Use of ifit1 in prognostic evaluation of patients with low-adhesion gastric cancer

Abstract

The present application relates to the application of IFIT1 in the prognosis evaluation of low adhesion type gastric cancer patients. The present application firstly finds that the expression level of IFIT1 and the level of neutrophil infiltration can more accurately reflect the immune characteristics of the microenvironment of low adhesion type gastric cancer tissue and the high and low risk of overall survival of gastric cancer patients after operation, and IFIT1 in TANs is an effective biomarker for the prognosis of this type of gastric cancer. Based on this, a detection kit for low adhesion type gastric cancer patients is provided, which can judge the prognosis of low adhesion type gastric cancer patients and the effect of immunotherapy by detecting the expression amount of IFIT1 in the microenvironment of gastric cancer. If the expression amount of IFIT1 is up-regulated, it indicates that the prognosis of low adhesion type gastric cancer patients and the effect of immunotherapy are not good. The kit can reflect the immune characteristics of the microenvironment of low adhesion type gastric cancer tissue and the risk of overall survival after operation, and is helpful for guiding the decision-making of patient immunotherapy and implementing precision treatment in clinical practice.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gene detection, and particularly relates to application of IFIT1 in prognosis evaluation of patients with poorly cohesive gastric cancer. BACKGROUND

[0002] As the fifth most commonly diagnosed cancer worldwide, gastric cancer (GC) causes the fourth highest cancer-related mortality. Although the overall incidence of gastric cancer has declined in the past few decades due to advances in early cancer screening tools, the relative incidence of poorly cohesive gastric cancer has been steadily rising. Poorly cohesive gastric cancer (PCC) is a relatively unique subtype of gastric cancer and was added to the World Health Organization classification in 2010. PCC is composed of isolated or small clusters of tumor cells and mainly includes two pathological subtypes of mucinous carcinoma and signet ring cell carcinoma in clinic. Compared with other histological subtypes of GC, PCC has poor differentiation, is prone to infiltration and metastasis, and mainly lacks adhesion between tumor cells, has a relatively fast speed of tumor cell metastasis, and has a poor overall prognosis of PCC patients, and is not good for various drug treatments, with a 5-year survival rate of 18.4% to 44.1%. At present, there are few studies on the molecular biological mechanism of PCC. In-depth understanding of the unique phenotype of PCC can provide a useful basis for exploring new treatment strategies for patients with poorly cohesive gastric cancer.

[0003] Tumor immunotherapy as a new diagnosis and treatment technology has attracted widespread attention worldwide, and the research on gastric cancer immunotherapy is also flourishing. Currently approved immune checkpoint inhibitors (ICIs) include antibodies targeting CTLA-4, targeting programmed cell death receptor 1 (PD1) of T cells, or targeting programmed death ligand 1 (PD-L1) of tumor cells. At present, some ICIs drugs such as pembrolizumab, avelumab, sintilimab, toripalimab and ipilimumab have been approved for clinical treatment of advanced gastric cancer.

[0004] However, due to the high heterogeneity of the gastric cancer microenvironment and the high price of ICIs drugs, not all gastric cancer patients can benefit from them, especially in poorly cohesive gastric cancer. Therefore, the precise treatment of gastric cancer still has a long way to go, and precise screening of suitable benefit populations and overcoming immune resistance are the keys to optimizing clinical treatment outcomes.

[0005] Existing, whether it is transcriptome sequencing or protein detection or mRNA expression level can only reflect the unbalanced expression of a certain gene or protein in all tissue levels. The response rate of immunotherapy mainly depends on the functional balance between immune effector cells and immune suppressor cells, and the immune escape ability of tumor cells.

[0006] The antiviral properties of IFIT1 have been extensively studied, and some studies have mentioned that it may exert its biological activity through protein-protein interaction in tumor progression, but the study is limited to the tissue level and does not conduct positioning analysis of IFIT1 at the single-cell level.

[0007] Therefore, there is an urgent need to screen specific molecular markers in the microenvironment of gastric cancer for low adhesion type gastric cancer patients to evaluate prognosis and predict the efficacy of immunotherapy. SUMMARY

[0008] The inventors of the present application first discovered that overexpression of IFIT1 located on TANs of neutrophils is closely related to poor prognosis and drug resistance to immunotherapy of low adhesion type gastric cancer, and is an important factor leading to immunosuppressive microenvironment, through cell sequencing of surgical samples of low adhesion type gastric cancer patients and other types of gastric cancer patients, using public data sets, and in vitro and in vivo experimental verification. IFIT1 in TANs can be used as a prognostic biomarker for human low adhesion type gastric cancer and a predictor of immunotherapy, and has important application prospects.

[0009] The purpose of the present application is to provide the application of IFIT1 in the prognosis evaluation of low adhesion type gastric cancer patients, to solve the problem that the molecular mechanism of low adhesion type gastric cancer is not clear in existing clinical diagnosis, the accuracy of prognosis judgment is not high, and the ability to predict the efficacy of immunotherapy is insufficient.

[0010] To achieve the purpose of the present application, the technical solution is as follows:

[0011] The application of IFIT1 in the prognosis evaluation of low adhesion type gastric cancer patients specifically refers to: detecting the expression level of IFIT1 in the microenvironment of gastric cancer to judge the prognosis of low adhesion type gastric cancer patients and the effect of immunotherapy; if the expression level of IFIT1 is up-regulated, it indicates that the prognosis of low adhesion type gastric cancer patients and the effect of immunotherapy are not good.

[0012] Further, the detection of the expression level of IFIT1 in the microenvironment of gastric cancer refers to the detection of the expression level of IFIT1 in neutrophils.

[0013] Further, the expression amount of IFIT1 of the detected neutrophil is determined by detecting the marker CD66b and the expression amount of IFIT1 of the neutrophil; if the expression amount of CD66b and the expression amount of IFIT1 are up-regulated at the same time, it indicates that the prognosis of the low-adhesion type gastric cancer patient and the effect of immunotherapy are poor.

[0014] Further, the detection object is a tumor tissue section.

[0015] Further, the immunotherapy includes immunotherapy targeting CTLA-4, PD1 or PD-L1.

[0016] Based on this, the application also provides a detection kit for a low-adhesion type gastric cancer patient: the detection kit is an immunofluorescence double-staining detection kit, which comprises a mixed primary antibody working solution labeled with an IFIT1 monoclonal antibody and a CD66b monoclonal antibody labeled with dextran, and a DAPI blue dye solution.

[0017] Further, the application also provides a determination method for immunotherapy prediction using the above-mentioned detection kit, which comprises the following steps:

[0018] S1, taking the tumor tissue of a gastric cancer patient, using the above-mentioned detection kit for a low-adhesion type gastric cancer patient for immunofluorescence antibody staining, and observing under a fluorescence microscope;

[0019] S2, if green and orange nuclei or cytoplasm appear in a cell at the same time, it is confirmed as double-staining positive, otherwise it is negative;

[0020] S3, randomly selecting 5 fields of view where double-staining positive cells appear for shooting, and directly counting the percentage of double-staining positive cells using ImageJ software, wherein the percentage of double-staining positive cells = the number of double-staining positive cells / the number of all cells in the random field of view;

[0021] S4, obtaining the baseline value of the percentage of double-staining positive cells by the median value of the percentage of double-staining positive cells of the samples of low-adhesion type gastric cancer patients collected in advance, if the percentage of double-staining positive cells of the gastric cancer patient tissue to be tested is higher than the baseline value, it indicates that the prognosis of the patient is poor and the effect of immunotherapy is poor.

[0022] Directly counting the percentage of double-positive cells using ImageJ software, comprising the following steps:

[0023] ① Open the software Image J, create a new file (New) in (File), and import the picture to be analyzed;

[0024] ② Click (Image)→(Type)→(8-bit) to convert the picture to black and white;

[0025] 3. Click (Edit) -> (Invert) to convert the picture background to black;

[0026] 4. Click (Image) -> (Adjust) -> (Threshold), select (B&W), adjust the scroll bar to make the picture contain all the cells and remove the impurities in the background as much as possible, and click (apply) to execute the current instruction;

[0027] 5. Click (Analyze) -> (Analyze Particles) to obtain the analysis result, wherein count is the number of all cells in the current field of view;

[0028] 6. Extract two channels (green and orange) respectively, perform cell segmentation based on the cell segmentation function - Trainable Weka Segmentation, and obtain the segmentation result after Create Result;

[0029] 7. Obtain the region of interest (ROI) of the overlapping part, select the segmentation result by Threshold, then create a selection area (Edit -> Selection -> Create Selection), add the selection area to the ROIManager, and rename them as orange and green respectively; then select the two ROIs, select the AND operation in More, and obtain the overlapping part;

[0030] 8. Add the overlapping part to the ROI Manager and rename it as Double Label: perform automatic cell counting on the overlapping part; obtain the binary picture (Edit -> Selection -> Create Mask), (in order to exclude the influence of impurities, first select a cell, measure the size of the cell, limit the size of the cell to 150 to remove false positive impurities, and obtain the total number of double positive cells after Analyze Particles).

[0031] The application finds that high infiltration of IFIT1+ tumor-associated neutrophils (TANs) is a significant feature of PCC patients, which can predict poor prognosis and drug resistance to immunotherapy, by combining single-cell transcriptomic data, bioinformatics analysis and molecular biology experiments of tumor tissues of tumor patients. As a major component of the tumor microenvironment (TME), tumor-associated neutrophils (TANs) promote tumor progression and metastasis through communication with various growth factors, chemokines, inflammatory factors and other immune cells, and these cells together establish an immunosuppressive TME, thereby severely inhibiting T cell-mediated tumor immunity and playing a key role in tumor progression.

[0032] Up-regulation of IFIT1 expression in TANs promotes the migration and invasion of gastric cancer cell lines (MKN45 and MKN74) and EMT phenotype, and stimulates the growth of tumor in cell-derived xenograft models. Up-regulation of IFIT1 helps PDL1 expression in TANs and induces acquired resistance to anti-PD-1 immunotherapy.

[0033] Through intercellular communication analysis, it is found that IFIT1 expression in TANs promotes the expression of phospho-protein 1 (SPP1) secreted by tumor-associated macrophages (TAMs) to be more inclined to differentiate into immunosuppressive macrophages; promotes the recruitment and activation of cancer-associated fibroblasts and endothelial cells, and promotes the stromal phenotype of cancer in an immunosuppressive cell-related signal-dependent manner, thereby impairing CD8+ T cell-mediated antitumor immunity and producing drug resistance to immunotherapy. Through in vivo animal studies, it is confirmed that IFIT1-overexpressing TANs play a pro-tumor growth function in vivo and produce resistance to IFN-γ-mediated immunotherapy.

[0034] IFIT1 in TANs can be used as a prognostic biomarker for human low-adhesion gastric cancer and a predictive biomarker for immunotherapy.

[0035] Compared with the prior art, the present application has the following advantages:

[0036] 1. The present application first finds that the expression level of IFIT1 and the infiltration level of neutrophils can accurately reflect the immune characteristics of the microenvironment of low-adhesion gastric cancer tissue and the high risk of poor overall survival of gastric cancer patients after surgery, and IFIT1 in TANs is an effective biomarker for the prognosis of this type of gastric cancer;

[0037] 2, On this basis, an immunofluorescence double staining kit for detecting gastric cancer microenvironment is provided, the standardization of gastric cancer microenvironment detection is realized, the gastric cancer detection process is simplified, and the market application value for promoting the precise typing, precise prognosis prediction and efficacy evaluation of gastric cancer is broad. The detection kit provided by the application can be made into an immunofluorescence double staining kit, compared with ordinary fluorescence, a plurality of color fluorescent antibodies can be excited simultaneously, direct staining can be realized, direct observation and shooting can be realized, the clinical result can be conveniently interpreted, and quantitative analysis can be realized by combining software. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a tissue cell description graph using single cell sequencing technology in embodiment 1 of the application;

[0039] wherein, Figure 1 A is a single cell transcriptome UMAP graph of all cells from 9 gastric cancer patients of different types; Figure 1 B is a Violin graph of smooth expression distribution of corresponding markers of different cell types; Figure 1 C is a UMAP graph of tissue sources of all cells; Figure 1 D is a different cell proportion graph in PCC, NPCC and NM.

[0040] Figure 2 is an HE staining graph of different tissue samples in embodiment 2 of the application.

[0041] wherein, Figure 2 A is an HE staining graph of NPCC components in a non-low adhesion gastric cancer sample; Figure 2 B is an HE staining graph of NPCC components in a PCC sample; Figure 2 C is an HE staining graph of PCC components in a PCC sample.

[0042] Figure 3 is a weighted gene co-expression network graph in embodiment 3 of the application.

[0043] wherein, Figure 3 A is a general schematic diagram of high-variation gene clustering of neutrophils in a PCC sample into 4 modules; Figure 3 B is a gene display graph of high-variation gene clustering of neutrophils in a PCC sample into 4 modules; Figure 3 C is a dot plot of scores of different modules in neutrophils; Figure 3 D is a gene regulation network graph in module 3.

[0044] Figure 4 is a Friends analysis and COX regression analysis graph of module 3 genes in embodiment 3 of the application.

[0045] wherein, Figure 4A is a Friends analysis plot of Module 3 genes; Figure 4 B is a forest plot of Cox regression analysis results of average overall survival OS of Module 3 related molecules; Figure 4 C is a forest plot of Cox regression analysis results of disease-specific survival DSS of Module 3 related molecules; Figure 4 D is a forest plot of Cox regression analysis results of progression-free interval PFI of Module 3 related molecules.

[0046] Figure 5 is a multiple immunofluorescence mIF staining plot of tissue samples in Example 4 of the present application.

[0047] wherein, Figure 5 A is a multiple immunofluorescence mIF staining plot of PDL1, IFIT1 and neutrophil marker CD66b in NPCC samples; Figure 5 B is a multiple immunofluorescence mIF staining plot of PDL1, IFIT1 and neutrophil marker CD66b in PCC samples.

[0048] Figure 6 is a photograph and volume curve plot of four types of tumors in the in vivo experiment of nude mice in Example 5 of the present application.

[0049] wherein, Figure 6 A is a photograph of different types of tumors after tumor removal from four groups of nude mice; Figure 6 B is a daily tumor volume curve plot after mixed cells were implanted in vivo in four groups of nude mice.

[0050] Figure 7 is a mIF plot of tumor tissue sections of nude mice in Example 5 of the present application.

[0051] wherein, Figure 7 A is a mIF plot of tumor tissue sections of nude mice; Figure 7 B is a table showing the content of PDL1 on tumors of four groups of nude mice; Figure 7 C is a table showing the content of ZEB1 on tumors of four groups of nude mice.

[0052] Figure 8 is a clinical data plot for prognosis and efficacy evaluation of low-adhesion gastric cancer in Example 6.

[0053] wherein, Figure 8 A is a CT plot of patients with high and low expression of IFIT1 before and after receiving immunotherapy; Figure 8 B is a ROC curve using the expression level of IFIT1 protein to evaluate the prognosis of all gastric cancer patients; Figure 8 C is a ROC curve using the expression level of IFIT1 to evaluate the prognosis of low-adhesion gastric cancer patients.

[0054] Figure 9 Figure 6 is a tissue fluorescence section image taken after using the immunofluorescence double staining detection kit in Example 6 of the present application. DETAILED DESCRIPTION

[0055] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0056] It is easy for those skilled in the art to understand that the expression levels of CD66b and IFIT1 in the gastric cancer tissue sample refer to the protein content or concentration of CD66b and IFIT1.

[0057] In the present application, "IFIT1+ tumor-associated neutrophils highly infiltrate in low-adhesion gastric cancer", where the symbol "+" represents positive, expression, which is easy for those skilled in the art to understand. Similarly, "IFIT1+TANs" means positive neutrophils with IFIT1 expression.

[0058] In the present application, "overexpression", "high expression", and "upregulation of expression" have the same meaning and are interchangeable.

[0059] Sample source: 12 patients from October 2022 to May 2023 in the Department of Oncology Surgery of Jiangsu Provincial Hospital of Traditional Chinese Medicine were first diagnosed as gastric cancer; among them, 12 samples of 9 patients were included in single-cell sequencing, and the specimen numbers were N1 to N3 and T1 to T9; in the clinical efficacy observation of immunotherapy in Example 6, the aforementioned 9 patients were also involved as observation objects, and another 3 PCC patients were only involved in the clinical efficacy observation of Example 6.

[0060] All patients were diagnosed based on pathological specimens obtained by preoperative endoscopic biopsy and CT imaging data;

[0061] The tissue sample number, pathological diagnosis, and pathological type and stage information of each patient are shown in Table 1.

[0062] Table 1

[0063]

[0064] Note: These patients did not receive adjuvant therapy, including chemotherapy or radiotherapy, before surgery to eliminate potential treatment-induced changes in gene expression profiles.

[0065] Example 1 Single-cell sequencing technology discovers highly infiltrated neutrophils in the immunosuppressive microenvironment of low-adhesion gastric cancer

[0066] Method steps: 12 gastric cancer specimens of 9 patients (3 patients with low-adhesion gastric cancer and 6 patients with other types of gastric cancer) in Table 1 were prepared into 12 single-cell suspensions. Among the 12 samples, 3 were PCC component samples, 6 were

[0067] For other types of gastric cancer samples (NPCC), 3 were normal gastric tissue samples (NM) more than 10 cm away from the tumor margin. Normal mucosa (NM) samples refer to samples taken at least more than 10 cm from the tumor margin (sample numbers: N1, N2, N3). Nine gastric cancer sample tissues were from 3 PCC (sample numbers: T3, T6, T9) patients and 6 NPCC patients (sample numbers: T1, T2, T4, T5, T7, T8).

[0068] The specific procedure of the single cell suspension is as follows:

[0069] 1) The biopsy tissue was finely cut with iris scissors within 1 hour of the specimen, and then incubated with a digestion solution composed of phosphate buffered saline (PBS) at 37°C, 800 rpm for 30 minutes.

[0070] 2) Incubated with trypsin, collagenase I and DNase at 37°C for one hour.

[0071] 3) Add 4 mL of culture medium (DMEM), filter the suspension with a 40-μm cell mesh.

[0072] 4) After centrifugation at 250g for 5 minutes, discard the supernatant and rinse the cells twice with PBS.

[0073] 5) After resuspension with 10 mL of pre-cooled PBS, centrifuge at 250g for 10 minutes.

[0074] 6) Resuspend the precipitated cells in 5 mL of PBS without calcium ions.

[0075] 7) Quantify the single cell suspension using an inverted microscope and a hemocytometer, detect the viable cell count using trypan blue reagent, and the proportion of viable cells must be greater than 90%.

[0076] After strict quality control, finally 12 single cell suspensions were mixed to retain a total of 64454 cells to draw a uniform manifold approximation and projection map (UMAP), as shown in Figure 1 A-B shows the single cell transcriptome all cell landscape map formed by mixing 12 single cell suspensions from 9 different types of gastric cancer patients. The green part in the figure represents the proportion of neutrophils in the number of 64454 cells.

[0077] By annotating and labeling different cells, the unique cell landscape and molecular characteristics of low adhesion gastric cancer were found. As shown in Figure 1 C shows that the UMAP map of the tissue source of all cells shows that neutrophils are mainly contributed by the cell suspension of PCC.

[0078] As shown inFigure 1 As shown in FIG. 2A, the proportion of different cells in PCC samples, NPCC samples, and NM samples, and the content of neutrophils in PCC samples were the highest.

[0079] Therefore, neutrophils were significantly increased in samples of patients with low adhesion gastric cancer.

[0080] Example 2. HE staining of samples of NPCC and PCC patients

[0081] The sample of the NPCC patient (sample No. T5) and the sample of the PCC patient (sample No. T6) were subjected to hematoxylin / eosin (HE) staining to evaluate the histopathological state of the tissue and the cellular infiltration components thereof.

[0082] The specific steps of the experiment were as follows: the gastric cancer tissue was subjected to ethanol gradient dehydration, the gastric cancer tissue sample was embedded in paraffin, and after being soaked in a 10% formaldehyde solution, it was cut into a 4-micron-thick slice. After deparaffinization, the slice was stained with HE, mounted, and then observed under an upright microscope (model: Nikon, Eclipse Ni-E).

[0083] As shown in FIG. 2A, the proportion of different cells in PCC samples, NPCC samples, and NM samples, and the content of neutrophils in PCC samples were the highest. Figure 2 As shown in FIG. 2A, the proportion of different cells in PCC samples, NPCC samples, and NM samples, and the content of neutrophils in PCC samples were the highest. Figure 2 The tissue staining photograph of the NPCC patient in A was compared with Figure 2 B and Figure 2 C shows the tissue staining photograph of the PCC patient. The number of neutrophils in the sample of the PCC patient was significantly higher than that of the NPCC patient (scale bar, 10 pm).

[0084] Example 3. Identification of core gene modules in neutrophils using weighted gene co-expression network (hdWGCNA)

[0085] The gene expression profiles of all neutrophils in the PCC sample subjected to single-cell sequencing in Example 1 were extracted, and four gene modules were obtained by weighted gene co-expression network as shown in FIG. 3A-B (different colors represent different modules. Based on the weighted correlation coefficient of genes, genes were classified according to the expression pattern, and genes with similar patterns were classified into a module). Figure 3 A-B.

[0086] Subsequently, the module gene expression level of the neutrophil cluster was evaluated, and it was found that the highly expressed genes in module 3 all came from neutrophils, and these neutrophils all came from PCC samples (as shown in FIG. 3C). Figure 3 C.

[0087] Based on this, a specific module network was generated to show the gene regulation network in module 3 as shown in FIG. 3D. Figure 3 D.

[0088] To determine the key driver genes in module 3 regulating the network, Friends analysis and COX regression analysis were performed on the genes in module 3. The results showed that IFIT1 was an independent prognostic factor for low-adhesion gastric cancer patients, and was related to the overall survival (OS) of the patients, disease-specific survival (DSS) and progression-free interval (PFI) with statistical significance (P<0.05) Figure 4 A-D).

[0089] Accordingly, it is believed that in the tumor microenvironment of low-adhesion gastric cancer, neutrophils are highly infiltrated, and IFIT1 is highly expressed therein, and is an independent prognostic factor for PCC patients, and is related to the overall survival (OS) of the patients, disease-specific survival (DSS) and progression-free interval (PFI).

[0090] Example 4. IFIT1+ tumor-associated neutrophils are highly infiltrated in low-adhesion gastric cancer and up-regulate the expression of PDL1

[0091] To verify the specific infiltration of IFIT1+PDL1+tumor-associated neutrophils in PCC samples, the potential physical co-localization relationship between IFIT1+ tumor-associated neutrophils and PCC cells was analyzed. Immunofluorescence staining was performed on T2 samples from NPCC and T9 samples from PCC.

[0092] The specific operation steps are as follows:

[0093] 1) Place the paraffin section in an oven at 60°C for 30 min. Dip the paraffin tissue section in xylene at room temperature for 2 times, 5 min each time, to completely deparaffinize.

[0094] 2) Submerge the section sample in anhydrous ethanol for 2 times, 5 min each time. Submerge the section sample in different concentration gradient of ethanol (95%, 90%, 80%, 70%) in order, 1 time for each concentration, 5 min each time. Submerge the section in pure water for 1 time, 3 min each time, and then submerge the section in 1xPBS for 1 time, 3 min each time, carefully absorb the excess liquid around the section sample with filter paper.

[0095] 3) Place the deparaffinized and rehydrated section in buffer and intermittent boiling for 10 min.

[0096] 4) After antigen retrieval, take it out and slowly cool it at room temperature. Wash with 1xPBS for 2 times. Add 3% hydrogen peroxide to cover the sample and incubate at room temperature for 60 min to quench the endogenous peroxidase activity of the sample.

[0097] 5) Subsequently, immunolabeling blocking: blocking buffer was used to block at room temperature for 1 hour. Primary antibody (IFIT1, CD66b, PDL1 monoclonal antibody) was diluted to 1:200 with blocking buffer.

[0098] 6) After incubating the sample with primary antibody at room temperature for 1 hour, wash with 1x PBS for 3 times, 5 min each time. Dilute HRP conjugated secondary antibody in blocking buffer. Incubate the above sample with this solution at room temperature for 1 hour. Wash with 1x PBS for 3 times, 5 min each time. Prepare 100 μL-300 μL tyramide fluorescent working solution for each sample and incubate the sample with the staining solution at room temperature for 10 min. Wash with 1x PBS for 3 times, 5 min each time.

[0099] 7) After covering the cover glass and sealing, image under fluorescence microscope (model Nikon, DS-QilMC).

[0100] As shown in Figure 5 A-B, respectively, show the multiplex immunofluorescence (mIF) staining of PDL1, IFIT1 and neutrophil marker CD66b in NPCC sample No. T5 and PCC sample No. T3, respectively, and representative areas are marked in the enlarged pictures on the right side of each figure. Figure 5 A and Figure 5 B, scale bar: 1000 μm left panel, 100 μm right panel). Compared with NPCC samples, PCC samples have higher levels of IFIT1+PDL1+TANs infiltration. It is proved that IFIT1+PDL1+tumor-associated neutrophils specifically infiltrate in PCC samples.

[0101] Example 5. IFIT1+neutrophils promote tumor generation in vivo

[0102] The effect of IFIT1+neutrophils on tumor proliferation in vivo was studied by constructing subcutaneous transplanted tumors.

[0103] Experimental subjects: 4-week-old male BALB / c nude mice (body weight 18-22 g) were purchased from Beijing Weitong Lihua Experimental Animal Technology Co., Ltd. (Certificate No. SYXK2019-0010).

[0104] Methods and procedures:

[0105] 1. All nude mice were placed in a specific pathogen-free environment and allowed to eat freely, and were adaptively fed for 3 days.

[0106] 2. All nude mice were divided into four groups, with 6 mice randomly allocated to each group.

[0107] 3. Extraction of neutrophils from tumor tissue: The specific steps are as follows: ① Place the PCC tissue block obtained from surgery into a 10cm diameter cell culture dish, add a small amount of tissue homogenate and 20% fetal bovine serum; ② Use ophthalmic scissors to cut the tissue into a homogenate, add 5ml of tissue homogenate and 20% fetal bovine serum; use a pipette to aspirate the tissue homogenate, and filter the cell suspension into a 15cm centrifuge tube through a 100-mesh stainless steel filter; ③ Use a horizontal centrifuge at 1500 rpm for 3 minutes, and collect the fine precipitate. Wash three times with PBS, centrifuging at 500 rpm for 3 minutes each time to remove cell debris; ④ Filter again through a 200-mesh stainless steel filter to remove cell clumps. Count the cells in the obtained cell suspension and adjust the cell concentration to 5×10⁶ cells / mL. 7 Quantity / ml, store at room temperature for later use.

[0108] 4. According to Stemcell's EasySep... TM The instructions for the human neutrophil enrichment kit describe the following steps for neutrophil isolation: ① Mix the cell suspension with a magnetic bead complex to allow the magnetic bead complex to bind to the target cells; ② Place the mixture in a magnetic field to allow the magnetic bead complex and target cells to settle to the bottom of the tube; ③ Discard the supernatant, wash and centrifuge the magnetic bead complex containing the target cells, and finally obtain the cell enrichment, which is neutrophils; flow cytometry analysis shows that the purity of the neutrophils is higher than 90%.

[0109] 5. The obtained single-cell suspension was divided into four groups for IFIT1 lentiviral transfection experiments: a complete blank control group (no viral plasmid added), a lentiviral plasmid empty vector group (NC), an IFIT1 knockdown group (sh-IFIT1), and an IFIT1 overexpression group (oe-IFIT1). The specific experimental steps were as follows: ① In 2×10 5 Polybrene and various lentiviral vectors were added to neutrophils at a density of / ml and mixed thoroughly. The mixture was incubated overnight at 37°C. Puromycin was added to screen for untransfected neutrophils, and the cells were cultured for another 3-4 days. The cells could be passaged or the medium changed as needed depending on cell growth.

[0110] 6. The four groups of neutrophils (Control, NC, sh-IFIT1, and oe-IFIT1) were mixed with MKN45 cells and injected subcutaneously into the right axilla of the four groups of nude mice. The ratio of MKN45 cells to neutrophils was 10:1, and the number of neutrophils was 1 × 102. 5 1×10 MKN45 cells 6 indivual.

[0111] 7. Starting from day 7 post-inoculation, subcutaneous xenografts were successfully constructed in nude mice. Neutrophils (1×10⁶ cells per group) were injected into the subcutaneous xenografts twice weekly, as described in step 5.5 The size of the subcutaneous tumor of the nude mice was measured twice a week for 4 weeks.

[0112] 8. At the fifth week, the nude mice were anesthetized with CO2 according to the guidelines for the humane euthanasia of animals of the American Veterinary Medical Association (AVMA). Serum samples were collected and tumor specimens were obtained, and the volume of the tumor was analyzed using the formula V = 1 / 2ab 2 where V represents the volume of the tumor, a represents the longest diameter of the tumor, and b represents the shortest diameter, and a tumor growth curve was plotted.

[0113] 9. The tumor specimens of the nude mice in each group were paraffin-embedded and subjected to immunofluorescence detection (the specific steps are the same as in Example 4).

[0114] As shown in Figure 6 A-B, the upregulation of IFIT1 led to a significant increase in tumor growth; however, this effect was reversed in tumor-bearing nude mice carrying sh-IFIT1 TANs. This result confirmed that neutrophils with overexpression of IFIT1 can promote tumor growth.

[0115] As shown in Figure 7 , ZEB1 (a marker of tumor stromalization, and the higher the expression level, the lower the immune activity of the tumor) and PDL1 in the subcutaneous tumor tissue of nude mice were labeled by IF staining, as shown in Figure 7 A-C, the overexpression of IFIT1 (TANs) significantly promoted the expression of ZEB1 and PDL1.

[0116] Example 6 verifies that high expression of IFIT1+ neutrophils is poor for the effect of immunotherapy in PCC patients

[0117] The percentage of double-stained cells positive for IFIT1 and CD66b in TANs in the surgical specimens of 12 patients in Table 1 was evaluated at baseline (immunofluorescence double staining of CD66b and IFIT1), and the percentage of double-stained cells positive was analyzed in 5 random fields, and the specific analysis steps were as follows:

[0118] ① Open the software Image J, create a new file (New) in (File), and import the picture to be analyzed;

[0119] ② Click (Image)→(Type)→(8-bit) to convert the picture to black and white;

[0120] ③ Click (Edit)→(Invert) to convert the background of the picture to black;

[0121] 4. Click (Image) -> (Adjust) -> (Threshold), select (B&W), adjust the scroll bar to make the picture contain all the cells as much as possible while remove the impurities in the background, click (apply) to execute the current instruction;

[0122] 5. Click (Analyze) -> (Analyze Particles) to get the analysis result, where count is the number of all cells in the current field of view;

[0123] 6. Extract two channels (green and orange) respectively, and perform cell segmentation based on the cell segmentation function - Trainable Weka Segmentation. After Create Result, the segmentation result is obtained;

[0124] 7. Get the region of interest (ROI) of the overlapping part by using the Threshold box to select the segmentation result, then create a selection (Edit -> Selection -> Create Selection), and add the selection to the ROIManager, and rename them as orange and green respectively. Then select the two ROIs, and select the AND operation in More, to get the overlapping part;

[0125] 8. Add the overlapping part to the ROI Manager and rename it as Double Label: automatically count the cells in the overlapping part; get the binary picture (Edit -> Selection -> Create Mask), (in order to exclude the influence of impurities, first select a cell, measure the size of the cell, and set the lower limit of the cell size to 150 to remove false positive impurities, and after Analyze Particles, the total number of double positive cells is obtained;

[0126] 9. The number of double positive cells / the number of all cells in the random field of view is the percentage of double positive cells.

[0127] The results showed that the median value of the percentage of double positive cells of IFIT1 and CD66b in 12 patients (including 6 PCC patients and 6 NPCC patients) was 5.16%. The median value of the percentage of double positive cells of 6 PCC patients was 9.57%. It was shown that the expression level of IFIT1 neutrophils in PCC patients was significantly increased (median value algorithm: sort all the percentages of double positive cells, and take the middle number, if it is an even number, take the middle two numbers and divide by 2).

[0128] Patients with IFIT1 expression higher than or equal to the median value were defined as IFIT1 high expression patients, and patients with IFIT1 expression lower than the median value were defined as IFIT1 low expression patients. Before analysis, all patient records were anonymized and de-identified. CT imaging data were independently verified by two different radiologists. Each patient had complete clinical data. The efficacy of patients receiving chemotherapy (SOX: S-1 plus oxaliplatin or XELOX: capecitabine plus oxaliplatin) combined with immunotherapy Nivolumab (intravenous injection of 360 mg every 3 weeks) was evaluated. CT scanning was performed every 6 weeks to evaluate the efficacy of immunotherapy.

[0129] As Figure 8 A shows the change in tumor volume of all cases recorded by computer tomography (CT). In the figure, the arrow represents the primary or metastatic tumor lesion, red represents disease progression (PD), green represents partial response (drug effect) (PR), and blue represents stable disease (SD). The results show that in all patients, the rate of acquired immunotherapy resistance in IFIT1 high expression cases is higher than that in IFIT1 low expression cases. These data strengthen the evidence that IFIT1+ neutrophils are poor responders to immunotherapy.

[0130] In addition, we used the protein expression level of IFIT1 as a prognostic evaluation indicator to evaluate the prognostic accuracy of all types of gastric adenocarcinoma and low adhesion gastric cancer, respectively. The receiver operating characteristic curve (ROC) was drawn. The ROC curve is the most core index for evaluating the discrimination of medical diagnostic tests and the performance of prediction models. The closer the area under the curve (AUC) is to 1, the stronger the identification ability. In all 12 cases of all types of gastric adenocarcinoma, the AUC of IFIT1 protein expression level for predicting 1-year survival rate was 0.501, and the AUC for predicting 3-year survival rate was 0.561 Figure 8 B). While the AUC for predicting 1-year survival rate of low adhesion gastric cancer was 0.628, and the AUC for predicting 3-year survival rate was 0.609 Figure 8 C). This shows that IFIT1+ neutrophils have higher accuracy in the prognostic evaluation of PCC patients. At the same time, based on the limited existing samples of the inventors, the median value of the percentage of double positive cells is not close enough to the true value. If the sample library is increased later, the median value of the percentage of double positive cells will be closer and closer to the true value, and the ROC curve will be closer and closer to 1.

[0131] Example 7. Immunofluorescence double staining kit for IFIT1+ CD66b

[0132] The embodiment provides an immunofluorescence double staining detection kit for predicting the postoperative immunotherapy reaction effect of a low adhesion type gastric cancer patient, which comprises a mixed primary antibody working solution composed of an IFIT1 monoclonal antibody labeled with dextran and a CD66b monoclonal antibody labeled with dextran. The IFIT1 monoclonal antibody is purchased from Proteintech Company (No. 13659-1-AP) by Nanjing Zhongding Biotechnology Co., Ltd. by commission; the CD66b monoclonal antibody is purchased from Proteintech Company (No. 24633-1-AP) by Nanjing Zhongding Biotechnology Co., Ltd. by commission and is coupled with a fluorescent dye. In addition, a DAPI blue dye solution for locating and observing the cell nucleus is also included.

[0133] The preparation method can refer to the existing Chinese patent application No. 201911363146.1, a kind of immunofluorescence double staining kit for auxiliary diagnosis of cervical cancer, specifically comprising:

[0134] ①Dextran is dissolved in phosphate buffer solution, sodium periodate is added for oxidation, and the reaction is stirred at 30℃-37℃ for 2-3 hours in the dark, and then terminated by adding excess ethylene glycol; the same phosphate buffer solution as the dextran matrix is used for dialysis overnight;

[0135] ②The required antibody is dialyzed in phosphate buffer solution overnight;

[0136] ③After mixing the oxidized dextran and the antibody, a suitable amount of sodium cyanoborohydride is added for reaction in the dark for 1.5-3 hours at a reaction temperature of 4℃-6℃;

[0137] ④After amine reaction by adding a suitable amount of ethylenediamine, sodium borohydride is used for reduction, and the phosphate buffer solution is dialyzed overnight;

[0138] ⑤The CD66b monoclonal antibody is labeled with fluorescein T-Sapphire wavelength (399-511nm) green, and the IFIT1 monoclonal antibody is labeled with fluorescein Dapoxyl (2-aminoethyl) sulfonamide wavelength (372-582nm) orange.

[0139] ⑥Take the antibody in a proportion of 0.01, and dissolve the activated fluorescein with dimethyl sulfoxide. Add the dissolved fluorescein to 100 μl mixed with two kinds of monoclonal antibodies (concentration: 1 mg / ml), and use phosphate solution containing 5% BSA as the antibody working storage solution (the mass ratio of fluorescein:dextran:monoclonal antibody is 0.01:50:1).

[0140] According to the above kit, the operation steps are consistent with the steps of commercially available directly labeled immunofluorescence antibodies, and the specific operation steps are as follows:

[0141] Gastric cancer patient surgical tissue specimens were taken. After baking the tissue sections at 60°C for 2 hours, they were deparaffinized. Xylene deparaffinization for 15 min, followed by anhydrous ethanol, 95%, 90%, 80%, 70% for 5 min each, and distilled water for 5 min each. PBS rinsing for 5 min, repeated three times. Antigen retrieval. Citric acid buffer (about 92-95°C) boiling (boiling water bath) for 15 minutes, and naturally cool to room temperature. PBS washing for 5 min, three times. Normal goat serum blocking, 37°C for 20 min. PBS washing for 5 min, three times, and primary antibody (mixed CD66b+IFIT1 special fluorescently labeled monoclonal antibody) incubation, 37°C for 1.5 hours (antibody concentration 1:200). PBS washing for 10 min, three times. Anti-quenching fluorescent mounting medium mounting, and observation under a fluorescence microscope (model Nikon, DS-QilMC).

[0142] Result determination: as shown in Figure 9 CD66b positive expression is green fluorescence in the nucleus and / or cytoplasm, and IFIT1 positive expression is orange in the nucleus and / or cytoplasm. When green and orange nuclei / cytoplasm appear in a cell at the same time, it is confirmed to be double-stained positive, and otherwise it is negative. Five fields of view with double-positive cells were randomly selected for photography, and the percentage of double-stained positive cells was directly counted using ImageJ software. The baseline value of the percentage of double-stained positive cells obtained from the (CD66b and IFIT1) of the low-adhesion gastric cancer patient samples collected initially was 9.57%. If the percentage of double-stained positive cells (CD66b and IFIT1) in the PCC patient tissue to be tested is greater than the baseline value, it indicates that the patient has a poor prognosis and the immunotherapy effect is poor.

[0143] The kit of the present embodiment can directly observe and photograph images under an ordinary fluorescence optical microscope by selecting special fluorescent dyes to excite double-color fluorescence and blue fluorescence of DAPI dye under ultraviolet laser 405 nm wavelength without switching different excitation wavelengths or image synthesis processing. Compared with similar fluorescent immunoreagent kits, the present kit has the special advantages of simultaneously and efficiently exciting multicolor fluorescence and directly observing and photographing for software quantitative analysis.

[0144] The above only describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered within the protection scope of the present application.

Claims

1. A use of a reagent for detecting the expression amount of IFIT1 in a gastric cancer microenvironment in the preparation of a reagent for evaluating the prognosis and immunotherapy efficacy of a low adhesion type gastric cancer patient, characterized in that: If the expression level of IFIT1 is up-regulated, it indicates that the prognosis of the low adhesion type gastric cancer patient and the effect of immunotherapy are poor; the expression level of IFIT1 in the microenvironment of the gastric cancer refers to the expression level of IFIT1 in the neutrophils of the tumor tissue; the immunotherapy refers to the immunotherapy of PD-L1. ​ 2.The use of the reagent for detecting the expression amount of IFIT1 in a gastric cancer microenvironment according to claim 1 in the preparation of a reagent for evaluating the prognosis and immunotherapy efficacy of a low adhesion type gastric cancer patient, characterized in that: The expression level of IFIT1 in the neutrophils of the tumor tissue is determined by detecting the marker CD66b and the expression level of IFIT1 of the neutrophils; if the expression level of CD66b and the expression level of IFIT1 are up-regulated at the same time, it indicates that the prognosis of the low adhesion type gastric cancer patient and the effect of immunotherapy are poor.

3. Use of a detection kit in the preparation of a reagent for judging the prognosis of a low adhesion type gastric cancer patient and the evaluation of the efficacy of immunotherapy, characterized in that: the detection kit comprises a monoclonal antibody capable of specifically recognizing a low adhesion type gastric cancer patient specific antigen; and the monoclonal antibody is an antibody capable of specifically recognizing a protein comprising the amino acid sequence of SEQ ID NO:

1. The detection kit is an immunofluorescence double staining detection kit, which comprises a mixed primary antibody working solution labeled with dextran IFIT1 monoclonal antibody and dextran CD66b monoclonal antibody and a DAPI blue dye solution, the immunotherapy refers to the immunotherapy of PD-L1, and the evaluation and determination method of the effect of immunotherapy comprises the following steps: S1, taking the tumor tissue of the gastric cancer patient, using the detection kit for immunofluorescence antibody staining, and observing under a fluorescence microscope; S2, if green and orange cell nuclei or cytoplasm appear in a cell at the same time, it is confirmed as double staining positive, otherwise it is negative; S3, randomly selecting 5 fields of view where double staining positive cells appear for shooting, using ImageJ software to directly count the percentage of double staining positive cells, the percentage of double staining positive cells = the number of double staining positive cells / the number of all cells in the random field of view; S4, obtaining the baseline value of the percentage of double staining positive cells by the median value of the percentage of double staining positive cells of CD66b and IFIT1 of the samples of the low adhesion type gastric cancer patient collected in advance, if the percentage of double staining positive cells of CD66b and IFIT1 of the gastric cancer patient tissue to be detected is higher than the baseline value, it indicates that the prognosis of the patient is poor, and the effect of PD-L1 immunotherapy is poor.

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