An esophageal cancer staging / metastasis prediction method, system or apparatus
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CANCER INST & HOSPITAL CHINESE ACADEMY OF MEDICAL SCI
- Filing Date
- 2025-09-30
- Publication Date
- 2026-07-21
Smart Images
Figure CN121260508B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of intelligent medical care, and specifically relates to a method, system or device for predicting esophageal cancer staging / metastasis. Background Technology
[0002] Esophageal cancer is one of the most common malignant tumors that seriously threaten human health. It mainly has two pathological subtypes: squamous cell carcinoma and adenocarcinoma. 90% of esophageal cancers are squamous cell carcinomas, and their distribution shows significant geographical differences. A 2016 report in the Clinical Physician's Cancer Journal stated that esophageal cancer has become one of the five most common cancers in China, ranking third in incidence among men and fifth among women.
[0003] Currently, there are no plasma tumor markers for assessing the severity of esophageal cancer. Endoscopy is the gold standard, but it is invasive and difficult for patients to tolerate. Therefore, providing a marker that can effectively assess the severity of esophageal cancer is crucial in this field. Summary of the Invention
[0004] To overcome the shortcomings of existing technologies, this invention provides a method, system, or device for predicting esophageal cancer staging / metastasis.
[0005] To achieve the above objectives, the present invention adopts the following technical solution:
[0006] A first aspect of the present invention provides a computer-based method for predicting the staging of esophageal cancer, the method comprising:
[0007] Obtain cell data from the sample to be tested;
[0008] Extract target cell data from the cell data, wherein the target cell data is a mononuclear cell;
[0009] Based on the target cell data, the esophageal cancer staging prediction result of the test sample is obtained; if the proportion of intermediate monocytes and non-classical monocytes in the test sample is high, the test sample is predicted to be early esophageal cancer; if the proportion of classical monocytes in the test sample is high, the test sample is predicted to be late esophageal cancer.
[0010] Furthermore, the early esophageal cancer is G2 stage and / or G3 stage esophageal cancer.
[0011] Furthermore, the advanced esophageal cancer mentioned is G4 stage esophageal cancer.
[0012] Furthermore, the monocytes include classical monocytes, intermediate monocytes, and non-classical monocytes.
[0013] Furthermore, the sample is a blood sample.
[0014] Furthermore, the esophageal cancer mentioned is esophageal squamous cell carcinoma.
[0015] A second aspect of the present invention provides a computer-based method for predicting esophageal cancer metastasis, the method comprising:
[0016] Obtain cell data from the sample to be tested;
[0017] Extract target cell data from the cell data, wherein the target cell data is γδT cells;
[0018] Based on the target cell data, a prediction result is obtained regarding whether the esophageal cancer in the test sample has metastasized; if the proportion of γδT cells in the test sample is high, a prediction result is obtained indicating that the test sample has metastasized; if the proportion of γδT cells in the test sample is low, a prediction result is obtained indicating that the test sample has not metastasized.
[0019] Furthermore, the esophageal cancer metastasis refers to esophageal cancer lymph node metastasis.
[0020] Furthermore, the sample is a blood sample.
[0021] Furthermore, the esophageal cancer mentioned is esophageal squamous cell carcinoma.
[0022] A third aspect of the present invention provides an esophageal cancer staging prediction system, the system comprising:
[0023] Acquisition module: Acquires cell data from the sample to be tested;
[0024] Extraction module: Extracts target cell data from the cell data, wherein the target cell data is a mononuclear cell;
[0025] Prediction module: Based on the target cell data, the prediction result of esophageal cancer stage of the test sample is obtained; if the proportion of intermediate monocytes and non-classical monocytes in the test sample is high, the prediction result of the test sample is early esophageal cancer; if the proportion of classical monocytes in the test sample is high, the prediction result of the test sample is late esophageal cancer.
[0026] Furthermore, the early esophageal cancer is G2 stage and / or G3 stage esophageal cancer.
[0027] Furthermore, the advanced esophageal cancer mentioned is G4 stage esophageal cancer.
[0028] Furthermore, the monocytes include classical monocytes, intermediate monocytes, and non-classical monocytes.
[0029] Furthermore, the sample is a blood sample.
[0030] Furthermore, the esophageal cancer mentioned is esophageal squamous cell carcinoma.
[0031] A fourth aspect of the present invention provides an esophageal cancer metastasis prediction system, the system comprising:
[0032] Acquisition module: Acquires cell data from the sample to be tested;
[0033] Extraction module: Extracts target cell data from the cell data, wherein the target cell data is γδT cells;
[0034] Prediction module: Based on the target cell data, it obtains a prediction result of whether the esophageal cancer in the test sample has metastasized; if the proportion of γδT cells in the test sample is high, it obtains a prediction result that the test sample has metastasized; if the proportion of γδT cells in the test sample is low, it obtains a prediction result that the test sample has not metastasized.
[0035] Furthermore, the esophageal cancer metastasis refers to esophageal cancer lymph node metastasis.
[0036] Furthermore, the sample is a blood sample.
[0037] Furthermore, the esophageal cancer mentioned is esophageal squamous cell carcinoma.
[0038] A fifth aspect of the present invention provides a computer device, the device including a memory and a processor;
[0039] The memory is used to store program instructions;
[0040] The processor is used to invoke program instructions, which, when executed, are used to perform the method described in the first or second aspect of the present invention.
[0041] A sixth aspect of the present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described in the first or second aspect of the present invention.
[0042] A seventh aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first or second aspect of the present invention.
[0043] The eighth aspect of the present invention provides a method for evaluating whether a drug to be screened has the effect of inhibiting esophageal cancer metastasis, the method comprising using γδT cells as a positive control to evaluate the effect of the drug to be screened.
[0044] Advantages and beneficial effects of the present invention:
[0045] This application is the first to discover the correlation between γδT cells and lymph node metastasis in esophageal cancer, and also found the correlation between different subgroups of monocytes and the stage of esophageal cancer, which was demonstrated experimentally. This provides a method, system, device, computer program product, and computer-readable storage medium for predicting esophageal cancer staging / metastasis. This application can effectively predict the metastasis or stage of esophageal cancer, providing a new direction for personalized treatment of esophageal cancer patients and has significant clinical implications. Attached Figure Description
[0046] Figure 1 This is a schematic diagram of the computer-based esophageal cancer staging prediction method provided in this application;
[0047] Figure 2 This is a schematic diagram of the computer-based esophageal cancer metastasis prediction method provided in this application;
[0048] Figure 3 This is a schematic diagram of the system provided in this application;
[0049] Figure 4 This is a schematic diagram of the computer equipment provided in this application;
[0050] Figure 5 This is a diagram showing the main immune cell types and their origins in ESCC patients;
[0051] Figure 6 This is a graph showing the average expression of 42 selected markers in each ESCC patient;
[0052] Figure 7 This is a frequency map of major immune cells in the blood, adjacent normal tissue, and cancerous tissue of ESCC.
[0053] Figure 8 These are box plots of proteomics of the main immune cells in ESCC. Among them, 8A shows a significant increase in γδT cells in patients with lymph node metastasis, and 8B shows a significant increase in macrophages in advanced tumors (G3 and G4 stages).
[0054] Figure 9 This is a graph showing the correlation between high PD-L1 expression in macrophages and improved immunotherapy outcomes. Among them, 9A is the distribution of myeloid cell subsets in ESCC patients, 9B is the average expression level of representative protein markers in myeloid cell subsets, 9C is the source of myeloid cell subsets in ESCC patients, 9D is the distribution of myeloid cell subsets in ESCC patients from different sources, and 9E is the frequency of major myeloid cells in ESCC blood, adjacent normal tissue, and cancerous tissue. Detailed Implementation
[0055] 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.
[0056] In some of the 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.
[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0058] Figure 1 This is a schematic diagram of a computer-based esophageal cancer staging prediction method provided in an embodiment of this application. Specifically, the method includes:
[0059] 101: Obtain cell data from the sample to be tested.
[0060] 102: Extract target cell data from the cell data, wherein the target cell data is a mononuclear cell.
[0061] 103: Based on the target cell data, the esophageal cancer staging prediction result of the test sample is obtained; if the proportion of intermediate monocytes and non-classical monocytes in the test sample is high, the test sample is predicted to be early esophageal cancer; if the proportion of classical monocytes in the test sample is high, the test sample is predicted to be late esophageal cancer.
[0062] Figure 2 This is a schematic diagram of a computer-based esophageal cancer metastasis prediction method provided in an embodiment of this application. Specifically, the method includes:
[0063] 201: Obtain cell data from the sample to be tested.
[0064] 202: Extract target cell data from the cell data, wherein the target cell data is γδT cells.
[0065] 203: Based on the target cell data, a prediction result is obtained regarding whether the esophageal cancer in the test sample has metastasized; if the proportion of γδT cells in the test sample is high, a prediction result is obtained indicating that the test sample has metastasized; if the proportion of γδT cells in the test sample is low, a prediction result is obtained indicating that the test sample has not metastasized.
[0066] Figure 3 This is a schematic diagram of a system provided in an embodiment of this application. Specifically, the system includes:
[0067] 301 Acquisition Module: Acquires cell data from the sample to be tested.
[0068] 302 Extraction Module: Extracts target cell data from the cell data, wherein the target cell data is a mononuclear cell.
[0069] 303 Prediction Module: Based on the target cell data, the esophageal cancer staging prediction result of the test sample is obtained; if the proportion of intermediate monocytes and non-classical monocytes in the test sample is high, the test sample is predicted to be early esophageal cancer; if the proportion of classical monocytes in the test sample is high, the test sample is predicted to be late esophageal cancer.
[0070] Alternatively, the 303 prediction module: based on the target cell data, obtains a prediction result of whether the esophageal cancer in the test sample has metastasized; if the proportion of γδT cells in the test sample is high, a prediction result of metastasis in the test sample is obtained; if the proportion of γδT cells in the test sample is low, a prediction result of no metastasis in the test sample is obtained.
[0071] In some implementations, the system includes a processor, which may be a single-core or multi-core processor, or more than one processor for parallel processing. The system also includes memory (e.g., random access memory, read-only memory, flash memory), electronic storage units (e.g., hard disks), communication interfaces (e.g., network adapters) for communicating with one or more other systems, and peripheral devices such as cache memory, other memory, data storage, and / or electronic display adapters. The memory, electronic storage units, communication interfaces, and peripheral devices communicate with the processor via a communication bus (solid line), such as a motherboard. The storage units may be data storage units (or databases) for storing data. The system may be operatively coupled to a computer network via the communication interface. The network may be the Internet, an intranet and / or an extranet, or an intranet and / or extranet communicating with the Internet. In some cases, the network is a communication and / or data network. The network may include one or more computer servers, which may support distributed computing, such as cloud computing. In some cases, the network may enable a peer-to-peer network, allowing devices coupled to the system to operate as clients or servers.
[0072] In some embodiments, the processor can execute a series of machine-readable instructions, which can be embodied in a program or software. The instructions can be stored in a memory location, such as memory. The instructions can be directed to the processor, which can then be programmed or otherwise configured to implement the methods of the present invention. Examples of operations performed by the processor can include reading, decoding, executing, and writing back.
[0073] In some implementations, the processor may be part of a circuit such as an integrated circuit, and one or more other components of the system may be included in the circuit, which in some cases is an application-specific integrated circuit (ASIC).
[0074] In some implementations, the electronic storage unit can store files such as drivers, libraries, and saved programs. The electronic storage unit can also store user data, such as user preferences and user programs. In some cases, the system may include one or more additional data storage units located outside the computer system, such as on a remote server that communicates with the system via an intranet or the Internet.
[0075] In some implementations, the system can communicate with one or more remote computer systems via a network. For example, the system can communicate with a user's (e.g., a physician's) remote computer system. Examples of remote computer systems include personal computers, tablet PCs, telephones, smartphones, or personal digital assistants. Users can access the system via the network.
[0076] Figure 4 This is a schematic diagram of a computer device provided in an embodiment of this application. Specifically, the device includes a memory and a processor.
[0077] The memory is used to store program instructions.
[0078] The processor is used to invoke program instructions, which, when executed, are used to perform the methods described above.
[0079] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.
[0080] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device that includes one or any combination of the above-mentioned memories.
[0081] In some implementations, executable instructions may take the form of programs, software, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
[0082] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).
[0083] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.
[0084] In some embodiments, executable instructions can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes described in the embodiments of the methods above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0085] In some implementations, the test sample, test subject, or participant refers to any animal (e.g., a mammal), including but not limited to humans, non-human primates, rodents, etc., which will become the recipient of a specific treatment. In a preferred implementation, the test sample is a human.
[0086] In some implementations, the sourcing and criteria for human ESCC tissue samples were as follows: all participants provided informed consent to have tumor tissue collected from the Cancer Hospital of the Chinese Academy of Medical Sciences and participate in the clinical study. The study protocol and all revisions regarding the efficacy of neoadjuvant therapy in the ESCC cohort were approved by the Institutional Review Committee and the Independent Ethics Committee of the National Cancer Center, Cancer Hospital of the Chinese Academy of Medical Sciences (Beijing, China). This study was conducted in accordance with the Declaration of Helsinki and international standards of good clinical practice. Twenty-five patients diagnosed with ESCC were recruited from the Cancer Hospital of the Chinese Academy of Medical Sciences to participate in this study. All patients had not received prior ESCC treatment, and tumors were classified according to WHO criteria. Cancer clinical staging was determined according to the Union for International Cancer Control (UICC) / American Joint Committee on Cancer (AJCC) TNM staging system (8th edition).
[0087] In some embodiments, tissue preparation is performed as follows: Following surgical resection, fresh tissue samples are immediately transferred to pre-cooled MACS tissue storage solution (Miltenyi Biotec) and transported at 4°C. Tissue processing is completed within 24 hours of collection. For dissociation, the tissue is minced using a scalpel and then further dissociated according to the manufacturer's instructions using a Human Tumor Dissociation Kit (Miltenyi Biotec) and a gentleMACS dissociator (Miltenyi Biotec). The resulting single-cell suspension is filtered sequentially through sterile 70 μm and 40 μm cell filters. The cell suspension is viability stained with 25 mM cisplatin (Enzo Life Sciences) in 1-minute pulses, followed by quenching with 10% FBS. Cells are then fixed with 1.6% paraformaldehyde (PFA, Electron Microscopy Sciences) at room temperature for 10 minutes and stored at -80°C.
[0088] In some implementations, CyTOF data acquisition is performed using a Helios™ mass cytometer (Fluidigm). Cellular events are normalized using EQ™ quaternary calibration beads (Fluidigm), and analysis is performed using Cytobank and FlowJo software. To ensure high data quality, a strict gating strategy is employed: DNA⁺ cell screening: Cells are first gated based on DNA content using the 191Ir and 193Ir channels (Ir191 / 193). Events with low DNA signal, such as fragmentation or asymmetric DNA distribution duplexes, are excluded. Live cell gating: Live cells are screened by excluding cisplatin-positive (Pt194⁺) events (indicating cell membrane damage (death)). Low signal in the 194Pt channel corresponds to live cells. Immune cell gating: Immune cells are identified using CD45 (89Y), and the CD45⁺ cell population is further analyzed. Singlet selection: Singlet cells are gated using the relationship between event length and DNA signal. Events with an event length >20 are retained to eliminate potential doublets or aggregates. Bead exclusion: Beads are excluded from sample collection based on 140Ce signal. Events with high 140Ce intensity are considered beads or bead aggregates and are removed. The cleaned CD45⁺ singlet viable cell population is used for downstream clustering and visualization, including t-SNE and FlowSOM analyses.
[0089] In some implementations, the preprocessing method for mass cytometry data is as follows: The mass cytometry data undergoes a series of steps to ensure quality and accuracy. First, the files are concatenated using a .fcs file concatenation tool (Cytobank, Inc.), and then normalized using the MATLAB version of the Normalizer tool. The data is debarcoded using the CATALYSTR / Bioconductor package, and then channel crosstalk is compensated using single-stained polystyrene beads. The adjusted .fcs file is uploaded to the Cytobank server for manual gating, focusing on excluding non-specific background cells and cisplatin-positive dead cells. The target cell population is then exported and analyzed in R to further validate the data and compare results between barcode plates, thereby improving the consistency of the results.
[0090] In some implementations, the dimensionality reduction and clustering methods involve using the t-SNE algorithm described by Van Der Maaten and Hinton to reduce the dimensionality of mass spectrometry flow cytometry data. The signal intensity of each channel is transformed using an arcsine function with a cofactor of 5 (counts_transf = asinh(x / 5)). Notably, the same principal components are also used for nonlinear dimensionality reduction to generate a visualization projection via uniform manifold approximation projection (UMAP) or t-distributed random neighborhood embedding (t-SNE). Specifically, t-SNE is used to analyze the entire tumor microenvironment. UMAP is used for analysis targeting specific immune subsets, such as T cells.
[0091] In some embodiments, one result of this application is: single-cell proteomics mapping of blood, adjacent normal tissue, and cancerous tissue from ESCC patients: approximately 10,000 immune cells (CD45+CD66b-) were isolated from the blood, adjacent normal tissue, and cancerous tissue of ESCC patients, allowing for in-depth analysis of TME composition. Cellular data were processed to examine the proportion and distribution of immune cell populations in these samples. Figure 5 The study primarily showcased the proteomic findings of major immune cells in human esophageal squamous cell carcinoma (ESCC), providing crucial information for understanding the characteristics of immune cells in the ESCC tumor microenvironment. Figure 5 This study demonstrates the use of t-distributed random neighborhood embeddings (t-SNE) to visually represent the major immune cell types and their origins in ESCC patients. Different colors and shapes of dots represent different immune cell types, clearly showing the distribution of various immune cells in the tissue, which helps to understand the diversity and heterogeneity of immune cells. Figure 5 The left figure reveals significant commonalities in the immune response mechanisms among 25 ESCC patients. CyTOF clustering analysis identified 53 distinct immune cell clusters in the ESCC patient cohort. These clusters were then classified into nine major immune cell subtypes using biomarkers: CD4+ T cells, CD8+ T cells, γδ T cells, B cells, natural killer (NK) cells, dendritic cells (DCs), granulocytes, macrophages, and monocytes. Figure 5 (Right image).
[0092] The results showed that there were similarities between the immune environments of cancerous and adjacent normal tissues, which contrasted sharply with the immune characteristics in peripheral blood, and significant differences existed in the distribution of immune cells in tissues and blood. Principal component analysis (PCA) plots were used to present the average expression of 42 selected markers in each ESCC patient. Different shapes and colors of dots represented different tissue groups, visually reflecting the differences in immune cell expression profiles among cancerous tissues, adjacent normal tissues, and peripheral blood samples, highlighting the differences in immune characteristics between tissues. Figure 6(This sentence appears to be incomplete and requires further context.)
[0093] Furthermore, further analysis revealed that although the distribution of immune cells in cancerous and adjacent tissues was similar, peripheral blood exhibited a unique pattern, particularly in subsets of dendritic cells (DCs), granulocytes, macrophages, monocytes, CD4+ T cells, B cells, γδ T cells, and NK cells. Figure 7 Stacked bar charts were used to illustrate the proportions of major immune cells in ESCC blood, adjacent normal tissue, and cancerous tissue. This allows for a direct comparison of the proportions of various immune cells in different tissues, revealing similar distributions of immune cells between cancerous and adjacent normal tissues.
[0094] In some embodiments, one result of this application is a significant increase in blood γδT cells in patients with lymph node metastases; furthermore, Figure 8 Box plots showed a significant increase in serum γδ T cells in patients with lymph node metastasis compared to ESCC patients without lymph node metastasis. Data distribution was visualized using median, quartiles, and whiskers, and analysis using the Mann-Whitney U test indicated a potential association between serum γδ T cells and tumor metastasis. This suggests that serum γδ T cells may serve as a biomarker for metastatic disease and may participate in the systemic immune response to tumor spread. Furthermore, macrophage infiltration was significantly higher in G4 and G3 stage ESCC patients than in G2 stage patients. Analysis using the Kruskal-Wallis test and Benjamini-Hochberg correction revealed the relationship between macrophages and tumor progression. This observation suggests a correlation between malignancy and macrophage infiltration, and these cells may play a role in promoting tumor progression.
[0095] In some embodiments, one result of this application is: the functional composition of myeloid cells in the blood of ESCC patients reveals a distinct pattern of monocyte distribution associated with disease progression: the functional composition of myeloid cells in the blood of ESCC patients was examined, and a distinct pattern of monocyte distribution associated with disease progression was found. Figure 9 D uses box plots to illustrate the distribution of myeloid cell subsets from different tissue origins in ESCC patients. The median and upper and lower quartiles in the box plots clearly demonstrate the distribution characteristics of the data, while also... P<0.05, P<0.01, P<0.001 indicates significant differences between different groups, visually presenting the distribution differences of each subgroup in different tissues, such as the enrichment of intermediate monocytes and non-classical monocytes in the blood, and the changing trend of classical monocytes in advanced tumor tissues. Figure 9E uses a stacked bar chart to show the proportion of major myeloid cells in the blood, adjacent normal tissue, and cancerous tissue in ESCC. This chart provides a direct comparison of the proportion of various myeloid cell types in different tissues, clearly showing the high enrichment of macrophages in tumor and adjacent normal tissues. Intermediate monocytes (50.3%) and non-classical monocytes (32.4%) are mainly enriched in the blood of ESCC patients, especially during early-stage disease (G2 and G3), where their presence in cancerous and adjacent normal tissues is less than 1%. Conversely, classical monocytes account for only 4.8% of the total blood volume, but exceed 30% in advanced (G4) ESCC patients. Figure 9 (D, 9E). Significant differences in the distribution of dendritic cell (DC) subsets were observed between cancerous and adjacent normal tissues. DCs co-expressing CD14 and CD11c were enriched in both cancerous (8.0%) and adjacent normal tissues (10.1%), particularly in the adjacent normal region. Plasma-like dendritic cells (pDCs), characterized by the expression of specific surface markers such as CD123 and HLA-DR, were more concentrated in cancerous (4.1%) than in adjacent normal tissues (2.6%). Figure 9 BD). A macrophage subtype characterized by high expression of CD206 and HLA-DR but low expression of CD204, significantly enriched in cancerous tissue (66.2%) and adjacent normal tissue (51.7%), compared with blood (3.6%). Figure 9 D).
[0096] This invention provides a method for evaluating whether a drug to be screened can inhibit esophageal cancer metastasis, the method comprising using γδT cells as a positive control to evaluate the effect of the drug to be screened.
[0097] In some implementations, the evaluation of whether the drug to be screened has therapeutic, preventive, alleviating, and / or resolving effects on esophageal cancer can be performed in any one or more of the following ways: 1) Cell proliferation assay: Esophageal cancer cells (such as KYSE30, KYSE150, etc.) are co-cultured with the drug to be screened, and cell viability is detected by MTT assay, CCK-8 assay, or high content screening technology to assess the inhibitory effect of the drug on cell proliferation; γδT cells are used as a positive control to compare the inhibitory effects of the drug to be screened with those of γδT cells; the half-maximal inhibitory concentration (IC50) is calculated to assess the inhibitory strength of the drug. 2) Apoptosis assay: The ability of the drug to be screened to induce apoptosis in esophageal cancer cells is detected by flow cytometry or TUNEL staining; γδT cells are used as a positive control to compare the apoptosis-inducing effects of the drug to be screened with those of γδT cells; the evaluation indicators are apoptosis rate and expression levels of apoptosis-related proteins (such as Bax, Bcl-2). 3) Cell cycle analysis: Flow cytometry was used to analyze the effect of the screening drug on the cell cycle of esophageal cancer cells; γδT cells were used as a positive control to compare the cell cycle arrest effects of the screening drug and γδT cells; the evaluation index was the proportion of cells in each phase of the cell cycle (G0 / G1, S, G2 / M).
[0098] 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.
[0099] 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 an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.
[0100] 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.
[0101] 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 unit can be implemented in hardware or as a software functional unit.
[0102] 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. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0103] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.
[0104] The computer device provided by the present invention has been described in detail above. For those skilled in the art, there will be changes in the specific implementation and application scope based on the ideas of the embodiments of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A computer-based method for predicting esophageal cancer staging, characterized in that, The method includes: Obtain cell data from the sample to be tested; Extract target cell data from the cell data, wherein the target cell data is a mononuclear cell; Based on the target cell data, the esophageal cancer staging prediction result of the test sample is obtained; if the proportion of intermediate monocytes and non-classical monocytes in the test sample is high, the test sample is predicted to be early esophageal cancer; if the proportion of classical monocytes in the test sample is high, the test sample is predicted to be late esophageal cancer.
2. The method according to claim 1, characterized in that, The early esophageal cancer mentioned refers to G2 and / or G3 stage esophageal cancer.
3. The method according to claim 1, characterized in that, The advanced esophageal cancer mentioned is G4 stage esophageal cancer.
4. The method according to claim 1, characterized in that, The monocytes include classical monocytes, intermediate monocytes, and non-classical monocytes.
5. The method according to claim 1, characterized in that, The sample is a blood sample.
6. The method according to claim 1, characterized in that, The esophageal cancer mentioned is esophageal squamous cell carcinoma.
7. A computer-based method for predicting esophageal cancer metastasis, characterized in that, The method includes: Obtain cell data from the sample to be tested; Extract target cell data from the cell data, wherein the target cell data is γδT cells; Based on the target cell data, a prediction result is obtained regarding whether the esophageal cancer in the test sample has metastasized; if the proportion of γδT cells in the test sample is high, a prediction result is obtained indicating that the test sample has metastasized; if the proportion of γδT cells in the test sample is low, a prediction result is obtained indicating that the test sample has not metastasized.
8. The method according to claim 7, characterized in that, The esophageal cancer metastasis mentioned refers to esophageal cancer lymph node metastasis.
9. The method according to claim 7, characterized in that, The sample is a blood sample.
10. The method according to claim 7, characterized in that, The esophageal cancer mentioned is esophageal squamous cell carcinoma.
11. An esophageal cancer staging prediction system, characterized in that, The system includes: Acquisition module: Acquires cell data from the sample to be tested; Extraction module: Extracts target cell data from the cell data, wherein the target cell data is a mononuclear cell; Prediction module: Based on the target cell data, the prediction result of esophageal cancer stage of the test sample is obtained; if the proportion of intermediate monocytes and non-classical monocytes in the test sample is high, the prediction result of the test sample is early esophageal cancer; if the proportion of classical monocytes in the test sample is high, the prediction result of the test sample is late esophageal cancer.
12. The system according to claim 11, characterized in that, The early esophageal cancer mentioned refers to G2 and / or G3 stage esophageal cancer.
13. The system according to claim 11, characterized in that, The advanced esophageal cancer mentioned is G4 stage esophageal cancer.
14. The system according to claim 11, characterized in that, The monocytes include classical monocytes, intermediate monocytes, and non-classical monocytes.
15. The system according to claim 11, characterized in that, The sample is a blood sample.
16. The system according to claim 11, characterized in that, The esophageal cancer mentioned is esophageal squamous cell carcinoma.
17. An esophageal cancer metastasis prediction system, characterized in that, The system includes: Acquisition module: Acquires cell data from the sample to be tested; Extraction module: Extracts target cell data from the cell data, wherein the target cell data is γδT cells; Prediction module: Based on the target cell data, it obtains a prediction result of whether the esophageal cancer in the test sample has metastasized; if the proportion of γδT cells in the test sample is high, it obtains a prediction result that the test sample has metastasized; if the proportion of γδT cells in the test sample is low, it obtains a prediction result that the test sample has not metastasized.
18. The system according to claim 17, characterized in that, The esophageal cancer metastasis mentioned refers to esophageal cancer lymph node metastasis.
19. The system according to claim 17, characterized in that, The sample is a blood sample.
20. The system according to claim 17, characterized in that, The esophageal cancer mentioned is esophageal squamous cell carcinoma.
21. A computer device, characterized in that, The device includes a memory and a processor; The memory is used to store program instructions; The processor is used to invoke program instructions, which, when executed, are used to perform the method described in any one of claims 1-6.
22. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-6.
23. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.