Prediction method of protein biomarker based on SKCM prognosis and immunotherapy response

Abstract

The invention discloses a prediction method of a protein biomarker based on SKCM prognosis and immunotherapy response. The prediction method comprises the following steps: S1, performing gene expression profile interaction analysis: S1, comparing expression levels of METTLs in SKCM and normal skin tissue by using online database gene expression profile interaction analysis; s2, analyzing prognosis by adopting a Kaplan-Meier curve; the invention relates to the technical field of biological detection. According to the prediction method of the protein biomarker based on SKCM prognosis and immunotherapy response, METTL9 expression up-regulation in SKCM tissue prompts poor prognosis, is closely related to tumor development and patient survival, and is expected to become a new therapeutic target; meanwhile, after immunotherapy, a patient with high expression of METTL9 has poor prognosis and poor reactivity to immunotherapy, so that a new method is provided for selecting a prognosis biomarker to predict the reactivity of SKCM to immunotherapy, and the survival condition of the SKCM patient can be predicted more accurately.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, in particular to a prediction method of protein biomarkers based on SKCM prognosis and immunotherapy response. Background Art

[0002] Cutaneous melanoma (SKCM) is a typical skin malignancy caused by excessive proliferation of abnormal melanocytes. Excessive sun exposure may increase the risk of SKCM, and other risk factors for the disease include family history and various dysplastic nevi. Although the incidence of melanoma is increasing year by year, there is no very successful systemic treatment. Studies have shown that models based solely on clinical pathological characteristics cannot accurately assess the prognosis of patients with SKCM tumors, let alone predict the response to personalized treatment. Therefore, further research on diagnostic and prognostic biomarkers found in SKCM patients is necessary to evaluate their clinical utility and gain a deeper understanding of the underlying molecular processes and gene networks that promote SKCM progression and prognosis.

[0003] SKCM is the most aggressive and lethal skin cancer, accounting for approximately 72% of all skin cancer deaths. Human SKCM patients have access to a wider range of treatment options today than ever before, including radiotherapy, photodynamic therapy, immunotherapy, chemotherapy, and biochemotherapy. Unfortunately, a large number of patients receiving these treatments show poor durable responses, increased drug resistance, and dismal prognosis. In the past few years, several reporters have reported differentially expressed genes and mutations in SKCM patients, and identified diagnostic and prognostic biomarkers for SKCM (PSME, BRAF CTLA-4, PD-1, etc.). There is still a lack of information about curative biomarkers currently used for the treatment of SKCM patients. Therefore, it is necessary to find certain therapeutic targets and prognostic biomarkers.

[0004] Human methyltransferase-like proteins (METTLs) are a class of proteins with s-adenosylmethionine binding domains and belong to a large protein family. The METTLs family has more than 27 members, but only a few of them have been studied. Due to the limited conserved domains shared by this family, their functions have been found to be diverse. Evidence has shown that METTLs are involved in the progression of tumors such as gastric cancer, breast cancer, choriocarcinoma, thyroid cancer, and hepatocellular carcinoma. Among them, RNA methyltransferases have been identified as METTL3, METTL16, METTL2b, and METTL8, and METTL7A may play an important role in the regulation of MTX resistance in choriocarcinoma cells. However, the role of METTLs in SKCM has not been explored, and it is unclear whether METTLs play a similar role in SKCM and its metastatic forms. Therefore, further research is necessary. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a prediction method based on protein biomarkers for SKCM prognosis and immunotherapy response, which solves the problems mentioned in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented by the following technical solutions: A prediction method based on protein biomarkers for SKCM prognosis and immunotherapy response, including the following prediction methods:

[0007] Gene expression profile interactive analysis:

[0008] S1. Compare the expression levels of METTLs in SKCM and normal skin tissues using online database gene expression profile interactive analysis;

[0009] S2, Kaplan-Meier curve was used to analyze prognosis;

[0010] S3. Use the limma program to detect expression differences between normal and melanoma;

[0011] S4. Genes in the GSE91061 data were divided into high and low expression groups according to their median expression levels;

[0012] Immunohistochemistry:

[0013] P1, take SKCM tissue paraffin specimens and make 4 μm thick paraffin sections;

[0014] P2, bake in an oven to remove wax, then block endogenous peroxidase with 3% H2O2 and inactivate it for 10 minutes;

[0015] P3, wash twice, then place in citrate buffer, heat at elevated temperature for antigen retrieval;

[0016] P4, after washing twice with PBS, cells were blocked with 5% BSA containing nonspecific antigens;

[0017] P5, samples were incubated in METTL9 primary antibody diluted 1:200 in 5% BSA;

[0018] P6. Incubate the sample in a 4°C refrigerator overnight;

[0019] P7, after washing, add the secondary antibody to the slide to completely cover the tissue;

[0020] P8, samples were counterstained with hematoxylin at room temperature.

[0021] Preferably, in S2, the p-value cutoff is 0.05.

[0022] Preferably, in S4, the TIDE database is used to study the interaction between high levels of cytotoxic T cell infiltration and METTL9 expression in SKCM and the possibility of predicting the immunotherapy response rate of SKCM patients.

[0023] Preferably, in P3, the temperature is raised to 90°C to 95°C and heated for 15 minutes.

[0024] Preferably, in P3, the concentration of citrate buffer II is 0.01 mol / L and the pH is 6.0.

[0025] Preferably, in P2, the temperature of the oven is 65° C. and the baking time is 30 minutes.

[0026] Preferably, the immunohistochemical detection further comprises washing and dehydrating the sample after counterstaining, and then fixing it with neutral glue and observing it under a microscope.

[0027] Beneficial Effects

[0028] The present invention provides a prediction method based on protein biomarkers for SKCM prognosis and immunotherapy response. Compared with the prior art, the method has the following beneficial effects: the up-regulated expression of METTL9 in SKCM tissue indicates a poor prognosis, which is closely related to tumor development and patient survival, and is expected to become a new therapeutic target; at the same time, patients with high expression of METTL9 after immunotherapy have a poor prognosis and poor responsiveness to immunotherapy, which provides a new method for selecting prognostic biomarkers to predict the responsiveness of SKCM to immunotherapy, and can more accurately predict the survival of SKCM patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is the mRNA level of the METTLs of the present invention in SKCM;

[0030] Figure 2 is the expression pattern of the input gene of the present invention in SKCM;

[0031] Figure 3 The present invention uses the GEPIA database to analyze the expression of METTLs in normal people and SKCM;

[0032] Figure 4 The present invention uses the GEO database to analyze the expression of METTLs in normal people and SKCM;

[0033] Figure 5 is the prognostic value of differently expressed METTLs in the overall survival curve of the present invention in SKCM patients;

[0034] Figure 6 The prognostic value of different METTLs expression in SKCM patients in the overall survival curve of the internal structure main view of the present invention;

[0035] Figure 7 The TIDE database analysis results of the present invention;

[0036] Figure 8 The IPS difference between the high METTL9 group and the low METTL9 group of the present invention was statistically significant;

[0037] Fig. 9 The present invention shows the relationship between METTL9 expression and clinical pathological characteristics in 40 cases of SKCM tissues. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In this technical scheme, the ONCOMINE database was used to analyze the expression differences of METTLs genes in SKCM tissues and adjacent tissues;

[0040] The UALCAN database was used to analyze the mRNA expression differences of METTLs in SKCM tissues and adjacent paracancerous tissues;

[0041] GraphPad Prism and SPSS software were used for data analysis and graph drawing. Student's t-test or one-way analysis of variance was used to evaluate the differences between the groups. Fisher's exact test or χ test was used to evaluate the differences between the groups. 2 The expression levels of related genes and common clinical pathological characteristics were compared and tested, and the Kaplan-Meier survival curve was used to analyze the correlation between METTL9 expression level and OS.

[0042] Prediction methods based on protein biomarkers of SKCM prognosis and immunotherapy response, including the following prediction methods:

[0043] Gene expression profile interactive analysis:

[0044] S1. Compare the expression levels of METTLs in SKCM and normal skin tissues using online database gene expression profile interactive analysis;

[0045] S2, Kaplan-Meier curve was used to analyze prognosis, with a p value cutoff of 0.05;

[0046] S3. Use the limma program to detect expression differences between normal and melanoma;

[0047] S4. The genes in the GSE91061 data were divided into high and low expression groups according to the median expression level, and the TIDE database was used to study the interaction between high-level cytotoxic T cell infiltration and METTL9 expression in SKCM and the possibility of predicting the immunotherapy response rate of SKCM patients;

[0048] Immunohistochemistry:

[0049] P1, take SKCM tissue paraffin specimens and make 4 μm thick paraffin sections;

[0050] P2, bake in an oven at 65°C for 30 minutes to remove the wax, then block the endogenous peroxidase with 3% H2O2 and inactivate it for 10 minutes;

[0051] P3, wash twice, then place in 0.01 mol / L, pH 6.0 citrate buffer, heat to 90°C for 15 min for antigen retrieval;

[0052] P4, after washing twice with PBS, cells were blocked with 5% BSA containing nonspecific antigens;

[0053] P5, samples were incubated in METTL9 primary antibody diluted 1:200 in 5% BSA;

[0054] P6. Incubate the sample in a 4°C refrigerator overnight;

[0055] P7, after washing, add the secondary antibody to the slide to completely cover the tissue;

[0056] P8. The samples were counterstained with hematoxylin at room temperature, then washed and dehydrated, fixed with neutral glue, and observed under a microscope.

[0057] A total of 40 patients with SKCM who underwent surgery in the hospital from January 2022 to December 2023 and had not received any systemic treatment before were selected by random number method. 40 matched adjacent cancer tissues and SKCM tumor tissues were obtained 3 cm from the edge of the malignant tissue. The inclusion criteria were as follows: receiving surgical treatment in the hospital; the postoperative disease was confirmed by the pathology department of this hospital, and comprehensive clinical and follow-up information was provided. Exclusion criteria: receiving neoadjuvant chemotherapy and radiotherapy; death due to other diseases, or a history of tumor-related diseases; the patient died of unrelated problems.

[0058] The results of the analysis predicting the response to immunotherapy showed that high expression of METTL9 in patients treated with PD1 was a risk factor for poor prognosis in patients with SKCM, and patients with high expression of METTL9 after PD1 treatment had a poor prognosis. Patients with low expression of METTL9 had a better immune response to treatment.

[0059] Table 1 Expression of METTL9 in 40 cases of skin melanoma tissues (n%)

[0060]

[0061] Table 2 Expression of METTL9 in 40 cases of cutaneous melanoma and its relationship with clinicopathological features

[0062]

[0063] Table 3 Univariate and multivariate analysis of overall survival in cutaneous melanoma

[0064]

[0065]

[0066] Note: ①HR: hazard ratio; CI: confidence interval; ②p<0.05 indicates that the difference is statistically significant.

[0067] The expression level of METTL9 in melanoma is significantly positively correlated with TNM stage, N stage and ulcer status. Patients with high METTL9 expression have shorter survival time, are more likely to relapse, and have worse prognosis than those with low METTL9 expression, which is an independent risk factor affecting the OS of SKCM patients.

[0068] Figure 1 In the ONCOMINE database, the number of datasets with statistically significant METTLs mRNA overexpression (red) or downregulated expression (blue) was shown, and the mRNA expression levels of METTL1, METTL4, and METTL9 were significantly increased in tumor tissues compared with adjacent adjacent tissues. The mRNA expression level of METTL7A was significantly decreased in SKCM tissues (p<0.05).

[0069] Figure 2 In the study, the expression patterns of all types of METTLs input genes in SKCM were compared and it was found that METTL9 had the highest expression level, while elevated expression levels of METTL1, METTL3, METTL5, and METTL13 were also observed. Therefore, changes in these expression levels may be potential indicators for the diagnosis of SKCM.

[0070] Figure 3In the study, METTLs included METTL1, METTL2A, METTL2B, METTL3, METTL4, METTL5, METTL6, METTL7A, METTL7B, METTL8, METTL9, METTL10, METTL12, METTL13, and METTL14. The expression levels of METTLs in SKCM tumors and normal tissues were analyzed through the GEPIA database. The expression of METTL1, METTL6, METTL7B, METTL9, and METTL13 in SKCM patients were higher than those in normal people. On the contrary, the expression of METTL7A in SKCM patients was lower than that in normal people.

[0071] Figure 4 In the study, METTLs included METTL1, METTL2B, METTL3, METTL4, METTL5, METTL7A, METTL8, and METTL9; the expression levels of METTL1 and METTL9 were upregulated, while the expression levels of METTL7A and METTL8 were downregulated. Based on the results of various databases, METTL1, METTL9, and METTL7A may be more useful biomarkers.

[0072] Figure 5 The overall survival curves are (a) METTL1, (b) METTL2A, (c) METTL2B, (d) METTL3, (e) METTL4, (f) METTL5, (g) METTL6, (h) METTL7A, (i) METTL7B, (j) METTL8, (k) METTL9, (l) METTL10, (m) METTL12, (n) METTL13, (o) METTL14; GEPIA database was used to obtain cases of patients with METTLs expression in TCGA database to evaluate the value of METTLs differential expression on the overall survival of SKCM patients. The overall survival curve analysis results showed that low expression of METTL1 (p = 0.035) and METTL9 (p = 0.026) significantly affected the overall survival time and survival rate of patients, and could prolong the overall survival time of patients. We can also find that the survival rate of SKCM patients with high transcription levels of METTL5 (p=0.043), METTL7A (p=0.0016), METTL7B (p=0.00025) and METTL14 (p=0.003) in the high expression group was significantly higher than that in the low expression group.

[0073] Figure 6The overall survival curves are (a) METTL1, (b) METTL2A, (c) METTL2B, (d) METTL3, (e) METTL4, (f) METTL5, (g) METTL6, (h) METTL7A, (i) METTL7B, (j) METTL8, (k) METTL9, and (l) METTL14; patient cases were collected from the GEO database for reanalysis to evaluate the role of METTLs in the overall mortality of SKCM patients. Only the METTL9 gene had a significant effect on the prognosis of patients. Therefore, high expression of METTL9 in SKCM significantly reduced the survival chances of patients.

[0074] Figure 7 In the figure, (a) is the relationship between METTL9 expression level and patient prognosis in the COX risk regression model; immune checkpoint inhibitor PD1 treatment of cutaneous melanoma can bring lasting clinical benefits, but only a small number of patients respond to treatment. In order to predict the response to immunotherapy, we used the TIDE database in our study and found that in the COX risk regression model, high expression of METTL9 in patients treated with PD1 was a risk factor for poor prognosis; (b) is the relationship between METTL9 biomarkers and patient overall survival analyzed by Kaplan-Meier curve; Kaplan-Meier analysis showed that patients with high expression of METTL9 after PD1 treatment had a poor prognosis (p=0.029).

[0075] Figure 8 In the study, the immune phenotype score (IPS) dataset obtained by TCIA was used to study whether METTL9 expression could predict the immunotherapy effect of SKCM patients. The IPS in the Low-METTL9 group was significantly larger, indicating that this group of patients responded better to immunotherapy.

[0076] in, Fig. 9In the figure, (a) shows the low and high expression of METTL9 immunohistochemical staining in 40 SKCM tissues; (b) shows the expression level of METTL9 in 40 SKCM tissues (p<0.001); (c) shows the effect of METTL9 expression on OS of 40 SKCM patients (p=0.0401); the high expression rate of METTL9 in SKCM tissues was 72.5% (29 / 40), and the high expression rate in normal skin tissues was 35.0% (14 / 40). The expression level of METTL9 in SKCM tissues was significantly higher than that in adjacent normal tissues. At the same time, the median survival time of 40 SKCM patients was 37 months. The expression level of METTL9 in SKCM was significantly positively correlated with TNM stage (p=0.025), N stage (p=0.027) and ulcer (p=0.003) in Table 2; univariate and multivariate survival analysis showed that high expression of METTL9 reduced the overall survival of patients, and the difference was statistically significant; METTL9 and TMN stage were independent prognostic factors for OS in SKCM patients (p<0.05, Table 3), suggesting that METTL9 has important clinical value in SKCM patients.

[0077] In this technical solution, the joint analysis of 4 databases showed that multiple METTLs were differentially expressed in SKCM tissues and normal tissues. Among them, METTL9 and METTL1 were both upregulated in SKCM tissues, and METTL7A was downregulated, which were more useful markers. From the prognostic analysis, the expression levels of multiple METTLs can affect the prognosis of SKCM patients, but the results of the GEO database showed that only METTL9 significantly affected the prognosis of SKCM patients. The expression of METTL9 significantly reduced the survival rate of SKCM patients. It is a risk factor for poor prognosis in patients with high expression of METTL9 treated in the PD1 stage. Patients with low expression of METTL9 responded better to immunotherapy. The immunohistochemical detection of clinical samples showed that the expression level of METTL9 in SKCM tissues was significantly higher than that in normal adjacent tissues, which was consistent with the database. The expression level of METTL9 in SKCM was positively correlated with TNM stage, N stage, and ulcer status. High expression of METTL9 significantly shortened the overall survival (OS) of SKCM patients. Like TNM stage, METTL9 is an independent prognostic factor for OS in patients with SKCM.

[0078] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0079] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0080] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Prediction methods based on protein biomarkers for SKCM prognosis and immunotherapy response, It is characterized in that The following forecasting methods are included: Gene expression profile interactive analysis: S1. Compare the expression levels of METTLs in SKCM and normal skin tissues using online database gene expression profile interactive analysis; S2, Kaplan-Meier curve was used to analyze prognosis; S3. Use the limma program to detect expression differences between normal and melanoma; S4. Genes in the GSE91061 data were divided into high and low expression groups according to their median expression levels; Immunohistochemistry: P1, take SKCM tissue paraffin specimens and make 4 μm thick paraffin sections; P2, bake in an oven to remove wax, then use 3% H 2 O 2 Block endogenous peroxidase and inactivate it for 10 min; P3, wash twice, then place in citrate buffer, heat at elevated temperature for antigen retrieval; P4, after washing twice with PBS, cells were blocked with 5% BSA containing nonspecific antigens; P5, samples were incubated in METTL9 primary antibody diluted 1:200 in 5% BSA; P6. Incubate the sample in a 4°C refrigerator overnight; P7, after washing, add the secondary antibody to the slide to completely cover the tissue; P8, samples were counterstained with hematoxylin at room temperature.

2. The method for predicting protein biomarkers for SKCM prognosis and immunotherapy response according to claim 1, Features: In S2, the p-value cutoff was 0.

05.

3. The method for predicting protein biomarkers for SKCM prognosis and immunotherapy response according to claim 1, Features: In S4, the TIDE database was used to study the possibility of high levels of cytotoxic T cell infiltration in SKCM interacting with METTL9 expression to affect patient survival and predict the response rate of immunotherapy in SKCM patients.

4. The method for predicting protein biomarkers for SKCM prognosis and immunotherapy response according to claim 1, Features: In the P3, the temperature is raised to 90°C to 95°C and heated for 15 minutes.

5. The method for predicting protein biomarkers for SKCM prognosis and immunotherapy response according to claim 1, Features: In the P3, the concentration of citrate buffer II is 0.01 mol / L and the pH is 6.

0.

6. The method for predicting protein biomarkers for SKCM prognosis and immunotherapy response according to claim 1, Features: In P2, the temperature of the oven is 65° C. and the baking time is 30 minutes.

7. The method for predicting protein biomarkers for SKCM prognosis and immunotherapy response according to claim 1, Features: The immunohistochemical test also includes washing and dehydrating the sample after counterstaining, and then fixing it with neutral gel and observing it under a microscope.

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