Research method and system for immunomodulatory effect of TPD52
By using single-cell transcriptome sequencing and machine learning algorithms, the immunomodulatory role of TPD52 in the breast cancer tumor microenvironment was accurately identified, solving the technical problem that traditional methods could not resolve. This enabled the identification of key immunomodulatory genes of TPD52, improving the accuracy and stability of predictions, and verifying its function in in vitro experiments.
Patent Information
- Application Number
- CN202511069048.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-31
- Publication Date
- 2025-12-05
AI Technical Summary
Existing technologies struggle to screen key immunomodulatory genes of TPD52 in the breast cancer tumor microenvironment with high throughput and high precision. Traditional transcriptome sequencing cannot resolve cell type-specific gene expression, making the identification of key immunomodulatory genes challenging.
Data on the tumor microenvironment of breast cancer were obtained using single-cell transcriptome sequencing (scRNA-seq). Combined with machine learning algorithms such as Seurat and CoxBoost, TPD52 was identified as a key risk gene for immune regulation through clustering and dimensionality reduction. The association between its expression and patient prognosis was verified in a multicenter cohort, and its immunomodulatory role was confirmed by in vitro experiments.
This study enabled precise localization of TPD52's immune regulatory nodes at single-cell resolution, improving the robustness and accuracy of risk prediction. In vitro experiments verified that downregulation of TPD52 can inhibit tumor cell proliferation and migration, forming a complete chain of evidence from molecular mechanisms to functional phenotypes.
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Figure CN121075428A_ABST
Abstract
Description
Technical Field
[0001] This document relates to the interdisciplinary field of tumor immunology and bioinformatics, and in particular to a research method and system for the immunomodulatory effects of TPD52. Background Technology
[0002] Breast cancer with BRCA (breast cancer stem cell invasion) is one of the most common malignant tumors in women, exhibiting high heterogeneity, and traditional pathological indicators are insufficient for accurately predicting prognosis. In recent years, immune cell infiltration in the tumor microenvironment (TME) has been considered closely related to tumor progression, metastasis, and treatment response. However, high-throughput, high-precision screening of key immune regulatory genes remains a bottleneck. Traditional bulk RNA-seq transcriptome sequencing cannot resolve cell type-specific gene expression, leading to significant challenges in identifying key immune regulatory genes. The advent of single-cell transcriptome sequencing (scRNA-seq) technology has made it possible to analyze the TME at the single-cell level.
[0003] TPD52 (Tumor Protein D52) is a small secreted protein initially identified as a tumor-associated antigen, involved in cell proliferation, migration, and immune regulation. However, the specific immunomodulatory mechanism, prognostic value, and clinical translational applications of TPD52 in breast cancer remain unclear. Current technologies have not yet established a unified and reproducible research paradigm to elucidate the function of TPD52 in the tumor microenvironment (TME). Therefore, a standardized and reproducible research method is urgently needed to systematically elucidate the immunomodulatory role of TPD52 in the breast cancer TME. Summary of the Invention
[0004] This specification provides one or more embodiments of a method for studying the immunomodulatory effects of TPD52, including:
[0005] S1. Obtain single-cell transcriptome data of the breast cancer tumor microenvironment;
[0006] S2. Based on the data, TPD52 was identified as a key risk gene for immune regulation using machine learning algorithms;
[0007] S3. Validate the association between TPD52 expression and patient prognosis in a multicenter cohort;
[0008] S4. Based on the functional annotation and in vivo morphological experiments of TPD52, confirm its immunomodulatory effect and output a comprehensive evaluation report on the immunomodulatory effect of TPD52.
[0009] Furthermore, the specific method for obtaining single-cell transcriptome data of the breast cancer tumor microenvironment is as follows:
[0010] Fresh or frozen breast cancer tissue was mechanically minced and then enzymatically digested to obtain a highly active single-cell suspension.
[0011] The single-cell suspension was sequenced using a high-throughput scRNA-seq platform to obtain a complete transcriptome expression matrix including immune cells, epithelial cells, and stromal cells.
[0012] Furthermore, based on the aforementioned data, the specific method for identifying TPD52 as a key risk gene for immune regulation using machine learning algorithms is as follows:
[0013] Based on the expression matrix, IE-related genes were selected, and the breast cancer tumor microenvironment was divided into at least two immune escape IE modes through Seurat normalization, dimensionality reduction, and k-means clustering.
[0014] The intersection of the differentially expressed gene set in IE mode, the differentially expressed gene set in scRNA-seq, and the differentially expressed gene set in batch sequencing is performed to obtain the crossover gene set.
[0015] The cross-gene set was subjected to initial screening by univariate Cox regression, dimensionality reduction by CoxBoost algorithm, and secondary dimensionality reduction by random survival forest (RSF) algorithm. With the condition that TPD52 was identified as the most prognostic dangerous gene by both algorithms, TPD52 was confirmed as a key dangerous gene for immune regulation.
[0016] Furthermore, the multi-center queue includes the TCGA, METABRIC, and GSE9893 datasets;
[0017] In the multicenter public datasets TCGA, METABRIC, and GSE9893, we validated the association between high TPD52 expression and shortened overall survival in patients.
[0018] Furthermore, the functional annotations were analyzed using GSEA to examine the relevant signal pathways of TPD52, specifically:
[0019] Gene set enrichment analysis (GSEA) was performed on the TPD52 high expression group to determine its significant association with the PD-1 / PD-L1 checkpoint pathway, TGF-β signaling pathway, PI3K-Akt signaling pathway, and MAPK signaling pathway.
[0020] Furthermore, the in vivo extrinsic experiments include the determination of cell proliferation, migration, and macrophage chemotaxis after TPD52 knockdown, specifically:
[0021] TPD52-siRNA was transfected into the MCF-7 breast cancer cell line to obtain the TPD52 knockdown group;
[0022] Cell proliferation, migration, and macrophage chemotaxis were measured using CCK-8, EdU, and Transwell assays, respectively. When the TPD52 knockdown group showed a significant decrease in at least two indicators compared to the negative control group, the immunomodulatory and tumor-promoting functions of TPD52 were confirmed.
[0023] Furthermore, the TPD52-siRNA is selected from:
[0024] siRNA1: GCATGAACCTGATGAATGAAT;
[0025] siRNA2: GCAGAGTTAGTTCAGCTAGAA; or
[0026] siRNA3: GAAGAGCTAAGAAGAGAACTT.
[0027] This specification provides one or more embodiments of a research system for the immunomodulatory effect of TPD52, comprising:
[0028] Data acquisition module: used to acquire single-cell transcriptome data of the breast cancer tumor microenvironment;
[0029] Gene identification module: used to identify TPD52 as a key risk gene for immune regulation based on the data and using machine learning algorithms;
[0030] Validation module: Used to validate the association between TPD52 expression and patient prognosis in a multicenter cohort;
[0031] Output module: Used for functional annotation and in vivo extrinsic experiments based on TPD52 to confirm its immunomodulatory effects and output a comprehensive evaluation report on the immunomodulatory effects of TPD52.
[0032] This specification provides one or more embodiments of an electronic device, including:
[0033] Processor; and,
[0034] A memory is configured to store computer-executable instructions, which, when executed, cause the processor to implement the steps of the research method for the immunomodulatory effect of TPD52 described above.
[0035] This specification provides one or more embodiments of a storage medium for storing computer-executable instructions that, when executed, implement the steps of the research method for the immunomodulatory effect of TPD52 described above.
[0036] Using the embodiments of this invention, the immune regulatory nodes of TPD52 in the breast cancer microenvironment are precisely located at single-cell resolution, and its independent prognostic value is cross-validated using a dual machine learning algorithm. This avoids the interference of cellular heterogeneity in traditional bulk sequencing and significantly improves the robustness and accuracy of risk prediction. In vitro experiments simultaneously confirm that downregulating TPD52 can simultaneously inhibit tumor cell proliferation and migration and weaken macrophage chemotaxis, forming a complete chain of evidence from molecular mechanisms to functional phenotypes, which is of great significance for tumor behavior and patient prognosis.
[0037] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0038] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 A flowchart illustrating a method for studying the immunomodulatory effects of TPD52, provided for one or more embodiments of this specification;
[0040] Figure 2 A schematic diagram of the composition of a research system for the immunomodulatory effect of TPD52 provided for one or more embodiments of this specification;
[0041] Figure 3 This is a schematic diagram of the structure of an electronic device provided for one or more embodiments of this specification. Detailed Implementation
[0042] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.
[0043] Method Implementation Examples
[0044] According to embodiments of the present invention, a method for studying the immunomodulatory effects of TPD52 is provided. Figure 1 A flowchart illustrating a research method for the immunomodulatory effect of TPD52 provided for one or more embodiments of this specification is shown below. Figure 1 As shown, the method for studying the immunomodulatory effect of TPD52 according to an embodiment of the present invention specifically includes:
[0045] S1. Obtain single-cell transcriptome data of the breast cancer tumor microenvironment.
[0046] Fresh surgical tissue specimens were collected from multiple breast cancer patients. The fresh or frozen breast cancer tissues were mechanically minced and enzymatically digested. The supernatant was removed by centrifugation and the tissues were resuspended in PBS to obtain a highly active single-cell suspension.
[0047] The single-cell suspension was sequenced using a high-throughput scRNA-seq platform to obtain a complete transcriptome expression matrix including immune cells, epithelial cells, and stromal cells.
[0048] S2. Based on the data, TPD52 was identified as a key risk gene for immune regulation using machine learning algorithms.
[0049] Based on the obtained whole transcriptome expression matrix, IE-related genes were selected, and the breast cancer tumor microenvironment was divided into at least two immune escape IE modes through Seurat normalization, dimensionality reduction and k-means clustering.
[0050] The intersection of the differentially expressed gene set in IE mode, the differentially expressed gene set in scRNA-seq, and the differentially expressed gene set in batch sequencing is performed to obtain the crossover gene set.
[0051] The cross-gene set was subjected to initial screening by univariate Cox regression, dimensionality reduction by CoxBoost algorithm, and secondary dimensionality reduction by random survival forest (RSF) algorithm. With the condition that TPD52 was identified as the most prognostic dangerous gene by both algorithms, TPD52 was confirmed as a key dangerous gene for immune regulation.
[0052] S3. Validate the association between TPD52 expression and patient prognosis in a multicenter cohort.
[0053] The multicenter cohort included the TCGA, METABRIC, and GSE9893 datasets; the association between high TPD52 expression and shortened overall survival was verified in the publicly available multicenter datasets TCGA, METABRIC, and GSE9893.
[0054] S4. Based on the functional annotation and in vivo morphological experiments of TPD52, confirm its immunomodulatory effect and output a comprehensive evaluation report on the immunomodulatory effect of TPD52.
[0055] The functional annotations were analyzed using GSEA to examine the relevant signal pathways of TPD52, specifically:
[0056] Gene set enrichment analysis (GSEA) was performed on the TPD52-overexpressing group to identify that the B-cell receptor signaling pathway, breast cancer, MAPK signaling pathway, NOTCH signaling pathway, PD-L1 and PD-1 checkpoint pathways in cancer, PI3K-Akt signaling pathway, T-cell receptor signaling pathway, TGF-β signaling pathway, TNF signaling pathway, VEGF signaling pathway, WNT signaling pathway, and p53 signaling pathway are highly correlated with TPD52.
[0057] In BRCA patients with high TPD52 expression, the number of T cells, cytotoxic lymphocytes, B cells, NK cells, monocytes, neutrophils, and myeloid dendritic cells was significantly reduced; TPD52 was significantly negatively correlated with immune score, matrix score, and ESTIMATE score; and TPD52 was significantly positively correlated with immune regulatory factors.
[0058] The in vivo phenotypic experiments included the determination of cell proliferation, migration, and macrophage chemotaxis after TPD52 knockdown, specifically:
[0059] TPD52-siRNA was transfected into the MCF-7 breast cancer cell line to obtain the TPD52 knockdown group;
[0060] Cell proliferation, migration, and macrophage chemotaxis were measured using CCK-8, EdU, and Transwell assays, respectively. The immunomodulatory and pro-tumor functions of TPD52 were confirmed when the TPD52 knockdown group showed a significant decrease in at least two indicators compared to the negative control group. TPD52-siRNA was selected from:
[0061] siRNA1: GCATGAACCTGATGAATGAAT;
[0062] siRNA2: GCAGAGTTAGTTCAGCTAGAA; or
[0063] siRNA3: GAAGAGCTAAGAAGAGAACTT.
[0064] The beneficial effects of this invention are as follows:
[0065] Using the embodiments of this invention, machine learning methods and scRNA-seq were employed to explore the role of TPD52 as a key immunomodulatory factor in BRCA. The immune regulatory nodes of TPD52 within the breast cancer microenvironment were precisely located at single-cell resolution, and its independent prognostic value was cross-validated using a dual-algorithm machine learning approach. This approach avoids interference from cellular heterogeneity in traditional bulk sequencing and significantly improves the robustness and accuracy of risk prediction. In vitro experiments simultaneously confirmed that downregulating TPD52 can simultaneously inhibit tumor cell proliferation and migration and weaken macrophage chemotaxis, forming a complete chain of evidence from molecular mechanisms to functional phenotypes, which is of great significance for tumor behavior and patient prognosis.
[0066] System Implementation Examples
[0067] According to embodiments of the present invention, a research system for the immunomodulatory effects of TPD52 is provided. Figure 2 A schematic diagram illustrating the composition of a research system for the immunomodulatory effect of TPD52, provided for one or more embodiments of this specification, is shown below. Figure 2 As shown, the research system for the immunomodulatory effect of TPD52 according to an embodiment of the present invention specifically includes:
[0068] Data acquisition module 20: Used to acquire single-cell transcriptome data of the breast cancer tumor microenvironment;
[0069] Gene identification module 22: used to identify TPD52 as a key risk gene for immune regulation based on the data and using machine learning algorithms;
[0070] Validation module 24: Used to validate the association between TPD52 expression and patient prognosis in a multicenter cohort;
[0071] Output module 26: Used for functional annotation and in vivo extrinsic experiments based on TPD52 to confirm its immunomodulatory effect and output a comprehensive evaluation report on the immunomodulatory effect of TPD52.
[0072] The embodiments of the present invention are system embodiments corresponding to the above method embodiments. The specific operation of each module can be understood by referring to the description of the method embodiments, and will not be repeated here.
[0073] Device Example 1
[0074] This invention provides an electronic device, such as... Figure 3 As shown, it includes: a memory 30, a processor 32, and a computer program stored in the memory 30 and executable on the processor 32. When the computer program is executed by the processor 32, it performs the following method steps:
[0075] S1. Obtain single-cell transcriptome data of the breast cancer tumor microenvironment;
[0076] S2. Based on the data, TPD52 was identified as a key risk gene for immune regulation using machine learning algorithms;
[0077] S3. Validate the association between TPD52 expression and patient prognosis in a multicenter cohort;
[0078] S4. Based on the functional annotation and in vivo morphological experiments of TPD52, confirm its immunomodulatory effect and output a comprehensive evaluation report on the immunomodulatory effect of TPD52.
[0079] Device Example 2
[0080] This invention provides a computer-readable storage medium storing an information transmission implementation program. When executed by a processor 32, the program performs the following method steps:
[0081] S1. Obtain single-cell transcriptome data of the breast cancer tumor microenvironment;
[0082] S2. Based on the data, TPD52 was identified as a key risk gene for immune regulation using machine learning algorithms;
[0083] S3. Validate the association between TPD52 expression and patient prognosis in a multicenter cohort;
[0084] S4. Based on the functional annotation and in vivo morphological experiments of TPD52, confirm its immunomodulatory effect and output a comprehensive evaluation report on the immunomodulatory effect of TPD52.
[0085] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.
[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for investigating immunomodulatory effects of TPD52, characterized by, Comprise: S1. Obtain single-cell transcriptome data of breast cancer tumor microenvironment; S2. Based on the data, identify TPD52 as an immune regulation key risk gene using machine learning algorithm; S3. Verify the association between TPD52 expression and patient prognosis in a multi-center cohort; S4. Confirm its immune regulation function based on the functional annotation of TPD52 and in vitro phenotype experiment, and output a comprehensive evaluation report of the immune regulation function of TPD52.
2. The method of claim 1, wherein, The specific method for obtaining single-cell transcriptome data of breast cancer tumor microenvironment is: After mechanical shearing and enzyme digestion of fresh or frozen breast cancer tissue, a high-activity single-cell suspension is obtained; Using high-throughput scRNA-seq platform to sequence the single-cell suspension, a whole transcriptome expression matrix containing immune cells, epithelial cells and stromal cells is obtained.
3. The method of claim 1, wherein, The specific method for identifying TPD52 as an immune regulation key risk gene based on the data using machine learning algorithm is: Based on the expression matrix, select IE-related genes, standardize, reduce dimension and k-means clustering by Seurat, and divide the breast cancer tumor microenvironment into at least two immune escape IE modes; Perform intersection operation on the IE mode differentially expressed gene set, the scRNA-seq differentially expressed gene set and the batch sequencing differentially expressed gene set to obtain the intersection gene set; Perform single-factor Cox regression screening, CoxBoost algorithm dimension reduction and random survival forest RSF algorithm secondary dimension reduction on the intersection gene set in turn, and take TPD52 being simultaneously identified as the most prognostically effective risk gene by the two algorithms as the output condition to confirm TPD52 as an immune regulation key risk gene.
4. The method of claim 1, wherein, The multi-center cohort includes TCGA, METABRIC and GSE9893 data sets; In multi-center public data sets TCGA, METABRIC and GSE9893, the correlation between high TPD52 expression and short overall survival of patients is verified.
5. The method of any one of claims 1, wherein, The functional annotation analyzes TPD52 related signaling pathways by GSEA, specifically: Perform gene set enrichment analysis GSEA on TPD52 high expression group to determine its significant association with PD-1 / PD-L1 checkpoint pathway, TGF-β signaling pathway, PI3K-Akt signaling pathway and MAPK signaling pathway.
6. The method of claim 1, wherein, The in vitro phenotype experiment includes determination of cell proliferation, migration and macrophage chemotaxis ability after TPD52 knockdown, specifically: TPD52-siRNA is transfected into MCF-7 breast cancer cell line to obtain TPD52 knockdown group; CCK-8, EdU and Transwell experiments are used to determine cell proliferation, migration and macrophage chemotaxis ability, respectively, and when TPD52 knockdown group is significantly decreased in at least two indicators compared with negative control group, the immune regulation and tumor promotion function of TPD52 are confirmed.
7. The method of claim 6, wherein, The TPD52-siRNA is selected from: siRNA1: GCATGAACCTGATGAATGAAT; siRNA2: GCAGAGTTAGTTCAGCTAGAA; or siRNA3: GAAGAGCTAAGAAGAGAACTT.
8. A system for investigating immunomodulatory effects of TPD52, characterized by Comprising: a data acquisition module for acquiring single-cell transcriptome data of breast cancer tumor microenvironment; a gene identification module for identifying TPD52 as an immune regulation key risk gene based on the data using a machine learning algorithm; a verification module for verifying the association between TPD52 expression and patient prognosis in a multi-center cohort; an output module for confirming the immune regulation function of TPD52 based on the functional annotation of TPD52 and in vitro phenotype experiments, and outputting a comprehensive evaluation report of the immune regulation function of TPD52.
9. An electronic device, comprising: Comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to implement the steps of a method of studying the immune regulation function of TPD52 as claimed in any of claims 1 to 7.
10. A storage medium, characterized by a computer program product for storing computer executable instructions that, when executed, implement the steps of a method of studying the immune regulation function of TPD52 as claimed in any of claims 1 to 7.