Melanoma prognosis biomarker combination and application thereof

Abstract

The invention relates to the field of medical detection, and particularly discloses a melanoma prognosis biomarker and application thereof. According to the multi-factor prediction model based on serological detection, GGCT is combined with lactic dehydrogenase (LDH), S100B protein and other biomarkers, and the prognosis risk of a melanoma patient can be accurately predicted. The content of GGCT in serum of a patient is detected through methods such as enzyme-linked immunosorbent assay (ELISA) and the like, comprehensive analysis is carried out in combination with other indexes, the area under a working characteristic curve (AUC) of a subject higher than 85% can be obtained, and high sensitivity and specificity are achieved. Compared with an existing single marker, the prediction effectiveness of the multi-factor model is remarkably improved, patients with high recurrence and metastasis risks can be recognized in the early stage, and individualized treatment is guided. The detection method is simple, convenient and rapid to operate, low in cost of required reagents, good in repeatability and stability and suitable for clinical popularization and application.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis, and in particular, to a combination of melanoma prognostic biomarkers and applications thereof. Background Art

[0002] Melanoma is a highly malignant skin tumor with a continuously increasing incidence in recent years. Although early-stage localized melanoma can be cured by surgery, once metastasis occurs, the prognosis is extremely poor. Clinical prognosis for melanoma patients is primarily based on clinicopathological parameters such as tumor thickness, ulceration, and stage. However, even among patients with the same clinical stage, significant differences in prognosis exist. This suggests the need to identify more reliable biological markers to assist in predicting melanoma recurrence and survival.

[0003] Serum biomarkers have been widely used in the diagnosis and prognosis of various diseases due to their convenient and minimally invasive sampling. Currently, several serum biomarkers are used as auxiliary assessments in melanoma, such as lactate dehydrogenase (LDH) and S100B protein. LDH is one of the criteria for staging advanced melanoma. Elevated LDH levels in advanced patients often indicate a high disease burden and short survival. However, LDH lacks specificity and can also be elevated in other tumors or tissue damage. S100B is a calcium-binding protein secreted by melanoma cells. Some studies and clinical practice use it to monitor the risk of recurrence and metastasis of melanoma; high levels of S100B are associated with poor survival. However, S100B is primarily positive in patients with advanced disease, and its prognostic efficacy remains limited in patients with early-stage disease or those without overt metastasis. Therefore, the sensitivity and specificity of existing melanoma serum biomarkers need to be improved.

[0004] With the development of tumor molecular biology, an increasing number of new biomarkers are being explored for melanoma prognosis assessment. For example, some studies have focused on indicators such as circulating tumor DNA, circulating tumor cells, and inflammatory factors. However, these tests are often expensive or technically complex and have not yet been widely used in routine clinical practice. Protein biomarkers remain an ideal choice due to their mature detection methods and low cost. Studies have shown that GGCT is highly expressed in various tumor tissues and is closely associated with tumor cell proliferation and survival. However, GGCT has not previously been used as a serological marker for melanoma prognosis. Summary of the Invention

[0005] Based on prospective studies, we speculate that the level of GGCT in serum may reflect tumor burden or invasiveness, and thus be related to the patient's survival prognosis. In view of this, the present invention introduces GGCT into the research of melanoma prognostic biomarkers, and develops a multi-factor prediction model combining GGCT with other indicators in order to improve the accuracy of prognostic assessment. The main purpose of the present invention is to provide a new combination of melanoma prognostic biomarkers to solve the technical problem of low prediction accuracy of single serum markers in the prior art. The present invention uses serum GGCT as a biomarker for melanoma prognosis for the first time, and combines it with other serum indicators to establish a multi-factor prognostic prediction model, which can more accurately distinguish high-risk from low-risk patients.

[0006] To achieve the above objectives, the present invention provides the following technical solutions:

[0007] First, in a first aspect, the present invention provides a use of GGCT as a biomarker in the preparation of a melanoma prognosis assessment product.

[0008] In one embodiment, the product is a kit.

[0009] In one embodiment, the kit is used to perform an enzyme-linked immunosorbent assay (ELISA) to detect the GGCT content in a biological sample.

[0010] In one embodiment, GGCT is combined with at least one other melanoma serum biomarker as a multifactorial model for prognostic prediction.

[0011] In one embodiment, the additional biomarkers include lactate dehydrogenase (LDH) and S100B protein.

[0012] In one embodiment, the application includes directly substituting the actual measured values of GGCT, LDH and S100B detected by the patient into the following formula:

[0013]

[0014] P is the probability of melanoma recurrence or death within 3 years after surgery; GGCT, LDH, and S100B are the original measured values of serum GGCT concentration, LDH activity, and S100B protein concentration, respectively; the regression coefficients of each indicator are: β0 = -8.27, β1 = 0.29, β2 = 0.02, and β3 = 3.15.

[0015] In one embodiment, P=0.35 is used as a cutoff value to evaluate the patient's prognostic risk: patients with a predicted risk ≥0.35 are classified as high risk.

[0016] Compared with the prior art, the present invention has the following significant improvements:

[0017] 1. This study proposes, for the first time, GGCT as a prognostic biomarker for melanoma, in combination with LDH and S100B to construct a predictive model. GGCT, a novel biomarker, demonstrates prognostic validity in an independent population, and its combination with two existing biomarkers (LDH and S100B) significantly improves predictive performance.

[0018] 2. The model of the present invention is based on serological testing, which is relatively simple in methodology and can be completed in a conventional laboratory. In particular, the determination of GGCT can be performed using a mature ELISA platform, making the overall testing cost low;

[0019] 3. Considering that postoperative follow-up monitoring of melanoma patients in clinical practice already routinely includes items such as LDH and S100B, the present invention only requires the addition of GGCT to obtain significantly higher prognostic assessment value, making it easy to integrate into the existing follow-up process and provide patients with more accurate treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0021] Figure 1 : ROC curves of the discovery set GGCT, S100B, LDH and MIA;

[0022] Figure 2 : ROC curve of the joint prediction model on the discovery set;

[0023] Figure 3 : ROC curves of the validation set GGCT, S100B, and LDH single indicators;

[0024] Figure 4 : ROC curve of the joint prediction model on the validation set. DETAILED DESCRIPTION

[0025] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0026] Example 1: Melanoma patient sample collection and serum biomarker detection

[0027] In this example, clinical samples of melanoma patients were collected for biomarker screening and model establishment. A total of 120 patients with histologically confirmed cutaneous melanoma were included in the study, and all patients had signed informed consent. Among them, 80 patients were used as the discovery set (training set), and the samples mainly came from patients who received surgical treatment and followed up at Hubei Provincial Cancer Hospital from 2017 to 2020; there were 40 patients as the validation set, and the samples came from cases at Tongji Hospital from 2018 to 2020. All patients were followed up regularly after surgical resection of the tumor, and complete prognostic follow-up data were obtained. The basic clinical data of the patients (including age, sex, Clark grade of tumor thickness, whether ulcers occurred, clinical stage, etc.) were collected for analysis. For the patients in the discovery set, the follow-up was at least 5 years after surgery to evaluate prognostic indicators such as median survival time and 3-year and 5-year survival rates; for the patients in the validation set, the follow-up was at least 3 years. According to the follow-up results, each group of patients was divided into a good prognosis group and a poor prognosis group according to their prognosis: the poor prognosis group was determined by tumor recurrence, metastasis or death within 3 years after surgery, while the good prognosis group was determined by no tumor recurrence during the follow-up period.

[0028] 1. Serum Sample Preparation: Collect 2-5 mL of peripheral venous blood from the patient within one week before surgery using a vacuum tube without anticoagulant. After the blood sample rests for 30 minutes, centrifuge it at 3000 rpm for 10 minutes to separate the serum. Aliquot the serum into enzyme-free cryovials and immediately store at -80°C until needed. All stored serum samples should be thawed and mixed at once before centralized biomarker testing to minimize inter-batch variability.

[0029] 2. GGCT detection: GGCT serum levels were detected using a commercial human GGCT ELISA kit (FineTest, Cat. No. EH8765). The specific steps are as follows:

[0030] (1) Sample preparation: Peripheral blood was collected from the subjects, placed at room temperature for 30 minutes, and then centrifuged at 3000 rpm for 10 minutes to separate the serum. The serum samples were aliquoted and stored at -80°C to avoid repeated freezing and thawing.

[0031] (2) Kit preparation: Remove the kit from the refrigerator and allow it to equilibrate at room temperature for 20 minutes. Prepare the standard and working solutions according to the instructions.

[0032] (3) Experimental procedure: Add 100 μL of standard or sample to a 96-well plate pre-coated with antibody and incubate at 37°C for 90 minutes. Discard the liquid and wash the plate twice. Add 100 μL of biotin-labeled antibody working solution and incubate at 37°C for 60 minutes. Discard the liquid and wash the plate three times. Add 100 μL of HRP-labeled streptavidin working solution and incubate at 37°C for 30 minutes. Discard the liquid and wash the plate five times. Add 90 μL of TMB substrate solution and incubate at 37°C in the dark for 10-20 minutes. Add 50 μL of stop solution and immediately measure the absorbance at a wavelength of 450 nm. Draw a standard curve and calculate the concentration of GGCT in the sample.

[0033] 3. Determination of other markers: This example also measured LDH and S100B in serum for joint analysis with GGCT. Serum LDH was measured using an automated biochemical analysis method: using a commercially available LDH detection reagent (lactate substrate method), the test was performed on a fully automatic biochemical analyzer according to standard operating procedures, and the results were expressed in units of U / L. Quality control showed that the daily variation of LDH was <5%. Serum S100B was detected using a commercial enzyme-linked immunosorbent assay kit (sandwich ELISA method) according to the instructions provided by the manufacturer. Quality control was also set up during the test to ensure that the data was reliable. The LDH and S100B determinations of all patient samples were completed in the same batch or at a similar time as GGCT, and the storage conditions were consistent to ensure the comparability of the results between the indicators.

[0034] Example 2: Screening of biomarkers (univariate analysis)

[0035] 1. Univariate analysis:

[0036] Before establishing a multifactor model, this example screened and analyzed candidate serum biomarkers obtained from prospective studies, aiming to identify factors closely related to melanoma prognosis from a wide range of potentially relevant indicators. The screening scope included multiple serum factors reported in the literature or suggested by preliminary experiments to be associated with tumor progression. The indicators we included in the study were: traditional tumor markers lactate dehydrogenase (LDH), S100B protein, melanoma inhibitory factor (MIA, also known as Melanoma Inhibitory Activity), C-reactive protein (CRP, reflecting inflammatory status), serum lactate levels, and several new candidate proteins related to tumor metabolism or immunity (including the gamma-glutamyl cyclotransferase GGCT, glutathione-S-transferase Pi, YKL-40, etc., which are of interest to this invention). The above indicators were simultaneously detected in the serum of 80 melanoma patients in the discovery set, and the differences in the levels of each indicator between the good prognosis group and the poor prognosis group were compared, and their prognostic prediction efficacy was evaluated.

[0037] Results of univariate statistical analysis: The 80 patients in the discovery set were divided into a good prognosis group and a poor prognosis group based on whether they had recurrence or death within 3 years. There were 33 patients (41.2%) in the poor prognosis group and 47 patients (58.8%) in the good prognosis group.

[0038] The mean serum concentrations or activity levels of candidate markers were compared between the two groups, and differences were assessed using t-tests or nonparametric tests. Receiver operating characteristic (ROC) curves and area under the curves (AUCs) for each marker predicting prognosis were calculated. The results showed that several of the markers studied showed statistically significant differences between the two groups. Among them, the serum concentration of GGCT was significantly higher in the poor prognosis group than in the good prognosis group: the mean GGCT level was 9.5 ng / mL in the poor prognosis group and 4.3 ng / mL in the good prognosis group, a highly significant difference (P < 0.001). Traditional markers such as LDH and S100B were significantly higher in the poor prognosis group than in the good prognosis group (P < 0.01), consistent with existing literature. MIA also showed some differences between the two groups, but these differences were less pronounced than those for S100B (P ≈ 0.05). CRP and lactate levels were elevated in some patients, but the correlation did not reach statistical significance. Although other new candidates such as YKL-40 tended to increase in the poor prognosis group, the variation was large and did not meet the statistical requirements.

[0039] Predictive efficacy of candidate markers: ROC curves were drawn for each indicator to evaluate its ability to distinguish good / poor prognosis. The results showed that ( Figure 1 ), the AUC of GGCT alone reached 0.82 (95% confidence interval 0.74-0.90), which is better than the AUC of 0.78 for S100B and 0.75 for LDH, which are traditional markers. The AUC of MIA is approximately 0.65, which has almost no practical value. The AUCs of CRP, lactate, etc. are even lower (about 0.6 or less) and are highly non-specific. Therefore, we selected GGCT, LDH, and S100B as candidate indicators for the prognostic model. In addition, the three indicators were not highly correlated with each other (Pearson correlation coefficient r was all <0.3), indicating that they may reflect tumor characteristics from different biological pathways and have combined value.

[0040] 2. Multifactor analysis:

[0041] Multivariate logistic regression analysis was further used to explore the independent predictive role of GGCT, LDH, and S100B in poor prognosis within 3 years. Poor prognosis (1 = recurrence / death) was used as the dependent variable, and GGCT, LDH, and S100B values were included in the model as independent variables. Logistic regression results showed that GGCT, LDH, and S100B all showed significant positive correlations in the multivariate model (P values approximately 0.002, 0.01, and 0.03, respectively). This means that after controlling for the influence of other variables, an increase in each indicator significantly increased the patient's probability of early recurrence or death. GGCT had the highest regression coefficient (standardized regression coefficient approximately 0.50), indicating that it contributed relatively more to the risk. The Hosmer-Lemeshow goodness-of-fit test for the multivariate model was P = 0.45, indicating that the model fit was good and there was no significant bias.

[0042] These screening and analysis results confirm the value of GGCT as a prognostic biomarker for melanoma. GGCT is not only significantly elevated in patients with a poor prognosis, but also outperforms traditional biomarkers in predicting disease progression. Based on this finding, we have incorporated GGCT into a prognostic prediction model, along with LDH and S100B, to further improve prediction accuracy.

[0043] Example 3: Establishment of a GGCT Combined Multi-marker Prognostic Model (Discovery Set)

[0044] According to the results of Example 2, this example established a prognostic prediction model of GGCT combined with LDH and S100B based on the data of 80 patients in the discovery set, and evaluated its performance.

[0045] A multivariate logistic regression model was used to construct a risk score for predicting recurrence or death within 3 years. The model used the presence or absence of an adverse prognostic event (yes / no) as the dependent variable, and continuous variables of GGCT concentration, LDH activity, and S100B concentration as independent variables. To facilitate model application, these indices were appropriately transformed: GGCT, LDH, and S100B were logarithmically (ln) transformed to minimize the effect of skewed distributions and then standardized (converted to Z scores). A stepwise method was used to select variables for inclusion in the regression analysis, with an α-level of significance of 0.05.

[0046] The final regression screening retained three indicators: GGCT, LDH, and S100B, and no variables were eliminated, which was consistent with our previous judgment. The resulting prediction model formula is as follows (standard errors are in parentheses):

[0047]

[0048] In the formula, P is the probability of melanoma recurrence or death within 3 years after surgery; GGCT, LDH, and S100B are the original measured values of the patient's serum GGCT concentration (unit: ng / mL), LDH activity (unit: U / L), and S100B protein concentration (unit: μg / L), respectively; the regression coefficients of each indicator are: β0 = -8.27, β1 = 0.29, β2 = 0.02, and β3 = 3.15. Based on the ROC curve, we selected the risk probability P corresponding to the maximum Youden index as the threshold for distinguishing high risk from low risk. The ROC curve of the discovery set shows ( Figure 2 ). When the Youden Index is set at a threshold of approximately P = 0.35, it reaches its maximum. The corresponding model predicts a sensitivity of 88% and a specificity of 85%. Therefore, we set the model cutoff value at P = 0.35 to assess patient prognostic risk: patients with a predicted risk ≥ 0.35 are classified as having a high risk (poor prognosis), while those with a risk < 0.35 are classified as having a low risk (good prognosis).

[0049] Using the above model formula, the actual values of a patient's GGCT, LDH, and S100B can be directly substituted into the equation to calculate the probability (P) of a patient experiencing an adverse prognostic event (relapse or death). For example, a patient with a serum GGCT level of 9.5 ng / mL, an LDH activity of 280 U / L, and an S100B protein level of 0.2 μg / L has a predicted risk of approximately 67.2% for experiencing an adverse prognostic event. Based on the risk threshold set by the model (e.g., 35%), this patient is considered high-risk and should be closely followed and considered for active adjuvant therapy.

[0050] Model performance evaluation (discovery set): The data of 80 patients in the discovery set were used to calculate the probability of poor prognosis P for each patient, and the ROC curve was drawn to evaluate the model's ability to distinguish good / poor prognosis. The results showed that the AUC of the multi-indicator joint model was 0.92 (95% confidence interval 0.87-0.97). Compared with the single indicator, the AUC of the joint model was significantly improved (GGCT alone AUC 0.82, LDH alone 0.75, S100B alone 0.78). The DeLong test was used to compare the AUC. The combined model was compared with GGCT alone P = 0.03, compared with LDH alone P < 0.001, and compared with S100B alone P = 0.01. The differences were all statistically significant. This shows that the combined model is significantly better than any single indicator in the training set.

[0051] Internal cross-validation: To verify the robustness of the model, we performed internal cross-validation on the discovery set data (both leave-one-out and 5-fold cross-validation were tested). The results showed that throughout the cross-validation process, the model AUC remained above 0.88, with sensitivity and specificity fluctuations within ±5%, and no signs of overfitting. This suggests that the model established in this paper has good robustness and generalization ability within the training set.

[0052] In summary, the GGCT combined with LDH and S100B multifactorial model of the present invention demonstrated excellent predictive performance in the discovery set. The model was able to differentiate patients with poor prognosis from those with good prognosis with an area under the curve exceeding 90%, and exhibited both high sensitivity and specificity at selected thresholds.

[0053] Example 4: Validation of a multifactor prognostic model (validation set)

[0054] The inclusion criteria for the 40 melanoma patients in the validation set were the same as those in the discovery set. Of these 40 patients, 15 experienced recurrence or death within 3 years of follow-up (poor prognosis group, 37.5%), while 25 remained event-free (favorable prognosis group, 62.5%). Basic characteristics of the two groups, including age, gender, and clinical stage, were similar to those in the discovery set, with no significant differences between the two groups (P>0.05). Serum samples from all validation patients were assayed for GGCT, LDH, and S100B levels according to the methods in Example 1. The mean serum GGCT concentration in the poor prognosis group was 10.1 ng / mL, significantly higher than the 4.7 ng / mL in the favorable prognosis group (P<0.001); the mean LDH concentration was 310 U / L vs. 240 U / L, respectively (P=0.008); and the mean S100B concentration was 0.421 μg / L vs. 0.125 μg / L, respectively (P=0.015). The difference trend of individual indicators in the validation set is consistent with that in the discovery set.

[0055] Model application and evaluation: For each patient in the validation set, the logistic model formula in Example 3 was used to calculate the probability P of poor prognosis risk. Subsequently, the ROC curve of the model on the validation set was plotted, and the result showed that the AUC was 0.88 (95% confidence interval 0.80-0.96). The threshold value P = 0.35 determined in the discovery set was applied to the validation set. The results showed that when the predicted P was greater than 0.35 and was judged as high risk, the sensitivity of the model was 86.7% and the specificity was 83.0%. Using Kaplan-Meier survival analysis, the progression-free survival and overall survival of patients in the high-risk group were significantly shorter than those in the low-risk group (log-rank test P < 0.001). These results fully demonstrate that the multifactor model based on GGCT established by the present invention still maintains a high predictive accuracy and robustness in an independent population.

[0056] To further illustrate the advantages of the combined model, we compared the model predictions with those of single markers in the validation set. In the validation set, the ROC AUC for GGCT alone was 0.81, for LDH alone was 0.73, and for S100B alone was 0.77 ( Figure 3 ). It can be seen that the effectiveness of a single indicator is limited, while the AUC of the model of the present invention reaches 0.88 ( Figure 4 ), significantly improved. When each individual indicator is predicted using its own optimal threshold, the combined sensitivity and specificity are inferior to the multifactor model. Therefore, the multifactor model demonstrated superior discriminatory power in the validation set compared to any single indicator. This is consistent with the results in the discovery set, confirming the applicability of the GGCT combined with LDH and S100B model across diverse patient populations.

[0057] It should be noted that, without departing from the spirit of the present invention, professional and technical personnel may also make various modifications or improvements to the above-mentioned embodiments, such as selecting other technical means for detecting GGCT (chemiluminescence, immunoturbidimetry, etc.) or combining more markers. These changes should be considered to fall within the scope of protection of the present invention.

Claims

1. Application of GGCT as a biomarker in the preparation of melanoma prognosis assessment products.

2. The use according to claim 1, characterized in that The product is a test kit.

3. The use according to claim 1 or 2, characterized in that The kit is used to perform an enzyme-linked immunosorbent assay (ELISA) to detect the GGCT content in a biological sample.

4. The use according to any one of claims 1 to 3, characterized in that GGCT was combined with at least one other melanoma serum biomarker as a multifactorial model for prognostic prediction.

5. The use according to claim 4, characterized in that The other biomarkers include lactate dehydrogenase (LDH) and S100B protein.

6. The use according to any one of claims 1 to 4, characterized in that The application includes: directly substituting the actual measured values of GGCT, LDH and S100B detected by the patient into the following formula: P is the probability of melanoma recurrence or death within 3 years after surgery; GGCT, LDH, and S100B are the original measured values of serum GGCT concentration, LDH activity, and S100B protein concentration, respectively; the regression coefficients of each indicator are: β0 = -8.27, β1 = 0.29, β2 = 0.02, and β3 = 3.

15.

7. The use according to claim 6, characterized in that The prognostic risk of patients was evaluated with P = 0.35 as the cutoff value: patients with a predicted risk ≥ 0.35 were classified as the high-risk poor prognosis group, and those with a predicted risk < 0.35 were classified as the low-risk good prognosis group.

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