A method for determining blood inflammatory markers with prognostic value for GBM and a GBM survival rate calculation system

Abstract

The present invention relates to a method for determining blood inflammatory markers with prognostic value for GBM and a GBM survival rate calculation system. The method comprises establishing a diffuse glioma patient group, which includes a LGG patient group and a GBM patient group; extracting blood inflammatory indicators from the routine blood test and liver function data of each patient in the diffuse glioma patient group and calculating the ratio of the blood inflammatory indicators; comparing the statistical differences in blood inflammatory indicators and the ratio of blood inflammatory indicators between the LGG patient group and the GBM patient group; and determining that NLR, LMR, and AGR have independent prognostic value for GBM. Statistical analysis methods are used to determine blood inflammatory markers with prognostic value for GBM from the blood inflammatory indicators and the ratio of blood inflammatory indicators, thereby evaluating the clinical outcomes of GBM patients and providing theoretical support for preoperative guidance of individualized treatment plans for GBM patients.

Description

Technical Field

[0001] The present invention relates to the field of GBM (glioblastoma) prognosis assessment, and in particular to a method for determining blood inflammatory markers with GBM prognostic value and a GBM survival rate calculation system. Background Art

[0002] GBM is the most common malignant brain tumor in adults. Although surgical resection and chemoradiotherapy have improved patient survival, the median overall survival of GBM patients remains less than 15 months. Several factors influencing GBM prognosis have been identified, among which IDH1 (isocitrate dehydrogenase 1) mutation and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation have been widely used in clinical practice. However, these molecular biomarkers are only available after surgery. Therefore, it is necessary to identify preoperative biomarkers for GBM patients to assess their clinical outcomes and guide individualized treatment plans. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for determining blood inflammatory markers with prognostic value for GBM, so as to solve the above-mentioned problems existing in the prior art.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] A method for determining blood inflammatory markers with prognostic value for GBM comprises the following steps:

[0006] Step 1: constructing a diffuse glioma patient group, wherein the diffuse glioma patient group includes a LGG (low-grade glioma) patient group and a GBM patient group;

[0007] Step 2, extracting blood inflammation indicators from the routine blood test and liver function data of each patient in the diffuse glioma patient group, and calculating the ratio of the blood inflammation indicators; wherein the blood inflammation indicators include neutrophils, lymphocytes, platelets, monocytes, albumin and globulin; the ratio of the blood inflammation indicators includes NLR (neutrophil / lymphocyte), PLR (platelet / lymphocyte), LMR (lymphocyte / monocyte), AGR (albumin / globulin);

[0008] Step 3, comparing the statistical differences in the blood inflammation indicators and the ratios of the blood inflammation indicators in the LGG patient group and the GBM patient group; obtaining that the GBM patient group had higher neutrophils, NLR and PLR, lower lymphocytes, LMR and AGR than the LGG patient group, and no significant differences in platelets, monocytes and albumin; suggesting that neutrophils, NLR, PLR, lymphocytes, LMR and AGR are blood inflammation markers with GBM differentiation significance, and determining that NLR, LMR and AGR have independent prognostic value for GBM.

[0009] The beneficial effect of the present invention is: using statistical analysis methods to determine blood inflammatory markers with GBM prognostic value from blood inflammatory indicators and the ratio of blood inflammatory indicators, so as to evaluate the clinical outcomes of GBM patients and provide theoretical support for preoperative guidance of individualized treatment plans for GBM patients.

[0010] On the basis of the above technical solution, the present invention can also be improved as follows.

[0011] Furthermore, the method further comprises the following steps:

[0012] Step 4: Identify the optimal cutoff values of neutrophils, lymphocytes, platelets, albumin, NLR, PLR, LMR and AGR in the GBM patient group, divide the GBM patient group into two groups based on each of the optimal cutoff values, compare the OS (overall survival) of the GBM patients in each two groups, and further determine that NLR, LMR and AGR have independent prognostic value for GBM.

[0013] Furthermore, step 3 includes the following steps:

[0014] Step 31, performing a normality test and a homogeneity of variance analysis on the blood inflammation index and the ratio of the blood inflammation index, and obtaining that albumin conforms to a normal distribution and homogeneity of variance, while neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR, and AGR all have non-normal distributions;

[0015] Step 32, using the Mann-Whitney U test to compare the statistical differences in neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR, and AGR between the LGG patient group and the GBM patient group;

[0016] Step 33, using an unpaired t-test to compare the statistical difference of albumin between the LGG patient group and the GBM patient group.

[0017] Furthermore, in step 4, the OS of the GBM patients in each two groups are compared respectively, specifically using the Mann-Whitney U test to compare the OS of the GBM patients in each two groups respectively, and the GBM patient groups with high neutrophils, high NLR, low lymphocytes, low albumin, low LMR and low AGR all show shorter OS.

[0018] Furthermore, in step 4, the OS of the GBM patients in each two groups are compared respectively by drawing the Kaplan-Meier survival curve of each two groups according to each of the blood inflammation indicators and the ratio of the blood inflammation indicators to compare the OS of the GBM patients in each two groups. The GBM patient groups with high NLR, low LMR, low AGR and low albumin all showed a shorter OS, and there was no significant difference in the OS of the GBM patients in each corresponding two groups of neutrophils, lymphocytes, platelets and PLR.

[0019] Furthermore, the method further comprises the following steps:

[0020] In step 5, the Cox univariate and multivariate regression models were used to evaluate the prognostic value of NLR, LMR, and AGR in the GBM patient group to further confirm that NLR, LMR, and AGR had independent prognostic value for GBM.

[0021] Furthermore, the method further comprises the following steps:

[0022] In step 6, NLR, LMR, and AGR were combined in pairs and in triplicate to evaluate the prognostic value of the combined blood inflammatory markers to obtain the AGR-NLR score. The AGR-LMR score and the LMR-NLR score had independent prognostic value.

[0023] Furthermore, the method further comprises the following steps:

[0024] In step 7, KPS (Kalman filter performance status score), whether postoperative radiotherapy and / or chemotherapy was received, and the extent of tumor resection had independent prognostic value with AGR-NLR score, AGR-LMR score, and LMR-NLR score in GBM. Nomograms were constructed to predict the 0.5-year, 1-year, and 1.5-year survival probabilities of the GBM patient group. The C index (Harrell's concordance index) was used to evaluate the predictive accuracy of the three nomograms. The internal validation calibration curve was used to evaluate the consistency between the predicted values and the actual observed values of the three nomograms. The time-dependent ROC (receiver operating characteristic) curve and AUC (area under the time-dependent ROC curve) were used to evaluate the accuracy of the three nomograms in predicting survival probability. The nomogram containing the AGR-NLR score had the highest predictive accuracy.

[0025] Another technical solution of the present invention is as follows:

[0026] A GBM survival rate calculation system, characterized by comprising a data acquisition module, a data calculation module, a data comparison module and a data analysis module;

[0027] The data acquisition module is used to collect blood routine and liver function data of GBM patients and obtain blood inflammation indicators in the blood routine and liver function data;

[0028] The data calculation module is used to calculate the ratio of the blood inflammation index;

[0029] The data comparison module is used to compare the blood inflammation index and the ratio of the blood inflammation index with the optimal cutoff value of the blood inflammation index and the optimal cutoff value of the ratio of the blood inflammation index obtained in the above-mentioned method for determining a blood inflammation marker with prognostic value for GBM, to determine whether the blood inflammation index and the ratio of the blood inflammation index are high or low, respectively;

[0030] A data analysis module is used to calculate the survival rate of the GBM patient based on whether the blood inflammation index and the ratio of the blood inflammation index are high or low, respectively, combined with the analysis results of the above-mentioned method for determining the blood inflammation marker with GBM prognostic value.

[0031] The beneficial effects of the present invention are: providing a preoperative GBM survival rate calculation system to evaluate the clinical outcomes of GBM patients and provide support for preoperative guidance of individualized treatment plans for GBM patients.

[0032] On the basis of the above technical solution, the present invention can also be improved as follows.

[0033] Furthermore, the blood inflammation indicators include neutrophils, lymphocytes, monocytes, albumin and globulin; the ratios of the blood inflammation indicators include NLR, LMR and AGR; the optimal cutoff values of NLR, LMR and AGR are 2.0, 2.3 and 1.7, respectively; the analysis results show that when the NLR is high, the LMR is low or the AGR is low, the survival rate of the GBM patient is poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of a method for determining blood inflammatory markers with prognostic value for GBM according to the present invention;

[0035] Figure 2A diagram showing optimal cutoff values for blood inflammation indicators and ratios of blood inflammation indicators in a method for determining blood inflammation markers with prognostic value for GBM according to the present invention;

[0036] Figure 3 A first survival curve diagram of a method for determining blood inflammatory markers with prognostic value for GBM according to the present invention;

[0037] Figure 4 A second survival curve diagram of a method for determining blood inflammatory markers with prognostic value for GBM according to the present invention;

[0038] Figure 5 This is a schematic diagram of the correlation between NLR, LMR and AGR in a method for determining blood inflammatory markers with prognostic value for GBM according to the present invention;

[0039] Figure 6 A third survival curve diagram of a method for determining blood inflammatory markers with prognostic value for GBM according to the present invention;

[0040] Figure 7 A nomogram for a method of determining blood inflammatory markers with prognostic value for GBM according to the present invention;

[0041] Figure 8 This is a time-dependent ROC curve diagram of a method for determining blood inflammatory markers with prognostic value for GBM according to the present invention;

[0042] Figure 9 This is a system principle block diagram of a GBM survival rate calculation system of the present invention. DETAILED DESCRIPTION

[0043] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0044] like Figure 1 As shown, a method for determining blood inflammatory markers with prognostic value for GBM comprises the following steps:

[0045] Step 1, constructing a diffuse glioma patient group, wherein the diffuse glioma patient group includes a LGG patient group and a GBM patient group;

[0046] Step 2, extracting blood inflammation indicators from the routine blood test and liver function data of each patient in the diffuse glioma patient group, and calculating the ratio of the blood inflammation indicators; wherein the blood inflammation indicators include neutrophils, lymphocytes, platelets, monocytes, albumin and globulin; the ratio of the blood inflammation indicators includes NLR, PLR, LMR, and AGR;

[0047] Step 3, comparing the statistical differences in the blood inflammation indicators and the ratios of the blood inflammation indicators in the LGG patient group and the GBM patient group; obtaining that the GBM patient group had higher neutrophils, NLR and PLR, lower lymphocytes, LMR and AGR than the LGG patient group, and no significant differences in platelets, monocytes and albumin; suggesting that neutrophils, NLR, PLR, lymphocytes, LMR and AGR are blood inflammation markers with GBM differentiation significance, and determining that NLR, LMR and AGR have independent prognostic value for GBM.

[0048] Inflammation is a hallmark of cancer. Pre-treatment blood inflammatory indicators and the ratio of blood inflammatory indicators can be easily obtained from routine blood tests. Statistical analysis methods are used to determine blood inflammatory markers with prognostic value for GBM from blood inflammatory indicators and the ratio of blood inflammatory indicators to evaluate the clinical outcomes of GBM patients and provide theoretical support for preoperative guidance of individualized treatment plans for GBM patients.

[0049] The method for determining blood inflammatory markers with prognostic value for GBM described in Example 2 of the present invention, based on Example 1, further includes the following steps:

[0050] Step 4: Identify the optimal cutoff values of neutrophils, lymphocytes, platelets, albumin, NLR, PLR, LMR, and AGR for the GBM patient group, divide the GBM patient group into two groups based on each optimal cutoff value, compare the OS of the GBM patients in each two groups, and further determine that NLR, LMR, and AGR are blood inflammatory markers with independent prognostic value for GBM.

[0051] The method for determining blood inflammatory markers with prognostic value for GBM described in Example 3 of the present invention is based on Example 1 or 2, and step 3 includes the following steps:

[0052] Step 31, performing a normality test and a homogeneity of variance analysis on the blood inflammation index and the ratio of the blood inflammation index, and obtaining that albumin conforms to a normal distribution and homogeneity of variance, while neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR, and AGR all have non-normal distributions;

[0053] Step 32, using the Mann-Whitney U test to compare the statistical differences in neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR, and AGR between the LGG patient group and the GBM patient group;

[0054] Step 33, using an unpaired t-test to compare the statistical difference of albumin between the LGG patient group and the GBM patient group.

[0055] The method for determining blood inflammatory markers with prognostic value for GBM described in Example 4 of the present invention is based on Example 2 or 3.

[0056] In step 4, the OS of the GBM patients in each two groups are compared respectively, specifically using the Mann-Whitney U test to compare the OS of the GBM patients in each two groups respectively, and the GBM patient groups with high neutrophils, high NLR, low lymphocytes, low albumin, low LMR and low AGR all show shorter OS.

[0057] The method for determining blood inflammatory markers with prognostic value for GBM described in Example 5 of the present invention is based on any one of Examples 2 to 3.

[0058] In step 4, the OS of the GBM patients in each two groups are compared respectively. Specifically, the Kaplan-Meier survival curve of each two groups is drawn according to each of the blood inflammation indicators and the ratio of the blood inflammation indicators to compare the OS of the GBM patients in each two groups. The GBM patient groups with high NLR, low LMR, low AGR and low albumin all showed a shorter OS, and there was no significant difference in the OS of the GBM patients in the two corresponding groups of neutrophils, lymphocytes, platelets and PLR.

[0059] The method for determining blood inflammatory markers with prognostic value for GBM described in Example 6 of the present invention, based on any one of Examples 2 to 5, further includes the following steps:

[0060] In step 5, the Cox univariate and multivariate regression models were used to evaluate the prognostic value of NLR, LMR, and AGR in the GBM patient group, and NLR, LMR, and AGR were further confirmed as blood inflammatory markers with independent prognostic value for GBM.

[0061] The method for determining blood inflammatory markers with prognostic value for GBM described in Example 7 of the present invention, based on any one of Examples 2 to 6, further includes the following steps:

[0062] The following steps are also included:

[0063] In step 6, NLR, LMR, and AGR were combined in pairs and in triplicate to evaluate the prognostic value of the combined blood inflammatory markers to obtain the AGR-NLR score. The AGR-LMR score and the LMR-NLR score had independent prognostic value.

[0064] The method for determining blood inflammatory markers with prognostic value for GBM described in Example 8 of the present invention, based on Example 7, further includes the following steps:

[0065] In step 7, KPS, whether postoperative radiotherapy and / or chemotherapy was received, and the extent of tumor resection had independent prognostic value with the AGR-NLR score, AGR-LMR score, and LMR-NLR score in GBM. Nomograms were constructed to predict the 0.5-year, 1-year, and 1.5-year survival probabilities of the GBM patient group. The C index was used to evaluate the predictive accuracy of the three nomograms. The internal validation curve was used to evaluate the consistency between the predicted values and the actual observed values of the three nomograms. The time-dependent ROC curve and AUC were used to evaluate the accuracy of the three nomograms in predicting survival probability. The nomogram containing the AGR-NLR score had the highest predictive accuracy.

[0066] The GBM survival rate calculation system described in Example 9 of the present invention includes a data acquisition module, a data calculation module, a data comparison module and a data analysis module;

[0067] The data acquisition module is used to collect blood routine and liver function data of GBM patients and obtain blood inflammation indicators in the blood routine and liver function data;

[0068] The data calculation module is used to calculate the ratio of the blood inflammation index;

[0069] The data comparison module is used to compare the blood inflammation index and the ratio of the blood inflammation index with the optimal cutoff value of the blood inflammation index and the optimal cutoff value of the ratio of the blood inflammation index obtained in the method for determining a blood inflammation marker with GBM prognostic value described in Example 2, to determine whether the blood inflammation index and the ratio of the blood inflammation index are high or low, respectively;

[0070] A data analysis module is used to calculate the survival rate of the GBM patient based on whether the blood inflammation index and the ratio of the blood inflammation index are high or low, respectively, in combination with the analysis results of the method for determining a blood inflammation marker with GBM prognostic value described in any one of Examples 1 to 8.

[0071] Provide a preoperative GBM survival rate calculation system to evaluate the clinical outcomes of GBM patients and provide support for preoperative guidance of individualized treatment plans for GBM patients.

[0072] The GBM survival rate calculation system described in Example 10 of the present invention is based on Example 9, wherein the blood inflammation indicators include neutrophils, lymphocytes, monocytes, albumin and globulin; the ratios of the blood inflammation indicators include NLR, LMR and AGR; the optimal cutoff values of the NLR, LMR and AGR are 2.0, 2.3 and 1.7, respectively; and the analysis result shows that when the NLR is high, the LMR is low or the AGR is low, the survival rate of the GBM patient is poor. Specific embodiment:

[0074] Step 1: All patients with diffuse glioma in the Department of Neurosurgery, Zhongnan Hospital of Wuhan University from January 2016 to May 2019 were retrieved through the electronic medical record system. A total of 187 patients with diffuse glioma were finally identified according to the following inclusion and exclusion criteria, including 52 patients with grade 2 gliomas and 47 patients with grade 3 gliomas. These 99 patients were included in the LGG patient group, and the other 88 patients with grade 4 glioblastoma were included in the GBM patient group.

[0075] Inclusion criteria: 1) Age ≥ 18 years; 2) All patients' diagnoses were confirmed by histology; 3) All patients' clinical information and preoperative blood routine and liver function data were available;

[0076] Exclusion criteria: 1) Patients who received chemotherapy (including hormone use) and / or radiotherapy before surgery; 2) Patients with a history of other malignant tumors or chronic inflammatory diseases (including autoimmune diseases and infections); 3) Patients with recurrent gliomas; 4) Patients who died during the perioperative period.

[0077] The acquisition, processing, and analysis of all patient data strictly adhered to the Declaration of Helsinki. This study was approved by the Ethics Committee of Zhongnan Hospital of Wuhan University, and each patient provided written informed consent. The last follow-up was August 31, 2020.

[0078] In step 2, demographic and clinical information of all 187 patients with diffuse glioma was extracted, including sex, age at diagnosis, tumor location, tumor grade, IDH1 mutation status, MGMT promoter methylation level, KPS, extent of tumor resection (complete resection with GTR ≥ 95%, subtotal resection with STR < 95%), and whether they received postoperative radiotherapy and / or chemotherapy.

[0079] Blood routine and liver function data were also extracted for all 187 patients with diffuse glioma, including neutrophil, lymphocyte, platelet, monocyte, albumin, and globulin levels. Proportions of these blood inflammatory markers, including NLR, PLR, LMR, and AGR, were also calculated. OS was defined as the period from the date of surgery to the date of death or the last follow-up date.

[0080] The clinical and pathological characteristics of all 187 patients with diffuse gliomas are shown in Table 1. The mean age was 50.3 years (range, 21–81). Among the GBM patients, 69.3% underwent nontotal resection, and 56.8% received postoperative radiotherapy and / or chemotherapy. Only 4 (4.5%) and 33 (37.5%) GBM patients had IDH1 mutations and MGMT promoter hypermethylation, respectively.

[0081] Table 1. Clinical and pathological characteristics of the patients

[0082]

[0083]

[0084] Step 3: Normality test and variance homogeneity analysis were performed on the blood inflammatory indicators and the ratios of blood inflammatory indicators. It was found that albumin conformed to the normal distribution and variance homogeneity, while neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR and AGR were all non-normally distributed; the Mann-Whitney U test was used to compare the statistical differences in neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR and AGR between the LGG patient group and the GBM patient group; the unpaired t-test was used to compare the statistical differences in albumin between the LGG patient group and the GBM patient group.

[0085] The GBM patient group had higher neutrophil counts, NLR, and PLR, lower lymphocyte counts, LMR, and AGR, and no significant differences in platelets, monocytes, and albumin compared with the LGG patient group, as shown in Table 1. This suggests that neutrophil counts, NLR, PLR, lymphocyte counts, LMR, and AGR are blood inflammatory markers with distinguishing significance for GBM, and confirms that NLR, LMR, and AGR have independent prognostic value for GBM.

[0086] Step 4: Since the blood inflammation index and the ratio of blood inflammation index are continuous variables, the optimal cutoff value of the blood inflammation index and the ratio of blood inflammation index was determined using X-tile software (version 3.6.1, http: / / medicine.yale.edu / lab / rimm / research / software.aspx). Figure 2As shown in the figure, the optimal cutoff values for neutrophils, lymphocytes, platelets and albumin were 4.7 (10 9 / L),2.3(10 9 / L),208(10 9 / L), 35.7 (g / L); the optimal cutoff values of NLR, PLR, LMR and AGR were 2.0, 213.0, 2.3 and 1.7, respectively. The GBM patient group was divided into two groups according to each optimal cutoff value, and the OS of GBM patients in each two groups were compared. First, the OS of GBM patients in each two groups were compared using the Mann-Whitney U test. The OS of GBM patients in the group with high neutrophils, high NLR, low lymphocytes, low albumin, low LMR and low AGR was shorter, as shown in Table 2. Next, the Kaplan-Meier survival curve of each two groups was drawn according to each blood inflammation index and the ratio of blood inflammation index to compare the OS of GBM patients in each two groups. It was found that in GBM patients, high NLR (p = 0.005) had a worse OS, while high LMR (p = 0.006), AGR (p = 0.002) or albumin (p = 0.006) had a better OS. Figure 3 Neutrophils (p = 0.057), lymphocytes (p = 0.224), platelets (p = 0.311), and PLR (p = 0.290) had no significant relationship with OS. Figure 4 It was further confirmed that NLR, LMR, and AGR have independent prognostic value for GBM.

[0087] Table 2. OS in GBM patients based on cutoff values of peripheral blood inflammatory markers

[0088]

[0089] In step 5, Cox univariate and multivariate analyses showed that, in addition to KPS, chemoradiotherapy, and extent of resection, NLR, LMR, and AGR were also significantly associated with survival. A high NLR (>2.0) and a low LMR (<2.3) were indicators of poor prognosis in GBM patients, whereas a high PLR (>213.0) was not. The optimal cutoff value of preoperative NLR of 4.0 is most commonly used in GBM studies and has been shown to be associated with glioma grade and poor survival in GBM patients. The data presented here suggest that a low optimal cutoff value of 2.0 for preoperative NLR may serve as a potential predictor of GBM survival, as shown in Table 3.

[0090] Table 3. Univariate and multivariate analysis of OS in GBM patients

[0091]

[0092] In addition, the Spearman correlation coefficient test showed that there was no significant correlation between NLR or LMR and AGR, while there was only a weak correlation between NLR and LMR (r = -0.613, p < 0.01). Figure 5 Surprisingly, the analysis did not show a significant association between MGMT promoter methylation or IDH1 mutation and survival. Univariate analysis showed that albumin was a significant variable associated with survival, but multivariate analysis failed to show significant prognostic significance.

[0093] In step 6, since NLR, LMR, and AGR may affect tumor progression through different mechanisms, combining these markers may provide a more comprehensive survival prediction tool. Therefore, four prognostic scoring systems were constructed by incorporating any two or all three variables of NLR, LMR, and AGR: AGR-NLR, AGR-LMR, LMR-NLR, and LMR-NLR-AGR. The scores in the systems were determined by the presence or absence of variable status associated with poor survival, namely, high NLR (>2.0), low LMR (<2.3), or low AGR (<1.7). For the AGR-NLR, AGR-LMR, and LMR-NLR scores, the systems were divided into three groups: 0 (both variables absent), 1 (one of the two variables present), and 2 (both variables present). The LMR-NLR-AGR scoring system was divided into four groups: 0 (all three variables absent), 1 (any one of the three variables present), 2 (any two of the three variables present), and 3 (all three variables present). The OS data for each scoring system are shown in Table 4.

[0094] Table 4. OS of GBM patients based on different scoring systems

[0095]

[0096] Kaplan-Meier survival curve analysis showed that AGR-NLR, AGR-LMR, LMR-NLR, and LMR-NLR-AGR scores were significantly correlated with OS, with p values of p<0.001, p<0.001, p=0.003, and p<0.001, respectively. Figure 6 shown.

[0097] The results of multivariate analysis showed that for the AGR-LMR and LMR-NLR scores, groups 1 and 2 were associated with unfavorable OS; in the AGR-NLR score, only group 2 was associated with worse OS; in the LMR-NLR-AGR score, groups 1, 2, and 3 were not predictors of survival outcomes; as shown in Table 5.

[0098] Table 5. Univariate and multivariate analyses of OS in GBM patients based on scoring systems

[0099]

[0100] In step 7, based on the results of multivariate analysis, several independent prognostic factors were identified, including KPS, chemoradiotherapy, extent of resection, AGR-NLR score, AGR-LMR score, and LMR-NLR score. KPS, chemoradiotherapy, and extent of resection, along with AGR-NLR score, AGR-LMR score, and LMR-NLR score, had independent prognostic value in GBM. Nomograms were constructed to assess the predictive value of these variables for 0.5-, 1-, and 1.5-year overall survival in GBM patients.

[0101] In the nomogram that included the AGR-NLR score, KPS had the greatest impact on prognosis, followed by the AGR-NLR score, chemoradiotherapy, and degree of resection. The c-index of the nomogram survival prediction model was 0.874, which had good accuracy in predicting survival. The boot-strapped calibration plot of the nomogram for predicting 0.5-, 1-, and 1.5-year survival rates, i.e., the validation curve of internal validation, performed well in the ideal model, such as Figure 7 a, indicating the predictive validity of the nomogram.

[0102] In the nomogram incorporating the AGR-LMR score, KPS had the greatest impact on prognosis, followed by the AGR-LMR score, degree of resection, and chemoradiotherapy. The c-index of the nomogram survival prediction model was 0.867, which had good accuracy in predicting survival. The boot-strapped calibration plots of the nomogram for predicting 0.5-, 1-, and 1.5-year survival rates performed well in the ideal model, such as Figure 7 b, Shows the predictive validity of the nomogram.

[0103] In the nomogram incorporating the LMR-NLR score, KPS had the greatest impact on prognosis. Chemoradiotherapy and the LMR-NLR score had similar weights on the risk score, followed by the extent of resection. The c-index of the nomogram survival prediction model was 0.866, which showed good accuracy in predicting survival. The boot-strapped calibration plots of the nomogram for predicting 0.5-, 1-, and 1.5-year survival rates performed well in the ideal model, such as Figure 7 c, indicating the predictive validity of the nomogram.

[0104] Among the three nomograms, the nomogram containing the AGR-NLR score had the highest c-index, showing a slight advantage in the accuracy of predicting survival. Figure 8 As shown in the figure, time-dependent ROC curve analysis also showed that the AUC value of the AGR-NLR score was the highest when predicting 0.5 years, 1 year, or 1.5 years, which were 0.703, 0.708, and 0.738, respectively, and the prediction accuracy was the highest.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A GBM survival rate calculation system, characterized in that: Including data acquisition module, data calculation module, data comparison module and data analysis module; The data acquisition module is used to collect blood routine and liver function data of GBM patients and obtain blood inflammation indicators in the blood routine and liver function data; The data calculation module is used to calculate the ratio of the blood inflammation index; The data comparison module is configured to compare the blood inflammation index and the ratio of the blood inflammation index with the optimal cutoff value of the blood inflammation index and the optimal cutoff value of the ratio of the blood inflammation index obtained in the method for determining a blood inflammation marker with prognostic value for GBM, respectively, to determine whether the blood inflammation index and the ratio of the blood inflammation index are high or low values, respectively; a data analysis module for calculating the survival rate of the GBM patient based on whether the blood inflammation index and the ratio of the blood inflammation index are high or low, respectively, in combination with the analysis results of the method for determining a blood inflammation marker with prognostic value for GBM; in, The blood inflammation indicators include neutrophils, lymphocytes, monocytes, albumin, and globulin; the ratios of the blood inflammation indicators include NLR, LMR, and AGR; the optimal cutoff values of NLR, LMR, and AGR are 2.0, 2.3, and 1.7, respectively; the analysis results show that when the NLR is high, the LMR is low, or the AGR is low, the survival rate of the GBM patient is poor; The method for determining blood inflammatory markers with prognostic value for GBM comprises the following steps: Step 1, constructing a diffuse glioma patient group, wherein the diffuse glioma patient group includes a LGG patient group and a GBM patient group; Step 2, extracting blood inflammation indicators from the routine blood test and liver function data of each patient in the diffuse glioma patient group, and calculating the ratio of the blood inflammation indicators; wherein the blood inflammation indicators include neutrophils, lymphocytes, platelets, monocytes, albumin and globulin; the ratio of the blood inflammation indicators includes NLR, PLR, LMR, and AGR; Step 3, comparing the statistical differences in the blood inflammation indicators and the ratios of the blood inflammation indicators between the LGG patient group and the GBM patient group; obtaining that the GBM patient group had higher neutrophils, NLR, and PLR, lower lymphocytes, LMR, and AGR than the LGG patient group, and no significant differences in platelets, monocytes, and albumin; suggesting that neutrophils, NLR, PLR, lymphocytes, LMR, and AGR are blood inflammation markers with distinguishing significance for GBM, and confirming that NLR, LMR, and AGR have independent prognostic value for GBM; Step 4: Identify optimal cutoff values for neutrophils, lymphocytes, platelets, albumin, NLR, PLR, LMR, and AGR in the GBM patient group; divide the GBM patient group into two groups based on each optimal cutoff value; compare the OS of the GBM patients in each two groups, and further determine that NLR, LMR, and AGR have independent prognostic value for GBM; Step 5: Cox univariate and multivariate regression models were used to evaluate the prognostic value of NLR, LMR, and AGR in the GBM patient group to further confirm that NLR, LMR, and AGR have independent prognostic value for GBM; Step 6: NLR, LMR, and AGR were combined in pairs and in triplicate to evaluate the prognostic value of the combined blood inflammatory markers to obtain the AGR-NLR score. The AGR-LMR score and the LMR-NLR score had independent prognostic value. In step 7, KPS, whether postoperative radiotherapy and / or chemotherapy was received, and the extent of tumor resection had independent prognostic value with the AGR-NLR score, AGR-LMR score, and LMR-NLR score in GBM. Nomograms were constructed to predict the 0.5-year, 1-year, and 1.5-year survival probabilities of the GBM patient group. The C index was used to evaluate the predictive accuracy of the three nomograms. The internal validation curve was used to evaluate the consistency between the predicted values and the actual observed values of the three nomograms. The time-dependent ROC curve and AUC were used to evaluate the accuracy of the three nomograms in predicting survival probability. The nomogram containing the AGR-NLR score had the highest predictive accuracy.

2. The GBM survival rate calculation system according to claim 1, characterized in that: The step 3 comprises the following steps: Step 31, performing a normality test and a homogeneity of variance analysis on the blood inflammation index and the ratio of the blood inflammation index, and obtaining that albumin conforms to a normal distribution and homogeneity of variance, while neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR, and AGR all have non-normal distributions; Step 32, using the Mann-Whitney U test to compare the statistical differences in neutrophils, lymphocytes, platelets, monocytes, NLR, PLR, LMR, and AGR between the LGG patient group and the GBM patient group; Step 33, using an unpaired t-test to compare the statistical difference of albumin between the LGG patient group and the GBM patient group.

3. The GBM survival rate calculation system according to claim 1, characterized in that: In step 4, the OS of the GBM patients in each two groups are compared respectively, specifically using the Mann-Whitney U test to compare the OS of the GBM patients in each two groups respectively, and the GBM patient groups with high neutrophils, high NLR, low lymphocytes, low albumin, low LMR and low AGR all show shorter OS.

4. The GBM survival rate calculation system according to claim 1, characterized in that: In step 4, the OS of the GBM patients in each two groups are compared respectively. Specifically, the Kaplan-Meier survival curve of each two groups is drawn according to each of the blood inflammation indicators and the ratio of the blood inflammation indicators to compare the OS of the GBM patients in each two groups. The GBM patient groups with high NLR, low LMR, low AGR and low albumin all showed a shorter OS, and there was no significant difference in the OS of the GBM patients in the two corresponding groups of neutrophils, lymphocytes, platelets and PLR.

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