Kit and method for evaluating malignant degree of digestive system cancer

Abstract

The embodiment of the invention discloses a kit and a method for evaluating the malignancy degree of digestive system cancer. The kit for evaluating the malignancy degree of the digestive system cancer comprises reagents for detecting the following indexes: a sialic acid accumulation level; the infiltration level of B cells, macrophages and neutrophils; hypoxia level. The sialic acid accumulation level is obtained; the infiltration level and hypoxia level of cells A, B, macrophages and neutrophils are key factors influencing the malignant degree of the digestive system cancer, and the model constructed by the key factors has good sensitivity and specificity and can systematically and comprehensively evaluate the malignant degree of the digestive system cancer. And the kit has great significance for early diagnosis and treatment of digestive system cancer.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of biomedical technology, and in particular to a kit and method for evaluating the malignancy of digestive system cancer. Background Art

[0002] Cancer is a major disease that seriously threatens human health worldwide, especially digestive system cancer, which has high morbidity and mortality. Advanced digestive system cancer is difficult to treat, patients have a short survival period, and are prone to recurrence and metastasis. Among digestive system cancers, cholangiocarcinoma is a rare and highly invasive cancer, so its research is particularly important. Cholangiocarcinoma originates from the bile duct and has an average five-year survival rate of 9%, the lowest among the 33 major cancer types publicly released worldwide, mainly due to its unique tumor microenvironment and metabolic reprogramming. Cholangiocarcinoma is the most easily metastatic type among the seven types of digestive system cancers (i.e., bile duct cancer, colon adenocarcinoma, esophageal cancer, hepatocellular carcinoma, pancreatic adenocarcinoma, rectal adenocarcinoma, and gastric adenocarcinoma) due to high levels of bile acid production. The latest data has been publicly released, with 988,627 cases and 847,780 deaths worldwide in 2022.

[0003] Current methods have certain limitations in the early diagnosis, efficacy judgment and prognosis evaluation of digestive system cancer. Although histopathological examination or imaging scanning technology is often used for qualitative and quantitative analysis of cancer. However, these two methods can only provide reference information for doctors and cannot fully and accurately assess the patient's condition. For example, tissue sections may not be able to fully obtain tumor samples, resulting in an increased risk of misdiagnosis; imaging cannot show organ function activities, affecting doctors' judgment. Therefore, there is an urgent need for a more accurate, simple, economical and reliable cancer malignancy assessment kit and method in clinical practice, which has great practical value in improving diagnostic accuracy, reducing costs and accelerating medical treatment. Summary of the invention

[0004] We conducted a comprehensive computational study, with the following main findings: (1) The biggest factor that makes cholangiocarcinoma a highly malignant cancer is that its sialic acid biosynthesis and accumulation on the surface of cancer cells are the highest among seven cancer types, thus driving cancer cell migration and invasion; (2) Bile acids play a key role in inhibiting cell proliferation rate and indirectly promoting sialic acid synthesis and accumulation; (3) Further extending the study to seven types of digestive system cancer, the five-year survival rate can be well explained by the accumulation level of sialic acid attached to the surface of cancer cells, the infiltration level of B cells, macrophages and neutrophils, and the level of hypoxia. This is the first study to explain the high malignancy of a cancer type from the perspective of cancer chemical state and host organ characteristics.

[0005] To this end, the embodiments of the present invention provide a kit and method for evaluating the malignancy of digestive system cancer, so as to solve the defects of low accuracy and low sensitivity of existing methods for evaluating the malignancy of digestive system cancer.

[0006] In order to achieve the above purpose, the embodiment of the present invention provides the following technical solutions:

[0007] According to a first aspect of an embodiment of the present invention, the present invention provides a kit for evaluating the malignancy of digestive system cancer, comprising the following indicators for detecting tumor samples respectively: sialic acid accumulation level; infiltration level of B cells, macrophages and neutrophils; hypoxia level.

[0008] Furthermore, it is characterized in that the calculation method of the sialic acid accumulation level is as follows:

[0009] The average level of sialic acid accumulation in tumor samples at the Localized, Regional, Distant, and Combined stages was detected by the following formula (1):

[0010]

[0011] Among them, i∈{Localized, Regional, Distant, Combined}, G1={ST6GALNAC1, ST6GALNAC2, ST6GALNAC4}, G2=NEU3, Ave i (G,S) represents the average expression value of G in all samples S at stage i;

[0012] The following formula (2) is used to evaluate the level of sialic acid accumulation up to stage I:

[0013] ΔSA(I)=∑ i≤I ΔSA' i (2)

[0014] Among them, I=Localized represents the tumor samples of the primary lesion, I=Regional represents the tumor samples near the primary lesion, and I=Distant represents the metastatic tumor samples.

[0015] Furthermore, the xCell calculation tool is used to calculate the enrichment score to detect the infiltration level of B cells, macrophages and neutrophils in the tumor sample. Specifically, the infiltration level of B cells, macrophages and neutrophils can be evaluated by the ratio of the enrichment score of B cells, macrophages and neutrophils in the tumor sample to the total enrichment score of all immune cell types in the tumor sample.

[0016] Furthermore, the expression level of HIF1A was used to detect the hypoxia level of tumor samples. HIF1A, or hypoxia-inducible factor 1α subunit, is a protein that plays a key role in cellular hypoxia response. The higher the expression level of HIF1A, the higher the level of hypoxia.

[0017] Furthermore, the digestive system cancer is bile duct cancer, colon adenocarcinoma, esophageal cancer, hepatocellular carcinoma, pancreatic adenocarcinoma, rectal adenocarcinoma or gastric adenocarcinoma.

[0018] According to a second aspect of an embodiment of the present invention, the present invention provides a method for evaluating the malignancy of digestive system cancer, the method comprising:

[0019] Detect tumor samples for sialic acid accumulation levels; infiltration levels of B cells, macrophages, and neutrophils; and hypoxia levels;

[0020] Comprehensive evaluation was performed based on the multivariate linear regression model established with the five-year survival rate.

[0021] Furthermore, the multivariate linear regression model includes:

[0022] Localized regression model model1:

[0023] SR=1.0158-0.4043ΔSA+0.2528H+0.0434B+0.004M-0.4158N (3)

[0024] Regional regression model model2:

[0025] SR=1.3789-0.5405ΔSA+0.3069H+0.1469B-0.1133M-0.8068N (4)

[0026] Distant regression model model3:

[0027] SR=0.3679-0.1086ΔSA-0.0948H+0.136B+0.0665M-0.124N (5)

[0028] Combined regression model model4:

[0029] SR=1.58-0.3908ΔSA+0.0104H+0.2268B+0.0937M-0.6073N (6)

[0030] Among them, SR represents the five-year survival rate; ΔSA represents the sialic acid accumulation level; H represents the hypoxia level; B, M and N represent the infiltration levels of B cells, macrophages and neutrophils, respectively.

[0031] Furthermore, 60% is set as a threshold. If the five-year survival rate is higher than the threshold, digestive system cancer is a low-malignancy tumor. If the five-year survival rate is lower than the threshold, digestive system cancer is a high-malignancy tumor.

[0032] The implementation of the present invention has the following advantages:

[0033] The present invention obtains the sialic acid accumulation level; the infiltration level of B cells, macrophages and neutrophils and the hypoxia level are key factors affecting the malignancy of digestive system cancer. The model constructed by the above key factors has good sensitivity and specificity, and can systematically and comprehensively evaluate the malignancy of digestive system cancer, which is of great significance for the early diagnosis and treatment of digestive system cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0035] Figure 1 : Hypoxia levels of tumor samples and control samples in Localized (A), Regional (B), Distant (C) and Combined (D), where HIF1A expression is used to reflect the hypoxia levels of tumor samples and control samples at each cancer stage in seven cancer types. The y-axis in each figure represents the logarithmic transformation of the expression value of TPM (gene expression format).

[0036] Figure 2 : Evaluated infiltration levels of B cells (A), macrophages (B) and neutrophils (C) in the control group (normal tissue), Localized, Regional, Distant and Combined in seven cancer types, where the y-axis represents the percentage of infiltrating cells in the tissue sample; (D) shows the relationship between the B cell infiltration level (y-axis) and the sialic acid biosynthesis and attachment level (x-axis) measured using the first principal component of the expression of marker genes corresponding to sialic acid biosynthesis and attachment in the relevant samples, with a correlation coefficient of 0.823, as shown in Table 3.

[0037] Figure 3 :(A) De novo biosynthesis of nucleotides (NT), biosynthesis of sialic acid (SA) and the use of OH produced by neutralization of Fenton reaction -The x-axis represents the contribution weight of nucleotide biosynthesis, the y-axis represents the contribution weight of sialic acid biosynthesis and utilization, and the points on the diagonal represent the contribution of the two biosynthetic processes to the neutralization of OH produced by the Fenton reaction. - The contribution weights of the two groups are the same; (B) Prediction of sialic acid accumulation level in each cancer type; (C) Correlation between bile acid (BA) biosynthesis and de novo nucleotide biosynthesis and between bile acid biosynthesis and sialic acid biosynthesis; (D) Gene enrichment signaling pathways related to cell cycle progression that are positively correlated with GPBAR1 in cholangiocarcinoma; (E) Bile acid receptor GPBAR1 is negatively correlated with cell cycle marker genes, where each point represents a cholangiocarcinoma sample, its standardized GPBAR1 expression level (x-axis) and the first principal component of the standardized cell cycle-related gene expression level (y-axis); (F) Correlation between de novo nucleotide synthesis and sialic acid biosynthesis and utilization at different stages. The x-axis represents the standardized de novo nucleotide synthesis level, and the y-axis represents the standardized sialic acid synthesis level, where all related marker genes are listed in Table 3.

[0038] Figure 4 : The five-year survival probability (y-axis) of seven cancers and the sialic acid accumulation level, hypoxia level, infiltration level of B cells, macrophages and neutrophils were used to obtain the predicted five-year survival probability (x-axis), where (A), (B), (C) and (D) are for Localized, Regional, Distant and Combined sample sets, respectively.

[0039] Figure 5 :Evaluate the accuracy of the model. The threshold is set at 60%. If the survival rate is higher than the threshold, it is a low-malignancy tumor, and if the survival rate is lower than the threshold, it is a high-malignancy tumor. ROC curves of the accuracy of the SVM evaluation model in (A) clinical stage I, (B) clinical stage II, and (C) clinical stage III-IV. DETAILED DESCRIPTION

[0040] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] The subjects of this study are cancers of the digestive organs, namely the esophagus, stomach, liver, bile duct, pancreas, colon and rectum. According to the five-year survival rate report provided by "The Surveillance, Epidemiology, and End Results Program and Pathology" (SEER for short), the five-year survival rates of the above seven cancers in the digestive system are summarized in Table 1 below.

[0042] Table 1. Five-year survival rates for seven cancers of the digestive system

[0043]

[0044] Note: The four stages in Table 1, Localized, Regional, Distant, and Combined, are in a progressive relationship. Localized-primary lesion; Regional-invasion to the periphery of the primary lesion; Distant-metastasis; Combined-a mixture of the first three. Localized is equivalent to clinical stage I-II; Regional is equivalent to clinical stage III; Distant is equivalent to clinical stage IV.

[0045] We compared transcriptome data from 1,835 cancer tissue samples and 330 control tissue samples from seven cancer types, all of which were obtained from the public database "The Cancer Genome Atlas" (TCGA). We investigated multiple cellular and molecular factors that statistically explained the five-year survival rates of the seven cancer types reported in the SEER report; we then inferred possible factors that contribute to the high malignancy of cholangiocarcinoma based on the statistical relationships detected.

[0046] Due to the complexity and heterogeneity of the tumor microenvironment, changes in the tumor immune microenvironment, and individual differences, the evaluation results vary greatly. In addition, the evaluation process focuses on unilateral factors and does not weigh multiple factors, making it difficult to fully capture the malignant biological behavior of the tumor. In this study, we focused on the following microenvironmental conditions in cancer tissues: (1) hypoxia level; (2) infiltration level of neutrophils, macrophages, and B cells; (3) sialic acid accumulation level on the surface of cancer cells. A multivariate linear regression model was established to compare the five-year survival rate with the predicted level of the factors in each tissue sample of the above seven cancer types; finally, the contribution level of each factor was analyzed based on the Bayesian information criterion. By comparing the contribution levels, it can be found which factors of cholangiocarcinoma jointly lead to the lowest five-year survival rate of cholangiocarcinoma.

[0047] The reasons for considering these factors are as follows: (1) It has long been known that hypoxia is associated with a poor prognosis in cancer. Hypoxia is usually caused by local neutrophils and macrophages consuming O2 to produce high levels of H2O2 and (1) The presence of B cells in cancer tissues negatively regulates anti-tumor immunity, thereby inhibiting anti-tumor responses. Therefore, the higher the level of B cell infiltration, the worse the prognosis. (2) Since the 1960s, people have found that cancers with higher levels of sialic acid biosynthesis are more likely to metastasize. Research on sialic acid and cancer metastasis has mainly focused on the signal transduction effect of sialic acid.

[0048] We also considered in our analysis that bile acids produced in the bile duct can activate the G protein-coupled bile acid receptor GPBAR1, which is known to inhibit cell proliferation in cancer cells. This is of great significance in our analysis, which follows the following chain of inferences: (1) All cancer tissues in TCGA have Fenton reaction (abbreviated as FR):

[0049] Fe 2+ +H2O2→Fe 3+ +OH - + OH (7)

[0050] Cytoplasm As a reducing molecule, Fe 3+ Reduction to Fe 2+ , so that the Fenton reaction continues and continuously produces OH - (2) A sustained alkaline environment can affect tumor cells and bring about stress that threatens cell life, driving tumor cells to initiate a variety of acid-producing metabolic reprogramming biological processes to maintain a stable pH value; (3) The two most important acidification metabolic reprogramming biological processes used by all cancers in TCGA are de novo nucleotide biosynthesis and sialic acid biosynthesis. For example, the biosynthesis of purines produces 8 to 9 H + , which together have the following properties:

[0051] R(OH - ,FR)=R(H + ,NT)+R(H + ,SA)+ε (8)

[0052] Where R(OH - ,FR) indicates that Fenton reaction in cancer tissue produces OH - The rate, R(H + ,NT) and R(H + , SA) are respectively produced by nucleotide biosynthesis and sialic acid biosynthesis +The rate of all other metabolic reprogramming. + , and R(H + ,NT)+R(H + ,SA), its number is relatively small. From the above formula (8), it can be seen that if R(H + ,NT) cannot be as high as other cancers, so R(H + ,SA) must be high enough so that their sum is equal to OH - The production rates were largely matched to keep the pH stable.

[0053] We estimated the levels of each of the above factors based on the expression levels of each cancer tissue-associated marker gene in all seven cancer types and verified that the five-year survival rate of each cancer type could be well represented as a function of the expression levels of these factors by multiple linear regression analysis with high statistical significance.

[0054] Example 1

[0055] 1 Materials and Methods

[0056] 1.1 Data

[0057] Transcriptome data of cancer tissues and matched controls of seven cancer types were downloaded from the TCGA database. The detailed information of the datasets is shown in Table 2.

[0058] Table 2. Number of samples of the seven cancer types used in this study

[0059]

[0060] For each factor analyzed, we used the expression levels of marker genes widely used in the literature (see Table 3): (1) hypoxia levels, (2) infiltration levels of B cells, macrophages, and neutrophils, and (3) sialic acid accumulation levels in seven cancer types (including bile acid levels related to sialic acid, and cell cycle).

[0061] Table 3. Marker genes for tumor microenvironment conditions

[0062]

[0063]

[0064] 1.2 Batch effect analysis

[0065] An empirical Bayesian approach was used to estimate batch effects by modeling the relationship between gene expression levels and batch variables. The estimated batch effects were then subtracted from the original data to ensure that individually collected transcriptome data could be directly compared with each other.

[0066] 1.3 Estimation of sialic acid accumulation on cancer cell surfaces

[0067] O-glycosylated proteins on the cell surface are involved in cancer metastasis. Therefore, the present invention evaluates the accumulation level of sialic acid on O-glycosylated proteins by sialyltransferases (i.e., ST6GALNAC1, ST6GALNAC2, ST6GALNAC3, ST6GALNAC4, and STA8SI6). We noted that only ST6GALNAC1, ST6GALNAC2, and ST6GALNAC4 were upregulated in digestive system cancers, so they were considered in our analysis. In addition, NEU3 is mainly involved in the degradation of sialic acid. We designed a computational method to evaluate the average level of sialic acid accumulation in samples of the Localized, Regional, Distant, and Combined stages of cancer types:

[0068]

[0069] Among them, i∈{Localized, Regional, Distant, Combined}, G1={ST6GALNAC1, ST6GALNAC2, ST6GALNAC4}, G2=NEU3, Ave i (G,S) represents the expression of G averaged over all samples S at stage i. The following is defined as the level of sialic acid accumulation assessed up to stage I:

[0070] ΔSA(I)=∑ i≤I ΔSA' i (2)

[0071] Among them, I=Localized represents the tumor samples of the primary lesion, I=Regional represents the tumor samples that have invaded near the primary lesion, and I=Distant represents the metastatic tumor samples. The duration of the three stages is roughly the same.

[0072] 1.4xCell is used to assess the infiltration level of immune cells

[0073] xCell is a widely used computational tool for calculating enrichment scores based on the expression of marker genes for 64 immune and stromal cell types in a given cancer tissue sample. Using the calculated enrichment scores, the infiltration level of B cells, neutrophils, and macrophages in each sample was assessed by evaluating the ratio of each enrichment score to the total enrichment score of the entire tissue sample.

[0074] 1.5 Principal component analysis for gene expression analysis

[0075] Principal component analysis (PCA) was used to capture the main change directions of multiple marker gene sets in terms of overall expression changes.

[0076] 1.6 Evaluating Contribution Based on Bayesian Information Criterion

[0077] The Bayesian Information Criterion (BIC) is used to select the best model among the given options and is defined as follows:

[0078] BIC=k×ln(n)-2ln(L) (9)

[0079] Where L is the maximum likelihood function of the model, n is the sample size, and k is the number of free parameters in the model.

[0080] Use | BIC k |-|BIC i |To evaluate the effect of the i-th free parameter on the model model k The contribution level of BIC k is to use R 2 The best model for fractional measurement k The quality of BIC i is the best model based on all parameters except the i-th parameter. Therefore, the contribution percentage of the i-th parameter can be calculated as follows:

[0081]

[0082] 1.7 Multiple Linear Regression

[0083] Multiple linear regression (MLR for short) is used to model the linear relationship between a single dependent variable and multiple free variables. We used BIC-based MLR to linearly regress a possible range of contributing factors in each sample of seven cancer types against the five-year survival rate, where BIC provides information about the contribution of each free variable to the regression outcome.

[0084] 1.8 SVM-ROC evaluation test

[0085] In the SVM model, by changing the threshold of the decision function, the true positive rate and false positive rate under the set threshold can be obtained, thereby providing parameters for ROC calculation. The area under the ROC curve (AUC) is an important indicator for measuring model performance. The higher the AUC value, the stronger the model's evaluation ability.

[0086] 2 Results

[0087] 2.1 Cholangiocarcinoma is the most hypoxic of the seven cancer types

[0088] We used the expression level of HIF1A to reflect the level of hypoxia in tissue samples; the higher the HIF1A expression level, the higher the degree of hypoxia. Figure 1 The expression levels of HIF1A in localized, regional, distant, and combined cancer samples and control samples of seven cancer types are shown respectively. Figure 1 As can be seen, cholangiocarcinoma has the largest increase in HIF1A expression among cancer samples of all stages compared with control samples, indicating that cholangiocarcinoma is the most hypoxic cancer type among the seven cancer types.

[0089] 2.2 Cholangiocarcinoma has the highest level of B cell infiltration

[0090] We used xCell to assess the infiltration levels of B cells, macrophages, and neutrophils in each tissue sample in the Control, Localized, Regional, Distant, and Combined groups of seven cancer types. Figure 2 The calculation results are shown, from which we can see that cholangiocarcinoma has the highest level of B cell infiltration on average among all seven cancer types.

[0091] To understand why cholangiocarcinoma had the highest concentration of B cell infiltration, pathway enrichment analysis was performed to identify key genes (see Table 3) whose expression was highly correlated with genes involved in sialic acid biosynthesis and attachment. Among the signaling pathways, B cell activation was one of the most relevant activities (see Figure 2 D) So we found that higher levels of sialic acid attracted more B cells into the tumor area.

[0092] 2.3 Cholangiocarcinoma cells have the highest level of sialic acid accumulation on their surface

[0093] As mentioned above, cancer cells utilize de novo nucleotide biosynthesis and sialic acid biosynthesis as major H + The generator is used to neutralize the OH produced by the Fenton reaction. - . Figure 3 AB shows the role of sialic acid in maintaining OH produced by the Fenton reaction in seven cancer types. - Neutralization contribution weight. We noticed that cholangiocarcinoma stood out in terms of the level of contribution to sialic acid biosynthesis. To understand why sialic acid has a particularly high contribution to maintaining the stability of cancer cell solute pH, we investigated the possible role of bile acids in this process. First, we noticed that: (1) bile acid levels measured using expression data of relevant marker genes (Table 3) were closely correlated with sialic acid biosynthesis levels as well as sialic acid accumulation levels, and (2) bile acid levels were negatively correlated with five-year survival rate, as shown in Table 3. Figure 3C, strongly suggesting that bile acids play a role in driving sialic acid biosynthesis and attachment accumulation.

[0094] To elucidate a possible link between the two, we examined the expression level of the bile acid receptor GPBAR1 and found it to be upregulated, consistent with the elevated bile acid levels in cholangiocarcinoma. Our literature search showed that GPBAR1 can inhibit cell proliferation by activating the cAMP signaling pathway, which is known to be involved in cancer bone metastasis. To examine whether this is the case in cholangiocarcinoma, we noticed that the expression of GPBAR1 is associated with cell cycle-related pathways ( Figure 3 D, Table 3) and cell cycle progression levels ( Figure 3 E) is negatively correlated, thus indicating that GPBAR1 inhibits cell proliferation in cholangiocarcinoma.

[0095] As mentioned above, de novo nucleotide biosynthesis and sialic acid biosynthesis maintain the OH produced by the Fenton reaction. - Neutralization plays a leading role. Figure 3 The data shown in F explain why sialic acid biosynthesis levels in cholangiocarcinoma stand out among the seven cancer types, namely because bile acids inhibit cell proliferation and inhibit nucleotide biosynthesis. Therefore, it is ultimately bile acids that drive elevated levels of sialic acid biosynthesis and accumulation, leading to the high mobility of cholangiocarcinoma and, therefore, the lowest survival rate among the seven cancer types.

[0096] 2.4 Key factors leading to low survival rate of cholangiocarcinoma

[0097] We established a quantitative relationship between the five-year survival rate and the above factors. We performed a multivariate linear regression analysis on the five-year survival rate and five factors: ΔSA, hypoxia level, and infiltration levels of B cells, neutrophils, and macrophages. The following shows four regression models for Localized (Model 1), Regional (Model 2), Distant (Model 3), and Combined (Model 4) samples. The regression results are as follows: Figure 4 shown.

[0098] Regression model model1:

[0099] SR=1.0158-0.4043ΔSA+0.2528H+0.0434B+0.004M-0.4158N (3)

[0100] Regression model model2:

[0101] SR=1.3789-0.5405ΔSA+0.3069H+0.1469B-0.1133M-0.8068N (4)

[0102] Regression model model3:

[0103] SR=0.3679-0.1086ΔSA-0.0948H+0.136B+0.0665M-0.124N (5)

[0104] Regression model model4:

[0105] SR=1.58-0.3908ΔSA+0.0104H+0.2268B+0.0937M-0.6073N (6)

[0106] Among them, SR represents the five-year survival rate; ΔSA represents the level of sialic acid accumulation; H represents the level of hypoxia; B, M and N represent the infiltration levels of B cells, macrophages and neutrophils, respectively.

[0107] We provide the contribution level of each factor based on BIC analysis, as shown in Table 4. We conclude that sialic acid accumulation determined by bile acid levels plays the most important role in making cholangiocarcinoma the most lethal of the seven cancers studied, from the early stage of cancer model 1 to the final late stage of cancer model 4. The level of B cell infiltration is the second most important factor for the worst survival in cholangiocarcinoma, which is partly due to the high sialic acid level discussed earlier.

[0108] Table 4. Performance results of regression models

[0109]

[0110] We used SVM-ROC to evaluate the accuracy of the above model. We set 60% as the threshold. If the survival rate is higher than the threshold, it is a low-malignant tumor, and if the survival rate is lower than the threshold, it is a high-malignant tumor. We randomly shuffled the samples of each clinical stage, put the model into a part of the samples to evaluate the malignancy of the cancer for training, and then put the model into another part of the samples to test the effect. Figure 5 It can be seen that the model evaluation effect is good in clinical phases I, II, and III+IV.

[0111] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A kit for evaluating the malignancy of digestive system cancer, characterized in that: The present invention comprises reagents for detecting the following indicators of tumor samples: sialic acid accumulation level; infiltration level of B cells, macrophages and neutrophils; and hypoxia level.

2. The kit for assessing the malignancy of digestive system cancer according to claim 1, characterized in that: The method for detecting the sialic acid accumulation level comprises: The average level of sialic acid accumulation in tumor samples at the Localized, Regional, Distant, and Combined stages was detected by the following formula (1): Among them, i∈{Localized, Regional, Distant, Combined}, G1={ST6GALNAC1, ST6GALNAC2, ST6GALNAC4}, G2=NEU3, Ave i (G,S) represents the expression of the average of G among all samples S at stage i; The following formula (2) is used to evaluate the level of sialic acid accumulation up to stage I: ΔSA(I)=∑ i≤I ΔSA' i (2) Among them, I=Localized represents the tumor sample of the primary lesion, I=Regional represents the tumor sample that has invaded near the primary lesion, and I=Distant represents the metastatic tumor sample.

3. The kit for assessing the malignancy of digestive system cancer according to claim 1, characterized in that: The xCell computational tool was used to calculate the enrichment scores to detect the infiltration levels of B cells, macrophages, and neutrophils in tumor samples.

4. The kit for assessing the malignancy of digestive system cancer according to claim 1, characterized in that: The expression level of HIF1A was used to detect the hypoxia level of tumor samples.

5. The kit for assessing the malignancy of digestive system cancer according to claim 1, characterized in that: The digestive system cancer is bile duct cancer, colon adenocarcinoma, esophageal cancer, hepatocellular carcinoma, pancreatic adenocarcinoma, rectal adenocarcinoma or gastric adenocarcinoma.

6. A method for evaluating the malignancy of digestive system cancer, characterized in that: The method comprises: The sialic acid accumulation level of tumor samples; the infiltration level of B cells, macrophages and neutrophils; and the hypoxia level were detected and comprehensively evaluated based on the multivariate linear regression model established with the five-year survival rate.

7. The method for evaluating the malignancy of digestive system cancer according to claim 6, characterized in that: The multivariate linear regression model includes: Localized regression model model1: SR=1.0158-0.4043ΔSA+0.2528H+0.0434B+0.004M-0.4158N (3) Regional regression model model2: SR=1.3789-0.5405ΔSA+0.3069H+0.1469B-0.1133M-0.8068N (4) Distant regression model model3: SR=0.3679-0.1086ΔSA-0.0948H+0.136B+0.0665M-0.124N (5) Combined regression model model4: SR=1.58-0.3908ΔSA+0.0104H+0.2268B+0.0937M-0.6073N (6) Among them, SR represents the five-year survival rate; ΔSA represents the sialic acid accumulation level; H represents the hypoxia level; B, M and N represent the infiltration levels of B cells, macrophages and neutrophils, respectively.

8. The method for evaluating the malignancy of digestive system cancer according to claim 7, characterized in that: The threshold value is set at 60%. If the five-year survival rate is higher than the threshold value, digestive system cancer is a low-malignancy tumor. If the five-year survival rate is lower than the threshold value, digestive system cancer is a high-malignancy tumor.

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