Marker for predicting risk of colorectal cancer lymphatic vessel invasion and application

Abstract

The invention discloses a marker for predicting the risk of colorectal cancer lymphatic vessel invasion and application, and belongs to the technical field of tumor biomarker research. Aiming at the problem that the current diagnosis of lymphatic vessel invasion is limited by a dyeing technology, the number of tissue blocks and the experience and professional degree of pathologists, the invention discovers that PLR, D-dimer, CA19-9 and CA724 in peripheral blood laboratory indexes are related to lymphatic vessel invasion through clinical pathological feature analysis; a risk factor scoring system constructed based on PLR, D-dimer, CA199 and CA724 is an independent prediction factor of lymphatic vessel invasion. The risk factor score is combined with other common clinical pathological variables to construct a column graph model for predicting the lymphatic vessel invasion risk, diagnosis of a pathologist can be assisted, and a clinician can be guided to make a treatment strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tumor biomarker research, and particularly relates to a marker for predicting the risk of lymphovascular invasion of colorectal cancer and application thereof. BACKGROUND

[0002] Colorectal cancer (CRC) is one of the most common malignant tumors of the gastrointestinal tract, and its incidence is mainly related to age, race, gender, body weight, living habits and gene mutations. In the past 30 years, the incidence and mortality of colorectal cancer in China have shown a rapid upward trend. The progress in treatment methods and the implementation of screening programs have reduced the mortality rate of colorectal cancer to a certain extent, but the long-term survival rate of patients with metastatic colorectal cancer is far lower than expected. Tumor cell invasion of lymphatic vessels is a key step in the metastasis process.

[0003] Lymphovascular invasion (LVI) refers to the involvement of small lymphatic vessels or blood vessels (usually veins) by the tumor, which is limited to the submucosal layer and / or the intrinsic muscle layer as intramural invasion, and beyond the intrinsic muscle layer as extramural invasion. Lymphovascular invasion has been shown to have prognostic significance in various cancers. Studies have shown that lymphovascular invasion is an independent prognostic factor for colorectal cancer, and Betge et al. believe that colorectal cancer patients with extramural lymphovascular invasion are more likely to have disease progression or cancer-related death. In addition, lymphovascular invasion is an independent predictor of lymph node metastasis.

[0004] Lymphovascular invasion is a relatively common pathological feature of colorectal cancer, and specific staining techniques are used in routine pathological diagnosis to identify lymphovascular invasion. These techniques include hematoxylin and eosin (H&E) staining, elastin staining, Factor VIII staining, Ulex europaeus I agglutinin staining, CD31, CD34, D2-40 and ERG immunohistochemical staining (Gonzalez et al., 2023). However, the diagnosis of lymphovascular invasion is often limited by staining techniques, the number of tissue blocks, and the experience and expertise of pathologists, resulting in the need for further improvement in the accuracy of lymphovascular invasion diagnosis. SUMMARY

[0005] In view of the above problems, the present application aims to provide a marker for predicting the risk of lymphatic and blood vessel invasion of colorectal cancer and an application thereof, according to the clinical pathological characteristics of colorectal cancer patients, the ability of these clinical pathological characteristics to predict lymphatic and blood vessel invasion is explored, and markers capable of predicting the risk of lymphatic and blood vessel invasion are screened, including PLR, D-dimer, CA19-9 and CA724 in peripheral blood. Based on the three markers, the risk factor score for predicting the risk of lymphatic and blood vessel invasion of colorectal cancer is determined, as an independent factor for predicting the risk of lymphatic and blood vessel invasion of colorectal cancer, and a nomogram for predicting the risk of lymphatic and blood vessel invasion is constructed in combination with other common clinical pathological variables.

[0006] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0007] In one aspect, the present application provides a marker for predicting the risk of lymphatic and blood vessel invasion of colorectal cancer, wherein the marker comprises PLR, D-dimer, CA19-9 and CA724.

[0008] In another aspect, the present application provides an application of a reagent for detecting peripheral blood biomarkers in a sample in the preparation of a product for predicting and / or evaluating the risk of lymphatic and blood vessel invasion of colorectal cancer, wherein the peripheral blood biomarkers comprise PLR, D-dimer, CA19-9 and CA724.

[0009] In another aspect, the present application also provides an application of a reagent for detecting peripheral blood biomarkers in a sample in the preparation of a product for diagnosing the risk of lymphatic and blood vessel invasion of colorectal cancer, wherein the peripheral blood biomarkers comprise PLR, D-dimer, CA19-9 and CA724.

[0010] In another aspect, the present application also provides a method for constructing a prediction model for the risk of lymphatic and blood vessel invasion of colorectal cancer, comprising the following steps:

[0011] S1: data acquisition; collecting clinical pathological characteristic data of colorectal cancer patients with lymphatic and blood vessel invasion and without lymphatic and blood vessel invasion;

[0012] S2: determining biomarkers for predicting the risk of lymphatic and blood vessel invasion of colorectal cancer according to the data collected in step S1, wherein the biomarkers comprise PLR, D-dimer, CA19-9 and CA724;

[0013] S3: calculating a risk factor score according to the biomarkers determined in step S2;

[0014] S4: constructing a prediction model for lymphatic and blood vessel invasion of colorectal cancer based on the risk factor score, histological grade and T stage as three independent prediction factors.

[0015] Specifically, the specific method for calculating the risk factor score in step S3 is as follows:

[0016] PLR≥157.28, D-dimer≥194.5 ng / ml, CA19-9≥12.25 KU / L, CA724≥8.621 U / mL are taken as risk factors of lymphatic and vascular invasion of colorectal cancer, any one of the risk factors is given 1 point, and the sum is the risk factor score, the range of the risk factor score is 0-4 points; the risk factor score <2 points is a low-risk group, the risk factor score =2 points is a medium-risk group, and the risk factor score >2 points is a high-risk group.

[0017] Specifically, the prediction model of lymphatic and vascular invasion of colorectal cancer in step S4 includes a nomogram model.

[0018] Specifically, the nomogram model includes:

[0019] a first behavior score scale, ranging from 0 to 100;

[0020] a second behavior histological grade, including good and moderate, and insufficient; the score of good and moderate is 0, and the score of insufficient is 63;

[0021] a third behavior T stage, including T1, T2, T3 and T4; the scores of T1, T2 and T3 are all 0, and the score of T4 is 63;

[0022] a fourth behavior risk factor score, including a low-risk group, a medium-risk group and a high-risk group; the score of the low-risk group is 0, the score of the medium-risk group is 15, and the score of the high-risk group is 100;

[0023] a fifth behavior total score, ranging from 0 to 260, being the sum of the scores of the second row, the third row and the fourth row;

[0024] a sixth behavior prediction risk value, ranging from 0.1 to 0.7; the prediction risk corresponding to a total score of 28-108 is 0.1-0.3, the prediction risk corresponding to a total score of 108-160 is 0.3-0.5, and the prediction risk corresponding to a total score of 160-210 is 0.5-0.7.

[0025] In another aspect, the present application also provides a prediction model of risk of lymphatic and vascular invasion of colorectal cancer, comprising a data analysis and prediction module, which is used to calculate the risk of lymphatic and vascular invasion of colorectal cancer according to the values of PLR, D-dimer, CA19-9 and CA724 in peripheral blood.

[0026] In another aspect, the present application also provides a prediction method of risk of lymphatic and vascular invasion of colorectal cancer for non-diagnostic and / or therapeutic purposes, which determines the risk factor score according to the values of PLR, D-dimer, CA19-9 and CA724, and further predicts the risk of lymphatic and vascular invasion of colorectal cancer.

[0027] Specifically, PLR is greater than or equal to 157.28, D-dimer is greater than or equal to 194.5 ng / ml, CA19-9 is greater than or equal to 12.25 KU / L, and CA724 is greater than or equal to 8.621 U / mL are taken as risk factors for lymphatic and blood vessel invasion of colorectal cancer, any one of the risk factors is given 1 point, and the sum is the risk factor score, the range of the risk factor score is 0-4 points; the risk factor score is less than 2 points for a low-risk group, the risk factor score is equal to 2 points for a medium-risk group, and the risk factor score is greater than 2 points for a high-risk group.

[0028] The present application has the following advantages:

[0029] 1. The present application discloses the close relationship between lymphatic and blood vessel invasion and inflammatory state, coagulation state and tumor invasion characteristics by analyzing the clinical pathological data of patients, and proposes a new marker combination, specifically, PLR, D-dimer, CA199 and CA724 in peripheral blood, which can reflect tumor invasion characteristics, whole body inflammatory state, coagulation state and tumor marker level of patients.

[0030] 2. The risk factor score based on PLR, D-dimer, CA199 and CA724 in the present application can be used as an independent predictor of lymphatic and blood vessel invasion risk. According to the optimal cutoff values of PLR, D-dimer, CA199 and CA724, PLR is greater than or equal to 157.28, D-dimer is greater than or equal to 194.5 ng / ml, CA199 is greater than or equal to 12.25 KU / L, and CA724 is greater than or equal to 8.621 U / mL are risk factors for lymphatic and blood vessel invasion of colorectal cancer. Any one of the above risk factors is given 1 point, that is, the risk factor score, and the score range is 0-4 points. According to the score, patients are divided into a low-risk group (less than 2 points), a medium-risk group (2 points), and a high-risk group (more than 2 points). The method can quickly determine the risk range of lymphatic and blood vessel invasion of colorectal cancer in patients.

[0031] 3. The nomogram model constructed based on the risk factor score and combined with pathological characteristics in the present application has good prediction performance, which can assist pathologists in diagnosis and guide clinicians in developing treatment strategies. DETAILED DESCRIPTION

[0032] Figure 1 ROC curve for sensitivity and specificity of PLR, D-dimer, CA19-9 and CA724 in the present application for diagnosing CRC lymphatic and blood vessel invasion.

[0033] Figure 2 Nomogram model of the lymphatic and blood vessel invasion risk prediction model in the present application.

[0034] Figure 3 ROC curve of the diagnostic efficacy of the lymphatic and blood vessel invasion risk prediction model in the training set and the validation set in the present application.

[0035] Figure 4 Calibration curve and decision curve of nomogram model for predicting the risk of lymphovascular invasion in the present application. DETAILED DESCRIPTION

[0036] In order for those skilled in the art to better understand the technical solutions of the present application, the technical solutions of the present application will be further described below in combination with the drawings and examples.

[0037] I. Research materials and methods

[0038] 1. Patient selection and study design

[0039] In this retrospective study, 745 patients with colorectal cancer diagnosed in a hospital in Shanxi Province from January 2017 to December 2018 were included. The inclusion criteria were as follows: (1) pathologically diagnosed as colorectal cancer; (2) complete clinical and pathological data; (3) radical surgery. The exclusion criteria were: (1) combined with infection, blood disease and other diseases directly affecting the hematological index; (2) preoperative anti-tumor therapy; (3) combined with other tumors: (4) incomplete clinical information and follow-up data. 745 patients with colorectal cancer were included in this study, and were randomly divided into training set (n=551) and validation set (n=194). This study followed the Declaration of Helsinki and was approved by the Ethics Committee of Shanxi Tumor Hospital (number: KY2023005). All patients signed the informed consent form before inclusion in the study.

[0040] 2. Data collection and definition

[0041] We reviewed the medical records of all patients and collected demographic and clinicopathological data, including age, sex, body mass index (BMI), underlying disease status (hypertension and diabetes), stool occult blood status, tumor location, tumor size, tumor histological grade, tumor (T) stage, regional lymph node (N) stage, metastasis (M) stage, and lymphovascular invasion status. All laboratory test indicators were from preoperative, including blood routine, serum tumor markers, D-dimer. Routine blood parameters include white blood cell count, lymphocyte count, neutrophil count, monocyte count and platelet count. Serum tumor markers include carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 724 (CA724). Peripheral blood inflammatory markers include monocyte-to-white blood cell ratio (MWR), platelet-to-white blood cell ratio (PWR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-neutrophil ratio (PNR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR).

[0042] 3. Construction of a peripheral blood laboratory indicator scoring system and analysis of decision curves

[0043] This study included peripheral blood laboratory parameters such as MWR, PWR, NLR, PNR, LMR, PLR, MLR, CEA, CA19-9, CA724, and D-dimer. A scoring system was established based on these peripheral blood laboratory parameters, and a predictive model was constructed by combining demographic and pathological characteristics. The core significance of decision curve analysis (DCA) lies in quantifying the net benefit of clinical intervention, evaluating the actual clinical utility of the predictive model, and helping physicians select the optimal decision-making strategy under different risk preferences. Therefore, this study applied DCA to evaluate the clinical application value of the predictive model.

[0044] 4. Statistical Analysis

[0045] Data analysis was performed using SPSS 22.0 and R 4.3.0 software. The chi-square test was used for categorical variables. For continuous variables, t-tests were used to ensure normality and homogeneity of variance; nonparametric tests were used for variables that did not meet the normality requirement. Logistic regression analysis was used for both univariate and multivariate analyses. ROC curves were used to determine the optimal cutoff value. R packages (VIM, rms, nomogramFormula, pROC, rmda) were used to fit the model and plot ROC curves and clinical decision curves.

[0046] II. Research Results

[0047] 1. Patient's clinicopathological characteristics

[0048] Of the 745 patients included in the study, 423 were male and 322 were female. 416 (61.9%) were over 60 years of age, and 135 (18.1%) of these patients had lymphovascular involvement. These patients were divided into a training set and a validation set. The training set included 98 cases of lymphovascular involvement and 453 cases of non-lymphovascular involvement; the remainder were in the validation set. Table 1 lists the clinicopathological characteristics of CRC patients in the training and validation sets. The two sets of data were comparable, and there were no statistically significant differences in any of the study endpoints between the groups (P>0.05).

[0049] Table 1. Clinicopathological characteristics of all patients

[0050]

[0051]

[0052] 2. Training set of patient clinicopathological characteristics

[0053] The clinicopathological characteristics of patients in the training and validation sets were analyzed, and the results are shown in Table 2 below. Table 2 shows that there were statistically significant differences in gender (P=0.027), BMI (P=0.025), diabetes (P=0.000), tumor size (P=0.007), histological grade (P=0.000), T stage (P=0.000), PLR (P=0.012), D-dimer (P=0.023), CA199 (P=0.021), and CA724 (P=0.022).

[0054] Table 2. Clinical and pathological characteristics of patients in the training set.

[0055]

[0056]

[0057] 3. Optimal cutoff values ​​and diagnostic efficacy of peripheral blood laboratory indicators

[0058] In the training set, statistically significant peripheral blood laboratory parameters between patients with lymphovascular involvement and those without lymphovascular involvement included PLR, D-dimer, CA19-9, and CA724.

[0059] Receiver operating characteristic (ROC) curves were plotted to assess the sensitivity and specificity of these indicators in diagnosing lymphovascular invasion of chronic rheumatoid arthritis (CRC). The optimal cutoff value was determined by calculating the Youden index, and the indicators were grouped. The results are shown in the appendix. Figure 1 As shown. In the appendix Figure 1 In the figure, A, B, C, and D are the ROC curves of PLR, D-dimer, CA19-9, and CA724, respectively.

[0060] From the appendix Figure 1As can be seen, the optimal cutoff value for PLR is 157.28, with a sensitivity of 0.602, a 1-specificity of 0.428, and an AUC of 0.587. The optimal cutoff value for D-dimer is 194.5 ng / mL, with a sensitivity of 0.473, a 1-specificity of 0.323, and an AUC of 0.575. The optimal cutoff value for CA19-9 is 12.25 U / mL, with a sensitivity of 0.702, a 1-specificity of 0.527, and an AUC of 0.603. The optimal cutoff value for CA724 is 8.621 U / mL, with a sensitivity of 0.387, a 1-specificity of 0.159, and an AUC of 0.615. This indicates that PLR ≥ 157.28, D-dimer ≥ 194.5 ng / ml, CA199 ≥ 12.25 KU / L, and CA724 ≥ 8.621 U / mL are risk factors for lymphovascular invasion in colorectal cancer.

[0061] Patients with colorectal cancer who have any of the above risk factors are assigned a score of 1, i.e., a risk factor score, ranging from 0 to 4 points. Based on the score, patients are divided into low-risk group (<2 points), intermediate-risk group (2 points), and high-risk group (>2 points).

[0062] 4. Univariate and multivariate analyses of factors related to lymphovascular invasion

[0063] Univariate and multivariate analyses were performed on factors related to lymphovascular invasion, and the results are shown in Table 3 below. As can be seen from Table 3, univariate logistic regression analysis showed that factors related to lymphovascular invasion included gender (OR: 1.638, 95% CI: 1.056–2.543, P = 0.028), diabetes (OR: 0.351, 95% CI: 0.124–0.996, P = 0.049), and tumor size (OR: 1.157, 95% CI: 1.040–1. 288, P = 0.007), histological grade (OR: 3.608, 95% CI: 2.298-5.664, P < 0.001), T stage (T4, OR: 3.590, 95% CI: 2.288-5.633, P < 0.001), risk factor score (high risk, OR: 4.451, 95% CI: 2.590-7.648, P < 0.001). Multivariate regression analysis showed that histological grade (OR: 2.853, 95% CI: 1.730–4.705, P<0.001), T stage (T4, OR: 2.684, 95% CI: 1.647–4.372, P<0.001), and risk factor score (high risk, OR: 4.936, 95% CI: 2.260–10.782, P<0.001) were independent predictors of lymphovascular invasion in colorectal cancer.

[0064] Table 3 Univariate and multivariate analyses of factors related to lymphovascular invasion.

[0065]

[0066] 5. Construction and validation of a lymphovascular invasion risk prediction model

[0067] Based on the results of multivariate regression analysis, histological grade, T stage, and risk factor score were selected as the three factors for predicting the risk of lymphovascular invasion. A nomogram model for predicting the risk of lymphovascular invasion was constructed, and the results are attached. Figure 2 As shown.

[0068] ROC curves were plotted to evaluate the diagnostic efficacy of the lymphovascular invasion risk prediction model in both the training and validation sets. The results are attached. Figure 3 As shown in the attached figure, the calibration curve and decision curve of the nomogram model for predicting lymphovascular invasion risk are constructed. Figure 4 As shown. In the appendix Figure 3 In the diagram, A represents the area under the nomogram (AUC) of the training set predicting the risk of lymphovascular invasion; B represents the AUC of the nomogram of the validation set predicting lymphovascular invasion. (See appendix...) Figure 4In the diagram, A and B are calibration curves for predicting the risk of lymphovascular invasion in the training and validation sets, respectively; C and D are decision curves for predicting the risk of lymphovascular invasion in the training and validation sets, respectively.

[0069] From the appendix Figure 3 and attached Figure 4 As can be seen, the AUC of the training set was 0.754 (95% CI: 0.702–0.806), and the AUC of the validation set was 0.710 (95% CI: 0.616–0.804). The calibration curves of the lymphovascular invasion prediction model show good agreement between the predicted and observed results in both the training and validation sets, indicating no bias and high confidence. Decision curves are a simple method for evaluating clinical prediction models, diagnostic tests, and molecular markers. The decision curves show that within the threshold range of 0.18–0.78, the net benefit of the model is consistently higher than both the All and None lines, indicating that the model has clinical application value.

[0070] In summary, this study reveals the close relationship between lymphovascular invasion and inflammatory status, coagulation status, and tumor invasiveness. The risk factor scoring system based on PLR, D-dimer, CA199, and CA724 is more helpful in predicting the risk of preoperative lymphovascular invasion.

[0071] More specifically, this study found a statistically significant difference in PLR between the lymphovascular invasion and non-invasion groups (P<0.05), and a high PLR (cutoff value: 157.28) was more likely to result in lymphovascular invasion; high D-dimer was associated with lymphovascular invasion in colorectal cancer (P<0.05); furthermore, this study also evaluated the efficacy of CA19-9 and CA724 in predicting lymphovascular invasion in colorectal cancer, and high CA19-9 and CA724 (cutoff values ​​of 12.25 U / mL and 8.62 U / mL, respectively) were significantly associated with lymphovascular invasion.

[0072] Based on the above results, this study uses risk factor scores constructed based on PLR, D-dimer, CA19-9, and CA724 as independent risk factors for predicting the risk of lymphovascular invasion, which can reflect the patient's systemic inflammatory status, coagulation status, and tumor marker levels. The nomogram model constructed in conjunction with pathological features has good predictive efficacy, which can assist pathologists in diagnosis and guide clinicians in formulating treatment strategies.

[0073] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A biomarker for predicting the risk of lymphovascular invasion in colorectal cancer, characterized in that: The markers include PLR, D-dimer, CA19-9, and CA724.

2. The use of a reagent for detecting peripheral blood biomarkers in a sample in the preparation of products for predicting and / or assessing the risk of lymphovascular invasion in colorectal cancer, characterized in that: The peripheral blood biomarkers include PLR, D-dimer, CA19-9, and CA724.

3. The application of a reagent for detecting peripheral blood biomarkers in a sample in the preparation of products for diagnosing the risk of lymphovascular invasion in colorectal cancer, characterized in that: The peripheral blood biomarkers include PLR, D-dimer, CA19-9, and CA724.

4. A method for constructing a risk prediction model for lymphovascular invasion in colorectal cancer, characterized in that, Includes the following steps, S1: Data Acquisition; Acquisition of clinicopathological features of lymphovascular invasion and non-lymphovascular invasion in colorectal cancer patients; S2: Based on the data collected in step S1, identify biomarkers for predicting the risk of lymphovascular invasion in colorectal cancer, including PLR, D-dimer, CA19-9, and CA724; S3: Calculate the risk factor score based on the biomarkers identified in step S2; S4: Based on three independent predictive factors—risk factor score, histological grade, and T stage—a predictive model for lymphovascular invasion in colorectal cancer was constructed.

5. The method for constructing a risk prediction model for lymphovascular invasion in colorectal cancer according to claim 4, characterized in that, The specific method for calculating the risk factor score in step S3 is as follows: PLR ≥ 157.28, D-dimer ≥ 194.5 ng / ml, CA19-9 ≥ 12.25 KU / L, and CA724 ≥ 8.621 U / mL were considered risk factors for lymphovascular invasion in colorectal cancer. One point was awarded for each risk factor, and the sum of these points yielded the risk factor score, which ranged from 0 to 4. A risk factor score < 2 indicated a low-risk group, a risk factor score = 2 indicated a medium-risk group, and a risk factor score > 2 indicated a high-risk group.

6. The method for constructing a risk prediction model for lymphovascular invasion in colorectal cancer according to claim 5, characterized in that, The colorectal cancer lymphangiogenic invasion prediction model described in step S4 includes a nomogram model.

7. The method for constructing a risk prediction model for lymphovascular invasion in colorectal cancer according to claim 6, characterized in that, The nomogram model includes: The first line is a score scale, ranging from 0 to 100; The second row is the histological grading, including good, moderate, and poor; the score for good and moderate is 0, and the score for poor is 63. The third row is the T-stage, including T1, T2, T3 and T4; the scores for T1, T2 and T3 are all 0, and the score for T4 is 63; The fourth row consists of risk factor scores, including low-risk, medium-risk, and high-risk groups; the low-risk group has a score of 0, the medium-risk group has a score of 15, and the high-risk group has a score of 100. The fifth row is the total score, ranging from 0 to 260, which is the sum of the scores from the second, third, and fourth rows; The sixth line is the predicted risk value, ranging from 0.1 to 0.7; the predicted risk for a total score of 28-108 is 0.1-0.3, the predicted risk for a total score of 108-160 is 0.3-0.5, and the predicted risk for a total score of 160-210 is 0.5-0.

7.

8. A predictive model for the risk of lymphovascular invasion in colorectal cancer, characterized in that, It includes a data analysis and prediction module, which is used to calculate the risk of colorectal cancer lymphovascular invasion based on the values ​​of PLR, D-dimer, CA19-9 and CA724 in peripheral blood.

9. A method for predicting the risk of lymphovascular invasion in colorectal cancer for non-diagnostic and / or therapeutic purposes, characterized in that, Risk factor scores were determined based on PLR, D-dimer, CA19-9, and CA724 values, which were then used to predict the risk of lymphovascular invasion in colorectal cancer.

10. The method for predicting the risk of lymphovascular invasion in colorectal cancer according to claim 9, characterized in that: PLR ≥ 157.28, D-dimer ≥ 194.5 ng / ml, CA19-9 ≥ 12.25 KU / L, and CA724 ≥ 8.621 U / mL were considered risk factors for lymphovascular invasion in colorectal cancer. One point was awarded for each risk factor, and the sum of these points yielded the risk factor score, which ranged from 0 to 4. A risk factor score < 2 indicated a low-risk group, a risk factor score = 2 indicated a medium-risk group, and a risk factor score > 2 indicated a high-risk group.

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