Ferroptosis molecular markers for NK / T cell lymphoma and their screening and identification methods and applications

Abstract

The present invention discloses ferroptosis molecular markers for NK / T cell lymphoma, screening and identification methods, and applications thereof. The ferroptosis molecular markers are differentially expressed ferroptosis genes: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3. The present invention identifies Fer-DEGs in NKTCL through bioinformatics analysis methods and analyzes the relationship between immune infiltration and Fer-DEGs in NKTCL, providing new targets for the diagnosis and treatment of NKTCL.

Description

Technical Field

[0001] The present invention belongs to the field of molecular biology, and specifically relates to ferroptosis molecular markers of NK / T cell lymphoma and screening and identification methods and applications thereof. Background Art

[0002] NK / T-cell lymphoma (NTKCL), or extranodal NK / T-cell lymphoma, is a highly aggressive and rare non-Hodgkin lymphoma. NTKCL primarily occurs in Asian and Latin American populations, a disparity in population distribution that is partly related to the prevalence of Epstein-Barr virus (EBV) in these regions. The neoplastic transformation of NKTCL tumor cells primarily occurs in peripheral mature NK cells and less frequently in cytotoxic T cells, meaning that extranodal involvement is more common, most commonly in the upper respiratory and digestive tracts (UADTs) and less frequently in regional lymph nodes. Treatment of NKTCL patients primarily relies on chemotherapy without radiation. Early anthracycline-based chemotherapy regimens, however, have limitations due to the high expression of p-glycoprotein in NKTCL, which can lead to intrinsic drug resistance. The probability of local tumor control in patients with stage I / II NKTCL is greater than 90%, and the 5-year overall survival rate is approximately 70%. For patients with stage III / IV NKTCL, the 1-year OS rate is 50% when combined with the SMILE regimen (dexamethasone, methotrexate, ifosfamide, l-asparaginase, and etoposide). Immunotherapy has become an important strategy for cancer treatment, but further exploration is needed. Studies have shown that programmed cell death ligand 1 (PD-L1) is upregulated in NKTCL cells and associated with a poor prognosis, particularly in mononuclear cells in the blood or tumor tissue, suggesting a role for immune escape in the development and progression of NKTCL. However, multiple studies have concluded inconsistently that PD-L1 is an excellent biomarker for predicting the efficacy of immunotherapy or conventional combined immunotherapy. In the Keynote052 study, patients with urothelial carcinoma treated with pembrolizumab had PD-L1 expression between 1% and 10%. As PD-L1 expression increased, the efficacy of pembrolizumab also increased accordingly. The ORR of patients with PD-L1 expression above 10% was significantly higher than that of patients with PD-L1 expression below 1% (39% vs. 11%). This study showed that high PD-L1 expression improved the therapeutic efficiency and clinical efficacy of immunotherapy. However, this conclusion is contrary to the results of the Checkmate032 study, which showed that there was no significant difference in the response to Opdivo treatment between patients with high PD-L1 expression (>1%) and low PD-L1 expression (<1%) in urothelial carcinoma (24% vs. 26.2%). Furthermore, studies have shown that in patients with stage I / II NKTCL, those with high serum soluble PDL1 concentrations (≥3.4 ng / ml) or high PDL1 expression in tumor specimens (≥38%) have significantly lower response rates to treatment and significantly worse survival, and PDL1 expression is an independent adverse prognostic factor. Therefore, in the diagnosis and treatment of NKTCL, the search for biomarkers to predict the efficacy of immunotherapy is urgently needed.

[0003] Ferroptosis, a novel form of programmed cell death discovered by Stockwell in 2012, is characterized by iron-dependent peroxidation of cell membrane lipids. The primary mechanism of ferroptosis is the catalytic peroxidation of highly expressed unsaturated fatty acids on the cell membrane by ferrous iron or esteroxygenases, leading to cell death. Furthermore, ferroptosis manifests as a decrease in GPX4, a core enzyme regulating the antioxidant system (glutathione system). Cysteine ​​is a key negative regulator of the ferroptosis pathway. Studies have shown that lymphocytes are unable to synthesize cysteine, a key finding that may represent a breakthrough in lymphoma treatment. Studies have shown that the ferroptosis inducer imidazole-ketone-erastin (IKE) significantly reduces the growth of diffuse large B-cell lymphoma (DLBCL) in rat and mouse models. Ferroptosis has been shown to play a positive role in radiotherapy, chemotherapy, and immunotherapy, and thus, activation of ferroptosis may be a potential strategy to overcome resistance to cancer treatment. Studies have shown that p53-mediated ferroptosis can occur after treatment with the natural product kayadiol, inducing ferroptosis in NKTCL cells. Kayadiol's mechanism of action is primarily through promoting p53 expression, inhibiting SLC7A11, and affecting GSH synthesis, thereby reducing GPX4 activity and leading to ferroptosis. However, the role of ferroptosis genes in NKTCL, particularly in immune regulation, has not been thoroughly studied. Therefore, the use of bioinformatics and molecular biology methods to detect ferroptosis molecular markers significantly associated with NKTCL and their correlation analysis with tumor immune cell infiltration and immune molecules is of great clinical significance. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a ferroptosis molecular marker for NK / T cell lymphoma and its screening and identification method and application.

[0005] The technical solution of the present invention is summarized as follows:

[0006] Ferroptosis molecular markers for NK / T cell lymphoma, wherein the ferroptosis molecular markers are ferroptosis differentially expressed genes: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3.

[0007] Furthermore, the ferroptosis molecular markers are ferroptosis differentially expressed genes: ANXA2, HSPA8, ANXA1, CFL1;

[0008] The ferroptosis molecular marker is a combination of at least two of the ferroptosis differentially expressed genes.

[0009] The present invention further provides a method for screening and identifying ferroptosis molecular markers for NK / T cell lymphoma, comprising the following steps:

[0010] (1) Gene expression profiles were retrieved from the NCBI GEO public database, and the GSE80632 and GSE169644 datasets were downloaded. After the two datasets were intersected, 26 differentially expressed genes in ferroptosis were identified: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3;

[0011] (2) Gene function enrichment analysis was performed on the 26 differentially expressed ferroptosis genes, and pathway analysis was performed using the Metscape database;

[0012] (3) The protein interaction pairs related to the 26 ferroptosis differentially expressed genes were obtained using the STRING database, and the PPI molecular interaction network was evaluated. Module analysis was performed using MCODE in Cytoscape to identify four key ferroptosis differentially expressed genes: ANXA2, HSPA8, ANXA1, and CFL1.

[0013] (4) Using the CIBERSORT algorithm, we first compared and analyzed the differences in the infiltration of immune cell subsets in the tumor microenvironment between NKTCL and normal controls, then performed Pearson correlation analysis on the expression levels of 26 ferroptosis differentially expressed genes and the infiltration levels of immune cell subsets, and further analyzed the correlation between 4 key ferroptosis differentially expressed genes and the infiltration of tumor immune cell subsets;

[0014] (5) Determine the correlation between the four key ferroptosis differential genes and different immune factors using the TISIDB database;

[0015] (6) 447 NKTCL disease-related genes were obtained through the GeneCards database, and the CIBERSORT algorithm was used to analyze the differential expression levels of the top 20 genes with the highest correlation coefficients with NKTCL disease in the normal control cohort and the NKTCL cohort. The expression levels of the four key ferroptosis differential genes were further analyzed with the expression of the top 20 genes with the highest correlation coefficients with NKTCL disease;

[0016] (7) The accuracy of the four key ferroptosis differential genes in the diagnosis of NKTCL was evaluated using the ROC curve and verified using the human protein atlas database.

[0017] Furthermore, the gene function enrichment analysis includes GO enrichment analysis, KEGG enrichment analysis, and GSEA enrichment analysis.

[0018] Furthermore, the different immune factors include chemokines, immunosuppressants, MHC, immunostimulants, and cell receptors.

[0019] Furthermore, the top 20 genes with the highest NKTCL disease correlation coefficients are: TP53, MTC, BCL2, BCL10, BCL6, ATM, ALK, PTPRC, JAK3, CDKN2A, KIT, PTEN, STAT3, FAS, KRAS, AKT1, BAX, IL6, FASLG, and PAX5.

[0020] Furthermore, in step (7), the method of evaluating using the ROC curve is: if the area under the ROC curve of the four key ferroptosis differential genes and the coordinate axis AUC>0.7, it means that the expression levels of the four key ferroptosis differential genes have good clinical predictive efficacy for the occurrence and development of NKTCL.

[0021] Furthermore, the AUC of the ANXA1 gene was 0.8704, the AUC of the ANXA2 gene was 0.7611, the AUC of the CFL1 gene was 0.8219, and the AUC of the HSPA8 gene was 0.7247.

[0022] Furthermore, in step (7), the method of verification using the human protein atlas database is: using the human protein atlas database to obtain the protein expression levels of the four key ferroptosis differential genes, and performing immunohistochemical analysis to verify the expression changes.

[0023] The present invention further provides the use of ferroptosis molecular markers of NK / T cell lymphoma in the diagnosis and treatment of NK / T cell lymphoma.

[0024] Beneficial effects of the present invention:

[0025] 1. The present invention retrieved gene expression profiles from the NCBI GEO public database and identified 26 differentially expressed genes involved in ferroptosis in the GSE80632 and GSE169644 datasets. Four key Fer-DEGs (ANXA2, HSPA8, ANXA1, and CFL1) were identified using the PPI molecular interaction network assessment. The CIBERSORT algorithm was used to analyze immune cell infiltration in NKTCL, and it was found that the four key Fer-DEGs were highly correlated with tumor immune cell infiltration and immune factors. GSEA was used to analyze the downstream regulatory network, and the analysis showed that the four key Fer-DEGs played a key role in ferroptosis, HIF-1 signaling pathway, reactive oxygen species response, and neutrophil extracellular trap formation in NKTCL. Finally, the expression differences of the four key Fer-DEGs at the tissue protein level were obtained through the Human Protein Atlas database, and the occurrence and development of NKTCL were predicted by ROC curves and protein expression levels.

[0026] 2. This paper identifies Fer-DEGs in NKTCL through bioinformatics analysis methods and analyzes the relationship between immune infiltration and Fer-DEGs in NKTCL, providing new diagnostic and therapeutic targets for NKTCL between ferroptosis biomolecular markers and tumor immune regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 shows differentially expressed genes and ferroptosis-related differentially expressed genes: Figure 1A 4068 differentially expressed genes between NKTCL and normal control cohorts in the GSE80632 dataset; Figure 1B The Venn diagram of gene expression of 71 Fer-DEGs between NKTCL and normal control cohorts in the GSE80632 dataset; Figure 1C These are the 46 Fer-DEGs differentially expressed genes between NKTCL and normal control cohorts in the GSE169644 dataset; Figure 1D The Venn diagram of gene expression of the same 26 Fer-DEGs between NKTCL and normal control cohorts in GSE80632 and GSE169644 datasets;

[0028] Figure 2 GO and KEGG enrichment analysis and Metascape database enrichment analysis diagram of 26 Fer-DEGs: Figure 2 A is the GO enrichment analysis diagram of 26 Fer-DEGs; Figure 2 B is the KEGG enrichment analysis diagram of 26 Fer-DEGs; Figure 2C is the enrichment analysis diagram of 26 Fer-DEGs in the Metascape database;

[0029] Figure 3 KEGG enrichment analysis diagram of four key Fer-DEGs ANXA1, ANXA2, CFL1, and HSPA8, and Figure 3 AD represent the KEGG enrichment analysis diagrams of ANXA1, ANXA2, CFL1, and HSPA8 respectively;

[0030] Figure 4 Figure 2 is the protein molecular interaction network model of 26 Fer-DEGs in NKTCL;

[0031] Figure 5 Figure 2 shows the immune cell infiltration between the NKTCL group and the normal control group: Figure 5 A is the relative percentage of immune cell infiltration of 22 subsets in each case of 13 normal controls and 19 NKTCL cases in the GSE80632 dataset; Figure 5 B is the difference in immune cell infiltration between the NKTCL group and the normal control group; Figure 5 C is the Pearson correlation analysis between the expression levels of 26 Fer-DEGs and the infiltration levels of 22 immune cell subsets;

[0032] Figure 6 Figure 2 is the correlation diagram between four key Fer-DEGs (ANXA1, ANXA2, HSPA8 and CFL1) and immune cell infiltration, among which, Figure 6 A indicates the association of ANXA1 with naive CD4 T cells and activated NK cells; Figure 6 B shows the association of ANXA2 with naive CD4 T cells, activated dendritic cells, and resting CD4 memory T cells; Figure 6 C indicates the correlation between CFL1 and activated dendritic cells, monocytes, and activated NK cells; Figure 6 D indicates the association of HSPA8 with memory B cells, M0 macrophages, and activated mast cells.

[0033] Figure 7 Figure 2 is the correlation diagram of four key Fer-DEGs (ANXA1, ANXA2, HSPA8 and CFL1) with different immune factors, among which, Figure 7 A-7E represent the correlations between the four key Fer-DEGs and chemokines, immunosuppressants, MHC, immunostimulants, and cell receptors, respectively;

[0034] Figure 8Figure 2 is the correlation expression level diagram between NKTCL-related genes and four key Fer-DEGs (ANXA1, ANXA2, HSPA8 and CFL1), among which, Figure 8 A represents the expression levels of NKTCL-related genes between the normal control group and the NKTCL group, Figure 8 B represents the relative expression levels of NKTCL-related significant genes and four key Fer-DEGs between the normal control group and the NKTCL group;

[0035] Figure 9 The ROC prediction curves of the four key Fer-DEGs are shown in Figure 2. Figure 9 A-9D represent the ROC prediction curves of ANXA1, ANXA2, CFL1, and HSPA8, respectively;

[0036] Figure 10 Representative immunohistochemistry of four key Fer-DEGs between non-Hodgkin's lymphoma and normal tissues in the Human Protein Atlas database;

[0037] Figure 11 The figure is a flow chart of the method for screening and identifying ferroptosis molecular markers for NK / T cell lymphoma of the present invention. DETAILED DESCRIPTION

[0038] The present invention is further described in detail below with reference to the embodiments so that those skilled in the art can implement the invention with reference to the description.

[0039] Example 1

[0040] The method for screening and identifying ferroptosis molecular markers for NK / T cell lymphoma comprises the following steps:

[0041] Gene expression profiles were retrieved through the NCBI GEO public database, and the GSE80632 and GSE169644 data sets were downloaded. After the two data sets were intersected, 26 ferroptosis differentially expressed genes were identified: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3; the ferroptosis molecular marker is a quantitative combination of the ferroptosis differentially expressed genes, that is, the ferroptosis molecular marker is a combination of at least two of the ferroptosis differentially expressed genes.

[0042] Specifically, gene expression profiles were retrieved from the NCBI GEO public database, and the GSE80632 dataset was downloaded. A total of 32 data sets were obtained, including 13 normal cases and 19 NKTCL cases. The differences in genes between the NKTCL and normal groups were statistically analyzed using the Limma package according to the genetic screening criteria of P value < 0.05 and |logFC| > 0.585. A total of 4068 differentially expressed genes (2001 up-regulated genes and 2067 down-regulated genes) were screened out. Figure 1A ), of which 71 ferroptosis genes (48 up-regulated genes and 23 down-regulated genes, Figure 1B Then, the GSE169644 dataset was downloaded from the NCBI GEO public database, and the expression levels of the 71 ferroptosis differentially expressed genes were analyzed and compared to screen out 46 ferroptosis genes (23 up-regulated genes and 23 down-regulated genes) that were differentially expressed in the GSE169644 dataset. Figure 1C However, only 26 differentially regulated ferroptosis genes showed the same trend in GSE80632 and GSE169644 datasets (18 up-regulated genes and 8 down-regulated genes, Figure 1D shown);

[0043] (1) GO enrichment analysis, KEGG enrichment analysis, and GSEA enrichment analysis were performed on the 26 differentially expressed ferroptosis genes, and pathway analysis was performed using the Metscape database; specifically:

[0044] The 26 ferroptosis differentially expressed genes (Fer-DEGs) were subjected to GO enrichment analysis (Gene Ontology (GO)). Figure 2 As shown in Figure A: The above 26 Fer-DEGs were enriched in 170 gene function enrichment items, including 127 biological process-related enrichment items, 21 cell localization-related enrichment items, and 22 molecular function-related enrichment items; and the above 26 Fer-DEGs candidate genes were mainly enriched in pathways regulating protein stability, pigment granules, and cadherin binding;

[0045] KEGG enrichment analysis was performed on the 26 differentially expressed ferroptosis genes. The results were as follows: Figure 2 As shown in B: the above 26 Fer-DEGs were mainly enriched in signaling pathways such as HIF-1 signaling pathway, ferroptosis and neutrophil extracellular trap formation;

[0046] GSEA enrichment analysis was performed on ANXA1, ANXA2, CFL1, and HSPA8 among the 26 differentially expressed ferroptosis genes. The results were as follows: Figure 3As shown in Figure 2, pathways positively enriched by ANXA1 include allogeneic rejection, endocytosis, graft-versus-host disease, and ribosomes, while pathways negatively enriched by ANXA1 include arachidonic acid metabolism and linoleic acid metabolism ( Figure 3 ANXA2 is positively enriched in pathways including Alzheimer's disease, chronic myeloid leukemia, citric acid cycle, TCA cycle, and nucleotide excision repair; its negatively enriched signaling pathways include glycosaminoglycan biosynthesis of chondroitin sulfate and glycosaminoglycan biosynthesis of heparan sulfate ( Figure 3 CFL1-positively enriched signaling pathways include chronic myeloid leukemia, glyoxylate and dicarboxylic acid metabolism, renal cell carcinoma, and systemic lupus erythematosus, while CFL1-negatively enriched signaling pathways include niacin and nicotinamide metabolism and olfactory transduction ( Figure 3 C shows); the signaling pathways positively enriched by HSPA8 in KEGG analysis include ErbB signaling pathway, insulin signaling pathway, mTOR signaling pathway and renal cell carcinoma; the signaling pathways negatively enriched include olfactory transduction and glycosphingolipid biosynthesis, lactose and neolactone series ( Figure 3 D shows);

[0047] The Metscape database was used to further analyze the pathways of the 26 differentially affected ferroptosis genes. Figure 2 As shown in C: These 26 Fer-DEGs were mainly enriched in the positive regulation of organelle organization, response to reactive oxygen species, and HIF-1 signaling pathway;

[0048] The above gene enrichment results showed that 26 Fer-DEGs played crucial roles in ferroptosis, HIF-1 signaling pathway, reactive oxygen species response, and neutrophil extracellular trap formation in NKTCL;

[0049] (2) The protein interaction pairs related to the 26 ferroptosis differentially expressed genes were obtained using the STRING database, and the PPI molecular interaction network was evaluated. The module analysis was performed using MCODE (molecular complex detection) in Cytoscape to identify four key ferroptosis differentially expressed genes: ANXA2, HSPA8, ANXA1, and CFL1.

[0050] (3) Using the CIBERSORT algorithm, we first compared and analyzed the differences in immune cell subset infiltration in the tumor microenvironment between NKTCL and normal controls. We then performed Pearson correlation analysis on the expression levels of 26 ferroptosis differentially expressed genes and the levels of immune cell subset infiltration. We further analyzed the correlation between four key ferroptosis differentially expressed genes and tumor immune cell subset infiltration. Specifically:

[0051] Using the CIBERSORT algorithm, we first analyzed the differences in immune cell infiltration in the tumor microenvironment between NKTCL and normal controls. The content of each immune cell subset in the tumor microenvironment of each sample was as follows: Figure 5 As shown in A; according to Figure 5 B (Differences in immune cell infiltration between the NKTCL group and the normal control group) shows that compared with the normal control group, the levels of resting CD4 memory T cells and monocytes in the NKTCL group were significantly reduced, while the levels of activated dendritic cells and naive CD4 T cells were significantly increased; Pearson correlation analysis was performed on the expression levels of 26 ferroptosis differentially expressed genes and the infiltration levels of immune cell subsets. Figure 5 C (Pearson correlation analysis between the expression levels of 26 Fer-DEGs and the infiltration levels of 22 immune cell subsets) shows that there is a significant correlation between the expression levels of these 26 Fer-DEGs and the infiltration levels of immune cell subsets; and further analysis of the correlation between the four key ferroptosis differential genes and the infiltration of tumor immune cell subsets, according to Figure 6 (Correlation diagram of 4 key Fer-DEGs (ANXA1, ANXA2, HSPA8 and CFL1) and immune cell infiltration) It can be seen that these 4 key Fer-DEGs are highly correlated with the infiltration of tumor immune cell subsets and play an important role in the tumor immune microenvironment; among them, according to Figure 6 A(Correlation between ANXA1 and naive CD4 T cells and activated NK cells) showed that ANXA1 was positively correlated with naive CD4 T cells (correlation coefficient, 0.55; P value, 0.001) and negatively correlated with activated NK cells (correlation coefficient, -0.41; P value, 0.016); Figure 6 B (Correlation between ANXA2 and naive CD4 T cells, activated dendritic cells, and resting CD4 memory T cells) showed that naive CD4 T cells (correlation coefficient, 0.41; P value 0.018) and activated dendritic cells (correlation coefficient, 0.55; P value 0.001) were positively correlated with ANXA2 expression, while resting CD4 memory T cells (correlation coefficient, -0.38; P value 0.031) were negatively correlated with ANXA2 expression; according to Figure 6 C (Correlation between CFL1 and activated dendritic cells, monocytes and activated NK cells) showed that CFL1 expression was positively correlated with activated dendritic cells (correlation coefficient, -0.43; P value 0.012), and negatively correlated with monocytes (correlation coefficient, -0.43; P value, 0.012) and activated NK cells (correlation coefficient, -0.36; P value, 0.039); Figure 6D (Correlation of HSPA8 with memory B cells, M0 macrophages, and activated mast cells) showed that HSPA8 was positively correlated with memory B cells (correlation coefficient, 0.42; P value, 0.014) and M0 macrophages (correlation coefficient, 0.56; P value, 0.001), but negatively correlated with activated mast cells (correlation coefficient, -0.41; P value, 0.018;

[0052] (4) Determine the correlation between the four key ferroptosis differential genes and chemokines, immunosuppressants, MHC, immunostimulants, and cell receptors using the TISIDB database. Figure 7 A-7E represent the correlations between the four key Fer-DEGs and chemokines, immunosuppressants, MHC, immunostimulants, and cell receptors, respectively: Figure 7 It was confirmed that four key ferroptosis-related differentially expressed genes (Fer-DEGs) were highly correlated with different immune factors in NKTCL;

[0053] (5) 447 NKTCL disease-related genes were obtained from the GeneCards database, and the CIBERSORT algorithm was used to analyze the differential expression levels of the top 20 genes with the highest correlation coefficients with NKTCL disease, including TP53, MTC, BCL2, BCL10, BCL6, ATM, ALK, PTPRC, JAK3, CDKN2A, KIT, PTEN, STAT3, FAS, KRAS, AKT1, BAX, IL6, FASLG, and PAX5 in the normal control cohort and the NKTCL cohort. Figure 8 A (representing the expression level of NKTCL-related genes between the normal control group and the NKTCL group) shows that: compared with the normal control group, the expression level of TP53 in the NKTCL group was significantly decreased, while the expression levels of PTPRC, JAK3, CDKN2A, FAS, AKT1, and IL6 were significantly increased, with statistical differences; and further correlation analysis was performed on the expression levels of the four key ferroptosis differential genes and the expression of the top 20 genes with the highest NKTCL disease correlation coefficients. Figure 8 B (represents the relative expression levels of NKTCL-related significant genes and four key Fer-DEGs between the normal control group and the NKTCL group) shows that: ANXA1 and CFL1 expression levels are positively correlated with CDKN2A expression levels, and there are significant differences. ANXA2 expression level is positively correlated with AKT1 expression level, and there are significant differences.

[0054] (7) The accuracy of the four key ferroptosis differential genes in the diagnosis of NKTCL was evaluated using ROC curves and verified using the human protein atlas database; specifically:

[0055] The ROC curve evaluation method is: if the area under the ROC curve of the four key ferroptosis differential genes and the coordinate axis AUC>0.7, it means that the expression levels of the four key ferroptosis differential genes have good clinical predictive efficacy for the occurrence and development of NKTCL. Figure 9 (ROC prediction curves of the four key Fer-DEGs) It can be seen that the AUC of the ANXA1 gene is 0.8704, the AUC of the ANXA2 gene is 0.7611, the AUC of the CFL1 gene is 0.8219, and the AUC of the HSPA8 gene is 0.7247, suggesting that the four key Fer-DEGs have good clinical prediction efficiency as biomolecular markers;

[0056] The method of using the human protein atlas database for verification is: using the human protein atlas database to obtain the protein expression levels of the four key ferroptosis differential genes, performing immunohistochemical analysis to verify the expression changes, and Figure 10 (Representative immunohistochemistry of four key Fer-DEGs between non-Hodgkin's lymphoma and normal tissues in the Human Protein Atlas Database) It can be seen that the protein expression of the four key Fer-DEGs in non-Hodgkin's lymphoma and normal tissues showed obvious differences, indicating that the four key Fer-DEGs as biomolecular markers have high accuracy in the diagnosis of NK / T cell lymphoma and good clinical predictive efficiency.

[0057] Example 1 screened out four key Fer-DEGs in NKTCL by bioinformatics methods, and verified that these four key Fer-DEGs were related to the tumor immune microenvironment of NKTCL, which can regulate the expression of NKTCL-related gene CDKN2A through ferroptosis, HIF-1 signaling pathway, and reactive oxygen species response signaling pathway. The expression levels of these four key Fer-DEGs have the function of predicting the clinical efficacy of NKTCL and can be applied to the diagnosis and treatment of NK / T cell lymphoma.

[0058] Example 2

[0059] The application of ferroptosis molecular markers for NK / T cell lymphoma includes the following steps:

[0060] Tumor specimens from newly diagnosed NK / T cell lymphoma patients (obtained by surgery or endoscopic biopsy) were collected and tested for mRNA and immunohistochemical protein levels to identify 26 differentially expressed ferroptosis genes: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3; their expression levels in tumor specimens from newly diagnosed NK / T cell lymph nodes were compared. The ferroptosis molecular marker is a quantitative combination of the differentially expressed ferroptosis genes, that is, a combination of at least two of the differentially expressed ferroptosis genes.

[0061] Specifically: tumor specimens from newly diagnosed NK / T cell lymphoma patients were collected and divided into two parts. One part was placed in 1 ml of Trizol reagent to lyse and extract mRNA, and the other part was placed in paraffin to form a paraffin tissue specimen.

[0062] 1) Extraction of mRNA for expression level verification of 26 differentially expressed ferroptosis genes: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3.

[0063] A. Collect a tumor specimen from a newly diagnosed NK / T cell lymphoma patient. Place an aliquot in 1 ml of Trizol reagent and pipette evenly with a de-enzyme pipette. Transfer the Trizol to a de-enzyme 1.5 ml EP tube using a pipette. (If not immediately processed, store at -20°C or -80°C.)

[0064] B. Add 200 μl of chloroform to each EP tube, shake vigorously up and down to mix for 1 minute, and then let it stand at room temperature for 5 minutes.

[0065] C. Refrigerate the high-speed refrigerated centrifuge and centrifuge at 12,000 rpm for 15 minutes.

[0066] D. Pipette 400 μl of the supernatant and transfer it to another enzyme-free EP tube. Add an equal volume (400 μl) of isopropanol to each tube, invert vigorously and rapidly to mix, and let it stand at room temperature for 10 minutes.

[0067] E. Centrifuge at 12000 rpm for 10 min.

[0068] F. Remove the supernatant and add 1 ml of 75% ethanol to the precipitate to wash it without blowing it away.

[0069] G. Centrifuge at 12000 rpm for 5 min.

[0070] H. Remove the supernatant and add 5-10ul of enzyme-free water to dissolve the precipitate.

[0071] I. Measure the concentration and purity of the extracted mRNA. Take 1 µl of mRNA sample and measure the OD value on a microfluidic analyzer. The λ260 / 280 ratio should be between 1.8 and 2.0. Record the mRNA concentration of each sample and adjust the reverse transcription system accordingly. (If not immediately proceeding, the mRNA sample can be stored at -80°C for long-term storage.)

[0072] J. Reverse Transcription: Using the Takara Reverse Transcription Kit, reverse transcribe mRNA into cDNA in a 10µl reaction. Prepare the common reagents and a 3.5µl mixture of 5X buffer, PrimeScript™ RT Enzyme, Oligo(dT) Primer (50µM), and Random 6mers (100µM). Add the prepared mixture to labeled 100µl enzyme-free EP tubes. Determine the amount of RNA to be added based on the RNA concentration and purity. Mix the reverse transcription system in each tube by pipetting and placing it in the PCR reverse transcription system. Reaction conditions: 37°C for 15 minutes; 85°C for 3 seconds; and 4°C for 5 min. Dilute the reverse transcribed cDNA with 30µl of DEPC water and store at 4°C.

[0073] K. Primer Dissolution and Dilution: Primer sequences are shown in Table 1. Primers are diluted as follows: Place the EP tube containing primer powder in a centrifuge for 10 seconds. Check the nmol number marked on the side of the tube. Add 10 times the amount of enzyme-free water to the tube and mix thoroughly by pipetting. This is labeled as the stock solution (store the stock solution at -20°C for long-term storage). Take 10 μl of this solution and dilute it with 90 μl of enzyme-free water to prepare the primer working solution (store the working solution at 4°C for long-term storage).

[0074] L. Real-time quantitative PCR: A 10-μl reaction mixture (3.6 μl of PCR water, 0.2 μl each of upstream and downstream primers, 5.0 μl of Taq enzyme, and 1.0 μl of cDNA) was added to the wells to be loaded. Then, 1 μl of cDNA was added to the corresponding wells. After loading, the wells were sealed with sealing film and the PCR plate was centrifuged at 1000 rpm for 5 minutes. After centrifugation, the PCR plate was placed in a Roche quantitative fluorescence PCR instrument. Amplification conditions: 95°C for 2 minutes, denaturation at 95°C for 30 seconds, and annealing at 60°C for 35 seconds. PCR was performed for a total of 40 cycles, with fluorescence signals collected at the end of each extension cycle to generate amplification curves. After 40 cycles, the following temperature control step was set: 95°C for 15 seconds, 60°C for 30 seconds, and 95°C for 15 seconds. Fluorescence signals were collected throughout the entire heating process from 60°C to 95°C to generate melting curves. Data processing: The relative expression of the target gene was calculated based on the measured cycle values ​​(Ct values) of the target and reference genes: ΔCt = Ct target gene - Ct reference gene, ΔΔCt = ΔCt tumor group - ΔCt control group; relative expression = 2 - ΔΔCt.

[0075] Table 1

[0076]

[0077]

[0078] 2) Tumor specimens were extracted for immunohistochemical staining, and 26 differentially expressed ferroptosis genes were detected: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3 protein expression levels. Application of verification: Tumor specimens were collected from patients with newly diagnosed NK / T cell lymphoma, and patients with newly diagnosed NK / T cell lymphoma were collected. Tumor specimens of patients were collected and one portion was placed in paraffin to form paraffin tissue specimens. Immunohistochemistry was used to detect the expression of 26 ferroptosis differentially expressed genes: PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3 in NK / T cell lymphoma tissues and calculate the immunohistochemical score. The specific steps are as follows:

[0079] A. Prepare paraffin sections of NK / T cell lymphoma tissue and oven-heat at 60°C overnight.

[0080] B. Dewaxing and rehydration: xylene 10 min - 100% ethanol 5 min - 95% ethanol 5 min - 90% ethanol 5 min - 85% ethanol 5 min - 80% ethanol 5 min - 75% ethanol 5 min - 60% ethanol 5 min - 50% ethanol 5 min - 30% ethanol 5 min - tap water 1 min - hydrogen peroxide 1 min.

[0081] C. Add 1 part 30% H2O2 to 10 parts distilled water, incubate at room temperature for 10 minutes, and then wash with distilled water three times, each time for 3 minutes.

[0082] D. Microwave repair: Place the slices in 0.01 M citrate buffer and heat in a microwave at maximum power (98-100°C) until boiling, then cool and repeat twice.

[0083] E. Cool the slices naturally to room temperature and wash them with PBS three times for 5 minutes each time.

[0084] F. Block with 5% BSA at room temperature for 20 min and remove excess liquid.

[0085] G. Add primary antibodies (PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, YTHDF3) and incubate at 4°C overnight.

[0086] H. Wash with PBS three times, 3 min each time.

[0087] I. Add secondary antibody and incubate at 37°C for 15-30 minutes.

[0088] J. Wash with PBS three times, 3 min each time.

[0089] K. Add SABC dropwise and incubate at 37°C for 30 min.

[0090] L. Wash with PBS three times, 3 min each time.

[0091] M. Add the color developer to 1ml of distilled water and mix well.

[0092] After the N.DAB color developer is prepared, add it dropwise to the slices and monitor the reaction time under a microscope at room temperature.

[0093] O. Rinse with tap water and rinse with distilled water.

[0094] P. Counterstain with hematoxylin for 2 minutes and rinse with tap water.

[0095] Q. Dehydration: 30% ethanol for 3 min - 50% ethanol for 3 min - 70% ethanol for 3 min - 80% ethanol for 3 min - 90% ethanol for 3 min - 95% ethanol for 3 min - 100% ethanol for 3 min - xylene for 20 min;

[0096] R. Seal the slides with resin and observe under a microscope. The above immunohistochemistry experimental procedures were used to detect the expression of 26 ferroptosis differentially expressed genes PARK7, ECH1, SLC39A7, ATF3, VDAC1, PDSS2, VDAC3, ATG7, ANXA2, HSPA8, KRT6B, RBX1, ANXA1, CFL1, CCT3, CCT8, RUFY1, DIAPH3, TFRC, RELA, ZFP36, OTUB1, TPM1, ENO2, PFN2, and YTHDF3 proteins in NK / T cell lymphoma tumor specimens.

[0097] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to specific details.

Claims

1. Use of a primer for detecting ferroptosis molecular markers in NK / T cell lymphoma in the preparation of a reagent for diagnosing NK / T cell lymphoma, characterized in that: The NK / T cell lymphoma iron death molecular marker is ANXA1; The primers include an upstream primer and a downstream primer, wherein the upstream primer is GCGGTGAGCCCCTATCCTA, and the downstream primer is TGATGGTTGCTTCATCCACAC.

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