Bone soft tissue tumor auxiliary diagnosis and monitoring method for circulating tumor cell detection

Abstract

The invention discloses a bone soft tissue tumor auxiliary diagnosis and monitoring method for circulating tumor cell detection, which analyzes tumor evolution and progression through DNA-NGS sequencing: collecting a bone primary focus tissue sample, a blood CTC sample and a pulmonary metastatic focus tissue sample of a patient, performing DNA-NGS sequencing, comparing genome differences of the three samples, and determining the progression of the tumor. Further, tumor generation and development and cancer metastasis mechanisms are prompted; single cell transcriptome ScRNA-seq sequencing analyzes changes of immune microenvironments of bone primary lesion and pulmonary metastasis lesion, and explores CTC to realize an immune escape mechanism: comparing expression differences of bone primary lesion, blood CTC and pulmonary metastasis lesion transcriptome, and exploring effects of the immune microenvironments in a tumor metastasis process; the prognostic marker of the osteosarcoma patient is explored by dynamically monitoring the change of the CTC; the recurrence and metastasis risk of the osteosarcoma is predicted through CTC typing, namely, the difference between the CTC of the recurrent patient and the CTC of the distant metastasis patient is compared. According to the invention, the blank of CTC sequencing research of circulating tumor cells in the field of osteosarcoma lung metastasis is filled, and diagnosis and monitoring of bone soft tissue tumors are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor diagnosis and monitoring, and in particular to a bone and soft tissue tumor auxiliary diagnosis and monitoring method for circulating tumor cell detection. Background Art

[0002] Osteosarcoma lung metastasis seriously endangers human health, especially the health of adolescents. Therefore, it is particularly important to study the occurrence and development mechanism of tumors and explore effective methods for treating tumors. However, research based on genomics alone can no longer explain deeper mechanisms. It is necessary to use other multi-omics to conduct a more comprehensive and systematic study on the mechanism of osteosarcoma lung metastasis. At the same time, only focusing on the comparison of molecular characteristics and / or immune microenvironment characteristics between primary bone lesions and lung metastases, while omitting the key intermediary in the metastasis process of osteosarcoma - circulating tumor cells CTC - will lose a lot of valuable information. Including CTC in the study of the mechanism of osteosarcoma lung metastasis will help us understand how CTC escapes immune monitoring and attack and realizes the metastasis and invasion of tumor cells. This will also help clinical discovery of methods to prevent tumor metastasis. CTC clarifies the potential mechanism of tumor cell metastasis and spread, and the development of new treatment strategies for tumor metastasis "seed" cells is of great significance for inhibiting tumor metastasis. At the same time, currently clinical patients mainly use chest CT for follow-up and disease monitoring, but some patients are unwilling to undergo examinations with these radioactive instruments, which makes it easy for patients to drop out; at the same time, CT imaging examinations have certain limitations. When observing tumor metastasis, it is necessary to wait until the tumor size reaches the observation value before it can be judged. Other detection methodologies are urgently needed in clinical practice to supplement it.

[0003] Therefore, this project aims to conduct relevant research through multi-omics technologies such as DNA-NGS and ScRNA-seq, systematically explore the dynamic changes from bone primary lesions → blood CTCs → lung metastases, compare the genomic characteristics and immune microenvironment differences of these three samples, analyze the mechanism of osteosarcoma lung metastasis, and explore possible drug targets; at the same time, with the help of CTC dynamic monitoring data, combined with patient clinical imaging information, efficacy, survival data and other information, a CTC prognosis prediction model is constructed to develop prognostic markers for patients with osteosarcoma lung metastasis. Summary of the invention

[0004] The purpose of the present application is to provide a bone and soft tissue tumor auxiliary diagnosis and monitoring method for circulating tumor cell detection to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present application provides the following technical solution: a method for auxiliary diagnosis and monitoring of bone and soft tissue tumors by detecting circulating tumor cells, comprising the following steps:

[0006] (1) Analyze tumor evolution and progression through DNA-NGS sequencing: Collect tissue samples from the primary bone tumor, blood CTC samples, and tissue samples from lung metastases of patients, perform DNA-NGS sequencing, compare the genomic differences among the three samples, and thereby reveal the mechanisms of tumor development and cancer metastasis;

[0007] (2) Analyze the changes in the immune microenvironment of the primary bone tumor and lung metastases through single-cell transcriptome ScRNA-seq sequencing, and explore the mechanism by which CTCs achieve immune escape: Compare the transcriptional expression differences among the primary bone tumor, blood CTCs, and lung metastases, and uncover the role of the immune microenvironment during tumor metastasis;

[0008] (3) Explore prognostic markers for osteosarcoma patients by dynamically monitoring changes in CTCs: Predict the risk of recurrence and metastasis of osteosarcoma through CTC typing, that is, compare the differences in CTCs between patients with recurrence and distant metastasis.

[0009] Preferably, the specific steps of step (1) are as follows:

[0010] The specific operations are as follows:

[0011] ① Simultaneously obtain tissue samples from the primary tumor, metastatic tumor, and blood CTC samples of the patient for NGS sequencing. After obtaining the sequencing data, perform bioinformatics analysis:

[0012] a) Analyze the genomic characteristics of the primary tumor, metastatic tumor, and CTCs, including the similarities among the three genomes, as well as the specific mutations in the primary tumor and the metastatic tumor;

[0013] b) Analyze the evolutionary history of pulmonary metastatic osteosarcoma, the tumor heterogeneity, and clonal evolution of pulmonary metastatic osteosarcoma;

[0014] c) Analyze the dynamic mutation spectrum and characteristics during lung metastasis;

[0015] d) Analyze the changes in the immunogenicity of pulmonary metastatic osteosarcoma;

[0016] ② Simultaneously obtain the primary tumor and metastatic tumor, while CTCs are obtained one year after the patient's diagnosis. Analyze the common mutation types of the primary tumor and CTCs, the metastatic tumor and CTCs, as well as the differences in molecular characteristics among the three;

[0017] ③ Collect blood samples during the diagnosis, before neoadjuvant chemotherapy, after neoadjuvant chemotherapy, after surgery, during adjuvant chemotherapy, and during follow-up after adjuvant chemotherapy, capture CTCs for DNA-NGS sequencing, and analyze the changes in the mutation characteristics of CTCs released into the blood by the patient during the diagnosis and treatment process, as well as the evolutionary and development paths of the tumor:

[0018] a) Analyze the similarities and differences in the genomic characteristics of CTCs captured at different time points;

[0019] b) Analyze the heterogeneity and clonal evolution of CTCs;

[0020] c) Analyze the dynamic mutation spectrum and characteristics of CTCs during lung metastasis;

[0021] d) Analyze the changes in CTC immunogenicity at different stages of pulmonary metastatic osteosarcoma;

[0022] ④ Deeply analyze the main driver genes during the lung metastasis of osteosarcoma, screen treatment markers, and verify them in basic experiments.

[0023] Preferably, the specific operation of step (2) is as follows:

[0024] ① Enroll patients with osteosarcoma lung metastasis at the time of diagnosis. At the same time, obtain the primary tumor, blood samples, and fresh tissue samples of the metastatic lesions of the patients, perform single-cell transcriptome sequencing, and conduct subsequent bioinformatics analysis on the collected sequencing data:

[0025] a) The cell ecosystem of osteosarcoma primary tumors, CTCs, and lung metastases;

[0026] b) Depict the transcriptional characteristics of primary, CTC, and metastatic osteosarcoma at the single-cell scale;

[0027] c) Copy number variation and clonal evolution analysis;

[0028] d) Compare the differences in the immune microenvironment, perform cell-cell communication analysis, and find molecular pairs of cell interactions;

[0029] ② Enroll patients with osteosarcoma at the time of diagnosis and develop lung metastasis during treatment and follow-up. First, obtain the fresh tissue of the primary tumor and blood samples of the patients, and then obtain the fresh tissue and blood samples of the metastatic lesions after lung metastasis occurs. Perform single-cell transcriptome sequencing on them, and conduct subsequent bioinformatics analysis on the sequencing data. The specific bioinformatics analysis requirements are as in ①;

[0030] ③ According to the data results of the above two parts, compare the differences in the changes of the transcriptional immune microenvironment characteristics of osteosarcoma patients with synchronous and metachronous metastases, and reveal different metastatic mechanisms and possible drug targets.

[0031] Preferably, the specific operation of step (3) is as follows:

[0032] ① Enroll the training cohort, that is, after the patients are diagnosed with osteosarcoma, collect blood samples at corresponding time points during their diagnosis and treatment, monitor the changes in CTCs, and perform correlation analysis with their chemotherapy efficacy, recurrence, metastasis, prognosis survival time, etc.;

[0033] a) To analyze the positive detection rate of CTC in the peripheral blood of patients with osteosarcoma lung metastasis and the distribution of CTC subtypes, namely epithelial, mesenchymal, and mixed epithelial and mesenchymal types;

[0034] b) Analyze the changes in CTC at various time points during the patient's diagnosis and treatment;

[0035] c) Analyze the changes in CTC during treatment and its correlation with DFS;

[0036] ② Determine the cut-off value for prognosis based on the prognostic survival data and the changes in the number of CTCs;

[0037] ③ Enroll patients in the validation cohort to verify the predictive efficacy of CTC.

[0038] In summary, the technical effects and advantages of the present invention are as follows:

[0039] In the present invention, relevant research is carried out through multi-omics technologies such as DNA-NGS and ScRNA-seq, and the dynamic change characteristics from bone primary lesions → blood CTC → lung metastasis lesions are systematically explored, the genomic characteristics and immune microenvironment differences of these three samples are compared, the mechanism of osteosarcoma lung metastasis is analyzed, and possible drug targets are explored; at the same time, with the help of CTC dynamic monitoring data, combined with patient clinical imaging information, efficacy, survival data and other information, a CTC prediction model for prognosis is constructed, and prognostic markers for patients with osteosarcoma lung metastasis are developed, which fills the gap in circulating tumor cell CTC sequencing research in the field of osteosarcoma lung metastasis, and is helpful for the diagnosis and monitoring of bone and soft tissue tumors. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment: A method for auxiliary diagnosis and monitoring of bone and soft tissue tumors by detecting circulating tumor cells comprises the following steps:

[0042] (1) Analysis of tumor evolution and progression through DNA-NGS sequencing: Collect bone primary lesion tissue samples, blood CTC samples, and lung metastasis tissue samples from patients, perform DNA-NGS sequencing, compare the genomic differences of the three samples, and then suggest the mechanism of tumor occurrence, development and cancer metastasis;

[0043] (2) Analyze the changes in the immune microenvironment of the primary bone tumor and lung metastases by single-cell transcriptome ScRNA-seq sequencing, and explore the mechanism of CTC immune escape: Compare the transcriptional expression differences among the primary bone tumor, blood CTCs, and lung metastases to uncover the role of the immune microenvironment during tumor metastasis;

[0044] (3) Explore prognostic markers for osteosarcoma patients by dynamically monitoring changes in CTCs: Predict the risk of recurrence and metastasis of osteosarcoma through CTC typing, i.e., compare the differences in CTCs between patients with recurrence and distant metastases.

[0045] The specific research methods of the present invention are as follows:

[0046] Patient enrollment:

[0047] Thirty clinical osteosarcoma patients with lung metastases were selected. The patient enrollment and exclusion criteria are as follows:

[0048] ① Enrollment criteria

[0049] 1) Gender: male + female;

[0050] 2) Histologically and radiologically confirmed as osteosarcoma patients with lung metastases;

[0051] 3) Accessible primary tumor, metastatic tumor tissue samples, and blood samples;

[0052] 4) If preparing to receive neoadjuvant chemotherapy and selecting the same treatment regimen, meet the conditions required for the regimen;

[0053] 5) Patients must undergo recent imaging examinations (CT or CT) after trial enrollment and before surgery to show measurable tumor lesions defined by RECIST 1.1;

[0054] 6) Good cardiopulmonary function, blood, and organ function;

[0055] 7) Voluntarily sign the informed consent form for enrollment.

[0056] ② Exclusion criteria

[0057] 1) Have previously received any treatment for the current tumor, including radiotherapy or chemotherapy;

[0058] 2) Have locally advanced unresectable / metastatic lesions;

[0059] 3) Have a history of autoimmune diseases / autoimmune diseases;

[0060] 4) Patients with severe chronic / active infections.

[0061] 3.2.2 Sampling of enrolled patients

[0062] a) Tumor tissue sampling

[0063] For the patients in the inclusion group, tissue samples of the primary tumor and lung metastases were collected. For each patient, two tissue samples were taken from each lesion, and the size of each sample was not less than 10mm×10mm×10mm. The collected tissue was placed into a 4mL EP tube and embedded with cryopreservation solution for low-temperature tissues. After sealing the EP tube, it was stored at low temperature in liquid nitrogen.

[0064] b) Venous blood sampling

[0065] For the patients in the inclusion group, at time points such as at the time of diagnosis, one year after diagnosis, before neoadjuvant chemotherapy, after neoadjuvant chemotherapy, after surgery (within one week to one month), during chemotherapy, within one month after chemotherapy cessation, and during follow-up (at three months, six months, nine months, and one year after follow-up), 2 tubes of venous blood were drawn using a dedicated vacuum blood collection tube, with 10mL in each tube. After sampling, the blood collection tube was gently inverted 8 - 10 times immediately. After the blood sample was mixed evenly, the blood collection tube was wrapped with foam and stored at room temperature; if the ambient temperature was relatively high, it was stored at low temperature with an ice pack and CTC detection was performed within 48 hours.

[0066] DNA - NGS sequencing of tumor samples:

[0067] c) DNA sample preparation and detection

[0068] DNA samples were extracted using the Nextera DNA Sample Preparation Kit. The DNA concentration was accurately quantified using Qubit 4.0. The nucleic acid fragment distribution was quality - checked using Agilent 4200 TapeStation.

[0069] d) Library construction and quality control

[0070] The DNA samples were fragmented into small fragments of 150 - 300bp. After end - repair, phosphorylation, and adding A - tails, adapters were ligated to both ends of the fragments to prepare a DNA library. The fragment size (insert size) of the library was detected. After the library concentration and fragments met the expectations, it was sequenced on the machine according to the requirements.

[0071] e) Sequencing on the machine

[0072] After the library passed the quality control, Illumina NovaSeq 6000 PE150 sequencing was performed according to the effective concentration of the library and the data output requirements. PE150 (Pair end 150) is high - throughput paired - end sequencing, with 150bp sequenced at each end. In the constructed small - fragment library, there are insert fragments (Insert DNA).

[0073] f) Sequencing data quality assessment

[0074] By statistically analyzing the sequencing data volume, error rate, effective sequencing depth, alignment rate, etc., evaluate whether the library construction and sequencing meet the standards. If they meet the standards, proceed with subsequent analysis.

[0075] g) Variant information mining and analysis

[0076] Align the high-quality sequences to the human reference genome, detect the variant information in the samples, and analyze and interpret the detected variants.

[0077] CTC blood sample DNA - NGS sequencing:

[0078] a) Configure the reaction system

[0079] ① Take out each component of the kit, thoroughly thaw and invert to mix well, and centrifuge quickly in a hand-held centrifuge for 15 seconds for standby.

[0080] ② Prepare the PCR reaction working solution. Preparation multiple = (number of test specimens + 3) × 1.1, adding an additional 10% for compensating pipetting errors.

[0081] The proportion is as follows:

[0082] 5X PCR reaction buffer: 5ul

[0083] Primer probe: 2.5ul

[0084] Polymerase: 0.3ul

[0085] Purified water: 4.7ul

[0086] Total: 12.5ul

[0087] ③ Gently invert and mix the reaction working solution, and centrifuge briefly.

[0088] ④ According to the sample volume, prepare PCR reaction eight-strip tubes, and dispense 12.5ul of the reaction working solution into each PCR reaction tube.

[0089] ⑤ Add 12.5μl of the test venous plasma sample to the reaction tube.

[0090] ⑥ Carefully cover the PCR reaction tube lid, flick the bottom of the tube and centrifuge briefly to ensure the liquid is well mixed and there are no bubbles in the tube.

[0091] ⑦ Transfer the eight-strip tubes to the droplet generator for droplet generation.

[0092] b) Prepare droplets

[0093] ① After configuring the reaction system in the eight-strip tubes, transfer the eight-strip tubes to the droplet generator.

[0094] ②Run on the JD Printer 100 droplet generator (Guangdong Shenzhen Medical Device Preparation No. 20210782) according to the set program, and the instrument will generate droplets through microdroplet array printing technology.

[0095] c) Microdroplet PCR amplification

[0096] Add the generated microdroplets into the PCR plate in sequence, and use the TC-XP-D PCR amplifier (Zhejiang Medical Device Approval No. 20172400742) to perform amplification according to the set program. The set program is as follows:

[0097]

[0098] d) Sequencing on the machine

[0099] Read the results of the amplified samples using the Digital PCR Reader 100 (Hunan Medical Device Approval No. 20212221807).

[0100] Single-cell ScRNA-seq sequencing of tumor samples (10X Genomics method):

[0101] a) Low-temperature storage

[0102] Before starting low-temperature storage, place the cell freezing container on ice for pre-cooling.

[0103] ①Place PBMCs on ice;

[0104] ②Gently mix the cells;

[0105] ③Use the Countess II Automated Cell Counter to measure cell viability and total cell count;

[0106] ④Centrifuge at 300 rcf for 5 min at 4°C;

[0107] ⑤Remove the supernatant;

[0108] ⑥Resuspend the cell pellet in an appropriate volume of Resuspension Medium to a final concentration of 20x106 cells / ml, and keep the cells on ice;

[0109] ⑦Add an equal volume of pre-cooled 2X Freezing Medium, and the final cell concentration reaches 10x106 cells / ml. Gently mix the cell suspension;

[0110] ⑧Aliquot the cell suspension into pre-cooled cryotubes, and place the cryotubes in a pre-cooled cell freezing container;

[0111] ⑨Place the cell freezing container in an -80°C refrigerator for ≥4 h. After 4 h, transfer the cryotubes to liquid nitrogen for long-term storage.

[0112] b) Thaw & Resuspend

[0113] Before starting cell thawing, set the water bath temperature to 37°C. All cell washes are performed at room temperature.

[0114] a. Take out the stored cryotubes and immediately thaw them in a 37°C water bath for 2 - 3 min;

[0115] b. In a biosafe environment, slowly transfer the thawed cells to a 50 mL conical tube using a wide-bore pipette; rinse the cryotube with 1 mL of complete growth medium and drip the rinse into the 50 mL conical tube (1 drop every 5 seconds), gently shaking the tube simultaneously;

[0116] c. In the 50 mL conical tube, gradually add medium to dilute the cells at a cell-to-medium ratio of 1:1, for a total of 5 dilutions (including the dilution in step b). Dilute once every 1 minute, adding media at a rate of 1 mL / 3 - 5 sec and stirring;

[0117] d. Centrifuge at 300 rcf for 5 min;

[0118] e. Use a regular-bore pipette to remove most of the supernatant, leaving ~1 mL and resuspend the cell pellet in this volume;

[0119] f. Add 9 mL of complete growth medium to bring the volume to ~10 mL (at a speed of 1 mL / 3 - 5 sec);

[0120] g. Use a Countess II Automated Cell Counter to measure the cell concentration;

[0121] h. Transfer ~2x106 cells to a new 50 - mL tube;

[0122] i. Centrifuge at 300 rcf for 5 min;

[0123] j. Remove the supernatant without disturbing the cell pellet;

[0124] k. Using a wide-bore pipette, add 1 mL of 1X PBS + 0.04% BSA and gently mix 5 times;

[0125] l. Transfer the cells to a 2 mL microcentrifuge tube, rinse the 50 mL conical tube with 0.5 mL of 1X PBS + 0.04% BSA and transfer the rinsed solution to the 2 mL microcentrifuge tube containing the cells;

[0126] Centrifuge at 300 rcf for 5 min;

[0127] n. Remove the supernatant without disturbing the cell pellet.

[0128] o. Based on the assumption of a ~50% cell loss in the initial cell concentration in step a, add an appropriate volume of 1X PBS + 0.04% BSA to finally obtain a cell concentration of 700 - 1,200 cells / μl. Gently mix using a regular-caliber pipette to obtain a single-cell suspension.

[0129] p. Measure the cell concentration and viability using a Countess II Automated Cell Counter or a hemocytometer, with a target concentration of 700 - 1,200 cells / μl.

[0130] q. Once the final cell concentration is obtained, store it on ice.

[0131] r. Immediately perform the 10x Genomics single-cell transcriptome experimental procedure.

[0132] CTC enrichment and typing detection:

[0133] Invert and mix the 8 mL peripheral blood sample collected from the patient, and enrich and screen CTC using the microfluidic chip platform CTC100. Subsequently, immunofluorescently stain the isolated detection sample containing CTC with antibodies such as CD45, EpCAM / PanCK, and N-cadherin, and stain the cell nuclei with Hochest33342. Identify positive CTC by combining cell size, cell morphology, nuclear-to-cytoplasmic ratio, and multiple cell markers, and perform counting and typing detection.

[0134] CTC surface marker staining:

[0135] a) Collect the CTC-enriched sample solution after cell sorter treatment into a 15 mL sterile centrifuge tube, centrifuge at 500 g for 10 minutes, and discard the supernatant.

[0136] b) Add 500 μL of buffer to the sample tube to resuspend, gently pipette until well mixed, and transfer the resuspended solution to a cytospin loaded with slides.

[0137] c) Add another 500 μL of buffer to the sample tube, wash the inner wall of the tube, and transfer it to the cytospin. Repeat this step three times.

[0138] d) After centrifuging at 300 g for 5 minutes, aspirate the liquid. After most of the liquid in the smearer has been aspirated, add 200 μL of sample fixative and let it stand for 5 minutes.

[0139] e) Aspirate the fixative, add 200 μL of buffer, let it stand for 5 minutes, and then aspirate the liquid. Repeat this washing step 3 times.

[0140] f) Add 200 μL of sample blocking solution along the inner wall of the smearer, let it stand for 1 hour, and then aspirate the liquid.

[0141] g) Add 1 μL of surface marker antibody solution to a new sterile and enzyme-free PCR tube, make up to 100 μL with sample blocking solution, mix well, and then vertically and slowly drop it onto the center of the smearer. Gently shake the smearer to cover the bottom with the liquid surface, and incubate in the dark at room temperature for 1 hour.

[0142] h) Aspirate the staining solution and wash 3 times with 200 μL of sample washing solution for 5 minutes each time.

[0143] i) Remove the specimen slide from the smearer and observe it under a fluorescence microscope.

[0144] Determine the prognostic cut-off value:

[0145] Kaplan-Meier database and biostatistical analysis: Consult the Kaplan-Meier cancer case database, and analyze the correlation between the gene mutation sites screened after studying the number, classification and metastasis mechanism of CTCs and the survival rate of patients with osteosarcoma lung metastasis by combining biostatistical methods, and obtain the prognostic cut-off value of patients according to the correlation results.

[0146] Prognostic prediction model verification:

[0147] Enroll a new group of patients to verify the prognostic role of CTCs.

[0148] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Auxiliary diagnosis and monitoring methods of bone and soft tissue tumors based on circulating tumor cell detection, Features: The steps include: (1) Analysis of tumor evolution and progression through DNA-NGS sequencing: Collect bone primary lesion tissue samples, blood CTC samples, and lung metastasis tissue samples from patients, perform DNA-NGS sequencing, compare the genomic differences of the three samples, and then suggest the mechanism of tumor occurrence, development and cancer metastasis; (2) Single-cell transcriptome ScRNA-seq sequencing was used to analyze the changes in the immune microenvironment of primary bone lesions and lung metastases, and to explore the mechanism by which CTCs achieve immune escape: The transcriptome expression differences of primary bone lesions, blood CTCs, and lung metastases were compared to explore the role of the immune microenvironment in tumor metastasis; (3) Explore prognostic markers for osteosarcoma patients by dynamically monitoring CTC changes: Predict the risk of osteosarcoma recurrence and metastasis by CTC typing, that is, compare the differences in CTCs between patients with recurrence and those with distant metastasis.

2. The method for auxiliary diagnosis and monitoring of bone and soft tissue tumors by detecting circulating tumor cells according to claim 1, Features: The step (1) is specifically as follows: The specific operations are as follows: ① Obtain the patient's primary lesion, metastatic lesion and blood CTC samples for NGS sequencing at the same time, and perform bioinformatics analysis after obtaining the sequencing data: a) Analyze the genomic characteristics of primary lesions, metastases, and CTCs, including the genomic similarities among the three, as well as the mutational characteristics specific to primary lesions and metastases; b) To analyze the evolutionary history, tumor heterogeneity, and clonal evolution of osteosarcoma with lung metastasis; c) Analyze the dynamic mutation spectrum and characteristics during lung metastasis; d) Analyze the immunogenicity changes of lung metastatic osteosarcoma; ② The primary lesions and metastatic lesions were obtained at the same time, while CTCs were obtained one year after the patient was diagnosed. The common mutation types of the primary lesions and CTCs, metastatic lesions and CTCs, and the differences in molecular characteristics among the three were analyzed; ③ Collect blood samples at the time of diagnosis, before neoadjuvant chemotherapy, after neoadjuvant chemotherapy, after surgery, during adjuvant chemotherapy, and during follow-up after adjuvant chemotherapy, capture CTCs for DNA-NGS sequencing, and analyze the changes in mutation characteristics of CTCs released into the blood during the diagnosis and treatment process, as well as the evolution and development path of the tumor: a) Analyze the similarities and differences in the genomic characteristics of CTCs captured at different time points; b) Analysis of CTC heterogeneity and clonal evolution; c) Analyze the dynamic mutation spectrum and characteristics of CTCs during lung metastasis; d) Analyze the changes in CTC immunogenicity at different stages of lung metastatic osteosarcoma; ④In-depth analysis of the main driving genes in the process of osteosarcoma lung metastasis, screening of therapeutic markers, and verification in basic experiments.

3. The method for auxiliary diagnosis and monitoring of bone and soft tissue tumors by detecting circulating tumor cells according to claim 1, Features: The specific operation of step (2) is as follows: ① Patients with osteosarcoma lung metastasis at the time of diagnosis were enrolled. At the same time, the primary lesion, blood samples, and fresh tissue samples of metastatic lesions were obtained for single-cell transcriptome sequencing. The collected sequencing data were then used for subsequent bioinformatics analysis: a) The cellular ecosystem of osteosarcoma primary lesions, CTCs, and lung metastases; b) Characterize the transcriptional features of primary, CTC, and metastatic osteosarcoma at the single-cell scale; c) Copy number variation and clonal evolution analysis; d) Compare the differences in the immune microenvironment, perform cell-cell communication analysis, and identify molecular pairs of cell-cell interaction; ② Enroll patients diagnosed with osteosarcoma who develop lung metastases during treatment and follow-up. First, obtain fresh tissue and blood samples from the primary tumor of the patient. After the occurrence of lung metastases, obtain fresh tissue and blood samples from the metastatic tumor, and perform single-cell transcriptome sequencing on them. The sequencing data will be subjected to subsequent bioinformatics analysis, and the specific bioinformatics analysis requirements are as described in ①; ③ Based on the data results of the above two parts, compare the differences in the changes of the transcriptional immune microenvironment characteristics between osteosarcoma patients with synchronous metastasis and metachronous metastasis, and reveal different metastatic mechanisms and potential drug targets.

4. The method for the auxiliary diagnosis and monitoring of bone and soft tissue tumors by detecting circulating tumor cells according to claim 1, characterized in that: The specific operation of step (3) is as follows: ① Enroll the training cohort, that is, after the patient is diagnosed with osteosarcoma, collect blood samples at corresponding time points during the diagnosis and treatment process, monitor the changes in CTC, and perform correlation analysis with its chemotherapy efficacy, recurrence, metastasis, prognosis survival time, etc.: a) Analyze the positive detection rate of CTC in the peripheral blood of osteosarcoma patients with lung metastases and the distribution of CTC subtypes, namely epithelial, mesenchymal, and epithelial-mesenchymal mixed types; b) Analyze the changes in CTC at each time point during the diagnosis and treatment process of the patient; c) Analyze the changes in CTC during the treatment process of the patient and its correlation with the patient's DFS; ② Determine the cut-off value for differentiating prognosis based on the prognosis survival data and the changes in CTC count; ③ Enroll patients in the validation cohort to verify the predictive efficacy of CTC.

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