Separation and identification of novel stable drug-resistant tumor cell population SRCC

Abstract

The invention discloses separation and identification of a novel stable drug-resistant tumor cell population SRCC, and relates to the technical field of solid tumor treatment and drug resistance. The novel stable drug-resistant tumor cell SRCC has the characteristics of A) and / or B): A) tumor cells are detected, and if any one or more of riboflavin fluorescence, NOTCH1 and CD82, riboflavin transporter protein, NOTCH1 and CD82 are positive in a tissue sample, the tumor cells are drug-resistant tumor cells; or B) the drug-resistant tumor cells have at least one of the following characteristics: the drug-resistant tumor cells have inherent and stable broad-spectrum drug resistance; the cells are in a resting state; the cell cycle is inhibited; the active oxygen level is high; apoptosis, ferroptosis and programmed necrosis are all inhibited; and an epithelial-mesenchymal transition pathway is inhibited. No matter whether drug treatment exists or not, the drug resistance of the SRCC is inherent and stable, and the SRCC is different from known tumor drug-resistant cell populations.

Description

Technical Field

[0001] This invention relates to the field of solid tumor treatment and drug resistance technology, specifically to the isolation and identification of a novel stable drug-resistant tumor cell population, SRCC. Background Technology

[0002] In recent years, the use of most types of anticancer drugs, alone or in combination, has significantly prolonged the survival of patients in clinical practice. However, for most cancer patients, especially those with advanced chemotherapy or metastatic disease, cancer recurrence is common after chemotherapy treatment. Tumor drug resistance is divided into hereditary resistance and non-hereditary resistance. During targeted therapy, genetic mutations occur at gene targets, leading to insensitivity of the mutated target protein to the targeted drug, resulting in resistance; this type of resistance is called hereditary resistance. Currently, in the drug development process, the development of new-generation targeted drugs that target gene mutations through iterative drug development can gradually solve the problem of hereditary resistance.

[0003] Non-hereditary tumor drug resistance includes resistance to radiotherapy and chemotherapy without a gene target, or resistance to targeted therapy unrelated to target gene mutations. The mechanisms underlying non-hereditary tumor drug resistance are not yet universally agreed upon. It is generally believed that non-hereditary tumor drug resistance may occur through non-genetic mutational mechanisms, such as epigenetic alterations and metabolic reprogramming, to adapt to and evade drug treatment, leading to treatment failure or recurrence. It is also thought to be related to drug-resistant tumor cells. Currently identified drug-resistant cells include cancer stem cells (CSCs), epithelial-mesenchymal transition (EMT) cells, and drug-tolerant persisters (DTP) cells.

[0004] Methods for isolating and identifying known drug-resistant tumor cell populations have been reported. Cellular subpopulations (CSCs) are key cell populations in tumorigenesis, maintenance, and drug resistance, and can be isolated and enriched in some tumors. For example, using CD166... + CD49f high CD104 - Lin - It can be enriched in clinical non-small cell lung tumors, while CD44 is used in breast tumors. + CD24 -Enrichment. EMT cells are an important cell population in tumor invasion and metastasis, and are also considered a key cell subpopulation for tumor drug resistance. These cells can be isolated from various solid tumors using markers such as E-cadherin and Vimentin. DTP is a reversibly drug-resistant tumor cell population. In the absence of targeted drugs, DTP cells appear as sensitive and rapidly proliferating tumor cells. However, under the pressure of targeted drugs, DTP cells become drug-resistant and slowly proliferating tumor cells. There are no specific isolation markers for DTP. Currently known DTP markers overlap with CSC or EMT cells, including ABCB5, CD133, CD271, JARID1B, and ALDH1.

[0005] To deepen our understanding of drug resistance in non-hereditary tumors and improve the clinical diagnosis of drug resistance in tumors, it is urgent to isolate and identify novel drug-resistant tumor cell populations and to provide corresponding methods for their isolation and identification. Summary of the Invention

[0006] The purpose of this invention is to provide the isolation and identification of a novel population of stably resistant cancer cells (SRCC).

[0007] Terminology and Declarations of this Invention: In this invention, the term "stable drug-resistant tumor cells" refers to tumor cells that remain resistant to chemotherapy, targeted therapy, and / or immunotherapy even without drug pressure.

[0008] In this invention, the term "reactive oxygen species (ROS)" refers to a class of oxygen-containing substances formed by oxygen in organisms or the environment that are more chemically reactive than oxygen (O2) itself.

[0009] In this invention, the term "ferroptosis" refers to an iron-dependent form of programmed cell death caused by the accumulation of lipid peroxides.

[0010] In this invention, the term "deep sequencing" refers to a method for obtaining DNA sequences in high throughput using second-generation sequencing technology.

[0011] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows: On the one hand, the present invention provides a method for identifying a stable drug-resistant tumor cell population (SRCC), wherein the drug-resistant tumor cells have characteristics (A) and / or (B): (A) The presence of one or more of riboflavin fluorescence, NOTCH1, and CD82 in tumor cells or tissue samples, or the presence of any one or more of riboflavin transporter protein, NOTCH1, and CD82, indicates stable drug-resistant tumor cells; or (B) Stable drug-resistant tumor cells possess at least one of the following characteristics: 1) It has inherent and stable broad-spectrum drug resistance, including resistance to chemotherapy, targeted therapy, and immunotherapy; 2) The cells are in a resting state; 3) Cell cycle is inhibited; 4) High levels of reactive oxygen species; 5) Apoptosis, ferroptosis, and programmed necrosis were all inhibited; 6) The epithelial-mesenchymal transition pathway is inhibited.

[0012] Specifically, the tissue sample is a cancer tissue sample or a tumor tissue sample.

[0013] Furthermore, the tumor cells are epithelial tumor cell lines, including A549, HCC827, PANC1, and HCT116 cell lines.

[0014] Furthermore, the cancers include lung cancer, colorectal cancer, pancreatic cancer, stomach cancer, liver cancer, esophageal cancer, bile duct cancer, gallbladder cancer, laryngeal cancer, nasopharyngeal cancer, bronchial cancer, breast cancer, cervical cancer, ovarian cancer, prostate cancer, endometrial cancer, kidney cancer, bladder cancer, thyroid cancer, leukemia, and bone cancer.

[0015] Furthermore, the tumors include gastric adenoma, intestinal polyp, hepatic hemangioma, gallbladder polyp, pancreatic pseudocyst, pulmonary hamartoma, bronchial adenoma, nasal polyp, breast fibroadenoma, uterine fibroid, ovarian cyst, cervical polyp, renal hamartoma, bladder papilloma, ureteral polyp, thyroid adenoma, lipoma, hemangioma, ganglion cyst, osteochondroma, meningioma, and leiomyoma.

[0016] Specifically, the riboflavin fluorescence positivity refers to the autofluorescence of riboflavin and its derivatives (including flavin mononucleotide and flavin adenine dinucleotide) accumulated in tumor cells; The positive riboflavin transporter protein indicates that the mRNA or protein of the riboflavin transporter protein is expressed in tumor cells; The NOTCH1 positivity refers to the expression of NOTCH1 mRNA or protein in tumor cells; The CD82 positivity refers to the expression of CD82 mRNA or protein in tumor cells.

[0017] Specifically, the detection methods in (A) include, but are not limited to, fluorescence microscopy, flow cytometry, immunoblotting, enzyme-linked immunosorbent assay (ELISA), quantitative PCR, single-cell RNA sequencing, or bioinformatics analysis techniques.

[0018] Specifically, the cell cycle inhibition program described in 3) includes cell cycle checkpoints, DNA replication, mitosis regulation, inhibited positive regulators, and activated negative regulators.

[0019] Furthermore, the cell cycle checkpoints include, but are not limited to, the G1 / S and G2 / M checkpoints.

[0020] Furthermore, the aforementioned mitotic regulation includes, but is not limited to, spindle assembly, chromosome separation, nuclear division, and cell division.

[0021] Furthermore, the suppressed positive regulatory factors include E2F, c-myc, and mTORC1.

[0022] Furthermore, the activated negative regulatory factors include p53 and p21.

[0023] Specifically, the drug-resistant tumor cells described herein have the characteristic of slow proliferation.

[0024] Furthermore, the stable drug-resistant tumor cells are Riboflavin. + Cells, NOTCH1 + Cells, CD82 + Cells, Riboflavin + NOTCH1 + Cells, Riboflavin + CD82 + Cells, NOTCH1 + CD82 + Cell or Riboflavin + NOTCH1 + CD82 + cell.

[0025] Secondly, this invention provides a method for enriching stable drug-resistant tumor cells, comprising the following steps: S1. Culture the tumor cells, add cisplatin at IC50 concentration for 3 days, discard the drug solution, and then replace with cisplatin-free complete culture medium for 3 days to recover. S2. After repeating step S1 3-4 times, collect surviving drug-resistant tumor cells for testing or sorting. The drug-resistant tumor cells isolated in step S2 were positive for riboflavin fluorescence or riboflavin transporter, NOTCH1, and CD82.

[0026] Specifically, the riboflavin fluorescence positivity refers to the autofluorescence of riboflavin and its derivatives (including flavin mononucleotide and flavin adenine dinucleotide) accumulated in tumor cells; The positive riboflavin transporter protein indicates that the mRNA or protein of the riboflavin transporter protein is expressed in tumor cells; The NOTCH1 positivity refers to the expression of NOTCH1 mRNA or protein in tumor cells; The CD82 positivity refers to the expression of CD82 mRNA or protein in tumor cells.

[0027] Furthermore, the positive riboflavin transporter protein indicates an elevated level of riboflavin transporter protein transcription or expression.

[0028] The NOTCH1 positivity refers to an elevated level of NOTCH1 protein transcription or expression.

[0029] The CD82 positivity refers to an elevated level of CD82 protein transcription or expression.

[0030] Specifically, in step S1, cisplatin is added when the cell density reaches 70-80%.

[0031] Specifically, the tumor cells mentioned in step S1 are epithelial tumor cell lines, selected from any one of the A549, HCC827, PANC1, and HCT116 cell lines.

[0032] Specifically, the culturing conditions in step S1 are 35-38℃ and 5-8% CO2.

[0033] Furthermore, the culturing conditions in step S1 are 37°C and 5% CO2.

[0034] Furthermore, in step S1, cisplatin is added when the cell density reaches 80%.

[0035] Furthermore, cisplatin is added and treated for 72 hours in step S1; Furthermore, after discarding the drug solution in step S1, the culture medium is replaced with a cisplatin-free complete culture medium and allowed to recover for 72 hours.

[0036] Furthermore, the detection methods described in step S2 include, but are not limited to, flow cytometry, single-cell RNA sequencing, and bioinformatics analysis techniques.

[0037] Furthermore, the steps of flow cytometry detection include: 1) Digest cells with TrypLE enzyme; 2) Incubate with NOTCH1 antibody and CD82 antibody; 3) Label dead cells with 7-AAD; 4) Use flow cytometry for detection.

[0038] According to some embodiments of the present invention, the processing conditions in step 1) are 37°C for 2 minutes; According to some embodiments of the present invention, the conditions for incubation in step 2) in the dark are 4°C and 15 minutes.

[0039] According to some embodiments of the present invention, the amount of 7-AAD added in step 3) is 1 μg / 10 6 Cells were incubated at 4°C in the dark for 15 minutes after the addition of 7-ADD.

[0040] According to some embodiments of the present invention, in step 4), the riboflavin signal is detected through the FITC channel.

[0041] According to some embodiments of the present invention, the present invention also provides a method for isolating tumor tissue from stable drug-resistant tumor cells, comprising the following steps: 1) Chop the tumor tissue and add collagenase for digestion; 2) Filter to obtain a single-cell suspension; 3) Incubate with a medium containing riboflavin; 4) Perform flow cytometry detection.

[0042] Specifically, in step 1), the collagenases include type I collagenase and type IV collagenase.

[0043] Specifically, the tissue described in step 1) is selected from any one of the following: clinical samples from lung cancer patients who have received or have not received immunotherapy and chemotherapy, clinical samples of advanced lung tumors before treatment, or mouse xenograft tumors.

[0044] According to some embodiments of the present invention, in step 1), the tumor tissue is broken into 1 mm pieces. 3 Fragments.

[0045] According to some embodiments of the present invention, the working concentration of type I collagenase and type IV collagenase added in step 1) is 8-12 mg / mL; Furthermore, in step 1), the working concentration of type I collagenase and type IV collagenase is 10 mg / mL.

[0046] According to some embodiments of the present invention, a 70 μm filter is used in step 2).

[0047] According to some embodiments of the present invention, in step 3), the cells are incubated for 2 hours with a cell culture medium containing 10-100 μM riboflavin.

[0048] According to some embodiments of the present invention, the isolation of drug-resistant tumor cells is performed using Riboflavin fluorescence, NOTCH and CD82 markers.

[0049] The beneficial effects of this invention are as follows: This invention provides a method for isolating and identifying a stable drug-resistant tumor cell population, SRCC. The isolated SRCCs possess inherent and stable drug resistance properties. SRCCs exist in tumor cell lines or clinical tumors regardless of the presence or absence of drug treatment (or treatment).

[0050] This novel stable, drug-resistant tumor cell population, SRCC, exhibits the following characteristics: it carries three signature markers, including autofluorescence of riboflavin or its metabolites, NOTCH1, and CD82. SRCC is widely present in solid cancers such as lung cancer, breast cancer, pancreatic cancer, and colorectal cancer; and it is resistant to chemotherapy, targeted therapy, and immunotherapy. SRCC exhibits stable and broad-spectrum drug resistance, a quiescent state, high levels of reactive oxygen species, and anti-apoptotic, anti-ferroptosis, and anti-programmed necrosis properties. In single-cell RNA sequencing data, riboflavin transporter protein... SLC52A2 It can replace riboflavin fluorescence as a marker for this type of drug-resistant tumor cells. These unique cell biological and molecular biological characteristics distinguish SRCC from other drug-resistant cell populations, including cancer CSC, EMT cells, and DTP.

[0051] In this invention, SRCC isolation can be performed by treating tumor cell lines with cisplatin at an IC50 concentration for 3-4 rounds (requiring approximately one month in total, see [link]). Figure 1 The SRCC markers can be enriched using flow cytometry from tumor cell lines, tumor cell line xenografts, primary cultured tumor cells, tumor organoids, PDX (patient-derived xenograft) tumors, or clinical tumor samples. Attached Figure Description

[0052] Figure 1 Establish a clinically relevant SRCC resistance model. A. SRCC screening flowchart; B. Cisplatin concentration sensitivity curve and IC50 detection of tumor cells; C. Calculation of cisplatin sensitivity of SRCC-rich A549CR and HCC827CR cells using the CCK8 method; D. Detection of tumor growth in A549CR cell xenografts. Error bar is expressed as mean ± standard deviation (SD).

[0053] Figure 2SRCCs in lung cancer cells A549 and A549CR were isolated and identified using riboflavin fluorescence, NOTCH1, and CD82 markers. A. Bright-field cell morphology and green riboflavin fluorescence of parental lung cancer cells A549 and corresponding cisplatin-resistant A549CR cells were observed under a fluorescence microscope (scale bar = 100 μm). B. Flow cytometry was used to detect riboflavin and its derivatives-positive cells in A549 and cisplatin-resistant A549CR. C. Flow cytometry was used to detect changes in riboflavin fluorescence signal in A549CR cells after 28 days of culture in riboflavin-free medium (R-free). D, E. Flow cytometry was used to detect NOTCH1 (D) and CD82 (E)-positive cells in A549 and A549CR. FH. Riboflavin in A549 (left image) and A549CR (right image) was analyzed using riboflavin fluorescence, NOTCH1, and CD82 markers. + NOTCH1 + riboflavin + CD82 + CD82 + NOTCH1 + and riboflavin + NOTCH1 + CD82 + Cell population. R represents Riboflavin; N represents NOTCH1; shNOTCH1-2 , NOTCH1 shRNA.

[0054] Figure 3 SRCCs in HCC827 and HCC827CR lung cancer cells were isolated and identified using riboflavin fluorescence, NOTCH1, and CD82 markers. A. Bright-field cell morphology and green riboflavin fluorescence of HCC827 and the corresponding cisplatin-resistant HCC827CR cells were observed under a fluorescence microscope (scale bar = 100 μm). B. Flow cytometry was used to detect riboflavin fluorescence (B), NOTCH1 (C), and CD82 (D) positive cells in HCC827 and HCC827CR cells. EG. Riboflavin in HCC827 (left image) and HCC827CR (right image) cells was analyzed using riboflavin fluorescence, NOTCH1, and CD82 markers. + NOTCH1 + riboflavin + CD82 + CD82 + NOTCH1+ and riboflavin + NOTCH1 + CD82 + Cell population. R represents Riboflavin; N represents NOTCH1; shNOTCH1-2 , NOTCH1 shRNA.

[0055] Figure 4 SRCCs in clinical lung cancer samples were isolated and identified using riboflavin fluorescence, NOTCH1, and CD82 markers. A. Clinical lung cancer sample information table; BD flow cytometry detection of riboflavin (B), NOTCH1 (C), and CD82 (D) positive cells in lung cancer sample LC005; EG analysis of riboflavin in A549 (left panel) and A549CR (right panel) using riboflavin fluorescence, NOTCH1, and CD82 markers. + NOTCH1 + riboflavin + CD82 + CD82 + NOTCH1 + and riboflavin + NOTCH1 + CD82 + Cell population; riboflavin in H 13 clinical samples + NOTCH1 + The percentage of SRCC cells was divided into three groups: the diagnosed group (n = 5) which had not yet received drug treatment, the responsive group (n = 5) which had responded to drug treatment, and the non-responsive group (n = 3) which had no significant response to drug treatment. I. Comparison of riboflavin in lung cancer samples. - NOTCH1 - and riboflavin + NOTCH1 + The percentage of CD82-positive cells in the population. R represents Riboflavin; N represents NOTCH1.

[0056] Figure 5 SRCCs were isolated and identified from pancreatic cancer cells (PANC1) using riboflavin fluorescence, NOTCH1, and CD82. AC flow cytometry was used to detect PANC1 cells positive for riboflavin (A), NOTCH1 (B), and CD82 (C); DF flow cytometry was used to detect riboflavin expression in PANC1 cells. +NOTCH1 + riboflavin + CD82 + CD82 + NOTCH1 + and riboflavin + NOTCH1 + CD82 + Cell population. R stands for Riboflavin, and N stands for NOTCH1.

[0057] Figure 6 SRCCs were isolated and identified in breast cancer cells HS578T and colorectal cancer cells HCT116 using riboflavin fluorescence, NOTCH1, and CD82. AC flow cytometry was used to detect cells in HS578T cells that expressed riboflavin (A), NOTCH1 (B), and CD82 (C); DF flow cytometry was used to detect riboflavin expression in HS578T cells. + NOTCH1 + riboflavin + CD82 + CD82 + NOTCH1 + and riboflavin + NOTCH1 + CD82 + Cell population; G flow cytometry detection of riboflavin in parental colorectal cancer cells HCT116 and corresponding cisplatin-resistant cells HCT116CR + NOTCH1 + Positive cells. R stands for Riboflavin, and N stands for NOTCH1.

[0058] Figure 7 Identify the cellular biological characteristics of SRCC. A. Analyze the cisplatin resistance flowchart of SRCC; B. Compare riboflavin in A549. + NOTCH1 + riboflavin in cell populations and A549CR + NOTCH1 + Cisplatin susceptibility of cell populations; the left figure shows representative flow cytometry analysis, and the right figure shows statistical analysis (n = 3); C. Riboflavin sorted from A549CR. + NOTCH1 + Flowchart for detecting the reversibility of drug resistance in SRCC cells; D. Determining the irreversibility of SRCC drug resistance based on diagram C; E. Comparison of riboflavin sorted from A549 and A549CR.+ NOTCH1 + Similarity in proliferative capacity of SRCC cell populations; F-comparison of untreated and cisplatin-treated riboflavin + NOTCH1 + Percentage of Ki-67 positive proliferating cells in the SRCC cell population (n = 3); G comparison of riboflavin sorted from A549 and A549CR. + NOTCH1 + The proportion of SRCC cell populations integrated with the nucleic acid analog EdU; H compared to Riboflavin in A549 and HCC827. + NOTCH1 + and Riboflavin - NOTCH1 - Untreated and cisplatin-treated riboflavin in cells + NOTCH1 + Similarity of ROS levels in SRCC cell populations. I. Detection of riboflavin in clinical lung cancer sample LC006. + NOTCH1 + and Riboflavin - NOTCH1 - Differences in ROS levels among cell populations. Zombie Aqua (ZA) and 7-AAD labeled dead cells; R indicates Riboflavin, N indicates NOTCH1; Error bar represents mean ± standard deviation (SD).

[0059] Figure 8 Identify the molecular biological characteristics of SRCC. A. GSEA analysis showed that A549CR was positively correlated with multiple drug resistance signatures; B, C. GSEA analysis showed that A549CR cells were not enriched in the apoptosis (B), ferroptosis (C), and programmed necrosis (D) pathways; E. GSEA analysis showed that A549CR cells were negatively correlated with the EMT pathway; F, G. GSEA analysis showed that A549CR was positively correlated with the p53 pathway (F), but not with the senescence regulation (G) pathway; H. GO enrichment analysis showed that A549CR was negatively correlated with cell cycle regulation; I. GSEA analysis showed that A549CR was negatively correlated with multiple cell cycle pathways; J. GSEA analysis showed that A549CR cells were positively correlated with G0 arrest. NES>0 indicates gene enrichment in the A549CR group, NES<0 indicates gene enrichment in the A549 group, FDR (False Discovery Rate) q <0.05 indicates a significant difference.

[0060] Figure 9 SRCCs were isolated and identified from clinical scRNA-seq datasets. RNA-seq differential expression analysis was used to reveal the riboflavin transporter gene. SLC52A1-3 The mRNA expression level of [unclear]; B. RT-PCR analysis revealed riboflavin isolated from clinical lung cancer samples. + NOTCH1 + SRCC cells SLC52A2 mRNA levels were significantly elevated; C used a protein-flow method to reveal riboflavin in the lung cancer sample LC011. + NOTCH1 + The SLC52A2 protein level was significantly elevated in SRCC cells; D. A novel PCA-IG method was used for bioinformatics analysis of SRCC; E. PCA clustering was used to identify 7 cell types in the GSE131907 dataset; F. A bubble chart was used to display the marker expression of the 7 cell types; G. Epithelial cells, fibroblasts, and B cells were selected from the E chart for further analysis. InferCNV Analysis shows that the four clusters marked with an asterisk (*) are CNVs. + Tumor cells; H were clustered using PCA-IG. SLC52A2 + NOTCH1 + SRCC cell population. The arrow points to the SRCC population. I SLC52A2 + NOTCH1 + CD82 + and SLC52A2 + NOTCH1 + SRCC cells and seurat WhichCells Module selected SLC52A2 + NOTCH1 + Differences among all cells; J adopts fgsea Analysis shows SLC52A2 + NOTCH1 + SRCC was not associated with apoptosis or ferroptosis; KM analysis using fgsea showed that SRCC was negatively correlated with EMT (L), hypoxia (K), and cell cycle (M) regulation. NES>0 indicated enrichment in SRCC, and NES<0 indicated enrichment in non-SRCC. p <0.05 indicates a significant difference. Detailed Implementation

[0061] To make the technical means, creative features, and achieved objectives and effects of this invention easier to understand, the invention is further illustrated below with specific embodiments. However, the following embodiments are merely preferred embodiments of this invention and not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments described herein without creative effort are all within the protection scope of this invention. Unless otherwise specified, the operating methods and equipment used in the following embodiments are conventional operating methods, and the materials and equipment used in each embodiment are the same.

[0062] Example 1: Construction of a lung cancer drug resistance model of SRCC, separation and identification by fluorescence microscopy and flow cytometry. 1.1 Experimental Methods Lung tumor cells A549 or HCC827 were cultured at 37°C and 5% CO2. When the cell density reached 80%, A549 cells (cisplatin IC50 = 20 μm) or HCC827 cells (cisplatin IC50 = 10 μm) were treated with cisplatin at their respective IC50 concentrations for 72 hours. After discarding the treatment, the cells were allowed to recover for 72 hours using cisplatin-free complete culture medium. This treatment was repeated 3-4 times. Cells that survived were identified as cisplatin-resistant tumor cells. The establishment of this tumor resistance model is similar to the clinical chemotherapy procedure.

[0063] 1.1.1 Flow cytometry detection of SRCC: 1) Digest A549 or A549CR cells cultured in 3 mm culture dishes with 500 μL TrypLE enzyme (37°C, 2 minutes). 2) Wash with HBSS buffer, centrifuge, resuspend, filter into a suspension using a cell strainer, mix with R-PE labeled NOTCH1 antibody (Clone D1E11, 1:200) and PE-Cy7 labeled CD82 antibody (Clone ASL-24, 1:100); and incubate at 4°C in the dark for 15 minutes. 3) After centrifugation and resuspending, mix with 7-AAD (0.5ug / 100ul), and then incubate at 4°C in the dark for 15 minutes to label dead cells; 4) Use the Cytek Aurora spectral flow cytometer to detect SRCC cells in the sample and detect riboflavin fluorescence signal through the FITC channel or BV510 channel.

[0064] 1.1.2 CCK-8 assay for cell proliferation 1) Tumor cells that have grown to 85-90% abundance and are in good condition are digested with trypsin to prepare a single-cell suspension and counted; 2) Seed cell suspension (100 μL / well, 10,000 cells) in 96-well plates and pre-cultured the plates at 37°C in a 5% CO2 incubator for 24 hours. 3) Different concentrations of cisplatin were added to the cell culture in 96 empty plates and treated for 72 hours; 4) Add 10 μL of CCK-8 solution to each well (be careful not to generate bubbles), and incubate at 37°C for 1 hour; 5) The absorbance at 450 nm was measured using a microplate reader; then, GraphPad nonlinear regression analysis was used to fit the tumor cell response curve to cisplatin, and the IC50 was calculated. 50 value.

[0065] 1.1.3 Xenotransplantation experiment in immunodeficient mice: Experimental use NCG ( NOD / ShiLtJGpt-Prkdc em26Cd52 Il2rg em26Cd22 / Gpt Mice were purchased from Nanjing Jicui Pharmaceutical Co., Ltd. and housed in an SPF-grade environment at the Experimental Animal Center of the Zhejiang University-University of Edinburgh Joint Institute. The environment was kept at a constant temperature and humidity of 25°C, with 12 hours of light and 12 hours of darkness. The laboratory animal euthanasia equipment was available. All animal experiments were conducted after training by professional instructors from the Experimental Animal Center of the Zhejiang University-University of Edinburgh Joint Institute. The experimental procedures strictly followed the "Regulations on the Management of Laboratory Animals" and the experimental ethics number was ZJU20230534.

[0066] 1) After digesting A549 and A549CR tumor cells with trypsin, the cells were collected by centrifugation at 1500 rpm for 5 minutes. The cell pellet was resuspended in 1× PBS at room temperature and the cells were counted. The resuspended cells were transferred to 1.5 mL Eppendorf tubes and placed in an ice box for temporary storage. 2) Each NCG Mice require subcutaneous transplantation of 1 × 10⁶ cells / mL. 6 100 μL of cells / cells, gently mix with pre-chilled Matrigel at a 1:1 ratio and place on ice to prevent Matrigel from solidifying. 3) Using an ice-cold insulin syringe, draw up 200 μL of Matrigel / cell mixture and inject it into the insulin syringe. NCG Subcutaneous tissue on the lateral abdomen of a mouse; 4) Observe the tumor formation in mice daily. Starting from day 10, measure and record the length and width of the mouse tumors using calipers. The formula for calculating the mouse tumor volume is L × W. 2 ×π / 6 (mm) 3 ).

[0067] 1.1.4 Results of Example Experiments To investigate different drug-resistant tumor cell populations, this study first established clinically relevant platinum-based drug-resistant cell models using A549 and HCC827 cells, such as... Figure 1 As shown in A in the diagram.

[0068] After four cycles of treatment with 20 µM or 10 µM cisplatin (the half-maximal inhibitory concentration, IC50, respectively) for the two cell types, few A549 and HCC827 cells survived. Most surviving A549 (or A549CR) and HCC827 (or HCC827CR) cells exhibited cisplatin resistance, with significantly elevated cisplatin IC50 values. Figure 1 (B in the original text). A549CR and HCC827CR cells differ phenotypedly from their parent cells. Compared to their parent cells, these drug-resistant cells exhibited significantly reduced growth rates in both cisplatin-free in vitro culture conditions and in vivo immunodeficient mouse transplantation models. Figure 1 C in Figure 1 (D in the middle).

[0069] Compared to A549 cells, A549CR cells accumulate large amounts of riboflavin (or its derivatives FMN and FAD), resulting in strong intracellular autofluorescence. Figure 2 A in Figure 2 The autofluorescence spectrum of riboflavin (B) is consistent with that of known riboflavin and its derivatives. When cultured in a medium without riboflavin, the autofluorescence of riboflavin and its derivatives (hereinafter referred to as riboflavin fluorescence) in A549CR cells significantly decreases. Figure 2 (C in the middle).

[0070] Flow cytometry analysis also revealed that, unlike a small number of A549 cells that express NOTCH1 and CD82, NOTCH1 and CD82 are expressed on the surface of most A549CR cells. Figure 2 D in Figure 2 The E in the text. Therefore, riboflavin fluorescence, NOTCH1, and CD82 can be used as markers to enrich and isolate this novel drug-resistant tumor cell type—stable drug-resistant tumor cells (SRCC). Examples 4 and 5 below demonstrate that these drug-resistant tumor cells are novel, stably drug-resistant, and inherently drug-resistant. Further flow cytometry analysis revealed that any two of these three markers can enrich SRCC (E in the text). Figure 2 F in Figure 2 G in Figure 2(H in the image, right side of the flow cytometry image). Among the enriched SRCCs, the proportion of cells expressing the third marker was also high. Notably, SRCCs were not only enriched in drug-resistant A549CRs, but also present in untreated parental A549 cells, albeit less frequently. Figure 2 F in Figure 2 G in Figure 2 (H in the left-hand flow cytometry group).

[0071] Analysis using fluorescence microscopy and flow cytometry revealed that SRCCs in HCC827CR exhibited three unique markers: riboflavin fluorescence, NOTCH1, and CD82. Figure 3 Using one, two, or three markers, the SRCC cell population in HCC827CR could be enriched. SRCCs enriched using any two markers also highly expressed the third marker. Similarly, these markers could also be used to isolate SRCCs from untreated HCC827 parental cells, although the number of SRCCs was very low.

[0072] Example 2: Isolation of drug-resistant SRCC from clinical lung tumors using flow cytometry 2.1 Clinical Tumor Sample Processing Methods Tumor samples were obtained from non-small cell lung cancer patients at the First Affiliated Hospital of Zhejiang University (who had signed informed consent forms) and were processed according to the following procedure for flow cytometry analysis: 1) Mechanically break the tissue to 1 mm 3 The fragments were added with 10 mg / mL type I collagenase and type IV collagenase, digested at 37°C for 45 minutes, and then filtered through a 70 μm filter to obtain a single-cell suspension. 2) Incubate with a medium containing 30 μM riboflavin for 2 hours; 3) Perform antibody staining and flow cytometry detection as in Example 1, Section 1.1.1.

[0073] 2.2 Results of Example Experiments Clinical tumor samples from 13 patients with non-small cell lung cancer (NSCLC) Figure 4 (A) was used for flow cytometry analysis of SRCC subsets carrying Riboflavin fluorescence, NOTCH1, and CD82 markers. Experimental results showed that, regardless of whether chemotherapy and immunotherapy, or targeted therapy, clinical lung tumor samples contained SRCC populations positive for Riboflavin fluorescence, or a single marker such as NOTCH1 or CD82. Figure 4 B in Figure 4 C in Figure 4(D in the text). Using two or three of the following markers—Riboflavin fluorescence, NOTCH1, and CD82—SRCC subgroups can also be enriched from clinical samples: Riboflavin... + NOTCH1 + Riboflavin + CD82 + CD82 + NOTCH1 + Groups, and purer Riboflavin + NOTCH1 + CD82 + group( Figure 4 E in Figure 4 F in Figure 4 (G in the middle).

[0074] Of these 13 clinical patients, 8 received immunotherapy and chemotherapy, or targeted therapy, while 5 were diagnosed but did not require drug treatment. Ribefalavin was isolated from all clinical tumors. + NOTCH1 + The SRCC group accounts for only 0.004% - 2.20% ( Figure 4 In the H), most of its cells express CD82 ( Figure 4 (I) in the middle. More importantly, riboflavin + NOTCH1 + The proportion of SRCC is negatively correlated with the patient's response to drug treatment. Figure 4 The proportion of SRCC cells in the tumors of patients who do not respond to drug treatment is more than 10 times higher than that in patients who have not received drug treatment. This is because patients who do not respond to drug treatment have received chemotherapy, immunotherapy, and targeted therapy (…). Figure 4 (A in the text), so these drug resistance data indicate that SRCC in clinical lung tumors is resistant to chemotherapy, immunotherapy and targeted therapy.

[0075] Example 3: Isolation and identification of SRCC from pancreatic cancer, breast cancer, and colorectal cancer The tumor cells were cultured at 37°C and 5% CO2 using the experimental method described in Example 1, Section 1.1. When the cell density reached 80%, the cells were collected for flow cytometry analysis.

[0076] SRCCs were isolated from various tumor cell lines using Riboflavin fluorescence, NOTCH1, and CD82 labeling. Flow cytometry analysis revealed a small population of SRCCs that were single-positive for Riboflavin fluorescence, NOTCH1, and CD82 in pancreatic cancer PANC1 cells. Figure 5 A in Figure 5 B in Figure 5 (C in the text). It can also enrich Riboflavin. + NOTCH1 + Riboflavin + CD82 + CD82 + NOTCH1 + SRCC cell population, and more purified Riboflavin + NOTCH1 + CD82 + SRCC group ( Figure 5 D in Figure 5 E in Figure 5 (F in the middle).

[0077] Similarly, Riboflavin fluorescence, NOTCH1, and CD82 markers can enrich SRCC cell populations from untreated cisplatin-treated breast cancer cells HS578T cells and colorectal cancer cells HCT116. Figure 6 These test results indicate that SRCC-resistant cell populations are prevalent in cancer cells.

[0078] Example 4: Identification of cellular biological characteristics of SRCC 4.1. Apoptosis detection First, the cells were incubated and stained with NOTCH1 or CD82 surface antibodies according to the method in 1.1 of Example 1. Then, Annexin V and 7-AAD staining were performed using the Annexin V-PE / Cyanine 7 / 7-AAD apoptosis detection kit (Elabscience, E-CK-A228) or the Annexin V-APC / Cyanine 7 / 7-AAD apoptosis detection kit (Elabscience, E-CK-A230). Finally, apoptosis of SRCC was detected by flow cytometry.

[0079] 4.2. Ki-67 Detection First, following the method in 1.1 of Example 1, cells were incubated and stained with NOTCH1 or CD82 surface antibodies. Then, cells were fixed and perforated using the FIX & PERM cell permeation kit (Invitrogen, GAS004). Dead cells were labeled using the Zombie Aqua fixable live cell detection kit (Biolegend, 423101). Subsequently, cells were stained with Ki-67 antibody (BDPharmingen, 561126, clone B56). Finally, Ki-67 expression in SRCC cells was detected by flow cytometry.

[0080] 4.3. Detection of Reactive Oxygen Species (ROS) First, the cells were incubated and stained with NOTCH1 or CD82 surface antibodies according to the method in 1.1 of Example 1. Then, the intracellular ROS level was labeled using the CellROX flow cytometry kit (Invitrogen, C10491). The operation procedure was strictly followed according to the manufacturer's instructions. Finally, flow cytometry was used for detection.

[0081] 4.4 Results of the example experiment: Our research, along with previous studies, has shown that lung cancer cell carcinoma cells (CSCs) possess inherent resistance to cisplatin. Conversely, DTP cell populations exhibit induced and reversible resistance. Therefore, we focused on a comparative analysis of the resistance and various cell biological characteristics of SRCC cell populations isolated from cisplatin-untreated lung cancer cells (hereinafter referred to as cisplatin-untreated SRCC cell populations) and cell populations surviving after four cycles of cisplatin treatment (hereinafter referred to as cisplatin-treated SRCC cell populations).

[0082] Apoptosis analysis revealed that both cisplatin-untreated and cisplatin-treated SRCC cell populations exhibited similar levels of tolerance to cisplatin-induced cytotoxicity. Figure 7 A in Figure 7 (B in the text) indicates that the SRCC cell population possesses inherent drug resistance and does not require cisplatin pretreatment for induction. Furthermore, the sorted SRCC cells still exhibited cisplatin resistance after 8 days of culture without cisplatin treatment. Figure 7 C in Figure 7 (D in the text). This inherent and persistent drug resistance indicates that the SRCC cell population is indeed different from the DTP population, which has reversible drug resistance.

[0083] Consistent with the slow proliferation of A549CR and HCC827CR cells, the cisplatin-untreated and cisplatin-treated SRCC populations isolated from A549 and A549CR cells, respectively, proliferated at a rate one-third slower than that of A549 cells. Figure 7 The E in this context is likely due to cell cycle arrest in G0 cells, which is characterized by almost no expression of the cell cycle proliferation marker Ki-67. Figure 7 The F in the thymine analogue 5-ethynyl-2'-deoxyuridine (EdU) cannot be integrated. Figure 7 (G in the text). These results indicate that SRCC cells are in a dormant or quiescent state regardless of cisplatin treatment. Dormant quiescent SRCCs differ from CSCs, which have a slow-cycling ability.

[0084] Unlike CSC, which achieves radiotherapy tolerance by maintaining low levels of reactive oxygen species (ROS), we found that SRCC cells have higher levels of ROS than Riboflavin. - NOTCH1 - The number of cells was approximately 13.5-44.7 times higher. Figure 7 The H in [the text is missing here]. Elevated ROS levels were also observed in SRCC cells derived from clinical lung tumor tissues that had undergone chemotherapy and immunotherapy. Figure 7 (I) Clearly, SRCCs with elevated ROS levels differ from CSCs with low ROS levels. In summary, the SRCC cell population exhibits cell biological functional characteristics distinct from CSCs and DTPs.

[0085] Example 5: RNA-seq analysis of the molecular biological characteristics of A549CR cells 5.1 RNA-seq experimental sample processing and analysis workflow Single-cell suspensions were washed with 1×PBS and lysed using TRI reagent. RNA lysis buffer was sent to Genewiz Suzhou for RNA sequencing. A simplified procedure was as follows: poly(A)+ mRNA was extracted using VAHTS mRNA capture beads and the VAHTS Universal V8 Illumina Sequencing Library Preparation Kit (#N401, #NR605-02, Vazyme) for library construction. cDNA libraries were sequenced on an Illumina sequencer using a 2×150 bp paired-end sequencing mode. Raw sequencing data were analyzed on the Galaxy platform (version 20.0965).

[0086] The specific experimental steps are as follows: 1) Through FastQC Tools are used for quality control of sequencing data, and... fastp Perform adapter clipping and low-quality read filtering. Utilize RNA STAR The software aligns and plots the transcriptome data onto a reference human genome (GRCh38); 2) Utilize featureCounts The tool calculates the count for each gene using the gencodeprimary assembly annotation v37 file, and then... DESeq2 Gene-level differential expression analysis was performed and normalized, and differentially expressed genes were analyzed (p value < 0.05). 3) Differentially expressed genes were analyzed using DAVID analysis, GO enrichment analysis, KEGG pathway analysis, and GSEA (Gene Set Enrichment Analysis). GSEA analysis used the Normalized Enrichment Score (NES) and statistical tests. p The three values ​​of value, false discovery rate (FDR) are used to screen meaningful gene sets that satisfy |NES|>1 and NOM. p The enrichment results for the three conditions -value<0.05, FDR<0.05 were considered statistically significant.

[0087] 5.2 Example Experiment Results: In clinically relevant cisplatin resistance models, the vast majority of A549CR cells are Riboflavin-resistant. + NOTCH1 + Since SRCC is prevalent, RNA sequencing (RNA-seq) was directly used to analyze and compare the transcriptomic differences between A549CR cells and A549 cells. Consistent with the stable drug resistance of SRCC, GSEA analysis revealed that SRCC in A549CR cells exhibits activation of the chemotherapy resistance pathway (…). Figure 8 (A) SRCC cells with high ROS levels gain drug resistance and survival advantage by inhibiting apoptosis, ferroptosis, and programmed necrosis pathways. Figure 8 B in Figure 8 C in Figure 8 In addition, the epithelial-mesenchymal transition (EMT) pathway in SRCC is also inhibited (D). Figure 8 In the E1 (E2) pathway, inhibition of the EMT pathway helps reduce the sensitivity of SRCC to ferroptosis. These survival mechanisms play a crucial role in the drug resistance of SRCC. It should also be noted that the presence of EMT inhibition in SRCC indicates that SRCC is distinct from other drug-resistant EMT cells.

[0088] RNA sequencing analysis further revealed that the dormant state of SRCC stems from the inhibition of its cell cycle program. GSEA showed a positive correlation between the p53 pathway and A549CR cells. Figure 8 (F in the text). However, activation of the p53 pathway did not lead to the activation of apoptotic ferroptosis or programmed necrosis programs (F in the text). Figure 8 B in Figure 8 C in Figure 8 In the D), and p16-mediated aging processes are activated ( Figure 8(G in the text). After ruling out these possibilities, it is concluded that p53 pathway activation is related to cell cycle inhibition. Inhibited cell cycle programs include: cell cycle checkpoints (G1 / S and G2 / M checkpoints), DNA replication, mitotic regulation (spindle assembly, chromosome separation, nuclear division, and cell division), as well as inhibited positive regulators (such as E2F, c-myc, and mTORC1) and activated negative regulators (such as p53 and p21). Figure 8 H in Figure 8 (I in the text). Further data also indicates that SRCC is controlled by the G0-period quiescent program (…). Figure 8 In J), and also highly expresses the mRNA and protein of the dormancy marker CD82 (in J), Figure 4 (I in the text). These molecular biological data further support the hypothesis that SRCC is in a dormant or quiescent state.

[0089] Example 6: Isolation of SRCC from scRNA-seq data of clinical lung tumors 6.1 Single-cell RNA-seq data analysis workflow 6.1.1 Obtain the raw data of the GSE131907 dataset from NCBI.

[0090] 6.1.2 Using the R language environment (version 4.4.2) and the Seurat package (version 5.0.1), we analyzed cell type clustering: pass NormalizeData() The function was standardized, and its parameters were set to default values. Then, the `FindVariableFeatures` function was used to filter for highly variable genes, with the `Features` parameter set to 2000 and the remaining parameters set to default values. scale() After normalizing the matrix using the function, the RunPCA function performs initial dimensionality reduction to obtain the principal component matrix. Using... FindNeighbors The function constructs a proximity graph, setting the `dims` parameter to 1:30, with the remaining parameters using their default values. Findcluster The function performs unsupervised clustering, setting the resolution parameter to 0.05, with the other parameters using their default values. RunUMAP The function performs dimensionality reduction for visualization, setting the `dims` parameter to 1:30, with the remaining parameters at their default values. Multiple cell lineage markers are used to annotate cell populations, including the following: T cells (CD3D, CD3E), Myeloid cells (CD68, MARCO), B cells (CD79A, IGHM), epithelial cells (EPCAM, CDH1), fibroblasts (COL1A1, DCN), Mast cells (KIT, MS4A2), and endothelial cells (PECAM1, CLDN5).

[0091] 6.1.3 Copy Number Variation (CNV) Analysis: From the PCA clustering above, select epithelial cells (EPCAM). + CDH1 + ), fibroblasts (COL1A1) + DCN + ), B cells (CD79A) + IGHM + CNV analysis was performed. InferCNV The package (version 1.21.0) calculates copy number mutations; the function called is... CreateInfercnvObject Except for the input gene expression matrix and the reference cell population (using B cells as the reference cell population), the parameter settings are all default values. After obtaining the copy number variation results, the top 4 cell populations (CNV score greater than 1.009) are taken as tumor cells. 6.1.4 Establish the PCA with input gain (PCA-IG) method for SRCC clustering: PCA-IG improves upon the PCA clustering method by incorporating a principle similar to that of an audio mixing amplifier circuit—amplifying and mixing multi-channel audio signals to specifically amplify the marker representation level of SRCC. The specific operation is as follows: In FindVariableFeatures() After the function, the marker gene SLC52A2 , NOTCH1 Add it to the Variable Features matrix. Scaledata() After function normalization, the weight of marker genes is set to 10, and the weights of other genes are set to 1 (unchanged). The weight vector is transformed into a diagonal sparse matrix, and the high-variable feature matrix is ​​right-multiplied by this diagonal sparse matrix to obtain the weighted high-variable feature matrix. svds() The function performs Singular Value Decomposition (SVD) on the weighted, highly variable eigenma matrix, yielding a result completely equivalent to PCA, which is then used to obtain a weighted principal component matrix. Subsequent clustering and visualization are then performed based on this weighted principal component matrix. FindNeighbors , FindClusters , RunUMAP The function parameter settings are consistent with those described in 6.1.2. Use FeaturePlot Function visualization SLC52A2 and NOTCH1 Distribution: The double-positive group was selected as the SRCC group, and the double-negative group was selected as the Non-SRCC group.

[0092] 6.2 Example Experiment Results: scRNA-seq technology has gradually become an important single-cell research and analysis tool in tumor research, so this study investigated SRCC isolation methods in scRNA-seq datasets. The fluorescence signal of Riboflavin was inferred to be related to one of its transport genes. SLC52A2 The expression of [something] is positively correlated because in clinical oncology... SLC52A2 Compare SLC52A1 and SLC52A3 The expression is much higher, and the separated riboflavin + NOTCH1 + SRCC cells SLC52A2 The expression is more than riboflavin - NOTCH1 - The tumor cell count was much higher ( Figure 9 A- Figure 9 (C in the original text). SRCC was analyzed using the scRNA-seq dataset GSE131907 derived from 44 confirmed advanced lung adenocarcinoma tumors. However, principal component analysis (PCA) and rare cell analysis tools such as CellSIUS, EDGE, and scCAD were unable to cluster SRCC subsets (e.g., from the 11,763 CNV-carrying cancer cells in GSE131907). SLC52A2 + NOTCH1 + Subgroups (these analytical data are not shown in the manual).

[0093] To address the challenge of clustering drug-resistant SRCC, this study improved the PCA clustering technique by adding an input signal gain parameter (PCA with input gain, or PCA-IG). Specifically, this amplifies the mRNA expression levels of SRCC markers SLC52A2 and NOTCH1 by 5-fold, while keeping the expression levels of other genes unchanged. Figure 9 D- Figure 9 (H in the text). PCA-IG effectively improved the clustering effect of rare, drug-resistant cell populations, clustering 285 cells from CNV+ cancer cells in the GSE131907 database. SLC52A2 + NOTCH1 + SRCC cells. SLC52A2 + NOTCH1 + 53.3% of the cells in the SRCC cluster were CD82 positive. Figure 9 (I) SRCC cell population in Seurat software WhichCells All filtered by the module SLC52A2 + NOTCH1+ 65.2% of the cells. Furthermore, the SRCC population comprised approximately 0.1% of the total cell count, consistent with riboflavin detected in NSCLC samples. + NOTCH1 + The proportion of SRCC cells was quite similar.

[0094] With 2,115 SLC52A2 - NOTCH1 - Compared to non-SRCC cells, SLC52A2 + NOTCH1 + Apoptosis and ferroptosis pathways were not activated in SRCC cell clusters. Figure 9 J), Epithelial-mesenchymal transition pathway (EMT) Figure 9 (K in) and hypoxia ( Figure 9 The L in the cell cycle was suppressed. Simultaneously, this SRCC population also showed suppressed cell cycle regulation, including the E2F, mTORC1-mediated pathway, G2M checkpoint, and mitotic spindle checkpoint (…). Figure 9 (M in the example). These features are consistent with the molecular and cellular biological characteristics of SRCC revealed in Examples 4 and 5, therefore, in the scRNA-seq dataset, SLC52A2 + NOTCH1 + Group representative riboflavin + NOTCH1 + SRCC subgroup.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying a stable drug-resistant tumor cell population (SRCC), characterized in that, The stable drug-resistant tumor cells have characteristics (A) and / or (B): (A) The presence of one or more of riboflavin fluorescence, NOTCH1, and CD82 in tumor cells or tissue samples, or the presence of any one or more of riboflavin transporter protein, NOTCH1, and CD82, indicates stable drug-resistant tumor cells; or (B) Stable drug-resistant tumor cells (SRCC) possess at least one of the following characteristics: 1) It has inherent and stable broad-spectrum drug resistance, including resistance to chemotherapy, targeted therapy, and immunotherapy; 2) The cells are in a resting state; 3) Cell cycle is inhibited; 4) High levels of reactive oxygen species; 5) Apoptosis, ferroptosis, and programmed necrosis were all inhibited; 6) The epithelial-mesenchymal transition pathway is inhibited.

2. The identification method according to claim 1, characterized in that, The tissue sample mentioned is a cancer tissue sample or a tumor tissue sample.

3. The identification method according to claim 1, characterized in that, The tumor cells are epithelial tumor cell lines, including A549, HCC827, PANC1, and HCT116 cell lines.

4. The identification method according to claim 2, characterized in that, The cancers mentioned include lung cancer, colorectal cancer, pancreatic cancer, stomach cancer, liver cancer, esophageal cancer, bile duct cancer, gallbladder cancer, laryngeal cancer, nasopharyngeal cancer, bronchial cancer, breast cancer, cervical cancer, ovarian cancer, prostate cancer, endometrial cancer, kidney cancer, bladder cancer, thyroid cancer, leukemia, and bone cancer.

5. The identification method according to claim 1, characterized in that, Riboflavin fluorescence positivity is caused by the autofluorescence of riboflavin and its derivatives accumulated in tumor cells. Riboflavin derivatives include flavin mononucleotide and flavin adenine dinucleotide. The positive riboflavin transporter protein indicates that the mRNA or protein of the riboflavin transporter protein is expressed in tumor cells; The NOTCH1 positivity refers to the expression of NOTCH1 mRNA or protein in tumor cells; The CD82 positivity refers to the expression of CD82 mRNA or protein in tumor cells.

6. The identification method according to claim 1, characterized in that, (A) The detection methods are fluorescence microscopy, flow cytometry, immunoblotting, enzyme-linked immunosorbent assay (ELISA), quantitative PCR, single-cell RNA sequencing, or bioinformatics analysis.

7. The identification method according to claim 1, characterized in that, The cell biological and molecular biological characteristics in claim 1 distinguish SRCC from other drug-resistant cell populations, including cancer stem cells, epithelial-mesenchymal transition (EMT) cells, and drug-tolerant persister cells.

8. The identification method according to claim 1, characterized in that, The stable drug-resistant tumor cells mentioned are riboflavin. + Cells, NOTCH1 + Cells, CD82 + Cells, riboflavin + NOTCH1 + Cells, riboflavin + CD82 + Cells, NOTCH1 + CD82 + Cells or riboflavin + NOTCH1 + CD82 + cell.

9. A method for enriching stable drug-resistant tumor cells, characterized in that, Includes the following steps: S1. Culture the cells, add cisplatin at IC50 concentration for 3 days, discard the drug solution, and then replace with cisplatin-free complete culture medium for 3 days to recover. S2. After repeating step S1 3-4 times, collect the remaining surviving drug-resistant tumor cells. The drug-resistant tumor cells isolated in step S2 were positive for riboflavin fluorescence or riboflavin transporter, NOTCH1, and CD82.

10. A method for isolating stable drug-resistant tumor cells, characterized in that, Includes the following steps: 1) Chop the tumor tissue and add collagenase for digestion; 2) Filter to obtain a single-cell suspension; 3) Incubate with a medium containing riboflavin; 4) Perform flow cytometry detection or sorting.

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