Methods, systems, and apparatuses for screening drugs for retroperitoneal leiomyosarcoma treatment based on junb and eno1

Abstract

The application discloses a method, a system and equipment for screening a retroperitoneal leiomyosarcoma treatment drug based on JUNB and ENO1. It is found for the first time that in retroperitoneal leiomyosarcoma cells, JUNB can regulate the expression level of ENO1 and further regulate tumor growth; based on this, the application provides a method for screening a retroperitoneal leiomyosarcoma treatment drug based on JUNB and ENO1 for the field. The application provides a high-efficiency, precise and rapid screening method for new drug research and development for treating retroperitoneal leiomyosarcoma, and has a wide application prospect in the technical field of new drug screening for treating retroperitoneal leiomyosarcoma.

Description

Technical Field

[0001] This invention belongs to the field of computer-aided drug screening technology. Specifically, this invention relates to a method, system, and device for screening drugs for the treatment of retroperitoneal leiomyosarcoma. Background Technology

[0002] Retroperitoneal leiomyosarcoma is a rare but highly aggressive soft tissue sarcoma originating from smooth muscle cells, accounting for approximately 20% of all retroperitoneal sarcomas, second only to liposarcoma in incidence. Due to its deep anatomical location within the retroperitoneal space, it lacks specific clinical symptoms in the early stages, and patients often present with a large tumor that frequently involves vital organs and structures such as the inferior vena cava, abdominal aorta, kidneys, pancreas, and duodenum. Epidemiological data show that this disease is most common in adults aged 40-70 years, with a significantly higher incidence in women than men.

[0003] Currently, complete surgical resection is the cornerstone and only potentially curative treatment for resectable retroperitoneal leiomyosarcomas. However, because the tumor often originates from or invades the walls of major retroperitoneal vessels, achieving complete resection (R0 / R1 resection) is technically extremely challenging, often requiring combined organ resection and major vascular reconstruction. This places extremely high demands on the surgical team, and even with complete resection, patient prognosis remains unsatisfactory. In terms of adjuvant therapy, the benefits of existing methods are also very limited. Therefore, the development of novel molecularly targeted drugs has become one of the hot topics in retroperitoneal leiomyosarcoma research.

[0004] Computer-aided drug design (Computer Aided Drug Design (CADD) is based on computer computation and simulation technology. By learning from the prior knowledge contained in massive amounts of drug data, it can uncover the interaction relationships between target molecules and candidate drugs, and rapidly select drug-like active drug molecules from millions of molecules. This greatly reduces the blind spots in screening candidate drug molecules and improves research and development efficiency. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the present invention aims to provide a method, system and device for screening drugs for the treatment of retroperitoneal leiomyosarcoma.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for screening potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0007] Furthermore, the method includes: S1, contact the test compound with the system expressing JUNB; S2, to detect the phosphorylation level of JUNB protein in the system after contact; S3, compounds that can reduce the phosphorylation level of JUNB protein were selected as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0008] Furthermore, the method also includes S4, detecting the effect of the compounds screened in step S3 on the binding ability of JUNB and ENO1, and selecting compounds that can inhibit the binding ability of JUNB and ENO1 as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0009] Furthermore, the system includes: a cellular system, a subcellular system, a solution system, a tissue system, an organ system, or an animal system.

[0010] Furthermore, the test compound includes a JNK pathway inhibitor.

[0011] A second aspect of the present invention provides a system for screening potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0012] Furthermore, the system includes: Processing unit: Contacts the test compound with the system expressing JUNB; Detection unit: Detects the phosphorylation level of JUNB protein in the system after contact; Screening Unit: Compounds that can reduce the phosphorylation level of JUNB protein are selected as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0013] Furthermore, the system also includes a verification unit: detecting the effect of compounds screened by the screening unit on the binding ability of JUNB and ENO1, and selecting compounds that can inhibit the binding ability of JUNB and ENO1 as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0014] A third aspect of the present invention provides a method for screening therapeutic drugs for retroperitoneal leiomyosarcoma based on JUNB and ENO1.

[0015] Furthermore, the method includes: Obtain protein or gene data for JUNB and ENO1; The binding sites of JUNB and ENO1 were determined based on the protein or gene data of JUNB and ENO1. Candidate drugs targeting the binding sites of both substances were obtained through computer-aided screening.

[0016] Furthermore, the method for determining the binding sites of JUNB and ENO1 includes: Construct n mutants of the ENO1 DNA sequence, where n is a natural number greater than or equal to 1; The n mutants were subjected to chromatin immunoprecipitation experiments with JUNB protein to detect the binding abundance or enrichment level of JUNB protein with different mutants. By comparing the binding results of JUNB with various mutants, the key recognition sequence or region of JUNB protein on ENO1 was located, which is the binding site of the two.

[0017] Furthermore, the computer-aided screening process is as follows: small molecule compounds are screened based on the spatial structure of the JUNB and ENO1 binding sites; the screened small molecule compounds are molecularly docked with the binding sites to obtain the scores of the docked molecules; and candidate drugs are obtained by sorting the results according to the scores.

[0018] Furthermore, the computer-aided screening process is as follows: obtaining the molecular structures of JUNB protein and ENO1 gene and inputting the molecular structures into a pharmacophore module library for matching; clustering all action sites based on the interaction patterns with JUNB and ENO1 to obtain a pharmacophore model; and inputting the pharmacophore model into a molecular compound database for high-throughput screening to obtain candidate drugs.

[0019] Furthermore, the computer-aided screening process is as follows: first, obtain the molecular structure of the small molecule inhibitor that inhibits JUNB and / or the molecular structure of the small molecule inhibitor that inhibits ENO1; then, screen for a small molecule library with similar structures based on the molecular structure of the small molecule inhibitor; then, perform molecular docking of the small molecule library with similar structures to the binding site to obtain the score of the docking molecules; finally, sort them to obtain candidate drugs.

[0020] Furthermore, the method further includes: obtaining a retroperitoneal leiomyosarcoma animal model or a retroperitoneal leiomyosarcoma cell model; and treating the retroperitoneal leiomyosarcoma animal model or the retroperitoneal leiomyosarcoma cell model with the screened candidate drug to verify the effectiveness of the candidate drug.

[0021] Furthermore, the candidate drug inhibits the growth, proliferation, migration and / or promotes apoptosis of retroperitoneal leiomyosarcoma cells.

[0022] Furthermore, the candidate drug inhibits ATP and / or lactate levels within retroperitoneal leiomyosarcoma cells.

[0023] Furthermore, the candidate drugs include small molecule compounds, peptide analogs, polypeptides, and antibodies.

[0024] Furthermore, the small molecule compounds include inorganic small molecule compounds, siRNA, shRNA, dsRNA, microRNA, or antisense nucleic acids.

[0025] A fourth aspect of the present invention provides a system for screening therapeutic agents for retroperitoneal leiomyosarcoma based on JUNB and ENO1.

[0026] Furthermore, the system includes: Data Acquisition Module: Used to acquire protein or gene data for JUNB and ENO1; Binding site determination module: Determine the binding sites of JUNB and ENO1 based on the protein or gene data of JUNB and ENO1; Candidate drug screening module: Candidate drugs targeting the binding sites of the two are obtained through computer-aided screening.

[0027] The fifth aspect of the present invention provides a computer device.

[0028] Furthermore, the computer device includes a memory and a processor, the memory being used to store program instructions; the processor being used to invoke the program instructions, and when the program instructions are executed, to implement the method described in the first or third aspect of the present invention.

[0029] The sixth aspect of the present invention provides a computer-readable storage medium.

[0030] Furthermore, it stores a computer program that, when executed by a processor, implements the method described in the first or third aspect of the present invention.

[0031] The seventh aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first or third aspect of the present invention.

[0032] The eighth aspect of the present invention provides a method for regulating the expression level of ENO1 in retroperitoneal leiomyosarcoma cells.

[0033] Furthermore, the method includes administering JUNB protein, a JUNB promoter, or a JUNB inhibitor to retroperitoneal leiomyosarcoma cells.

[0034] Furthermore, the JUNB protein and JUNB promoter promote the expression level of ENO1.

[0035] Furthermore, the JUNB inhibitor suppresses the expression level of ENO1.

[0036] The ninth aspect of the present invention provides any of the following applications: (1) Application of JNK pathway inhibitors in the preparation of drugs for the treatment of retroperitoneal leiomyosarcoma; Furthermore, the JNK pathway inhibitor is JNK-IN-8; (2) Application of substances that inhibit the binding of JUNB and ENO1 in the preparation of screening drugs for the treatment of retroperitoneal leiomyosarcoma; (3) Application of JUNB in ​​regulating ENO1 expression; Furthermore, the JUNB positively regulates the expression level of ENO1.

[0037] Advantages and beneficial effects of the present invention: In this invention, the inventors have demonstrated for the first time that JUNB enhances aerobic glycolysis in retroperitoneal leiomyosarcoma cells by positively regulating ENO1 expression levels, thereby promoting the progression of retroperitoneal leiomyosarcoma. ChIP-qPCR experiments have further demonstrated a direct binding effect between the two. Inhibiting ENO1 expression levels can reverse the cell survival enhancement effect induced by JUNB overexpression. In other words, this application is the first to discover that the JNK-JUNB pathway can regulate ENO1 expression levels and thus regulate tumor growth in retroperitoneal leiomyosarcoma cells. Based on this, this application provides a method for screening therapeutic drugs for retroperitoneal leiomyosarcoma. This invention provides an efficient, precise, and rapid screening method for the development of new drugs for the treatment of retroperitoneal leiomyosarcoma, and has broad application prospects in this technical field. Attached Figure Description

[0038] Figure 1 A flowchart illustrating a method for screening potential therapeutic agents for retroperitoneal leiomyosarcoma, provided in an embodiment of the present invention; Figure 2 A schematic diagram of a system for screening potential therapeutic drugs for retroperitoneal leiomyosarcoma provided in an embodiment of the present invention; Figure 3 A flowchart illustrating a method for screening therapeutic drugs for retroperitoneal leiomyosarcoma based on JUNB and ENO1, provided for an embodiment of the present invention; Figure 4 A schematic diagram of a drug screening system for retroperitoneal leiomyosarcoma based on JUNB and ENO1 is provided for an embodiment of the present invention. Figure 5 A schematic diagram of a computer device provided by the present invention; Figure 6 The results showed that ENO1 was significantly upregulated in RPLMS; among which... Figure 6 A is a volcano plot of differential gene expression between tumors (FM_T) and adjacent normal tissues (FM_N) based on RNA-seq data. Red dots indicate gene upregulation, and blue dots indicate gene downregulation. Figure 6 B represents the KEGG pathway enrichment analysis of differentially expressed genes; Figure 6 C represents the RT-qPCR results of ENO1 mRNA expression in SK-LMS-1, FM1, and FM2 cell lines; Figure 6 D represents the Western blot results of clinical samples from patients; Figure 7 Transcriptomic analysis of RNA-seq data; among which... Figure 7Principal component analysis (PCA) showed a clear separation between the FM_T and FM_N samples; Figure 7 B is a hierarchical clustering heatmap of differentially expressed genes, which shows that there is obvious clustering between tumor and normal tissues; Figure 7 C represents the results of GO enrichment analysis, showing that differentially expressed genes are significantly enriched in cellular metabolic processes; Figure 8 The results showed that ENO1 knockdown inhibited the proliferation and migration of RPLMS cells; among which... Figure 8 A shows the results of Western blot analysis, which indicates that in FM1 and FM2 cells, two independent siRNAs effectively reduced the expression level of ENO1. Figure 8 B represents the results of the MTS experiment, showing that cell viability decreased after ENO1 silencing in FM1 and FM2 cells. Figure 8 C represents the results of the EdU incorporation experiment, showing that cell proliferation decreased after ENO1 silencing. Figure 8 D represents the results of the clonogenic assay, showing that the long-term proliferative capacity of FM1 and FM2 cells was impaired after ENO1 silencing. Figure 8 E represents the results of the Transwell migration assay, indicating that ENO1 knockdown reduced the cell's migration ability. Figure 9 This study demonstrates that the JNK-JUNB signaling pathway regulates the transcriptional and glycolytic metabolism of ENO1 in retroperitoneal leiomyosarcoma; among which, Figure 9 A shows that JUNB was enriched in the ENO1 promoter region, as indicated by ChIP-qPCR analysis. Figure 9 B is a Western blot analysis of phosphorylated JNK ( p Expression of JNK and JUNB in ​​tumors and adjacent normal tissues; Figure 9 C represents cells FM1 and FM2. p -Western blot analysis of JNK, ENO1 and JUNB protein levels; Figure 9 D indicates that RT-qPCR analysis showed decreased ENO1 mRNA expression after JNK pathway inhibition; Figure 9 ChIP-qPCR analysis showed that JUNB binding to the ENO1 promoter was reduced after JNK pathway inhibition. Figure 9 F represents the intracellular ATP levels in FM1 and FM2 cells treated with JNK pathway inhibitors; Figure 9 G represents lactate production in FM1 and FM2 cells after JNK pathway inhibition; Figure 10 JUNB overexpression was shown to enhance ENO1-mediated glycolytic metabolism in retroperitoneal leiomyosarcoma cells; among which... Figure 10A. Western blot analysis showed that JUNB overexpression increased ENO1 expression, while JUNB levels did not change after ENO1 knockdown in FM1 and FM2 cells. Figure 10 The MTS experiment showed that ENO1 knockdown could significantly rescue the survival of cells with enhanced JUNB overexpression. Figure 10 C represents the intracellular ATP levels in FM1 and FM2 cells after JUNB overexpression and ENO1 knockdown; Figure 10 D represents lactate production in FM1 and FM2 cells after JUNB overexpression and ENO1 knockdown; Figure 10 E-Cellular Acidification Rate (ECAR) analysis showed that ENO1 knockdown partially restored JUNB overexpression-induced glycolytic activation. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0040] In some processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0041] Unless otherwise specified in the following examples, the conditions should be performed under standard conditions or conditions recommended by the manufacturer. Reagents or instruments whose manufacturers are not specified are all commercially available products.

[0042] Figure 1 This is a flowchart illustrating a method for screening potential therapeutic agents for retroperitoneal leiomyosarcoma, as provided in an embodiment of the present invention. The method includes: S1, contact the test compound with the system expressing JUNB; S2, to detect the phosphorylation level of JUNB protein in the system after contact; S3, compounds that can reduce the phosphorylation level of JUNB protein were selected as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0043] Furthermore, the method also includes S4, detecting the effect of the compounds screened in step S3 on the binding ability of JUNB and ENO1, and selecting compounds that can inhibit the binding ability of JUNB and ENO1 as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0044] Furthermore, the system includes: a cellular system, a subcellular system, a solution system, a tissue system, an organ system, or an animal system; Furthermore, the test compound includes a JNK pathway inhibitor.

[0045] In this invention, the inventors have demonstrated for the first time that substances capable of regulating JUNB transcriptional activity can affect the progression of retroperitoneal leiomyosarcoma. Therefore, this application provides a method for screening potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0046] Figure 2 This is a schematic diagram of a system for screening potential therapeutic agents for retroperitoneal leiomyosarcoma provided by the present invention, the system comprising: 201 Processing Unit: Contacts the test compound with the system expressing JUNB; Detection Unit 202: Detects the phosphorylation level of JUNB protein in the system after contact; 203 Screening Unit: Select compounds that can reduce the phosphorylation level of JUNB protein as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0047] Furthermore, the system also includes a verification unit: detecting the effect of compounds screened by the screening unit on the binding ability of JUNB and ENO1, and selecting compounds that can inhibit the binding ability of JUNB and ENO1 as potential therapeutic agents for retroperitoneal leiomyosarcoma.

[0048] Figure 3 The following is a flowchart illustrating a method for screening therapeutic agents for retroperitoneal leiomyosarcoma based on JUNB and ENO1, as provided in an embodiment of the present invention. Specifically, the screening method includes the following steps: 301: Obtain protein or gene data for JUNB and ENO1; In this invention, the inventors have demonstrated for the first time that JUNB enhances aerobic glycolysis in retroperitoneal leiomyosarcoma cells by positively regulating the expression level of ENO1, thereby promoting the progression of retroperitoneal leiomyosarcoma. ChIP-qPCR experiments have also demonstrated a direct binding effect between the two. Furthermore, inhibiting the expression level of ENO1 can reverse the cell survival enhancement effect induced by JUNB overexpression. In other words, this application is the first to discover that JUNB can regulate the expression level of ENO1 in retroperitoneal leiomyosarcoma cells, thereby regulating tumor growth. Based on this, this application provides a method for screening therapeutic drugs for retroperitoneal leiomyosarcoma based on JUNB and ENO1.

[0049] In some implementations, the corresponding Gene IDs of JUNB and ENO1 in NCBI are 3726 and 2023, respectively, and their detailed information (including sequence information, etc.) can be obtained at https: / / www.ncbi.nlm.nih.gov / gene / .

[0050] 302: Determine the binding sites of JUNB and ENO1 based on the protein or gene data of the two; In some embodiments, the binding sites of JUNB and ENO1 in the JUNB and ENO1 complex may be determined by nucleic acid-protein docking or any other prior art known to be able to determine the interaction sites between nucleic acids and proteins.

[0051] In some embodiments, nucleic acid-protein docking refers to a computational simulation process that uses specific algorithms and procedures to dock the structures of proteins and nucleic acids (such as DNA or RNA) together. This process can be used to study the interactions between proteins and nucleic acids, as well as their potential biological functions.

[0052] In protein-nucleic acid docking, software such as NAflex is commonly used for computational simulation. NAflex is a software specifically developed for nucleic acid structure prediction and design, capable of accurate modeling and docking of DNA or RNA molecules. NAflex employs a fragment-based scoring method, enabling fast and accurate docking. It considers various complex interactions, such as hydrogen bonds, ionic bonds, and hydrophobic interactions, and can precisely adjust the side-chain conformation during docking. The basic algorithm of NAflex consists of two stages. The first stage is a low-precision stage, which mainly searches for a coarse match between the protein and nucleic acid. The second stage is a high-precision stage, which considers all side-chain conformations and calculates more accurate interaction energies. In the low-precision stage, NAflex randomly translates and rotates the nucleic acid molecule and performs a certain number of rigid body movements before calculating the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program performs further optimization and adjustments to achieve more accurate docking.

[0053] In some implementations, the method for determining the binding sites of both JUNB and ENO1 includes: Construct n mutants of the ENO1 DNA sequence, where n is a natural number greater than or equal to 1; The n mutants were subjected to chromatin immunoprecipitation experiments with JUNB protein to detect the binding abundance or enrichment level of JUNB protein with different mutants. By comparing the binding results of JUNB with various mutants, the key recognition sequence or region of JUNB protein on ENO1 was located, which is the binding site of the two.

[0054] 303: Candidate drugs targeting the binding sites of both were obtained through computer-aided screening.

[0055] In some implementations, the computer-aided screening process is as follows: screening small molecule compounds based on the spatial structure of the JUNB and ENO1 binding sites; performing molecular docking of the screened small molecule compounds with the binding sites to obtain a score for the docked molecules; and sorting the docked molecules according to the scores to obtain candidate drugs.

[0056] In some implementations, the computer-aided screening process is as follows: obtaining the molecular structures of the JUNB protein and ENO1 gene and inputting the molecular structures into a pharmacophore module library for matching; clustering all action sites based on the interaction patterns with JUNB and ENO1 to obtain a pharmacophore model; and inputting the pharmacophore model into a molecular compound database for high-throughput screening to obtain candidate drugs.

[0057] In some implementations, the computer-aided screening process is as follows: first, obtain the molecular structure of the small molecule inhibitor that inhibits JUNB and / or the molecular structure of the small molecule inhibitor that inhibits ENO1; then, screen for a library of small molecules with similar structures based on the molecular structure of the small molecule inhibitor; then, perform molecular docking of the small molecule library with similar structures to the binding site to obtain the score of the docking molecules; and finally, sort them to obtain candidate drugs.

[0058] In some embodiments, the small molecule compound includes, but is not limited to: inorganic small molecule compounds, protein analogs, nucleic acid analogs, peptides, antibodies, siRNA, shRNA, dsRNA, microRNA, or antisense nucleic acids.

[0059] It should be noted that the present invention does not impose any particular limitation on the specific type of small molecule compound. Any small molecule compound that may be used in the computer-aided drug screening method provided by the present invention and may produce the corresponding effect falls within the protection scope of the present invention.

[0060] In some embodiments, during the molecular docking process, affinity refers to the tightness of the binding between the molecule and the acceptor. High affinity means a more stable binding, while low affinity indicates a less stable binding. Affinity is typically obtained through calculations, such as by calculating the binding free energy (ΔG) or the binding constant (Kd).

[0061] In some implementations, during the molecular docking process, affinity depends on the interactions between the molecule and the receptor, including hydrogen bonds, van der Waals forces, electrostatic interactions, etc. These interactions collectively determine the binding mode between the molecule and the receptor, thus affecting affinity. To assess affinity, scoring systems or methods are typically used to quantify the interactions between the molecule and the receptor. These scoring methods are based on different algorithms and physical models and can reflect the binding energy, interaction type, and affinity between the molecule and the receptor.

[0062] In some implementations, the mode of screening drugs using computer-aided drug screening technology may include any one or more of the following modes, depending on the type of small molecule compound (inorganic small molecule compound, protein analog, nucleic acid analog, peptide, antibody, siRNA, shRNA, dsRNA, microRNA, or antisense nucleic acid, etc.): protein Small molecule docking, protein Protein docking, protein Nucleic acid testing docking.

[0063] Specifically, the protein Small molecule docking refers to the computational simulation process of docking the structures of proteins and small molecules (such as drug molecules) together using specific algorithms and programs. This process can be used to study the interactions between proteins and small molecules, as well as their potential biological functions. Software such as DOCK is typically used for computational simulation. DOCK is a highly automated drug design software capable of docking small molecule ligands with biomolecular receptors. It employs a fragment-based scoring method, enabling fast and accurate docking. The basic algorithm of DOCK software consists of two stages. The first stage is a low-precision stage, which mainly searches for a coarse match between the small molecule ligand and the biomolecular receptor. The second stage is a high-precision stage, which considers all side chain conformations and calculates more precise interaction energies. In the low-precision stage, DOCK software randomly translates and rotates the small molecule ligand and performs a certain number of rigid body movements before calculating the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program performs further optimization and adjustments to achieve more accurate docking.

[0064] Specifically, the protein Protein docking is a computational simulation process that uses specific algorithms and programs to dock the structures of two proteins together. This process can be used to study protein-protein interactions and potential biological functions. RosettaDock is a commonly used protein docking technique. RosettaDock is a protein docking software that uses a fragment-based scoring method to achieve fast and accurate protein docking. The software can precisely adjust the side-chain conformation during docking and considers various complex interactions, such as hydrogen bonds, ionic bonds, and hydrophobic interactions. RosettaDock's basic algorithm consists of two stages. The first stage is a low-precision stage, which mainly searches for the degree of skeletal shape compatibility between two proteins. The second stage is a high-precision stage, which considers all side-chain conformations and calculates more precise interaction energies. In the low-precision stage, the program randomly translates and rotates a component of a protein molecule and performs a certain number of rigid body movements, then calculates the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program performs 50 MCMPCCcycles: rearranging the conformations and minimizing the interaction energy, using this as the initial starting conformation.

[0065] Specifically, the protein Nucleic acid docking refers to the computational simulation process of connecting the structures of proteins and nucleic acids (such as DNA or RNA) using specific algorithms and procedures. This process can be used to study the interactions between proteins and nucleic acids, as well as their potential biological functions. Computational simulations are typically performed using software such as NAflex. NAflex is a software specifically developed for nucleic acid structure prediction and design, capable of accurately modeling and docking DNA or RNA molecules. NAflex employs a fragment-based scoring method, enabling fast and accurate docking. It considers various complex interactions, such as hydrogen bonds, ionic bonds, and hydrophobic interactions, and can precisely adjust the side-chain conformations during docking. The basic algorithm of NAflex consists of two stages. The first stage is a low-precision stage, which mainly searches for a coarse match between the protein and nucleic acid. The second stage is a high-precision stage, which considers all side-chain conformations and calculates more precise interaction energies. In the low-precision stage, NAflex randomly translates and rotates the nucleic acid molecule and performs a certain number of rigid body movements before calculating the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program will undergo further optimization and adjustments to achieve more accurate docking.

[0066] In some implementations, computer-aided drug screening is a technique that uses computer-aided drug design methods to screen drugs. It can help researchers quickly screen a large number of small molecule compounds for candidate drugs that have strong binding affinity to target proteins and potential efficacy.

[0067] In some implementations, molecular docking is a method for drug design based on the characteristics of the receptor and the interaction between the receptor and the drug molecule. It is a theoretical simulation method that primarily studies intermolecular interactions (such as ligand-receptor) and predicts their binding modes and affinities. This method is widely used in the early stages of drug development to help researchers quickly screen compounds with potential efficacy. Molecular docking methods mainly focus on spatial matching and energy matching. Spatial matching refers to the geometric complementarity between the drug molecule and the receptor protein, while energy matching refers to the minimization of the interaction between the drug molecule and the receptor protein. For geometric matching calculations, methods such as grid computing and fragment growth are commonly used, while energy calculations use methods such as simulated annealing and genetic algorithms. Based on the degree and method of simplification, molecular docking methods can be divided into rigid docking, semi-flexible docking, and flexible docking. In rigid docking, the conformation of the molecules involved in docking does not change during the calculation; only the spatial position and orientation of the molecules are altered. Semi-flexible docking allows for partial conformational changes during the calculation. Flexible docking allows for even more conformational changes.

[0068] In some implementations, the molecular libraries used in virtual drug screening primarily include: ZINC: containing over 250 million commercially available compounds for small molecule virtual screening; PubChem: containing bioactive substances for small molecule virtual screening; DrugBank: containing drugs and small molecules for drug design and discovery; ChEMBL: containing small molecules for drug discovery and chemical genomics research; ChemDB: containing a large number of known small molecules for chemical genomics research and drug discovery; HMDB: containing a large number of known small molecules for chemical genomics research and drug discovery; BindingDB: containing a large number of known small molecules for chemical genomics research and drug discovery; and SMPDB: containing a large number of known small molecules for chemical genomics research and drug discovery. In addition, there are some commercial databases such as ChemDiv, Enamine, Lifechemicals, Specs, Chembridge, Maybridge, Microsource, and Vitas. Databases such as M and Interbioscreen are also frequently used for virtual drug screening.

[0069] In some implementations, the method further includes: 304: Obtain an animal model or cell model of retroperitoneal leiomyosarcoma; treat the animal model or cell model of retroperitoneal leiomyosarcoma with the candidate drug obtained from screening to verify the effectiveness of the candidate drug.

[0070] Furthermore, the candidate drug inhibits the growth, proliferation, migration and / or promotes apoptosis of retroperitoneal leiomyosarcoma cells.

[0071] In some implementations, the animal model is not particularly limited and can be constructed using conventional construction methods well known to those skilled in the art. The animals include, but are not limited to, mice, rats, guinea pigs, rabbits, pigs, chickens, pigeons, monkeys, and dogs.

[0072] In one specific embodiment of the present invention, the applicant demonstrated that overexpression of ENO1 in patients with retroperitoneal leiomyosarcoma and knockdown of ENO1 could inhibit the proliferation and migration of retroperitoneal leiomyosarcoma cells.

[0073] Specifically, to explore aberrantly expressed genes and dysregulated pathways in retroperitoneal leiomyosarcoma, three patients were recruited from Peking University People's Hospital. Clinical tumors (FM_T) and adjacent normal tissues (FM_N) were collected, and RNA sequencing (RNA-seq) was performed. Differential expression analysis showed that compared with FM_N, 2995 genes were significantly downregulated and 1515 genes were upregulated in FM_T. Figure 6 A). Principal component analysis (PCA) confirmed a clear separation between tumor and normal tissue samples, with distinct clustering within each group. Figure 7 A). Similarly, hierarchical cluster analysis further revealed significant differences in gene expression between tumor and normal tissues, with samples within the same group exhibiting clustering characteristics. Figure 7 B).

[0074] Furthermore, through gene ontology (GO) analysis ( Figure 7 C) KEGG pathway enrichment analysis explored the biological significance of differentially expressed genes (DEGs). KEGG pathway analysis identified several metabolically related pathways, including the PI3K-AKT and mTOR signaling pathways, which were significantly enriched in DEGs. Figure 6 (B) Notably, both glycolysis and oxidative phosphorylation pathways were enriched, indicating a metabolic reprogramming shift in retroperitoneal leiomyosarcoma. Among the identified glycolytic genes, ENO1, a key rate-limiting enzyme in the glycolytic pathway, was significantly upregulated in FM_T tissues.

[0075] To verify the above findings, RT-qPCR analysis was performed on the SK-LMS-1, FM1, and FM2 cell lines. FM1 and FM2 are patient-derived cell lines, which we established ourselves from freshly excised tumor tissue at Peking University People's Hospital. The specific procedure was as follows: the tumor specimen was first cut into small fragments, then digested in a 37°C water bath with a mixture containing papain, collagenase, and DNase I to break down the tissue fragments into single cells. The resulting cell suspension was filtered through sterile gauze and seeded into culture dishes. Subsequently, we selected single cell clones and expanded their culture to obtain stable cell lines FM1 and FM2. FM1 and FM2 cells were also cultured in MEM medium containing 10% fetal bovine serum and 1% penicillin-streptomycin. The results showed that ENO1 mRNA expression was significantly upregulated in these cell lines (…). Figure 6 C). Furthermore, Western blot (WB) analysis of patient clinical samples confirmed that the ENO1 protein level in tumor tissue was significantly higher than that in adjacent normal tissue. Figure 6 D), which further supports the significant upregulation of ENO1 in retroperitoneal leiomyosarcoma.

[0076] Furthermore, to investigate the biological role of ENO1 in retroperitoneal leiomyosarcoma, we used two independent small interfering RNAs (siRNAs, siENO1-1 with the sequence CCACUGUUGAGGUUGAU CUCU (SEQ ID NO:1); and siENO1-2 with the sequence GAAUGUCAUCAAGGAGAAAUA (SEQ ID NO:2)) to silence ENO1 expression in patient-derived leiomyosarcoma cell lines FM1 and FM2. Western blot analysis confirmed that both siRNAs effectively reduced ENO1 protein levels in FM1 and FM2 cells. Figure 8 A). Cell viability was then assessed using the MTS assay. Figure 8 As shown in Figure B, compared with the control group, ENO1 knockdown significantly reduced the survival rate of FM1 and FM2 cells. EdU labeling experiments further confirmed that ENO1 deficiency significantly impaired the proliferative capacity of leiomyosarcoma cells. Figure 8 C). To assess long-term proliferative potential, we performed a colony formation assay. The results showed that ENO1 knockdown significantly impaired the colony-forming ability of FM1 and FM2 cells. Figure 8 D). Furthermore, Transwell migration assays showed that silencing ENO1 significantly inhibited the migration ability of leiomyosarcoma cells. Figure 8 E). The above results suggest that ENO1 may serve as a potential therapeutic target for retroperitoneal leiomyosarcoma.

[0077] In one specific embodiment of the present invention, the applicant demonstrated that JUNB enhances aerobic glycolysis in retroperitoneal leiomyosarcoma cells by positively regulating the expression level of ENO1, thereby promoting the progression of retroperitoneal leiomyosarcoma.

[0078] Specifically, to elucidate the molecular mechanism of ENO1 upregulation in retroperitoneal leiomyosarcoma, we focused on transcriptional regulation, as ENO1 mRNA levels were significantly upregulated in patient tumor tissues. Through bioinformatics prediction analysis, JUNB was identified as a potential transcription factor capable of binding to the ENO1 promoter. Furthermore, chromatin immunoprecipitation (ChIP-qPCR) experiments confirmed significant enrichment of JUNB in ​​the ENO1 promoter region. Figure 9 A), which supports a direct transcriptional regulatory relationship between JUNB and ENO1.

[0079] To further validate the clinical relevance of this regulatory axis, we performed Western blot analysis on tumor and adjacent normal tissues. The results showed that JNK phosphorylation was significantly increased in tumor tissues, while JUNB protein expression was also increased (…). Figure 9 B), suggesting activation of the JNK-JUNB signaling pathway in retroperitoneal leiomyosarcoma. Next, FM1 and FM2 cells were treated with a JNK pathway inhibitor (JNK-IN-8, purchased from MCE, catalog number HY-13319) to investigate its effect on ENO1 regulation. Western blot analysis showed that inhibition of the JNK pathway led to a dose-dependent decrease in JNK phosphorylation and ENO1 protein expression. Figure 9 C). Consistent with this, RT-qPCR analysis showed that inhibition of the JNK pathway significantly reduced the expression of ENO1 mRNA. Figure 9 D). Importantly, ChIP-qPCR analysis further showed that JUNB binding to the ENO1 promoter was significantly reduced after JNK pathway inhibition. Figure 9 (E), indicating that JNK signaling regulates ENO1 transcription by modulating the transcriptional activity of JUNB rather than its expression level.

[0080] Furthermore, functional metabolism experiments showed that inhibiting the JNK pathway significantly reduced intracellular ATP levels in FM1 and FM2 cells. Figure 9 F) and lactate production ( Figure 9 (G) confirms that inhibiting JNK signaling impairs aerobic glycolysis. In summary, these results indicate that activation of the JNK-JUNB axis promotes ENO1 transcription and enhances glycolytic metabolism in retroperitoneal leiomyosarcoma cells.

[0081] To further confirm the role of JUNB in ​​regulating ENO1 and its impact on the malignant phenotype of retroperitoneal leiomyosarcoma, we overexpressed JUNB in ​​FM1 and FM2 cells and performed a rescue experiment by combining it with ENO1 knockdown. Western blot analysis showed that JUNB overexpression significantly increased ENO1 expression, while ENO1 knockdown did not affect JUNB protein levels. Figure 10 A). Next, we used the MTS assay to assess cell viability. ENO1 knockdown significantly rescued the enhanced cell viability induced by JUNB overexpression ( Figure 10 B). Furthermore, functional metabolic analysis confirmed that ENO1 is involved in JUNB-driven glycolysis. ATP levels ( Figure 10 C) Lactic acid production ( Figure 10 D) and extracellular acidification rate (ECAR) analysis ( Figure 10 E) Both results indicate that ENO1 knockdown partially reversed JUNB overexpression-induced glycolytic activation. These results suggest that JUNB enhances aerobic glycolysis in retroperitoneal leiomyosarcoma cells by upregulating ENO1, thereby promoting a more aggressive tumor phenotype.

[0082] In summary, the above results confirm that JUNB promotes the progression of retroperitoneal leiomyosarcoma by upregulating ENO1, suggesting that targeting the binding sites of JUNB and ENO1 to screen drugs can treat retroperitoneal leiomyosarcoma.

[0083] Figure 4 A schematic diagram of a drug screening system for retroperitoneal leiomyosarcoma based on JUNB and ENO1 provided in this embodiment of the invention. Specifically, the system includes: 401 Data Acquisition Module: Used to acquire protein or gene data for JUNB and ENO1; 402 Binding Site Determination Module: Determines the binding sites of JUNB and ENO1 based on the protein or gene data of JUNB and ENO1; 403 Candidate Drug Screening Module: This module uses computer-aided screening to identify candidate drugs that target the binding sites of both substances.

[0084] In some implementations, the method for determining the binding sites of both JUNB and ENO1 includes: Construct n mutants of the ENO1 DNA sequence, where n is a natural number greater than or equal to 1; The n mutants were subjected to chromatin immunoprecipitation experiments with JUNB protein to detect the binding abundance or enrichment level of JUNB protein with different mutants. By comparing the binding results of JUNB with various mutants, the key recognition sequence or region of JUNB protein on ENO1 was located, which is the binding site of the two.

[0085] In some implementations, the computer-aided screening process is as follows: screening small molecule compounds based on the spatial structure of the JUNB and ENO1 binding sites; performing molecular docking of the screened small molecule compounds with the binding sites to obtain a score for the docked molecules; and sorting the docked molecules according to the scores to obtain candidate drugs.

[0086] In some implementations, the computer-aided screening process is as follows: obtaining the molecular structures of the JUNB protein and ENO1 gene and inputting the molecular structures into a pharmacophore module library for matching; clustering all action sites based on the interaction patterns with JUNB and ENO1 to obtain a pharmacophore model; and inputting the pharmacophore model into a molecular compound database for high-throughput screening to obtain candidate drugs.

[0087] In some implementations, the computer-aided screening process is as follows: first, obtain the molecular structure of the small molecule inhibitor that inhibits JUNB and / or the molecular structure of the small molecule inhibitor that inhibits ENO1; then, screen for a library of small molecules with similar structures based on the molecular structure of the small molecule inhibitor; then, perform molecular docking of the small molecule library with similar structures to the binding site to obtain the score of the docking molecules; and finally, sort them to obtain candidate drugs.

[0088] In some embodiments, the system further includes a 204 drug efficacy verification module for obtaining a retroperitoneal leiomyosarcoma animal model or a retroperitoneal leiomyosarcoma cell model; and for verifying the efficacy of the candidate drug by treating the screened candidate drug in the retroperitoneal leiomyosarcoma animal model or the retroperitoneal leiomyosarcoma cell model.

[0089] Figure 5 This is a schematic diagram of the computer device provided in this embodiment. Specifically, the computer device includes: 501 is a memory and 502 is a processor, wherein the memory is used to store program instructions; and the processor is used to invoke the program instructions, which, when executed, implement the method described in the first or third aspect of the present invention.

[0090] In some embodiments, the computer device may further include an input device and an output device.

[0091] In some embodiments, the memory, processor, input device, and output device may be connected via a bus or other means. Figure 5 The diagram illustrates a bus-connected configuration as an example; the memory stores program instructions, and the processor invokes these instructions. When the program instructions are executed, they are used to implement the methods described above. In some embodiments, the memory can be understood as any device for storing the program, and the processor can be understood as a device for using the program.

[0092] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in the first or third aspect of this invention.

[0093] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0095] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The present invention does not impose any particular limitation on the specific implementation forms.

[0097] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer device provided by the present invention has been described in detail above. For those skilled in the art, based on the ideas of the embodiments of the present invention, there will be changes in specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for screening potential therapeutic agents for retroperitoneal leiomyosarcoma, characterized in that, The method includes: S1, contact the test compound with the system expressing JUNB; S2, to detect the phosphorylation level of JUNB protein in the system after contact; S3, select compounds that can reduce the phosphorylation level of JUNB protein as potential therapeutic agents for retroperitoneal leiomyosarcoma. Preferably, the method further includes S4, detecting the effect of the compounds screened in step S3 on the binding ability of JUNB and ENO1, and selecting compounds that can inhibit the binding ability of JUNB and ENO1 as potential therapeutic drugs for retroperitoneal leiomyosarcoma. Preferably, the system includes: a cell system, a subcellular system, a solution system, a tissue system, an organ system, or an animal system; Preferably, the test compound includes a JNK pathway inhibitor.

2. A system for screening potential therapeutic agents for retroperitoneal leiomyosarcoma, characterized in that, The system includes: Processing unit: Contacts the test compound with the system expressing JUNB; Detection unit: Detects the phosphorylation level of JUNB protein in the system after contact; Screening Unit: Selecting compounds that can reduce JUNB protein phosphorylation levels as potential therapeutic agents for retroperitoneal leiomyosarcoma; Preferably, the system further includes a verification unit: detecting the effect of compounds screened by the screening unit on the binding ability of JUNB and ENO1, and selecting compounds that can inhibit the binding ability of JUNB and ENO1 as potential therapeutic agents for retroperitoneal leiomyosarcoma.

3. A method for screening therapeutic drugs for retroperitoneal leiomyosarcoma based on JUNB and ENO1, characterized in that, The method includes: Obtain protein or gene data for JUNB and ENO1; The binding sites of JUNB and ENO1 were determined based on the protein or gene data of JUNB and ENO1. Candidate drugs targeting the binding sites of both substances were obtained through computer-aided screening.

4. The method according to claim 3, characterized in that, The method for determining the binding sites of JUNB and ENO1 includes: Construct n mutants of the ENO1 DNA sequence, where n is a natural number greater than or equal to 1; The n mutants were subjected to chromatin immunoprecipitation experiments with JUNB protein to detect the binding abundance or enrichment level of JUNB protein with different mutants. By comparing the binding results of JUNB with various mutants, the key recognition sequence or region of JUNB protein on ENO1 was located, which is the binding site of the two. Preferably, the computer-aided screening process is as follows: screening small molecule compounds based on the spatial structure of the JUNB and ENO1 binding sites; performing molecular docking of the screened small molecule compounds with the binding sites to obtain the scores of the docked molecules; and sorting the docked molecules according to the scores to obtain candidate drugs. Preferably, the computer-aided screening process is as follows: obtaining the molecular structures of JUNB protein and ENO1 gene and inputting the molecular structures into a pharmacophore module library for matching; clustering all action sites based on the interaction patterns with JUNB and ENO1 to obtain a pharmacophore model; and inputting the pharmacophore model into a molecular compound database for high-throughput screening to obtain candidate drugs. Preferably, the computer-aided screening process is as follows: first, obtain the molecular structure of the small molecule inhibitor that inhibits JUNB and / or the molecular structure of the small molecule inhibitor that inhibits ENO1; then, screen for a small molecule library with similar structures based on the molecular structure of the small molecule inhibitor; then, perform molecular docking of the small molecule library with similar structures to the binding site to obtain the score of the docking molecules; finally, sort them to obtain candidate drugs. Preferably, the method further includes: obtaining a retroperitoneal leiomyosarcoma animal model or a retroperitoneal leiomyosarcoma cell model; and treating the retroperitoneal leiomyosarcoma animal model or retroperitoneal leiomyosarcoma cell model with the screened candidate drug to verify the effectiveness of the candidate drug; Preferably, the candidate drug inhibits the growth, proliferation, migration and / or promotes apoptosis of retroperitoneal leiomyosarcoma cells; Preferably, the candidate drug inhibits ATP and / or lactate levels in retroperitoneal leiomyosarcoma cells; Preferably, the candidate drug includes small molecule compounds, peptide analogs, polypeptides, and antibodies; Preferably, the small molecule compound includes inorganic small molecule compounds, siRNA, shRNA, dsRNA, microRNA, or antisense nucleic acid.

5. A system for screening therapeutic agents for retroperitoneal leiomyosarcoma based on JUNB and ENO1, characterized in that, The system includes: Data Acquisition Module: Used to acquire protein or gene data for JUNB and ENO1; Binding site determination module: Determine the binding sites of JUNB and ENO1 based on the protein or gene data of JUNB and ENO1; Candidate drug screening module: Candidate drugs targeting the binding sites of the two are obtained through computer-aided screening.

6. A computer device, characterized in that, The computer device includes a memory and a processor, the memory being used to store program instructions; the processor being used to invoke the program instructions, which, when executed, implement the method of claim 1 or any one of claims 3-4.

7. A computer-readable storage medium, characterized in that, It contains a computer program that, when executed by a processor, implements the method described in claim 1 or any one of claims 3-4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method described in claim 1 or any one of claims 3-4.

9. A method for regulating the expression level of ENO1 in retroperitoneal leiomyosarcoma cells, characterized in that, The method includes administering JUNB protein, a JUNB promoter, or a JUNB inhibitor to retroperitoneal leiomyosarcoma cells. Preferably, the JUNB protein and JUNB promoter promote the expression level of ENO1; Preferably, the JUNB inhibitor inhibits the expression level of ENO1.

10. Any of the following applications: (1) Application of JNK pathway inhibitors in the preparation of drugs for the treatment of retroperitoneal leiomyosarcoma; preferably, the JNK pathway inhibitor is JNK-IN-8; (2) Application of substances that inhibit the binding of JUNB and ENO1 in the preparation of screening drugs for the treatment of retroperitoneal leiomyosarcoma; (3) Application of JUNB in ​​regulating ENO1 expression; preferably, the JUNB positively regulates the expression level of ENO1.

Images