CN113470814A - Application of substances for detecting ALR, NLR, PLR and ANRI in predicting risk of vascular invasion - Google Patents
Application of substances for detecting ALR, NLR, PLR and ANRI in predicting risk of vascular invasion Download PDFInfo
- Publication number
- CN113470814A CN113470814A CN202110727385.1A CN202110727385A CN113470814A CN 113470814 A CN113470814 A CN 113470814A CN 202110727385 A CN202110727385 A CN 202110727385A CN 113470814 A CN113470814 A CN 113470814A
- Authority
- CN
- China
- Prior art keywords
- alr
- risk
- anri
- plr
- nlr
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention discloses an application of substances for detecting ALR, NLR, PLR and ANRI in predicting the risk of occurrence of vascular invasion. The invention discovers that the ALR content, the NLR, the PLR and the ANRI are independent risk factors for predicting the vascular invasion of HBV-related HCC patients, the constructed prediction model of the vascular invasion risk of the HBV-related HCC patients containing the four indexes of the ALR content, the NLR, the PLR and the ANRI can predict the vascular invasion risk of the HBV-related HCC patients, the sensitivity and the sensitivity are good, and the risk of the vascular invasion of the HBV-related HCC patients can be predicted by using the prediction model of the vascular invasion risk of the HBV-related HCC patients constructed according to the four factors.
Description
Technical Field
The invention relates to the application of substances for detecting ALR, NLR, PLR and ANRI in predicting the risk of vascular invasion in the biomedical field.
Background
Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors in the world, with high malignancy and poor prognosis. Chronic inflammation induced by viruses such as Hepatitis B Virus (HBV) is closely related to the cause of HCC.
Vascular invasion (i.e., vascular invasion) is mainly characterized by the involvement of malignant tumors in the vascular system, which may partially or completely surround the blood vessels, or may result in the occurrence of diffuse metastasis through the blood circulation. At this time, the malignant tumor usually reaches the middle and late stage, and serious and large blood loss is easily caused during surgical excision.
Therefore, the search for markers for predicting the risk of occurrence of vascular invasion is of great significance to cancer patients, especially hepatocellular carcinoma patients.
Disclosure of Invention
The invention aims to solve the technical problem of predicting the occurrence risk of vascular invasion of HBV related liver cancer patients.
In order to solve the technical problems, the invention firstly provides the application of substances for detecting the content of the hepatic regeneration enhancement factor (ALR), NLR, PLR and ANRI in preparing products for predicting or assisting in predicting the occurrence risk of vascular invasion; NLR is the ratio of the number of neutrophils to the number of lymphocytes in unit volume of peripheral blood; PLR is the ratio of the number of platelets to lymphocytes in a unit volume of peripheral blood; ANRI is the ratio of the content of aspartate Aminotransferase (AST) per unit volume of peripheral blood (U/L) to the number of neutrophils.
In the above application, the ALR content may be a liver regeneration-enhancing factor content in peripheral blood.
In the above application, the risk of occurrence of vascular invasion may be a risk of occurrence of vascular invasion in a patient with HBV-associated HCC.
In the above application, the HBV-related HCC patients all satisfy the following four conditions: the etiology is clear HBV infection; imaging examination (including ultrasound, CT, MRI, DSA) to confirm HCC; thirdly, diagnosing the patient as HCC through liver puncture biopsy, wherein the occupational disease does not accord with the imaging characteristics; serological diagnosis: serum alpha-fetoprotein (AFP) was diagnosed as HCC.
In the above application, the substances for detecting ALR content, NLR, PLR and ANRI include substances for detecting ALR content, substances for detecting neutrophil count, substances for detecting lymphocyte count, substances for detecting platelet count and substances for detecting glutamic-oxaloacetic transaminase content.
In one embodiment of the present invention, the material for detecting the ALR content is hepatocyte regeneration enhancing factor (ALR) ELISA kit (shanghai jianlei biotechnology limited).
The substance for measuring the number of neutrophils, the substance for measuring the number of monocytes, the substance for measuring the number of lymphocytes and the substance for measuring the number of platelets is a whole blood cell analyzer (Sysmex XN-2000).
The substance for detecting the content of the glutamic-oxaloacetic transaminase is an aspartate aminotransferase determination kit (rate method) (SIEMENS healthcare products, cat # 03039631).
In the above application, the substance for detecting ALR, NLR, PLR and ANRI may further include a blood vessel invasion occurrence risk prediction model, and the blood vessel invasion occurrence risk prediction model is used for determining the risk of occurrence of blood vessel invasion according to the values of ALR content, NLR, PLR and ANRI.
The substances for detecting ALR, NLR, PLR and ANRI may be composed of the substance for detecting ALR content, the substance for detecting neutrophil count, the substance for detecting lymphocyte count, the substance for detecting platelet count and the substance for detecting glutamic-oxaloacetic transaminase content, and may be further composed of the prediction model, the substance for detecting ALR content, the substance for detecting neutrophil count, the substance for detecting lymphocyte count, the substance for detecting platelet count and the substance for detecting glutamic-oxaloacetic transaminase content.
The method for predicting the occurrence risk of vascular invasion by using the model for predicting the occurrence risk of vascular invasion comprises the following steps:
the ALR content in the peripheral blood of a patient to be detected is more than or equal to 6387pg/ml, and the ALR high group (namely ALR is 1), and the ALR less than 6387pg/ml is the ALR low group (namely ALR is 0); NLR is more than or equal to 1.975 and is NLR high group (namely NLR is 1), NLR <1.975 and is NLR low group (namely NLR is 0); PLR is more than or equal to 121.8, the PLR is high group (namely PLR is 1), PLR is less than 121.8, the PLR is low group (namely PLR is 0); ANRI greater than or equal to 15.37 is ANRI high group (i.e., ANRI of 1), ANRI <15.37 is ANRI low group (i.e., ANRI of 0);
when the ALR, the PLR, the ANRI and the NLR of the patient to be detected are respectively 0, 1 and 1, the risk of the HBV-related HCC patient suffering from vascular invasion is highest, when at least one of the ALR, the PLR, the ANRI and the NLR of the patient to be detected does not meet the condition, the risk of the HBV-related HCC patient suffering from vascular invasion is reduced, and when the ALR, the PLR and the ANRI of the patient to be detected are respectively 1, 0 and 0, the risk of the HBV-related HCC patient suffering from vascular invasion is lowest.
Further, when the ALR of the patient to be tested is 1, the PLR is 1, the ANRI is 1 and the NLR is 1, the risk of the HBV-related HCC patient suffering from vascular invasion is about 40%; when the ALR of a patient to be detected is 1, the PLR is 1, the ANRI is 1 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is about 18 percent; when the ALR of a patient to be detected is 1, the PLR is 1, the ANRI is 0 and the NLR is 1, the risk of the HBV-related HCC patient to generate vascular invasion is about 27 percent; when the ALR of the patient to be detected is 1, the PLR is 1, the ANRI is 0 and the NLR is 0, the risk of the HBV-related HCC patient to suffer from vascular invasion is less than 10 percent; when ALR of a patient to be detected is 1, PLR is 0, ANRI is 1 and NLR is 1, the risk of vascular invasion of HBV-related HCC patients is about 20%; when the ALR of a patient to be detected is 1, the PLR is 0, the ANRI is 1 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is about 5 percent; when ALR of a patient to be detected is 1, PLR is 0, ANRI is 0 and NLR is 1, the risk of vascular invasion of HBV-related HCC patients is about 10%; when the ALR of the patient to be detected is 1, the PLR is 0, the ANRI is 0 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is 0; when the ALR of a patient to be detected is 0, the PLR is 1, the ANRI is 1 and the NLR is 1, the risk of the HBV-related HCC patient to generate vascular invasion is about 86 percent; when ALR of a patient to be detected is 0, PLR is 1, ANRI is 1 and NLR is 0, the risk of the HBV-related HCC patient suffering from vascular invasion is about 70%; when ALR of a patient to be detected is 0, PLR is 1, ANRI is 0 and NLR is 1, the risk of vascular invasion of HBV-related HCC patients is about 75%; when the ALR of a patient to be detected is 0, the PLR is 1, the ANRI is 0 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is about 45 percent; when the ALR, PLR, ANRI and NLR of a patient to be detected are 0, 1 and 1 respectively, the risk of the HBV-related HCC patient suffering from vascular invasion is about 80 percent; when the ALR, PLR and ANRI of a patient to be detected are 0, 1 and 0 respectively, the risk of the HBV-related HCC patient suffering from vascular invasion is about 50%; when the ALR, PLR, ANRI and NLR of a patient to be detected are 0, 0 and 1 respectively, the risk of the HBV-related HCC patient suffering from vascular invasion is about 55 percent; when the ALR, PLR, ANRI and NLR of a patient to be tested are 0, 0 and 0 respectively, the risk of vascular invasion of HBV-related HCC patients is about 27%.
The invention also provides a product for predicting or assisting in predicting the risk of occurrence of vascular invasion, wherein the product is the substance for detecting ALR content, NLR, PLR and ANRI.
The product may be a kit or system.
The invention also provides application of the substances for detecting the ALR content, NLR, PLR or ANRI in preparing products for predicting or assisting in predicting the occurrence risk of vascular invasion.
In the above application, the ALR content may be a liver regeneration-enhancing factor content in peripheral blood.
In the above application, the risk of occurrence of vascular invasion may be a risk of occurrence of vascular invasion in a patient with HBV-associated HCC.
In the above application, the HBV-related HCC patients all satisfy the following four conditions: the etiology is clear HBV infection; imaging examination (including ultrasound, CT, MRI, DSA) to confirm HCC; thirdly, diagnosing the patient as HCC through liver puncture biopsy, wherein the occupational disease does not accord with the imaging characteristics; serological diagnosis: serum alpha-fetoprotein (AFP) was diagnosed as HCC.
In the above application, the substance for detecting the ALR content is hepatocyte regeneration enhancing factor (ALR) ELISA kit (shanghai jianlei biotechnology limited).
The substance for detecting the NLR content consists of the substance for detecting the number of the neutrophils and the substance for detecting the number of the lymphocytes.
The substance for detecting PLR consists of the substance for detecting the number of platelets and the substance for detecting the number of lymphocytes.
The substance for detecting ANRI comprises the substance for detecting the content of glutamic-oxalacetic transaminase and the substance for detecting the number of neutrophils.
The method for predicting the risk of occurrence of vascular invasion by using the ALR content, NLR, PLR or ANRI is as follows:
the blood vessel invasion risk of a patient to be detected with the ALR content of more than or equal to 6387pg/ml in peripheral blood is lower than or is lower than that of a candidate patient to be detected with the ALR of less than 6387 pg/ml;
the risk of occurrence of vascular invasion of a patient to be detected with NLR more than or equal to 1.975 in peripheral blood is higher than or is higher than that of the patient to be detected with NLR less than 1.975 in a candidate way;
the incidence risk of vascular invasion of a patient to be detected with PLR more than or equal to 121.8 in peripheral blood is higher than or candidate for the patient to be detected with PLR < 121.8;
the blood vessel invasion risk of the patient to be tested with ANRI more than or equal to 15.37 in peripheral blood is higher than or candidate higher than that of the patient to be tested with ANRI < 15.37.
The invention also provides a product for predicting or assisting in predicting the risk of occurrence of vascular invasion, wherein the product is the substance for detecting ALR, NLR, PLR or ANRI.
The product may be a kit.
The invention also provides application of substances used in the method for predicting or assisting in predicting the risk of occurrence of vascular invasion by taking ALR as a marker in preparing products for predicting or assisting in predicting the risk of occurrence of vascular invasion.
The experiment of the invention proves that: ALR levels (OR ═ 0.096, 95% CI:0.055-0.162, p ═ 0.000), NLR (OR ═ 2.467, 95% CI:1.348-4.578, p ═ 0.012), PLR (OR ═ 2.389, 95% CI:1.282-4.505, p ═ 0.022) and ANRI (OR ═ 2.292, 95% CI:1.118-4.740, p ═ 0.050) are independent risk factors for predicting vascular invasion in HBV-associated HCC patients; 2. the nomogram (namely a prediction model of the vascular invasion risk of HBV-related HCC patients) containing the four indexes of ALR, NLR, PLR and ANRI constructed by the invention can predict the vascular invasion risk of the HBV-related HCC patients, and has good sensitivity and sensitivity. The ALR, the NLR, the PLR and the ANRI are independent risk factors for predicting the vascular invasion of the HBV-related HCC patient, and the risk of the vascular invasion of the HBV-related HCC patient can be predicted by using the prediction model of the vascular invasion risk of the HBV-related HCC patient, which is constructed by the four factors.
Drawings
FIG. 1 is a nomogram for assessing the risk of vascular invasion in HBV-associated HCC patients using ALR, PLR, ANRI, and NLR.
FIG. 2 is a ROC curve of a prediction model.
Fig. 3 is a calibration curve using a training set and a validation set. The left side is the training set and the right side is the validation set.
Detailed Description
The present invention is described in further detail below with reference to specific embodiments, which are given for the purpose of illustration only and are not intended to limit the scope of the invention. The experimental procedures in the following examples are conventional unless otherwise specified. Materials, reagents, instruments and the like used in the following examples are commercially available unless otherwise specified. The quantitative tests in the following examples, all set up three replicates and the results averaged.
ALR: augmenter of liver regeneration, enhancement factor of liver regeneration;
NLR: neutrophil-to-lymphocyte ratio, absolute number of neutrophils to lymphocytes per unit volume of peripheral blood;
PLR: platelet-to-lymphocyte ratio, ratio of the absolute number of platelets to lymphocytes per unit volume of peripheral blood;
ANRI: AST-to-neutrophile ratio index, the unit volume peripheral blood glutamic-oxaloacetic transaminase (AST) content (U/L) to the absolute ratio of neutrophils.
WPR: the ratio of white blood cells (lymphocytes and neutrophils) to the absolute number of platelets per unit volume of peripheral blood.
LMR: the ratio of lymphocyte to monocyte absolute number per unit volume of peripheral blood.
APRI: index of the ratio of glutamic-oxaloacetic transaminase (AST) content (U/L) to absolute number of platelets per unit volume of peripheral blood.
ALRI: index of the ratio of glutamic-oxaloacetic transaminase (AST) content (U/L) per unit volume of peripheral blood to absolute number of lymphocytes.
Example 1, ALR, NLR, PLR and ANRI can be used to predict the risk of developing vascular invasion
In this example, 317 cases of HBV-related HCC patients who were treated in beijing youan hospital affiliated to the university of capital medical science and signed with informed consent were used as samples to be tested in a training set, the content of ALR in peripheral blood was measured by ELISA, and then the cases were queried to obtain the content of peripheral blood indicators AFP (alpha fetoprotein), ALT (alanine aminotransferase), AST (aspartate aminotransferase), Tbil (total bilirubin), Dbil (combined bilirubin), TP (total protein), ALB (albumin), GGT (g-glutamyltransferase) and ALP (alkaline phosphatase) in peripheral blood, and the levels of inflammation indicators NLR, PLR, WPR, LMR, ANRI, APRI and ALRI were calculated from the cases. And screening out independent risk factors for vascular invasion through single-factor and multi-factor logistic regression analysis, and constructing a nomogram for diagnosing the vascular invasion. Then, the ALR content in the peripheral blood of 130 additional HBV-related HCC patients (used as a verification set) who visit the doctor of Beijing Youyan Hospital affiliated to the university of capital medical science and sign informed consent and are inquired about cases to obtain various peripheral hematological indexes and inflammation indexes, the peripheral hematological indexes and the inflammation indexes are brought into a model, and the accuracy of the model is verified through ROC and a correction curve.
The HBV-related HCC patients all meet the following four conditions: the etiology is clear HBV infection; imaging examination (including ultrasound, CT, MRI, DSA) to confirm HCC; thirdly, diagnosing the patient as HCC through liver puncture biopsy, wherein the occupational disease does not accord with the imaging characteristics; serological diagnosis: serum alpha-fetoprotein (AFP) was diagnosed as HCC.
The HBV-related HCC patients all meet the following conditions: (1) the ages are all over 17 years old; (2) all patients were treated in the Beijing Youton Hospital after the first visit of HCC; (3) all peripheral hematological tests were the most recent prior to primary treatment; (4) peripheral blood samples were retained in the Beijing Youton Hospital biological sample library before primary treatment in all group-enrolled people.
Among them, 191 patients (60.3%) in the training set had no vascular invasion, 126 patients (39.7%) had vascular invasion; the validation focused on 73 patients (56.2%) with no vascular invasion and 57 patients (43.8%) with vascular invasion. Wherein whether or not Vascular Invasion (VI) occurs is diagnosed by pathological diagnosis or/and CT/MRI.
The content of ALR in peripheral blood was performed using hepatocyte regeneration enhancing factor (ALR) ELISA kit from shanghai jianlai biotechnology ltd, according to the kit instructions:
1) rewarming the kit at room temperature 1 hour in advance;
2) uniformly taking the peripheral blood samples out of the refrigerator at the temperature of-80 ℃, melting the samples overnight at the temperature of 4 ℃, centrifuging the samples at 3000rpm for 20 minutes;
3) peripheral blood samples were diluted 20-fold (3 μ Ι sample +57 μ Ι sample dilution);
4) distributing the plate according to the experiment requirement, adding 50 mul of sample diluent into the blank control hole, adding 50 mul of corresponding standard substance into the standard substance hole, and adding 50 mul of diluted peripheral blood sample into the sample hole;
5) adding 100 μ l of HPR (horseradish peroxidase) -labeled detection antibody to each well, sealing the reaction wells with a sealing plate membrane, and incubating in a 37 ℃ incubator for 60 minutes;
6) preparing a washing liquid: diluting the 20x washing solution concentrate into 1x washing solution, namely 95ml double distilled water and 5ml concentrated washing solution;
7) discarding the liquid, patting the liquid on absorbent paper, and washing with 350 mul/hole lotion for 5 times and 1 minute/time;
8) adding 50 mul of substrate A liquid and B liquid into each hole, and incubating for 12 minutes in a constant temperature box at 37 ℃;
9) according to the gradient reaction condition of the standard substance, adding 50 mu l of stop solution, and immediately measuring the OD value of each hole at the wavelength of 450 nm;
10) the concentration value of each sample was calculated by plotting a standard curve. For samples outside the range, the test was repeated with varying dilution factor.
Whole blood cells were analyzed by a Whole blood cell analyzer (Sysmex XN-2000). Leukocytes (including lymphocytes, neutrophils, and monocytes) were detected by an optical detection unit using a semiconductor laser and by flow cytometry. The number of platelets was measured by a sheath flow DC detection method using an RBC detecting unit.
The detection of the content of aspartate Aminotransferase (AST) in peripheral blood is carried out by adopting an aspartate aminotransferase assay kit (rate method), wherein the kit is a SIEMENS healthcare product with a product number of: 03039631.
and further obtaining results of verification indexes PLR, ANRI and NLR.
1. Comparison of general clinical data of HBV-associated HCC patients in the training and validation set of non-vascular-invading and vascular-invading groups
The results of comparing the difference in age, sex, ALR content in peripheral blood, each peripheral hematological index, and each inflammatory index among the groups (table 1) show: the ALR content in peripheral blood is obviously reduced in patients with vascular invasion HCC whether in a training set or a verification set, and the Tbil, Dbil, GGT, ALP and AFP levels are obviously increased after vascular invasion.
TABLE 1 results of the respective indices of the patients in the training and validation sets
In Table 1, "-" indicates no vessel invasion, "+" indicates vessel invasion, and n indicates the number of patients.
2. And performing ROC curve analysis on each index by using the version 3.5.2 of the R software, calculating a critical value (cut off value) of each index through ROC, and performing classification grouping. Wherein the ALR content is more than or equal to 6387pg/ml, and the ALR content is less than 6387 pg/ml; NLR is more than or equal to 1.975 and is NLR high group, NLR <1.975 and is NLR low group; PLR is more than or equal to 121.8, is PLR high group, PLR is less than 121.8, is PLR low group; ANRI greater than or equal to 15.37 is ANRI high group, ANRI less than 15.37 is ANRI low group; APRI greater than or equal to 0.382 is APRI high group, APRI <0.382 is APRI low group; ALRI greater than or equal to 77.34 is ALRI high group, and ALRI less than 77.34 is ALRI low group. The sensitivity and specificity of using these indices to differentiate patients with and without vascular invasion in HBV-associated HCC are shown in Table 2.
TABLE 2 Critical values for ALR, NLR, PLR, ANRI, APRI and ALRI
| Index (I) | Critical value | AUC | 95％CI | Sensitivity (%) | Specificity (%) | P value |
| ALR(pg/ml) | 6387 | 0.754 | 0.698-0.810 | 81.10 | 67.02 | 0.000＊ |
| NLR | 1.975 | 0.672 | 0.611-0.733 | 72.22 | 57.07 | 0.000＊ |
| PLR | 121.8 | 0.588 | 0.523-0.653 | 42.06 | 79.58 | 0.008＊ |
| ANRI | 15.37 | 0.528 | 0.463-0.594 | 56.35 | 57.07 | 0.406 |
| APRI | 0.382 | 0.573 | 0.508-0.638 | 58.73 | 56.54 | 0.028＊ |
| ALRI | 77.34 | 0.633 | 0.569-0.696 | 32.54 | 87.96 | 0.000＊ |
3. Single-factor and multi-factor logistic regression screening of independent risk factors
And performing single-factor and multi-factor logistic regression analysis on each index by using the R software version 3.5.2, and screening independent risk factors.
The results show that: in the one-way logistic regression analysis, the risk of developing vascular invasion in male HCC patients is higher than in women (OR ═ 0.486, 95% CI: 0.263-0.860, p ═ 0.044); the risk of vascular invasion of the ALR low group and the LMR low group is higher than that of the ALR high group and the LMR high group respectively; the risk of vascular invasion was higher in the AFP, AST, ALB, ALP, TBIL, DBIL, TP, NLR, PLR, WPR, ANRI, APRI, and ALRI high groups than in the corresponding low groups, respectively. 4 independent risk factors, namely ALR, NLR, PLR and ANRI, which can be used for diagnosing the occurrence of vascular invasion are obtained by screening through multi-factor logistic regression analysis.
TABLE 3 Single and Multi-factor logistic regression analysis of the contributing factors to vascular invasion in HBV-related liver cancer patients
In the variable columns of Table 3, the commas in parentheses are on the left and on the right the high and low groups of the index.
The normal range indicated in Table 3 is shown in Table 4, and all the values outside the normal range are abnormal.
TABLE 4 Normal biochemical index value ranges and units
4. Nomograms for constructing predictive models
Nomograms (i.e. predictive models of the risk of developing vascular invasion in HBV-associated HCC patients) for assessing the development of vascular invasion using independent risk factors ALR, PLR, ANRI and NLR were constructed using version 3.5.2 of the R software, see fig. 1. In FIG. 1, 1 of ALR indicates the ALR high group (i.e., the ALR content is 6387pg/ml), and 0 indicates the ALR low group (i.e., the ALR content is <6387 pg/ml); 1 for PLR indicates PLR high group (i.e. ALR ≧ 121.8), 0 indicates PLR low group (i.e. PLR < 121.8); ANRI of 1 means ANRI high group (i.e., ANRI ≧ 15.37), and 0 means ANRI low group (i.e., ANRI < 15.37); NLR 1 represents the NLR high group (i.e., NLR ≧ 1.975), 0 represents the NLR low group (i.e., NLR < 1.975).
Specifically, the risk of the blood vessel invasion of the HBV-related HCC patient to be detected is predicted by using a prediction model of the risk of the blood vessel invasion of the HBV-related HCC patient: when the ALR, the PLR, the ANRI and the NLR of the patient to be detected are respectively 0, 1 and 1, the risk of the HBV-related HCC patient suffering from vascular invasion is highest, when at least one of the ALR, the PLR, the ANRI and the NLR of the patient to be detected does not meet the condition, the risk of the HBV-related HCC patient suffering from vascular invasion is reduced, and when the ALR, the PLR and the ANRI of the patient to be detected are respectively 1, 0 and 0, the risk of the HBV-related HCC patient suffering from vascular invasion is lowest.
The method comprises the following specific steps: when ALR of a patient to be detected is 1, PLR is 1, ANRI is 1 and NLR is 1, the risk of vascular invasion of HBV-related HCC patients is about 40%; when the ALR of a patient to be detected is 1, the PLR is 1, the ANRI is 1 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is about 18 percent; when the ALR of a patient to be detected is 1, the PLR is 1, the ANRI is 0 and the NLR is 1, the risk of the HBV-related HCC patient to generate vascular invasion is about 27 percent; when the ALR of the patient to be detected is 1, the PLR is 1, the ANRI is 0 and the NLR is 0, the risk of the HBV-related HCC patient to suffer from vascular invasion is less than 10 percent; when ALR of a patient to be detected is 1, PLR is 0, ANRI is 1 and NLR is 1, the risk of vascular invasion of HBV-related HCC patients is about 20%; when the ALR of a patient to be detected is 1, the PLR is 0, the ANRI is 1 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is about 5 percent; when ALR of a patient to be detected is 1, PLR is 0, ANRI is 0 and NLR is 1, the risk of vascular invasion of HBV-related HCC patients is about 10%; when the ALR of the patient to be detected is 1, the PLR is 0, the ANRI is 0 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is 0; when the ALR of a patient to be detected is 0, the PLR is 1, the ANRI is 1 and the NLR is 1, the risk of the HBV-related HCC patient to generate vascular invasion is about 86 percent; when ALR of a patient to be detected is 0, PLR is 1, ANRI is 1 and NLR is 0, the risk of the HBV-related HCC patient suffering from vascular invasion is about 70%; when ALR of a patient to be detected is 0, PLR is 1, ANRI is 0 and NLR is 1, the risk of vascular invasion of HBV-related HCC patients is about 75%; when the ALR of a patient to be detected is 0, the PLR is 1, the ANRI is 0 and the NLR is 0, the risk of the HBV-related HCC patient to generate vascular invasion is about 45 percent; when the ALR, PLR, ANRI and NLR of a patient to be detected are 0, 1 and 1 respectively, the risk of the HBV-related HCC patient suffering from vascular invasion is about 80 percent; when the ALR, PLR and ANRI of a patient to be detected are 0, 1 and 0 respectively, the risk of the HBV-related HCC patient suffering from vascular invasion is about 50%; when the ALR, PLR, ANRI and NLR of a patient to be detected are 0, 0 and 1 respectively, the risk of the HBV-related HCC patient suffering from vascular invasion is about 55 percent; when the ALR, PLR, ANRI and NLR of a patient to be tested are 0, 0 and 0 respectively, the risk of vascular invasion of HBV-related HCC patients is about 27%.
5. Verifying constructed predicted vascular invasion model
And (3) respectively carrying out ROC analysis on the model obtained in the step (4) by utilizing the samples of the training set and the verification set (see figure 2), and carrying out ROC analysis by adopting the version 3.5.2 of R software, wherein the results show that: AUC of training set 0.830, 95% CI:0.786-0.875, p 0.000; the AUC of the validation set was 0.771, 95% CI:0.691-0.850, and p ═ 0.000. The model obtained in the step 4 can well predict the vascular invasion risk of HBV-related HCC patients.
And 3.5.2 versions of R software are adopted to respectively use a training set and a verification set to make a calibration curve, the abscissa represents the prediction condition of the model, and the ordinate represents the actual condition of the vascular invasion. And (4) predicting the consistency of the vascular invasion and the actual vascular invasion by a calibration curve observation model. The results show that: whether it is a training set (fig. 3, a) or a validation set (fig. 3, B), the model predicts better consistency of vessel invasion with actual occurrence of vessel invasion. The accuracy of predicting the risk of the HBV-related HCC patient to vascular invasion by the prediction model obtained in the step 4 is relatively good.
And evaluating the accuracy of the model obtained in the step 4 through a verification set by using the version 3.5.2 of the R software.
The results (fig. 3 and table 5) show: the prediction sensitivity of the prediction model obtained in the step 4 is 65.38%, the specificity is 88.46%, and the prediction accuracy is 74.62%.
TABLE 5 prediction accuracy of regression model
The present invention has been described in detail above. It will be apparent to those skilled in the art that the invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with reference to specific embodiments, it will be appreciated that the invention can be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The use of some of the essential features is possible within the scope of the claims attached below.
Claims (10)
1. The application of the substances for detecting ALR content, NLR, PLR and ANRI in preparing products for predicting or assisting in predicting the risk of occurrence of vascular invasion; NLR is the ratio of the number of neutrophils to the number of lymphocytes in unit volume of peripheral blood; PLR is the ratio of the number of platelets to lymphocytes in a unit volume of peripheral blood; ANRI is the ratio of the glutamic oxaloacetic transaminase content per unit volume of peripheral blood to the number of neutrophils.
2. Use according to claim 1, characterized in that: the ALR content is the content of liver regeneration enhancement factors in peripheral blood.
3. Use according to claim 1 or 2, characterized in that: the risk of occurrence of vascular invasion is that of HBV-associated HCC patients.
4. Use according to any one of claims 1 to 3, characterized in that: the substances for detecting ALR, NLR, PLR and ANRI comprise substances for detecting ALR content, substances for detecting neutrophil count, substances for detecting lymphocyte count, substances for detecting platelet count and substances for detecting glutamic-oxaloacetic transaminase content.
5. Use according to any one of claims 1 to 3, characterized in that: the substances for detecting the ALR content, the NLR, the PLR and the ANRI further comprise a blood vessel invasion occurrence risk prediction model, and the blood vessel invasion occurrence risk prediction model is used for determining the risk of occurrence of blood vessel invasion according to the ALR content, the NLR, the PLR and the ANRI.
6. Product for predicting or aiding in the prediction of the risk of developing vascular invasion, being a substance for the detection of ALR, NLR, PLR and ANRI according to any one of claims 1 to 5.
7. The application of the substances for detecting ALR content, NLR, PLR or ANRI in preparing products for predicting or assisting in predicting the risk of occurrence of vascular invasion; NLR is the ratio of the number of neutrophils to the number of lymphocytes in unit volume of peripheral blood; PLR is the ratio of the number of platelets to lymphocytes in a unit volume of peripheral blood; ANRI is the ratio of the glutamic oxaloacetic transaminase content per unit volume of peripheral blood to the number of neutrophils.
8. Use according to claim 7, characterized in that: the ALR content is the content of liver regeneration enhancement factors in peripheral blood;
and/or, the risk of occurrence of vascular invasion is a risk of occurrence of vascular invasion in a patient with HBV-associated HCC.
9. Product for predicting or aiding in the prediction of the risk of developing vascular invasion, as claimed in claim 7, for the detection of ALR, NLR, PLR or ANRI.
10. The application of substances used in the method for predicting or assisting in predicting the occurrence risk of vascular invasion by using ALR as a marker in preparing products for predicting or assisting in predicting the occurrence risk of vascular invasion.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110727385.1A CN113470814A (en) | 2021-06-29 | 2021-06-29 | Application of substances for detecting ALR, NLR, PLR and ANRI in predicting risk of vascular invasion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110727385.1A CN113470814A (en) | 2021-06-29 | 2021-06-29 | Application of substances for detecting ALR, NLR, PLR and ANRI in predicting risk of vascular invasion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113470814A true CN113470814A (en) | 2021-10-01 |
Family
ID=77873707
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110727385.1A Pending CN113470814A (en) | 2021-06-29 | 2021-06-29 | Application of substances for detecting ALR, NLR, PLR and ANRI in predicting risk of vascular invasion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113470814A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114778417A (en) * | 2022-04-29 | 2022-07-22 | 复旦大学附属中山医院 | Biomarker, kit, model establishing method and application for immune checkpoint inhibitor related adverse reaction risk prediction |
| CN114974595A (en) * | 2022-05-13 | 2022-08-30 | 江苏省人民医院（南京医科大学第一附属医院） | Crohn's disease patient mucosa healing prediction model and method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101062946A (en) * | 2007-05-22 | 2007-10-31 | 中国人民解放军第二军医大学东方肝胆外科医院 | High-active liver regeneration reinforced factor and usage thereof |
| CN108565024A (en) * | 2018-03-19 | 2018-09-21 | 首都医科大学附属北京地坛医院 | The system of risk of recurrence in a kind of postoperative 1 year of the HBV correlation primary small hepatocellular carcinomas of determining single-shot |
| CN109473175A (en) * | 2018-11-07 | 2019-03-15 | 中山大学附属第三医院（中山大学肝脏病医院） | A kind of Noninvasive serology Rating Model and its design method for liver fibrosis |
| CN111584082A (en) * | 2020-05-15 | 2020-08-25 | 高春芳 | Establishment and application of primary hepatocellular carcinoma microvascular invasion regression prediction model based on clinical examination multidimensional data |
-
2021
- 2021-06-29 CN CN202110727385.1A patent/CN113470814A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101062946A (en) * | 2007-05-22 | 2007-10-31 | 中国人民解放军第二军医大学东方肝胆外科医院 | High-active liver regeneration reinforced factor and usage thereof |
| CN108565024A (en) * | 2018-03-19 | 2018-09-21 | 首都医科大学附属北京地坛医院 | The system of risk of recurrence in a kind of postoperative 1 year of the HBV correlation primary small hepatocellular carcinomas of determining single-shot |
| CN109473175A (en) * | 2018-11-07 | 2019-03-15 | 中山大学附属第三医院（中山大学肝脏病医院） | A kind of Noninvasive serology Rating Model and its design method for liver fibrosis |
| CN111584082A (en) * | 2020-05-15 | 2020-08-25 | 高春芳 | Establishment and application of primary hepatocellular carcinoma microvascular invasion regression prediction model based on clinical examination multidimensional data |
Non-Patent Citations (6)
| Title |
|---|
| JIAN ZHENG, MD ET AL.: "Utility of Serum Inflammatory Markers in Predicting Microvascular Invasion and Survival in Patients with Hepatocellular Carcinoma", 《ANN SURG ONCOL》, vol. 12, no. 24 * |
| YING ZHU ET AL.: "A new laboratory-based algorithm to predict microvascular invasion and survival in patients with hepatocellular carcinoma", 《INTERNATIONAL JOURNAL OF SURGERY》, vol. 57, pages 45 - 53, XP085465843, DOI: 10.1016/j.ijsu.2018.07.011 * |
| 何羽童等: "基于临床实验室指标的HBV 相关性原发性肝癌微血管侵犯预测模型的建立", 《临床检验杂志》, vol. 38, no. 7, pages 498 - 502 * |
| 常秀娟等: "原发性肝癌发生血管侵犯的危险因素分析", 《临床肝胆病杂志》, vol. 34, no. 5 * |
| 张超等: "肝再生增强因子研究进展", 《世界华人消化杂志》, vol. 25, no. 36, pages 3175 * |
| 陈况等: "术前预测肝细胞癌微血管侵犯研究进展", 《中国实用外科杂志》, vol. 39, no. 10, pages 1101 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114778417A (en) * | 2022-04-29 | 2022-07-22 | 复旦大学附属中山医院 | Biomarker, kit, model establishing method and application for immune checkpoint inhibitor related adverse reaction risk prediction |
| CN114778417B (en) * | 2022-04-29 | 2024-02-13 | 上海顿慧医疗科技发展有限公司 | Model building method for immune checkpoint inhibitor related adverse reaction risk prediction |
| CN114974595A (en) * | 2022-05-13 | 2022-08-30 | 江苏省人民医院（南京医科大学第一附属医院） | Crohn's disease patient mucosa healing prediction model and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Toshima et al. | A novel serum marker, glycosylated Wisteria floribunda agglutinin-positive Mac-2 binding protein (WFA+-M2BP), for assessing liver fibrosis | |
| RU2122212C1 (en) | Method for determining systemic inflammation | |
| US8489335B2 (en) | Method of diagnosing the presence and/or severity of a hepatic pathology in an individual and/or of monitoring the effectiveness of a treatment for one such pathology | |
| Pinzani | Non-invasive evaluation of hepatic fibrosis: don’t count your chickens before they’re hatched | |
| BRPI0709374A2 (en) | apolipoprotein fingerprinting technique and related methods | |
| CN113470814A (en) | Application of substances for detecting ALR, NLR, PLR and ANRI in predicting risk of vascular invasion | |
| CN105705652B (en) | Method for assisting in the differential diagnosis of stroke | |
| Gabr et al. | Prediction of fibrosis in hepatitis C patients: assessment using hydroxyproline and oxidative stress biomarkers | |
| AU2018247291A1 (en) | Biomarker-based methods and biochips for aiding the diagnosis of stroke | |
| Mattioli et al. | Longitudinal serum neurofilament light chain (sNfL) concentration relates to cognitive function in multiple sclerosis patients | |
| CN114093517A (en) | Cancer screening method and system based on blood indexes and cfDNA | |
| Omran et al. | Enhanced liver fibrosis test using ELISA assay accurately discriminates advanced stage of liver fibrosis as determined by transient elastography fibroscan in treatment naïve chronic HCV patients | |
| Ozdemir et al. | Could systemic immune inflammation index be a new parameter for diagnosis and disease activity assessment in systemic lupus erythematosus? | |
| Khoontawad et al. | Proteomic identification of plasma protein tyrosine phosphatase alpha and fibronectin associated with liver fluke, Opisthorchis viverrini, infection | |
| CN105021827B (en) | The application in preparation detection inflammation and fibrosis product of the material of detection Serum angiogenin sample albumen 2 content | |
| US20230274840A1 (en) | Disease risk evaluation method, disease risk evaluation device, and disease risk evaluation program | |
| Ding et al. | Nomogram for predicting advanced liver fibrosis and cirrhosis in patients with chronic liver disease | |
| CN111477268B (en) | Method for establishing glycoprotein N-glycoset graph spectrum model and application | |
| Yankov et al. | Comparative characterization of procalcitonin (sensitivity, specificity, predictability, and cut-off reference values) as a marker of inflammation in odontogenic abscesses of the head and neck in the female population | |
| Bui et al. | Evaluating M2BPGi as a Marker for Liver Fibrosis in Patients with Chronic Hepatitis B | |
| CN114994327A (en) | Application of biomarker GSDME | |
| Perera et al. | Percentage of small platelets on peripheral blood smear and Child-Turcott-Pugh class can predict the presence of oesophageal varices in newly diagnosed patients with cirrhosis: development of a prediction model for resource limited settings | |
| WO2013119279A2 (en) | Assays and methods for the diagnosis of ovarian cancer | |
| EP2534491B1 (en) | Compositons and methods for predicting cardiovascular events | |
| CN111521821A (en) | Biomarkers of vascular calcification and uses thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |