Application of targeted EIF3F inhibitor in preparation of tumor treatment medicine

Abstract

The invention discloses the immunotherapy effect of the targeted EIF3F gene on hepatocellular carcinoma for the first time. The high-expression EIF3F is related to poor prognosis in hepatocellular carcinoma, and as a potential cancer promoting gene, the high-expression EIF3F can promote the proliferation and metastasis ability of tumors in vitro and in vivo. In mechanism, EIF3F promotes malignant progression of hepatocellular carcinoma by remodeling fatty acid biosynthesis. Meanwhile, increased free fatty acids caused by EIF3F are discharged into a tumor microenvironment, CD8 + T cells can be indirectly inhibited, and the hepatocellular carcinoma is resistant to immunotherapy. Therefore, the targeting EIF3F gene provides a potential new way for immunotherapy of hepatocellular carcinoma, can enhance the curative effect of anti-PD-1 treatment in hepatocellular carcinoma, and represents a new human cancer promoting gene and a potential tumor treatment target.

Description

Technical Field

[0001] The present invention relates to the field of life science research, and particularly to the use of a targeted EIF3F inhibitor in the preparation of a tumor therapeutic drug. Background Art

[0002] HCC is one of the most prevalent malignant cancers globally, with over 800,000 new cases each year. Recently, significant progress has been made in treatment strategies such as immunotherapy, surgical resection, and liver transplantation. However, the prognosis of HCC patients and the 5-year survival rate after surgery remain unsatisfactory, accompanied by a high recurrence rate and metastasis rate. Therefore, exploring the progression mechanism of HCC and revealing potential diagnostic and therapeutic targets is crucial. Poor prognosis and resistance to treatment remain a major challenge. Lipid metabolism is an important metabolic activity in hepatocellular carcinoma and plays a crucial role in regulating the development and treatment response of HCC. Lipid metabolism reprogramming is one of the main factors leading to the development of HCC.

[0003] Ubiquitination plays a crucial role in regulating cellular functions, including protein degradation, signal transduction, and the cellular response to stress. The recently reported association between ubiquitination and lipid metabolism has revealed complex interactions between these processes. Understanding the molecular mechanisms behind this interaction may help identify new therapeutic targets and develop treatment strategies for diseases associated with dysregulated cell death, such as hepatocellular carcinoma.

[0004] Identifying the regulatory mechanisms of hepatocellular carcinoma cells in lipid metabolism is crucial for developing effective treatment strategies. In recent years, the development of single-cell sequencing technology has helped researchers better explore the regulatory molecules and mechanisms of lipid metabolism. Summary of the Invention

[0005] The purpose of the present invention is to address the drawbacks in the prior art and propose the use of a targeted EIF3F inhibitor in the preparation of a tumor therapeutic drug.

[0006] To achieve the above purpose, the present invention adopts the following technical scheme:

[0007] In a first aspect of the present invention, there is provided the use of a targeted EIF3F inhibitor in the preparation of a tumor therapeutic drug.

[0008] In one embodiment, the tumor therapeutic drug has at least one of the following functions:

[0009] Inhibiting the proliferation of tumor cells; inhibiting the growth of tumors.

[0010] In one embodiment, the EIF3F inhibitor inhibits EIF3F activity, or inhibits EIF3F gene transcription or expression.

[0011] The EIF3F inhibitor is siRNA, shRNA, an antibody or a small molecule compound.

[0012] In a second aspect of the present invention, a method for treating a tumor is provided, which is to administer an EIF3F inhibitor to a subject.

[0013] In a third aspect of the present invention, an antitumor drug is provided, which comprises an effective amount of an EIF3F inhibitor and a pharmaceutical carrier.

[0014] In a fourth aspect of the present invention, an antitumor drug combination is provided, which comprises an effective amount of an EIF3F inhibitor and at least one other antitumor drug.

[0015] In a fifth aspect of the present invention, a method and a composition for enhancing the sensitivity of hepatocellular carcinoma cells to ferroptosis are disclosed.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: A strategy of inhibiting fatty acid synthesis in HCC cells and enhancing the efficacy of immunotherapy by targeting EIF3F focuses on identifying EIF3F as a key factor promoting fatty acid synthesis in HCC cells, and thus provides a new treatment strategy with the prospect of being applied to the preparation of HCC-targeted drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram for exploring genes that simultaneously affect fatty acid metabolism and malignancy of HCC by combining organoid single-cell transcriptome sequencing and database analysis;

[0018] (A) Schematic diagram of the collection and scRNA-seq analysis process of PDO samples;

[0019] (B) UMAP map of all single cells in five PDO samples;

[0020] (C) Heat map display of representative DEGs and features in five PDO samples:

[0021] (D) UMAP map of the overall distribution of FAM scores calculated using AddModuleScore;

[0022] (E) Violin plot showing the distribution of FAM scores in five PDO samples;

[0023] (F) Single-cell trajectory analysis and pseudotime analysis were performed on five PDO samples respectively. According to the differentiation trajectory and FAM scores, the clusters were divided into a FAM high-expression group and a FAM low-expression group, and DEGs were identified;

[0024] (G) Intersection map of DEGs in five PDO samples;

[0025] (H) Comparison diagram of DEGs using bulk RNA-seq data of five patient samples and the TCGA database;

[0026] Figure 2 Schematic diagram showing that elevated EIF3F levels can promote the malignant phenotypes of HCC in vivo;

[0027] (A) Schematic diagram of the types and detection parameters of in vivo models;

[0028] (B) PDOX tumor images of tumors induced in NCG mice by subcutaneous transplantation of HCC 13T and HCC133T transfected with organism fragments;

[0029] (C) Schematic diagram of the volume of PDOX tumors induced by subcutaneous transplantation;

[0030] (D) Schematic diagram of the mass of PDOX tumors induced by subcutaneous transplantation;

[0031] (E) Representative photographs of the mouse liver and micro-MRI schematic diagrams after orthotopic transplantation of PDOX tumors using HCC 13T and HCC133T transfected organisms;

[0032] (F) Schematic diagram of the volume of PDOX tumors induced by orthotopic transplantation;

[0033] (G) Schematic diagram of measuring the maximum cross-sectional area of PDOX tumors induced by orthotopic transplantation using micro-MRI;

[0034] (H) Schematic diagrams of representative H&E staining and EIF3F and Ki-67 IHC staining of PDOX tumors;

[0035] (I-K) Representative bioluminescence images and photographs (I), RFI charts (J), and tumor volumes (K) of orthotopic tumor transplantation models established using Hep3B and Huh7 transfected cells;

[0036] (L) Representative bioluminescence images of mice injected with Hep3B and Huh7 transfected cells via the tail vein;

[0037] (M) RFI of mice injected with Hep3B and Huh7 transfected cells;

[0038] (N) Representative photographs and H&E staining of lung metastasis lesions;

[0039] (O) Number of metastasis lesions;

[0040] Figure 3 Schematic diagram showing that EIF3F promotes fatty acid biosynthesis and lipid accumulation in HCC;

[0041] (A) Schematic diagram of the experimental process for exploring the effect of EIF3F on the metabolism of Huh7 cells;

[0042] (B) PLS-DA analysis of metabolomics;

[0043] (C) Heatmap highlighting the differential metabolites identified by metabolomics;

[0044] (D) Metabolic pathways of KEGG enrichment analysis;

[0045] (E) KEGG metabolite set enrichment analysis of two groups of FAB metabolites;

[0046] (F) Inter-group differences of FAB-related proteins in proteomics;

[0047] (G) Schematic diagram showing the evaluation of metabolic flux in Huh7 cells using U- 13 C6 glucose tracer method;

[0048] (H) Differences in the relative abundances of C16:0, C16:1, C18:0, and C18:1 fatty acids in the metabolic flux between the two groups;

[0049] (I) Transmission electron microscopy images of lipid droplets in Hep3B and Huh7 transfected cells;

[0050] (J) Average number of lipid droplets observed by transmission electron microscopy;

[0051] (K) Grayscale image and corresponding pseudo-color MRI image showing the signal intensity of in-situ tumor sections;

[0052] (L) Pseudo-color signal intensity of micro-MRI images of in-situ tumor sections, and the signal intensity is normalized by the tumor section area;

[0053] Figure 4 Schematic diagram of the specific interaction between EIF3F and the key FAB factor ACSL4;

[0054] (A) Schematic diagram showing the process of identifying specific targets of EIF3F.

[0055] (B) Mass spectrometry analysis was used to detect proteins interacting with EIF3F (silver staining).

[0056] (C) Overlap of the results of proteomics, IP-MS analysis and FAB-related proteins.

[0057] (D-F) Co-IP experiments were used to detect the interaction between (D, E) exogenous and (F) endogenous EIF3F and ACSL4 in HEK293T cells.

[0058] (G, H) GST pull-down assays were used to detect the direct interaction between EIF3F and ACSL4.

[0059] (I) Immunofluorescence staining of EIF3F, ACSL4, and DAPI in Hep3B cells (scale bar for whole image: 10 μm, scale bar for enlarged image: 2 μm).

[0060] (J) PLA was used to detect the interaction between EIF3F and ACSL4 (scale bar for whole image: 10 μm, scale bar for enlarged image: 2 μm).

[0061] (K) Design of truncated EIF3F and ACSL4.

[0062] (L, M) Co-IP assays were used to explore the binding region between EIF3F and ACSL4.

[0063] (N) 3D structure simulation of EIF3F and ACSL4 and their binding sites.

[0064] (O) IP and Western blotting results showing the interaction between EIF3F and ACSL4 in HEK293T cells transfected with several mutant plasmids;

[0065] Figure 5 Schematic diagram of EIF3F inhibiting K48-linked ubiquitin-mediated proteasomal degradation of ACSL4;

[0066] (A) ACSL4 levels in transfected Hep3B and Huh7 cells;

[0067] (B) ACSL4 levels in transfected Hep3B and Huh7 cells after CHX treatment for a specific duration;

[0068] (C) Statistical chart of relative ACSL4 levels;

[0069] (D) ACSL4 levels in transfected Huh7 cells after treatment with CHX combined with MG132 (10 μM) or CQ (10 μM);

[0070] (E) Ubiquitination levels of ACSL4 in cell lines with EIF3F knockdown or overexpression;

[0071] (F, G) Screening of the ubiquitination types of ACSL4 by EIF3F in HEK293T cells using different types of ubiquitin;

[0072] (H) Effect of EIF3F overexpression on the ubiquitination level of ACSL4 in HEK293T cells transfected with mutant Myc-Ub plasmids (K48O Ub and K48R Ub, with intact and mutant Lys48 residues respectively);

[0073] (I) Schematic diagram showing the conserved amino acid sequences within the MPN domain of seven deubiquitinating enzymes and their conservation across different species;

[0074] (J) Effect of MPN mutations on the deubiquitination of ACSL4 by EIF3F in HEK293T cells;

[0075] (K) Detection of the ubiquitination level of ACSL4 in HEK293T cells transfected with the binding site mutant plasmid;

[0076] (L) Schematic diagram showing the identification of ubiquitinated lysines at the possible ubiquitination sites of ACSL4 by the double-segmentation method;

[0077] (M) Ubiquitination level of ACSL4 mutants in HEK293T cells transfected with the indicated mutant Flag-ACSL4 plasmids;

[0078] Figure 6 Schematic diagram showing that the elevated level of EIF3F is associated with reduced CD8+ T cell infiltration in HCC;

[0079] (A) Quantitative analysis of tumor-infiltrating immune cells;

[0080] (B) t-SNE plot showing the protein levels of cytotoxic CD8+ T cell markers (CD8α, GZMB, and IFN-γ) (left and middle panels) and the cell distribution patterns of two groups;

[0081] (C) Representative multiplex immunofluorescence staining images in mouse orthotopic tumor tissues; The right side shows the automated cell segmentation results based on digital image analysis for evaluating CD8+

[0082] GZMB+ and CD8+ IFN-γ+ cytotoxic T cell subsets;

[0083] (D) Quantification of related immune cells by multiplex immunofluorescence staining analysis;

[0084] (E) Representative multiplex immunofluorescence staining images in tumor tissues of HCC patients;

[0085] (F) 3D scatter plot of EIF3F, ACSL4, CD8, and GZMB based on multiplex immunofluorescence staining analysis of HCC patients;

[0086] Figure 7 Schematic diagram showing that targeting EIF3F can reduce tumor burden and improve the efficacy of anti-PD-1 therapy in mice;

[0087] (A) Hepa1-6 cell implantation protocol and treatment protocol using LNP-siNC or LNP-siEIF3F in combination with IgG or anti-mouse PD-1 antibody;

[0088] (B) In vivo imaging of LNP within 12 hours after injection;

[0089] (C) Representative images of orthotopic tumors in the liver of each group. Tumor growth was tracked by bioluminescence on day 7 and day 21;

[0090] (D) Relative luminescence intensity of orthotopic tumors established using Hepa1-6 cells;

[0091] (E) Volume of orthotopic tumors established using Hepa1-6 cells;

[0092] (F) Overall survival rate of mice with orthotopic tumors established using Hepa1-6 cells.

[0093] (G) Representative multiplex fluorescence immunohistochemical staining images;

[0094] (H) Visualization results on the right show automated cell segmentation results based on digital image analysis for evaluating the proportion of CD8+ GZMB+ cytotoxic T cell subsets;

[0095] (I) Quantification of related immune cells by multiplex fluorescence immunohistochemical staining analysis. Detailed implementation manners

[0096] To further understand the purpose, structure, characteristics, and functions of the present invention, the following is a detailed description in conjunction with embodiments.

[0097] The present invention provides the use of an EIF3F inhibitor in the preparation of a tumor therapeutic drug.

[0098] The present invention discloses a method and composition for enhancing the sensitivity of hepatocellular carcinoma cells to immunotherapy. The key findings of the invention are as follows:

[0099] Identification of EIF3F: Through bioinformatics analysis, in combination with reference Figure 1 , it was found that EIF3F is actively related to fatty acid metabolism and is related to malignancy. In addition, the expression of EIF3F is elevated in patients with hepatocellular carcinoma and is related to poor prognosis, highlighting the clinical significance of targeting EIF3F.

[0100] Mechanism research: In combination with reference Figure 4 and Figure 5Mechanistically, EIF3F promoted fatty acid synthesis and malignant progression of HCC cells by stabilizing ACSL4; meanwhile, the increased fatty acids promoted immune escape by being excreted into the immune microenvironment, leading to HCC resistance to immunotherapy.

[0101] Therapeutic potential: Targeting EIF3F can improve the therapeutic effect of anti-programmed cell death ligand-1 (PD-1) therapy in hepatocellular carcinoma.

[0102] The tissue samples used in the following examples were from patients undergoing hepatocellular carcinoma surgery by the applicant. The patients and their families were fully informed of the research purpose and procedures before surgery and signed the informed consent form. This study has been approved by the ethics committee of our unit.

[0103] The hepatocellular carcinoma cell lines Huh7, Hep3B, H22, etc. used were all purchased from Shanghai Anwei Biotechnology Co., Ltd.

[0104] The cell culture medium used was prepared from DMEM / F12, 10% fetal bovine serum, 1% penicillin, and 1% streptomycin, Gibco Technology Company;

[0105] TRIzol used for RNA extraction was purchased from Invitrogen;

[0106] The reagents used for qRT-PCR were all purchased from Nanjing Novizan Company.

[0107] Interfering gene sequences involved in the examples:

[0108] shEIF3F HumanshRNA1: CTCTCAAGTGACTTGCAGCAA

[0109] shRNA2: CCGCATGAGCATCAAAGCCTA

[0110] shRNA3: GTACTACGACACTGAACGCAT

[0111] shEIF3F Mouse shRNA: AGATTGCCCTCAACGAGAAAC

[0112] Example 1: Single-cell RNA sequencing analysis combined with database analysis was used to explore genes that simultaneously affect FAM and malignancy in HCC.

[0113] Combined reference Figure 1 , by constructing patient-derived hepatocellular carcinoma (HCC) organoids and performing single-cell RNA sequencing (scRNA-seq) analysis to explore the fatty acid metabolism differences of HCC at the single-cell level, and at the same time combining spatial transcriptomics for verification.

[0114] Organoids were successfully established from 5 HCC patients, and detailed clinicopathological information was recorded. These organoids had different morphologies under the microscope but were highly similar to the tumor tissues of the corresponding patients in terms of cell morphology and pathological features, indicating that the organoids retained the tumor characteristics of different patients.

[0115] The FAM score was calculated based on the FAM gene set of the MsigDB database, and the distribution of the FAM score in five datasets was shown in UMAP plots and violin plots.

[0116] To further explore the developmental trends affecting the FAM activity level in tumor cells, trajectory inference and pseudotime analysis were performed using Monocle2 to identify cell clusters with high and low FAM activity in each organoid and to identify their differentially expressed genes.

[0117] A total of 12 common genes were identified among the DEGs of the five organoids. To screen for candidate genes most relevant to the malignancy of HCC, we used the Cancer Genome Atlas (TCGA) database for differential analysis of HCC and adjacent tissues, overall survival (OS), and disease-free survival (DFS) analysis.

[0118] At the same time, bulk RNA-seq analysis was also performed on the HCC and adjacent tissues corresponding to the five patients, and the results showed that eukaryotic translation initiation factor 3F subunit (EIF3F) and transmembrane protein 14C (TMEM14C) were the genes most closely related to the malignancy and prognosis of HCC.

[0119] To further verify the expression of genes in HCC, 90 pairs of HCC and adjacent tissues were collected and a tissue microarray was constructed.

[0120] The results showed that compared with TMEM14C, the expression difference of EIF3F between HCC and adjacent tissues was more obvious and its correlation with tumor malignancy was stronger. Protein gel electrophoresis further confirmed the high expression of EIF3F in HCC.

[0121] Example 2: Elevated EIF3F expression promotes HCC malignant progression in vivo.

[0122] In combination with reference Figure 2 , five-week-old male BALB / c nude mice and C57BL / 6 mice were obtained from the Experimental Animal Center of Nanjing Medical University and were housed in a specific pathogen-free (SPF) facility with a 12 / 12-hour light / dark cycle and had free access to food and water.

[0123] This study was approved by the Ethics Committee of the Laboratory Animal Management of Nanjing Medical University.

[0124] Animals with a weight loss of more than 20%-25% or a tumor weight exceeding 10% of the body weight will be euthanized.

[0125] Subcutaneous tumor formation experiment:

[0126] Transfected cells (1×106 cells / 100 μL) were subcutaneously injected into the inguinal region of BALB / c nude mice.

[0127] The mice were monitored every four days and sacrificed after 28 days.

[0128] The tumor volume was calculated every 4 days using the formula 1 / 2 (length × width^2).

[0129] Lung metastasis experiment:

[0130] Fluorescein-labeled transfected cells (1×106 cells / 100 μL) were injected into BALB / c nude mice via the tail vein. Four weeks later, the mice were intraperitoneally injected with 100 mg / kg of d-luciferin (Xenogen, Hopkinton, MA, USA) and monitored using an IVIS100 imaging system (Xenogen). Then the lungs were surgically excised, placed in PBS, photographed, fixed, and evaluated by HE staining. The overall survival was recorded at 12 weeks.

[0131] From the results of the subcutaneous tumor experiment, compared with the control group, the tumor volume of the mice in the EIF3F overexpression group was significantly increased, and the tumor growth rate was higher. The mice were sacrificed on the 28th day, and the tumor weight was measured. It was found that the tumor weight in the EIF3F overexpression group was significantly higher than that in the control group. The above results demonstrated that the high expression of EIF3F significantly promoted tumor growth and cell proliferation of HCC in vivo. From the results of the lung metastasis experiment, after intravenous injection of fluorescein-labeled HCC cells via the tail vein, the bioluminescence signal in the lungs of the mice in the EIF3F overexpression group was significantly enhanced, indicating an increased metastasis burden. H&E staining showed that there were more tumor metastases in the lungs of the EIF3F overexpression group, accompanied by strong tumor cell infiltration. The number of lung metastases in the EIF3F overexpression group was significantly higher than that in the control group. Based on the above experimental results, overexpression of EIF3F can promote the malignant progression of HCC in vivo. Example 3: Transmission electron microscopy observation of the effect of EIF3F on the number of lipid droplets in hepatoma cells.

[0132] Combined with reference to Figure 3 : The designated hepatoma cells were seeded on a 6-cm diameter culture dish and treated with Erastin (5 μM) or RSL3 (2 μM) for 12 hours. Images were obtained using a transmission electron microscope (Hitachi; HT7700, Japan), and all fields of view were evaluated at a magnification of 15,000 times.

[0133] Transmission electron microscopy showed that lipid droplets (LDs) were significantly reduced after EIF3F knockdown, while lipid droplet accumulation increased after EIF3F upregulation.

[0134] Example 4: Discovery of EIF3F interactors by IP-MS.

[0135] Combined reference Figure 4 : Immunoprecipitation combined with mass spectrometry (IP / MS) experiments were performed. Proteins were extracted from transfected cells and immunoprecipitation (IP) was carried out using a primary antibody against protein A / G agarose beads (Santa Cruz Biotechnology, Shanghai, China). Subsequently, the isolated immunoprecipitates were analyzed by mass spectrometry (MS, manufactured by Thermo Scientific, Waltham, Massachusetts, USA).

[0136] 554 proteins with potential interactions with EIF3F were identified using IP / MS, and ACSL4 was among them, which facilitated subsequent screening.

[0137] Example 5: Ubiquitination experiments confirmed that EIF3F could remove ubiquitin chains from ACSL4.

[0138] Combined reference Figure 5 ; Cells transfected as indicated were treated with 15 μM MG132 (Sigma) for 8 hours before harvest and then collected in ice-cold PBS. The collected cells were suspended in RIPA buffer (containing 200 mmol of NaCl, 20 mmol of Tris-HCl, pH 8.0, 1 mmol of EDTA, 1 mmol of EGTA, 1% Nonidet P-40, 0.5% sodium dodecyl sulfate, 0.1% SDS, 2.5 mmol of sodium pyrophosphate, 1 mmol of β-glycerophosphate, 1 mmol of Na3VO4, protease inhibitor mixture and 15 μM MG132) on ice and lysed for 10 minutes. The lysates were immunoprecipitated for ubiquitinated proteins using specific antibodies. The immunoprecipitated samples were analyzed by Western blotting (WB) via SDS-PAGE.

[0139] Protein extraction and Western blotting (WB)

[0140] HCC cell and tissue samples were lysed in radioimmunoprecipitation assay (RIPA) lysis buffer (Beyotime, Haimen, China). Protein concentration was determined using an enhanced BCA protein assay kit (Beyotime). Proteins were separated by 10% or 12.5% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, Massachusetts, USA). The membranes were then blocked with blocking buffer (Beyotime, Haimen, China) for 2 hours at room temperature and incubated with primary antibodies overnight at 4°C. After washing three times (15 minutes each time) with Tris-buffered saline solution containing 0.1% Tween (TBST), the membranes were incubated with horseradish peroxidase-conjugated secondary antibodies for 1 hour at room temperature. The membranes were then washed again, and protein expression levels were visualized using a Super ECL chemiluminescent substrate kit (US Everbright, Suzhou, China). Relative protein expression was quantified using ImageJ software (National Institutes of Health, Bethesda, MD, USA).

[0141] Example 6: Mass cytometry experiment.

[0142] In combination with reference Figure 6: Wash the cells once with 1X phosphate-buffered saline (PBS), stain with 100 μL of 250 nM cisplatin (Fluidigm) on ice for 5 minutes to exclude dead cells, then incubate in Fc receptor blocking solution, and then stain with surface antibodies on ice for 30 minutes. Then, wash the cells 2 times with FACS buffer [1X PBS + 0.5% bovine serum albumin (BSA)] and fix overnight in 200 μL of permeabilization solution (Maxpar Fix and Permeabilization buffer containing 250 nM 191 / 193Ir, Fluidigm). After fixation, the cells are washed once with FACS buffer, then once with permeabilization buffer (eBioscience), and finally stained with intracellular antibodies on ice for 30 minutes. Wash and resuspend the cells in deionized water, add 20% EQ beads (Fluidigm), and acquire on a mass cytometer (Helios, Fluidigm). Process and normalize the file (.fcs) as described and upload it to Cytobank (Beckman Coulter Inc.), manually gate the CD45+ population, and perform biaxial marker expression in Cytobank for visualization. For downstream analysis, load the.fcs file into R v4.1.3 using the FlowCore package. We sampled CD45+ cells from each group and performed high-resolution unsupervised clustering and meta-clustering using the FlowSOM and ConsensusClusterPlus packages, respectively. Use cytometry data analysis tools for data visualization.

[0143] Finally, to visualize high-dimensional cell populations in two dimensions, the t-SNE algorithm was applied to represent the characteristics of annotated cell populations and identified biomarkers.

[0144] Example 7: Experiment on the combination of EIF3F interference and anti-PD-1 monoclonal antibody treatment in immunocompetent black mice. Refer to Figure 7 : H22 cells stably expressing fluorescence were subcutaneously injected under the left rib of C57BL / 6J mice. After subcutaneous tumors grew, small pieces were excised and a mouse orthotopic tumor model was constructed. Then, these mice were treated with LNP-siEIF3F, anti-PD-1 alone, or in combination according to the prescribed dosing regimen.

[0145] Both LNP-siEIF3F or anti-PD-1 alone showed certain tumor inhibitory effects, while combination treatment further enhanced the anti-tumor effect.

[0146] The above examples illustrate that EIF3F discovered by the present invention as a therapeutic target can promote the sensitivity of HCC cells to anti-PD-1 monoclonal antibody, thus providing a new strategy in the immunotherapy of hepatocellular carcinoma.

[0147] The present invention has been described by the above related embodiments. However, the above embodiments are only examples for implementing the present invention. It should be noted that the disclosed embodiments do not limit the scope of the present invention. On the contrary, changes and modifications made without departing from the spirit and scope of the present invention fall within the scope of patent protection of the present invention.

Claims

1. Use of a targeting EIF3F inhibitor in the preparation of a tumor therapeutic drug.

2. Use of the targeted EIF3F inhibitor as described in claim 1 in the preparation of a tumor therapeutic drug, characterized in that: The tumor therapeutic drug has at least one of the following functions: inhibiting the proliferation of tumor cells; inhibiting the growth of tumors.

3. Use of the targeted EIF3F inhibitor according to claim 2 in the preparation of a tumor therapeutic drug, characterized in that: The tumor is a tumor overexpressing EIF3F; the tumor includes hepatocellular carcinoma.

4. Use of the targeting EIF3F inhibitor as claimed in claim 1 in the preparation of a tumor therapeutic drug, characterized in that: The EIF3F inhibitor inhibits the activity of EIF3F, or inhibits the transcription or expression of the EIF3F gene.

5. Use of the EIF3F inhibitor targeted as claimed in claim 1 in the preparation of a tumor therapeutic drug, characterized in that: The EIF3F inhibitor is siRNA, shRNA, an antibody or a small molecule compound.

6. Use of the targeted EIF3F inhibitor as claimed in claim 1 in the preparation of a tumor therapeutic drug, characterized in that: The EIF3F inhibitor is the sole active ingredient or one of the active ingredients of the tumor therapeutic drug.

7. Use of an EIF3F inhibitor in the preparation of a drug having at least one of the following functions: activating fatty acid synthesis in tumor cells; promoting deubiquitination of ACSL4 in tumor cells.

8. A method for treating a tumor, which is to administer an EIF3F inhibitor to a subject.

9. A tumor therapeutic drug, comprising an effective amount of an EIF3F inhibitor and a pharmaceutical carrier.

10. A tumor therapeutic drug combination, comprising an effective amount of an EIF3F inhibitor and at least one other tumor therapeutic drug.

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