal occult blood status. All subjects’ peripheral blood samples were collected on an empty stomach before surgery and before receiving any neoadjuvant chemotherapy or targeted therapy. All patients’ follow-up data are tracked over time through outpatient follow-up records, telephone follow-up, and hospital electronic medical record systems. The survival status and metastasis occurrence of patients are regularly collected. The focus of follow-up is on the occurrence of distant metastasis or the last follow-up, with a maximum follow-up time of 79 months and a median of 60 months. The data is complete and traceable. All participants signed informed consent forms before study enrollment. This research was conducted per the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Shanxi Cancer Hospital (Approval No: KY2023005).
Inclusion and exclusion criteria
Inclusion Criteria: (1) Patients who underwent CRC resection at this hospital and received a postoperative pathological diagnosis of CRC; (2) No prior antineoplastic therapy administered before surgery; (3) Relevant clinical and pathological data are complete.
Exclusion Criteria: (1) Patients whose hematologic parameters are influenced by factors unrelated to the tumor, such as infection or hematologic disorders; (2) Patients who died from accidental causes or non-tumor-related causes; (3) Patients with dual malignancies.
Statistical methods
Data analysis was performed using SPSS 27.0, and results were visualized with GraphPad Prism 10.0. Determine the optimal cutoff value by plotting the ROC curve. In this study, the optimal cutoff value was calculated using the receiver operating characteristic curve (ROC curve), with the detection index value corresponding to the maximum value of the Jordan index (sensitivity + specificity − 1) being the optimal cutoff value. The larger the value of this index, the higher the overall accuracy of diagnosis or prediction. Survival analysis was performed using the Kaplan-Meier method. The chi-square test and t-test were used to compare group characteristics, respectively, with statistical significance set at P < 0.05. Conduct a multivariate logistic regression analysis in R 4.3.3 to build a predictive model, and plot a nomogram based on the analysis results. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration curves (Bootstrap method, n = 1000). Clinical utility was evaluated via decision curve analysis (DCA) curves, while predictive model discrimination was evaluated using receiver operating characteristic (ROC) curves.
Result
Distant metastasis in CRC patients and survival analysis between the two groups
Among the 270 CRC patients included in this study, 45 (16.7%) developed distant metastases (Fig. 1A). Compared with the non-distant metastasis group, the distant metastasis group showed a 29.78-month reduction in overall survival (33.69 ± 3.42 months vs. 63.47 ± 1.66 months), with a statistically significant difference (P < 0.001) (Fig. 1B).
Fig. 1.
Distribution of metastatic versus non-metastatic groups among 270 CRC patients and survival analysis between the two groups. A Among the 270 CRC patients, 45 cases (16.7%) were metastatic and 225 cases were non-metastatic. B Compared with the non-distant metastasis group, the distant metastasis group showed significantly reduced overall survival (P < 0.001)
Clinical and pathological characteristics of CRC patients
Based on distant metastasis status, 270 CRC patients were divided into distant and non-distant metastasis groups to analyze the relationship between distant metastasis occurrence and clinical-pathological characteristics. Results indicate that distant metastasis in CRC is significantly associated with gender, drinking history, Th cell, Tc cell, the Th/Tc ratio, regulatory T cells (Treg cells), CEA, CA199, CA242, CA724, carbohydrate antigen 50 (CA50), N staging, p53, and tumor diameter (P < 0.05). However, no significant association was found with patient smoking history, fecal occult blood, diabetes, hypertension, age, body mass index(BMI), total count cells (TTC), double-positive T cells (DPT), natural killer (NK) cells, natural killer T (NKT) cells, B cells, alpha-fetoprotein (AFP), differentiation grade, T stage, primary tumor location or microsatellite status (MSS/MSI) (P > 0.05) (Table 1).
Table 1.
Clinical and pathological characteristics of the two patient groups
| Characteristics | mCRC, (n = 45) | Non-mCRC, (n = 235) | P value |
|---|---|---|---|
| Gender, n (%) | 0.025 | ||
| Male | 27(60.0) | 94(41.8) | |
| Female | 18(40.0) | 131(58.2) | |
| Age, years, n (%) | 0.312 | ||
| < 60 | 22(48.9) | 92(40.9) | |
| ≥ 60 | 23(51.1) | 133(59.1) | |
| Smoker, n (%) | 0.554 | ||
| No | 33(73.3) | 155(68.9) | |
| Yes | 12(26.7) | 70(31.1) | |
| Drinking history, n (%) | 0.042 | ||
| No | 42(93.3) | 178(79.1) | |
| Yes | 3(6.7) | 47(20.9) | |
| Hypertensive, n (%) | 0.659 | ||
| No | 35(77.8) | 168(74.7) | |
| Yes | 10(22.2) | 57(25.3) | |
| Diabetes, n (%) | 0.795 | ||
| No | 41(91.1) | 199(88.4) | |
| Yes | 4(8.9) | 26(11.6) | |
| Fecal occult blood, n (%) | 0.115 | ||
| No | 12(26.7) | 88(39.1) | |
| Yes | 33(73.3) | 137(60.9) | |
| BMI, Kg/m2 (Q1,Q3) | 22.68(20.45, 26.67) | 23.55(21.22, 25.64) | 0.626 |
| Degree of Differentiation, n (%) | 0.128 | ||
| Well and Moderately | 25(55.6) | 155(68.9) | |
| Poorly | 20(44.4) | 70(31.1) | |
| T stage, n (%) | 0.176 | ||
| T1 + T2 | 1(2.2) | 29(12.9) | |
| T3 | 24(53.3) | 122(54.2) | |
| T4 | 20(44.5) | 74(32.9) | |
| N stage, n (%) | < 0.001 | ||
| N0 | 12(26.7) | 125(55.6) | |
| N1 | 13(28.9) | 54(24.0) | |
| N2 | 20(44.4) | 46(20.4) | |
| P53, n (%) | < 0.001 | ||
| Wild type | 27(60.0) | 190(84.4) | |
| Mutated | 18(40.0) | 35(15.6) | |
| MSI | 0.631 | ||
| No | 40(88.9) | 194(86.2) | |
| Yes | 5(11.1) | 31(13.8) | |
| Tumor Diameter (Q1,Q3) | 5.00(4.00, 6.75) | 4.50(3.50, 6.00) | 0.032 |
| Primary Site, n (%) | 0.149 | ||
| Left colon | 20(44.4) | 67(29.8) | |
| Right colon | 7(15.6) | 50(22.2) | |
| Rectum | 18(40.0) | 108(48.0) | |
| TTC (Q1,Q3) | 69.35(61.35, 77.05) | 70.30(62.90, 77.00) | 0.613 |
| Th cell (Q1,Q3) | 41.40(32.95, 48.53) | 35.90(30.70, 41.00) | 0.004 |
| Tc cell (Q1,Q3) | 24.15(19.78, 32.60) | 29.60(22.40, 37.30) | 0.011 |
| DP T (Q1,Q3) | 1.00(0.70, 1.95) | 1.10(0.70, 2.10) | 0.743 |
| Th/Tc (Q1,Q3) | 1.70(1.24, 2.21) | 1.25(0.90, 1.65) | < 0.001 |
| NK (Q1,Q3) | 21.10(11.90, 26.53) | 18.40(12.50, 26.30) | 0.498 |
| NKT (Q1,Q3) | 7.55(3.45, 10.78) | 6.40(3.90, 10.30) | 0.923 |
| Treg (Q1,Q3) | 4.40(3.60, 5.70) | 5.70(4.40, 6.80) | 0.009 |
| B cells (Q1,Q3) | 8.15(5.15, 11.53) | 9.10(6.20, 12.00) | 0.307 |
| CEA (Q1,Q3) | 5.50(2.54, 35.02) | 2.26(1.14, 5.58) | < 0.001 |
| CA199 (Q1,Q3) | 37.54(12.13, 166.18) | 12.35(6.50, 24.06) | < 0.001 |
| CA242 (Q1,Q3) | 27.01(3.53, 157.08) | 4.25(1.66, 13.11) | < 0.001 |
| AFP (Q1,Q3) | 2.55(1.25, 3.88) | 2.50(1.45, 4.34) | 0.905 |
| CA724(Q1,Q3) | 6.59(2.18, 15.73) | 2.32(1.05, 5.45) | < 0.001 |
| CA50 (Q1,Q3) | 8.48(0.87, 37.74) | 1.25(0.67, 3.08) | 0.001 |
Note: Bold font indicates a statistical significance with P < 0.05
Cutoff values and diagnostic performance
To evaluate the practical diagnostic value of immune cell levels and tumor marker indicators for distant metastasis in CRC and determine their optimal cutoff values, this study further constructed ROC curves for analysis. This curve is used to evaluate the sensitivity and specificity of these markers in diagnosing distant metastasis of CRC. By calculating the Yorden index, optimal cutoff values are determined to group the markers.
Results indicate that the optimal cutoff value for Th cells is 40.45%, with a sensitivity of 0.553, a 1-specificity of 0.274, and an AUC of 0.642 (P = 0.007) (Fig. 2A); The optimal cutoff value for Tc cells was 26.55%, with a sensitivity of 0.632, specificity of 0.370, and an AUC of 0.630 (P = 0.007) (Fig. 2B); The optimal cutoff value for the Th/Tc ratio was 1.68, with a sensitivity of 0.526, a 1-specificity of 0.224, and an AUC of 0.671 (P < 0.001) (Fig. 2C); The optimal cutoff value for Treg was 5.35%, with a sensitivity of 0.289, a 1-specificity of 0.562, and an AUC of 0.366 (P = 0.011); The optimal cutoff value for CEA was 4.96 µg/L, with a sensitivity of 0.600, a 1-specificity of 0.259, and an AUC of 0.701 (P < 0.001) (Fig. 2D); The optimal cutoff value for CA199 was 37.23 U/mL, with a sensitivity of 0.511, a 1-specificity of 0.155, and an AUC of 0.707 (P < 0.001) (Fig. 2E); The optimal cutoff value for CA242 was 18.40 U/mL, with a sensitivity of 0.591, a 1-specificity of 0.174, and an AUC of 0.7 (P < 0.001) (Fig. 2F); The optimal cutoff value for CA724 was 4.34 U/mL, with a sensitivity of 0.614, a 1-specificity of 0.297, and an AUC of 0.699 (P < 0.001) (Fig. 2G); The optimal cutoff value for CA50 was 8.47 U/mL, with a sensitivity of 0.514, a 1-specificity of 0.120, and an AUC of 0.665 (P = 0.004) (Fig. 2H).
Fig. 2.
Diagnostic performance of immunological and tumor marker indicators for distant metastasis in CRC. Select indicators with AUC > 0.60 to construct a risk factor scoring system, comprising immune cell levels (Th ≥ 40.45%(Fig. 2A), Tc < 26.55%(Fig. 2B), Th/Tc ≥ 1.68(Fig. 2C)) and tumor markers (CEA ≥ 4.96 µg/L(Fig. 2D), CA199 ≥ 37.23 U/mL(Fig. 2E), CA242 ≥ 18.40 U/ml(Fig. 2F), CA724 ≥ 4.34 U/ml(Fig. 2G), CA50 ≥ 8.47 U/ml(Fig. 2H))
Grading system
Based on the analysis, each CRC patient with any risk factor in the risk factor scoring system is assigned 1 point, with a score range of 0 to 8 points. Subsequently, the 270 patients were categorized into a low-risk group (0–4 points) and a high-risk group (5–8 points) based on their scores. Statistical analysis was conducted on the status of distant metastasis in both groups, and the results showed a significant difference (P < 0.001) (Table 2; Fig. 3).
Table 2.
Scoring system
| Index | Score |
|---|---|
| Th cell | |
| ≧ 40.450 | 1 |
| < 40.450 | 0 |
| Tc cell | |
| ≧ 26.55 | 0 |
| < 26.55 | 1 |
| Th/Tc | |
| ≧ 1.675 | 1 |
| < 1.675 | 0 |
| CEA | |
| ≧ 4.955 | 1 |
| < 4.955 | 0 |
| CA199 | |
| ≧ 37.225 | 1 |
| < 37.225 | 0 |
| CA242 | |
| ≧ 18.400 | 1 |
| < 18.400 | 0 |
| CA724 | |
| ≧ 4.340 | 1 |
| < 4.340 | 0 |
| CA50 | |
| ≧ 8.465 | 1 |
| < 8.465 | 0 |
Fig. 3.
Differences in distant metastasis between the low-risk and high-risk groups (P < 0.001)
Note: Immunological and tumor marker indicators with AUC values greater than 0.6 were selected. Each risk factor in CRC patients is assigned 1 point, with a total score ranging from 0 to 8 points.
Logistic regression analysis of patients with distant metastasis in CRC
Univariate logistic regression analysis revealed that distant metastasis in CRC patients was influenced by gender (P = 0.027), T stage (T4, P = 0.049), N stage (N1, P = 0.033; N2, P < 0.001), P53 (P < 0.001), and risk factor score (P < 0.001). Logistic multivariable regression analysis identified N stage, P53 status, and risk factor score as independent predictors of distant metastasis in CRC (Table 3).
Table 3.
Univariate and multivariate logistic regression analysis of distant metastasis in CRC
| Univariate analysis | Multivariate analysis | |||
|---|---|---|---|---|
| OR (95%CI) | P | OR (95%CI) | P | |
| Gender, Female | 2.090 (1.088,4.015) | 0.027 | ||
| Smoker | ||||
| No | ||||
| Yes | 0.805 (0.393,1.652) | 0.554 | ||
| Drinking history | ||||
| No | ||||
| Yes | 0.380 (0.129,1.114) | 0.078 | ||
| Hypertensive | ||||
| No | ||||
| Yes | 0.842 (0.392,1.808) | 0.659 | ||
| Diabetes | ||||
| No | ||||
| Yes | 0.747 (0.247,2.255) | 0.604 | ||
| Fecal occult blood | ||||
| No | ||||
| Yes | 1.766 (0.866,3.603) | 0.118 | ||
| Age | ||||
| < 60 | ||||
| ≧ 60 | 0.723 (0.381,1.374) | 0.322 | ||
| BMI | 0.980 (0.902,1.064) | 0.625 | ||
| Degree of Differentiation | ||||
| Well and Moderately | ||||
| Poorly | 1.771 (0.923,3.401) | 0.086 | ||
| T stage | ||||
| T1 + T2 | ||||
| T3 | 5.705 (0.741,43.916) | 0.094 | ||
| T4 | 7.838 (1.005,61.113) | 0.049 | ||
| N stage | ||||
| N0 | ||||
| N1 | 2.508(1.075,5.580) | 0.033 | 4.136(1.431,11.952) | 0.009 |
| N2 | 4.529(2.052,9.994) | < 0.001 | 5.166(1.799,15.003) | 0.003 |
| P53 | ||||
| Wild type | ||||
| Mutated | 3.619 (1.803,7.265) | < 0.001 | 6.009 (2.182,16.550) | < 0.001 |
| Tumor Diameter | 1.153 (0.988,1.346) | 0.071 | ||
| MSI | 0.632 | |||
| No | ||||
| Yes | 0.782 (0.287,2.135) | |||
| Primary site | ||||
| Left colon | ||||
| Right colon | 0.469(0.184,1.195) | 0.113 | ||
| Rectum | 0.558(0.276,1.131) | 0.106 | ||
| Risk factor score | ||||
| Low risk | ||||
| High risk | 12.250(5.583,26.877) | < 0.001 | 15.175 (5.428,42.420) | < 0.001 |
Note: Bold font indicates a statistical significance with P < 0.05
Construction and validation of CRC distant metastasis nomogram
Based on the three variables of N staging, P53 and risk factor scores, a Logistic regression analysis was conducted to construct a predictive model. The nomogram was drawn according to the analysis results, and the probability assessment of the risk of distant metastasis was achieved by summing the scores of these three indicators (Fig. 4A). The calibration accuracy of the model was evaluated through the Hosmer-Lemeshow goodness-of-fit test (P = 0.91 > 0.05) and the calibration curve (Bootstrap method, n = 1000) (consistency index = 0.805, 95% CI = 0.776–0.834). The results showed that the predictive results of the model were in good consistency with the observed results, proving that the model has good reliability (Fig. 4B). The clinical utility of the model was evaluated through the decision curve, and the results indicated that the model was superior to the “all intervention” (All) and “no intervention” (None) strategies within the threshold range of 0.1–0.7, indicating that the model has significant clinical application potential in this threshold range (Fig. 4C). The nomogram was evaluated through the ROC curve, and the result showed that the area under the curve (AUC) of the line graph model reached 0.818 (95% CI: 0.747–0.890, specificity = 87.6%, sensitivity = 64.4%, optimal cut-off value = 0.226, P < 0.05), indicating that the model has good identification ability for high-risk individuals (Fig. 4D).
Fig. 4.
Construction and validation of the CRC distant metastasis prediction model. A A nomogram for predicting distant metastasis of CRC was constructed based on N staging, P53 status, and risk factor scores. B The calibration curve of the predictive model is used to assess consistency; (C) Decision curves of predictive models are used to evaluate clinical benefits. D The area under the ROC curve of a predictive model is used to evaluate the model’s predictive performance
Comparison of prediction model based on N stage, p53 and risk factor score with traditional indicators
The AUC, sensitivity, specificity and 95% confidence intervals of the CEA, CA199, CEA + CA199 combined model, and the “N staging + P53 + risk factor score” combined prediction model were compared and analyzed (Table 4). The results showed that among the four prediction models, the AUC values were 0.701, 0.707, 0.729 and 0.818, respectively, all of which were statistically significant (all P < 0.001). Among them, the AUC value of the “N staging + P53 + risk factor score” combined prediction model was the highest (0.818), with a 95% confidence interval of 0.747–0.890, indicating better overall discrimination. Especially in terms of specificity, this model demonstrated extremely strong discriminatory ability (0.876), far exceeding traditional indicators, while maintaining a very low risk of misdiagnosis and a high sensitivity (0.644). In conclusion, this combined prediction model has higher clinical diagnostic value and predictive efficacy.
Table 4.
Comparison of prediction model based on N stage, p53 and risk factor score with traditional indicators
| cutoff | sensitivity | specificity | 95%CI | AUC | P | |
|---|---|---|---|---|---|---|
| CEA | 4.96 | 0.600 | 0.259 | 0.619–0.782 | 0.701 | < 0.001 |
| CA199 | 37.23 | 0.511 | 0.155 | 0.614–0.799 | 0.707 | < 0.001 |
| CEA+CA199 | 0.5 | 0.307 | 0.693 | 0.642–0.815 | 0.729 | < 0.001 |
| N +P53 + risk factor score | 0.226 | 0.644 | 0.876 | 0.747–0.890 | 0.818 | < 0.001 |
Discussion
In recent years, CRC has become the third most common cancer globally, following lung and breast cancer, and the second leading cause of cancer-related deaths after lung cancer [14]. With the shift in dietary habits and lifestyles among Chinese residents, the number of smokers, obese individuals, and diabetes patients has increased significantly, leading to a marked rise in the number of CRC patients [15]. The early screening rate for CRC is lower than that for breast cancer and cervical cancer [16], leading many patients to present at hospitals with already advanced-stage CRC. This is the primary reason for the high mortality rate among CRC patients [17]. The results of this study indicate that compared with CRC patients without distant metastasis, those with distant metastasis exhibit significantly reduced overall survival, suggesting that distant metastasis is a major factor contributing to poor prognosis in CRC patients. Currently, the diagnosis of mCRC relies on imaging examinations and histopathological examinations. However, imaging examinations carry risks such as high false-positive rates and radiation exposure, while histopathological examinations present drawbacks including invasiveness and time-consuming procedures [18, 19]. In contrast, immune cell and tumor marker indicators can be obtained from patient blood samples and dynamically monitored throughout treatment to assess tumor progression.
Previous studies have demonstrated that immune cell levels and tumor markers play a significant role in the development and progression of CRC [20, 21]. Th17 is significantly associated with reduced survival in patients with distant metastasis of colorectal cancer [22]. Additionally, Th17 cells promote tumor angiogenesis and accelerate liver metastasis in colorectal cancer [23]. The level of tumor infiltration by Tc cells (i.e., CD8⁺ T cells) is also directly correlated with patient prognosis. Higher CD8⁺ T cell infiltration correlates with better patient outcomes. Conversely, insufficient CD8⁺ T cell infiltration or functional suppression leads to tumor immune escape, increasing the risk of CRC progression and metastasis [24]. It is well known that tumor markers provide crucial evidence regarding the occurrence, progression, and prognosis of distant metastasis in CRC. Patients with colorectal cancer (CRC) who exhibit elevated levels of CA199 and CEA in their blood typically have shorter overall survival compared to patients with normal CA199 and CEA levels [25]. Studies have also shown that CA19-9 levels exhibit a gradient increase with tumor progression. Persistently elevated CEA levels or a recurrence of elevated CEA after returning to normal post-surgery serve as early warning signs of disease recurrence [26]. CA724 is primarily associated with gastric cancer [27], but it also finds application in colorectal cancer (CRC). Its high specificity enhances the accuracy of CRC diagnosis and reduces the likelihood of false positives [28]. Another study indicates that patients with liver metastasis exhibit significantly higher average levels of serum CA242, CA50, CA724, and CA199 compared to those without liver metastasis. Furthermore, combined testing of these four markers significantly enhances the predictive accuracy for liver metastasis occurrence [29]. This study found that the distant metastasis group exhibited an increased proportion of Th cells, a decreased proportion of Tc cells, and an elevated Th/Tc ratio. These findings indicate immune dysregulation and disruption of immune homeostasis, which may accelerate the progression of distant metastasis in CRC. CEA, CA199, CA242, CA724, and CA50 were all significantly upregulated in the distant metastasis group, consistent with previous reports on the role of these five tumor markers in CRC and gastric cancer. These findings suggest that immune dysfunction and abnormally elevated tumor marker levels are key factors contributing to distant metastasis in CRC.
This study innovatively integrated peripheral blood immune cell levels and serum tumor marker indicators to successfully establish a risk prediction model for distant metastasis in colorectal cancer (CRC). A risk factor scoring system based on immune cell levels (Th, Tc, Th/Tc) and tumor markers (CEA, CA199, CA242, CA724, CA50) classified patients into low-risk (0–4 points) and high-risk (5–8 points) groups. Analysis revealed that high-risk patients exhibited significantly higher rates of distant metastasis (P < 0.001). Results from univariate and multivariate logistic regression analyses indicate that the risk factor score is an independent predictor of distant metastasis in colorectal cancer (OR = 22.978, 95% CI: 7.996–66.035). This finding demonstrates the importance of combined multi-marker testing, which can prevent missed diagnoses and misdiagnoses that are prone to occur with single-marker testing, enabling a more comprehensive assessment of the risk of distant metastasis in CRC. This is consistent with previous studies showing that combined testing of the four markers CA242, CA50, CA724, and CA199 can enhance the predictive efficacy for liver metastasis [13]. This study also employed a nomogram to calculate the probability of distant metastasis risk in individual cases by summing the scores for N staging, P53 status, and risk factor assessment, further demonstrating the model’s significant practical potential. At the same time, validation results indicate that the model demonstrates high calibration accuracy and strong capability in identifying high-risk individuals. To further assess the advantages of this model in predicting risk, this study also compared the new model with CEA, CA199, and the combined score of CEA + CA199. The results showed that the ‘N staging + P53 + risk factor score’ combined model constructed in this study achieved a qualitative improvement in predictive ability: its AUC value reached 0.818 (95% CI: 0.747–0.890), significantly superior to CEA (0.701) and CA199 (0.707), and achieved a leap from ‘moderate accuracy’ to ‘high accuracy’. More importantly, the new model demonstrated a clear advantage in specificity (0.876), effectively addressing the problem of high false positives caused by the low specificity of traditional markers (0.155–0.259). This integration of multi-dimensional indicators not only compensates for the limitations of a single serological indicator but also provides a more precise and stable risk assessment tool for clinical practice, with significant clinical application value.
Compared to previous research methods, this study combines the dynamic changes of immune cells with tumor marker levels, enabling a more objective and comprehensive prediction of distant metastasis in CRC patients and further improving patient prognosis. The model established in this study will enable early assessment of distant metastasis risk in CRC without imposing additional financial burden on patients, thereby facilitating early screening. It can effectively guide clinicians in adjusting treatment plans promptly during therapy, contributing to personalized treatment for CRC. However, this study still has the following limitations. Firstly, regarding the sample size and the limitations of a single center: The total sample size of this study is relatively small. More samples need to be selected for verification, and all the research subjects in this study were from Shanxi Cancer Hospital.Although the Shanxi Provincial Cancer Hospital is the only specialized cancer hospital in Shanxi Province and the patients it treats have a certain regional representativeness, there may still be some result errors in the research.However, the study may still have result errors. Secondly, the model’s depth and fineness are insufficient. In terms of predictive indicators, this study did not include surgical-related information (such as specific surgical types, resection range, and dissection methods), which may have potential impacts on local control and subsequent metastasis. In terms of outcomes, this study examined only whether distant metastasis occurred, without distinguishing specific metastatic sites, which, to some extent, limited the model’s role in guiding precise, individual prevention. Finally, this study only constructed a simple binary model based on whether distant metastasis occurred and failed to capture the temporal dynamics of disease progression; at the same time, it lacked dynamic monitoring data during treatment, which would not reflect changes in indicators over time. In response to these deficiencies, subsequent studies will collect specific time data of patients’ metastasis occurrence, upgrade the simple binary prediction to a survival risk assessment with a time dimension; at the same time, increase the dynamic collection of blood indicators, and integrate dynamic changes of surgical parameters, biological markers, and multiple time points of clinical characteristics to construct a more valuable dynamic risk prediction model, achieving the full cycle management from assessment to monitoring of patients.
Conclusion
In summary, this study demonstrates significant potential in predicting distant metastasis of CRC. The risk factor scoring system, constructed by integrating immune cell levels and tumor marker indicators, holds high clinical value for assessing the risk of distant metastasis in CRC. This model provides additional options for early screening and prognostic evaluation of distant metastasis in CRC.
Acknowledgements
Thanks to Professor Yanfeng Xi for her suggestions and guidance, as well as the fund support she provided.
Authors’ contributions
Chentong Mao, Zhongyuan Bai and Hongling Zhang jointly conceived the research idea and designed the research plan. Data collection was mainly carried out by Chentong Mao, Hongling Zhang, Shuzhe Yang and Jiayi Wu. Data analysis and interpretation were accomplished by Chentong Mao, Zhongyuan Bai and Jianghong Guo. Chentong Mao wrote the first draft of the paper. Hongling Zhang and Yanfeng Xi conducted a strict review and revision of the paper. Yanfeng Xi was responsible for the direction guidance and supervision of this research and provided the research funds. All authors read and approved the final manuscript.
Funding
This work was supported by the Natural Science Foundation of China (grant numbers: 82172659).
Data availability
All data generated and analyzed during this study are available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
Publisher’s note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated and analyzed during this study are available from the corresponding author upon reasonable request.