Method, system and equipment for predicting lung cancer treatment effect

Abstract

The invention discloses a method, a system and equipment for predicting a lung cancer treatment effect. Based on clinical patient screening, the marker Staphylococcus allati (SA) for evaluating the potential treatment reaction and drug resistance risk of a patient before treatment is obtained, and further through systematic in-vitro and in-vivo experiments, the complex effect of SA in the lung cancer chemotherapy immunotherapy microenvironment is deeply explored. The result reveals that SA is not a simple bystander or a single promotion / inhibition factor, but is a critical microenvironment conditioner. When no treatment pressure exists, SA shows a strong innate immune activation capability; however, under the pressure of chemotherapy immunotherapy, it drives a dysfunctional immune state, resulting in treatment resistance. The research system disclosed by the invention reveals the key effect and multi-dimensional mechanism of the staphylococcus allati in the drug resistance of the lung cancer chemotherapy immunotherapy, and lays a theoretical foundation for subsequent clinical transformation and application.

Description

Technical Field

[0001] This invention belongs to the field of intelligent healthcare, specifically relating to a method, system, and device for predicting the treatment effect of lung cancer. Background Technology

[0002] Lung cancer treatment has entered the era of precision medicine for over two decades. During this period, targeted therapies against driver gene mutations and immune checkpoint blockade therapies against tumor immune escape have emerged. Evidence-based clinical trials have provided the basis for the continuous updating of clinical guidelines, thereby guiding clinical practice. Despite these achievements, the problem of primary drug resistance in tumors has not been fundamentally solved, prompting researchers to explore the nature of cancer more deeply.

[0003] Recent studies have increasingly revealed the significant role of the tumor microbiome in the occurrence and development of cancer. One study analyzed bacterial DNA from 1010 tumor samples and 516 normal tissue samples, finding significant differences in the positive detection rate of bacterial DNA across different cancer types, ranging from 14.3% in melanoma to over 60% in breast cancer, pancreatic cancer, and sarcoma. Even in solid tumors isolated from the external environment, such as ovarian cancer, glioblastoma multiforme, and bone cancer, traces of bacterial DNA have been detected. These microorganisms can alter the immune characteristics of the tumor microenvironment, affecting the recruitment, activation, and function of immune cells, thereby regulating tumor immune escape mechanisms and treatment responses. Therefore, the composition of the tumor microbiota may be a key factor influencing tumor progression and treatment efficacy. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a method, system, and device for predicting the treatment effect of lung cancer.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for predicting the treatment effect of lung cancer, the method being computer-based and specifically including: Obtain bacterial abundance data in the sample to be tested; Extract the abundance data of the target species from the bacterial abundance data, wherein the target species is Staphylococcus alright. Based on the abundance data of the target bacterial species, classification prediction is performed to obtain a classification result of whether the lung cancer treatment of the test sample is effective. If the abundance of Staphylococcus albus is low, the lung cancer treatment of the test sample is effective; if the abundance of Staphylococcus albus is high, the lung cancer treatment of the test sample is ineffective.

[0006] Furthermore, the lung cancer in question is non-small cell lung cancer.

[0007] Furthermore, the treatment includes one or more of targeted therapy, chemotherapy, or immunotherapy.

[0008] A second aspect of the present invention provides a computer-aided method for screening drugs based on Staphylococcus aureus, the method comprising: Obtain the genomic / proteomic information of Staphylococcus aureus; Extracting target proteins from Staphylococcus aureus; Substances that inhibit Staphylococcus aureus were identified through computer-aided screening as candidate drugs for enhancing the drug sensitivity of lung cancer.

[0009] Furthermore, the lung cancer in question is non-small cell lung cancer.

[0010] Furthermore, the method of promoting lung cancer drug sensitivity refers to promoting the sensitivity of lung cancer to one or more of the following drugs: docetaxel, gefitinib, PD-1, and PD-L1.

[0011] A third aspect of the present invention provides a system for predicting the treatment effect of lung cancer, the system comprising: Acquisition Unit: Acquires bacterial abundance data in the sample to be tested; Extraction unit: Extracts the abundance data of the target species from the bacterial abundance data, wherein the target species is Staphylococcus alright. Prediction Unit: Based on the abundance data of the target bacterial species, classification prediction is performed to obtain a classification result of whether the lung cancer treatment of the test sample is effective. If the abundance of Staphylococcus albus is low, the classification result of lung cancer treatment of the test sample is effective; if the abundance of Staphylococcus albus is high, the classification result of lung cancer treatment of the test sample is ineffective.

[0012] Furthermore, the lung cancer in question is non-small cell lung cancer.

[0013] Furthermore, the treatment includes one or more of targeted therapy, chemotherapy, or immunotherapy.

[0014] A fourth aspect of the present invention provides a computer-aided drug screening system based on Staphylococcus aureus, the system comprising: Acquisition Unit: Acquire genomic / proteomic information of Staphylococcus aureus; Extraction unit: Extraction of target proteins from Staphylococcus aureus; Screening Unit: Substances that inhibit Staphylococcus aureus were identified through computer-aided screening as candidate drugs to enhance the drug sensitivity of lung cancer.

[0015] Furthermore, the lung cancer in question is non-small cell lung cancer.

[0016] Furthermore, the method of promoting lung cancer drug sensitivity refers to promoting the sensitivity of lung cancer to one or more of the following drugs: docetaxel, gefitinib, PD-1, and PD-L1.

[0017] A fifth aspect of the present invention provides a computer device, the computer device comprising: Memory: The memory is used to store program instructions; Processor: The processor is used to invoke program instructions, which, when executed, are used to perform the steps of the method described in the first or second aspect of the present invention.

[0018] A sixth 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.

[0019] A seventh 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.

[0020] An eighth aspect of the present invention provides a method for screening candidate drugs that enhance the drug sensitivity of lung cancer, the method comprising: treating a culture system containing Staphylococcus alrightis with a substance to be screened; and detecting the abundance of Staphylococcus alrightis in the system; wherein, when the substance to be screened reduces the abundance of Staphylococcus alrightis, the substance to be screened is a candidate drug that enhances the drug sensitivity of lung cancer.

[0021] Furthermore, the lung cancer in question is non-small cell lung cancer.

[0022] Furthermore, the method of promoting lung cancer drug sensitivity refers to promoting the sensitivity of lung cancer to one or more of the following drugs: docetaxel, gefitinib, PD-1, and PD-L1.

[0023] A ninth aspect of the present invention provides a method for increasing the activity of T cells, macrophages or NK cells / reducing T cell, macrophage or NK cell apoptosis / increasing dendritic cell activation, said method comprising administering Staphylococcus aureus.

[0024] Furthermore, the method described is not for therapeutic purposes.

[0025] The tenth aspect of the present invention provides the use of Staphylococcus alright in the preparation of medicaments that enhance the activity of T cells, macrophages or NK cells / reduce apoptosis of T cells, macrophages or NK cells / enhance dendritic cell activation.

[0026] Advantages and beneficial effects of the present invention: This application, based on clinical patient screening, identified *Staphylococcus alright* (SA) as a biomarker for assessing potential treatment response and drug resistance risk before treatment. Further, through systematic in vitro and in vivo experiments, it explored the complex role of SA in the microenvironment of lung cancer chemotherapy-immunotherapy. The results revealed that SA is not simply a bystander or a single promoter / inhibitor, but a key microenvironment regulator. In the absence of treatment stress, SA exhibits strong innate immune activation capabilities; however, under the stress of chemotherapy-immunotherapy, it drives a dysfunctional immune state, leading to treatment resistance. This study systematically elucidates the crucial role and multidimensional mechanisms of *Staphylococcus alright* in lung cancer chemotherapy-immunotherapy resistance, laying a theoretical foundation for subsequent clinical translation and application. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the method for predicting the treatment effect of lung cancer provided in this application; Figure 2 This is a schematic diagram of the computer-aided drug screening method based on Staphylococcus aureus provided in this application; Figure 3 This is a schematic diagram of the system for predicting the treatment effect of lung cancer provided in this application; Figure 4 This is a schematic diagram of a computer-aided drug screening system based on Staphylococcus aureus provided in this application; Figure 5 This is a schematic diagram of the computer equipment provided in this application; Figure 6 This is a graph showing the differences in bacterial community levels and survival time between the treatment-effective group and the treatment-ineffective group; Figure 7 This is a graph showing the proliferative and metabolic activity of T cells detected by CCK8 assay; Figure 8 This is a comparison chart of the total apoptosis rate of cells in each group; Figure 9 The graph shows the proliferation rate of PC9 and H23 cell lines after 12h, 24h, and 48h of docetaxel treatment. Figure 10 The graph shows the proliferation rate of PC9 and H23 cells treated with different concentrations of PD-1 and PD-L1. Figure 11 The IC50 values ​​of PC9 and H23 cells treated with different concentrations of PD-1 and PD-L1 are shown. Figure 12 This refers to the Western blotting detection of NFκB electrophoresis films and statistical graphs. Figure 13 This is a Western blotting analysis of P-NF-κBp65 electrophoresis film and statistical graphs; Figure 14 This is an image of a tumor in a C57 mouse. Figure 15 This is a graph showing the tumor volume and weight in C57 mice. Figure 16 This is a graph showing the percentage of T cell subtypes in mouse tumors as detected by flow cytometry. Figure 17 These are experimental graphs of CCK8 proliferation activity at 12h, 24h, and 48h. Figure 18 This is a flow cytometry assay for the CD11c marker on the surface of dendritic cells in Staphylococcus aureus. + CD80 + Experimental diagram showing the effects of CD11c+HLA-DR and CD11c+CD86; Figure 19 This is a graph showing the proliferation and metabolic activity of cells in each group as detected by CCK8. Figure 20 This is an experimental diagram showing the CCK8 proliferation activity of Staphylococcus aureus on lung cancer-dendritic cells; Figure 21 This is a comparison chart of the total apoptosis rate of cells in each group; Figure 22 This is a graph showing the apoptosis rate of cells in each group as detected by flow cytometry. Figure 23 This is a diagram showing the results of co-culturing antitumor drugs with Staphylococcus aureus-PC9 cells / H23 cells-macrophages; Figure 24 This is a diagram showing the results of co-culturing anti-tumor drugs with SA-PC9 cells / H23 cells-NK92 cells; Figure 25 This is an image of a tumor in a C57 mouse. Figure 26 This is a graph showing the tumor volume and weight in nude mice; Figure 27 This is a flow cytometry assay for detecting F4 / 80, an intratumoral marker of immune cells in mice. + CD86 + Scale diagram. Detailed Implementation

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 1 This is a schematic diagram of the method for predicting the treatment effect of lung cancer provided in this application, which specifically includes: 101: Obtain the abundance data of bacterial species in the sample to be tested.

[0032] 102: Extract the abundance data of the target species from the bacterial abundance data, wherein the target species is Staphylococcus Arlettae (SA).

[0033] 103: Based on the abundance data of the target bacterial species, classification prediction is performed to obtain a classification result of whether the lung cancer treatment of the test sample is effective. If the abundance of Staphylococcus albus is low, the classification result of the lung cancer treatment of the test sample is effective; if the abundance of Staphylococcus albus is high, the classification result of the lung cancer treatment of the test sample is ineffective.

[0034] Figure 2 This is a schematic diagram of the computer-aided drug screening method based on Staphylococcus aureus provided in this application, specifically including: 201: Obtain genomic / proteomic information of Staphylococcus alright.

[0035] 202: Extraction of target proteins from Staphylococcus aureus.

[0036] 203: Substances that inhibit Staphylococcus aureus were identified through computer-aided screening as candidate drugs for enhancing the drug sensitivity of lung cancer.

[0037] In some embodiments, the target proteins of Staphylococcus alright include proteins associated with bacterial biofilm formation, virulence expression, or drug resistance (such as DNA gyrase, penicillin-binding proteins PBPs, quorum sensing signaling molecule synthases, etc.).

[0038] In some implementations, virtual screening is an important method in targeted drug design, and its process is roughly as follows: First, based on the known structures of drug-target protein complexes, the binding modes of the drug and target protein are studied to identify key amino acid residues. This step helps to understand how the drug interacts with the target protein and provides a basis for subsequent virtual screening.

[0039] Next, computer-aided drug design methods are used to virtually screen a large number of small molecule compounds. During this process, the binding affinity of the small molecule compounds to the target protein is predicted and evaluated.

[0040] Finally, the selected candidate drugs were further experimentally validated to confirm their interaction with the target protein and their biological activity.

[0041] In some implementations, the molecular libraries used in virtual screening primarily include the following: ZINC: containing over 250 million commercially available compounds for small molecule virtual screening; PubChem: containing bioactive substances for small molecule virtual screening; DrugBank: containing drugs and small molecules for drug design and discovery; ChEMBL: containing small molecules for drug discovery and chemical genomics research; ChemDB: containing a large number of known small molecules for chemical genomics research and drug discovery; HMDB: containing a large number of known small molecules for chemical genomics research and drug discovery; BindingDB: containing a large number of known small molecules for chemical genomics research and drug discovery; and SMPDB: containing a large number of known small molecules for chemical genomics research and drug discovery.

[0042] In addition, there are some commercial databases such as ChemDiv, Enamine, Lifechemicals, Specs, Chembridge, Maybridge, Microsource, Vitas-M, and Interbioscreen, which are also often used for virtual screening.

[0043] In some embodiments, the candidate drug includes protein analogs, antibodies, and RNA drugs.

[0044] Figure 3 This application provides a schematic diagram of a system for predicting the treatment effect of lung cancer, specifically including: 301 Acquisition Unit: Acquires bacterial abundance data in the sample to be tested.

[0045] 302 Extraction Unit: Extracts the abundance data of the target species from the bacterial abundance data, wherein the target species is Staphylococcus alright.

[0046] 303 Prediction Unit: Based on the abundance data of the target bacterial species, it performs classification prediction to obtain a classification result of whether the lung cancer treatment of the test sample is effective. If the abundance of Staphylococcus albus is low, the classification result of the lung cancer treatment of the test sample is effective; if the abundance of Staphylococcus albus is high, the classification result of the lung cancer treatment of the test sample is ineffective.

[0047] Figure 4 This is a schematic diagram of a computer-aided drug screening system based on Staphylococcus aureus provided in this application, specifically including: 401 Acquisition Unit: Acquire genomic / proteomic information of Staphylococcus alright.

[0048] 402 Extraction Unit: Extracts target proteins from Staphylococcus aureus.

[0049] 403 Screening Unit: Through computer-aided screening, substances that inhibit Staphylococcus aureus were identified as candidate drugs for promoting the sensitivity of lung cancer drugs.

[0050] Figure 5 This is a schematic diagram of the computer device provided in this application, specifically including: Memory: The memory is used to store program instructions; Processor: The processor is used to call program instructions, which, when executed, are used to perform the steps of the above method.

[0051] This invention provides a method for screening candidate drugs that enhance the sensitivity of lung cancer drugs. The method includes: treating a culture system containing Staphylococcus alrightis with a substance to be screened; and detecting the abundance of Staphylococcus alrightis in the system; wherein, when the substance to be screened reduces the abundance of Staphylococcus alrightis, the substance to be screened is a candidate drug that enhances the sensitivity of lung cancer drugs.

[0052] In some implementations, candidate drugs that enhance lung cancer drug sensitivity may be antibiotics, metabolic pathway inhibitors (such as drugs that target amino acid transport or glutathione synthesis), or drugs targeting specific NF-κB nodes.

[0053] This application is verified through the following embodiments: Example 1: Correlation between intratumoral bacteria in non-small cell lung cancer and the clinical efficacy of antitumor drug therapy Materials and Methods 1.1 Case Collection Patients with newly diagnosed lung malignancies admitted to the Department of Oncology at the First Affiliated Hospital of Dalian Medical University between July 2021 and July 2022 (the period of ethics approval) underwent CT-guided lung biopsy to confirm the pathological diagnosis. Simultaneously, a tissue sample from the tumor was taken for metagenomic microbiota testing. Once pathological confirmation of lung cancer was achieved, driver gene testing for EGFR, ALK, and ROS1 was performed. Subsequently, based on the pathological and driver gene testing results, clinical treatment was administered according to the Chinese Society of Clinical Oncology (CSCO) Guidelines for the Diagnosis and Treatment of Lung Cancer, including targeted therapy, chemotherapy, and chemotherapy combined with immunotherapy. Treatment efficacy and toxicities were recorded.

[0054] 1.2 Metagenomic sequencing detection of bacterial communities (1) Collection and transport: Under the premise of following the clinical aseptic operation guidelines, lung tumor tissue samples from patients are collected and transported to the laboratory under cold chain conditions of 2-4℃.

[0055] (2) Nucleic acid extraction: Nucleic acid extraction from tissue samples was performed using the TIANamp Magnetic DNA Kit (Tiangen Biotech Co., Ltd.). The first step was sample incubation, in which proteinase K, lysis buffer, and magnetic beads worked synergistically to release genomic DNA from the cells. Then, magnetic beads were adsorbed using a magnetic rack, and nucleic acids were washed away with enzyme-free water, collecting the DNA in the filtrate. The extracted DNA-containing solution was vortexed in a centrifuge tube and then quantitatively analyzed using Qubit fluorescence quantitative quantification (Thermo Fisher).

[0056] (3) Library construction and sequencing: DNA samples were sonicated to generate fragments of 150-300 bp, and then DNA libraries were prepared using the KAPA Hyper Prep kit (KAPA Biosystems). The process included DNA fragment purification, end repair, adapter ligation, fragment size selection, PCR amplification, and product purification. After completion, the library quality was controlled. Library quality was assessed using an Agilent 2100, and library concentration was quantified using Qubit. The constructed libraries were finally sequenced at 150 bp ends on an Illumina Novaseq 6000 platform.

[0057] (4) Bioinformatics Analysis and Pathogen Identification: After sequencing, the raw BCL format data was converted using FASTQ (bcl2fastq2) software to obtain raw FASTQ format data. Trimmomatic software was used for quality control of the raw data, removing low-quality sequences, removing adapter contamination, and eliminating repetitive and short sequences (less than 36 bp in length). Then, Bowtie2 software was used to align high-quality sequences with the human genome sequence (hs37d5), excluding human genome and mitochondrial DNA sequences. For reads that could not be mapped to the human genome, Kraken2 software was used to align them with a microbial genome database to identify the microorganisms. Simultaneously, Bracken software was used to estimate the relative abundance of each species. The microbial genome database used included genomes of bacteria, fungi, viruses, and parasites (based on GenBank version 238, ftp: / / ftp.ncbi.nlm.nih.gov / genomes / genbank / ). Finally, pathogenic microorganisms meeting the criteria were retained according to the set thresholds, and corresponding clinical diagnostic reports were generated.

[0058] 1.3 Grouping and Basis Based on the 3rd-4th cycle of treatment (or 3rd-4th month for targeted therapy), the RECIST criteria (version 1.1) were used to evaluate treatment efficacy, categorized into four groups: Complete Response (CR), Partial Response (PR), Stable Disease (SD), and Progressive Disease (PD). Patients were further divided into the treatment-responsive group (R0, including CR, PR, and SD) and the treatment-ineffective group (R1, PD) based on their treatment response. Based on the detection results of driver genes such as EGFR, ALK, and ROS1, and the treatment methods used, patients were subdivided into driver gene mutation group A and driver gene negative group B. To investigate changes in the tumor microbiota as the tumor grows, patients were divided into the large-diameter group (BD, tumor diameter ≥ 3 cm) and the small-diameter group (SD, tumor diameter < 3 cm) based on pre-treatment imaging results.

[0059] 1.4 Statistical Analysis This study used R software (version 4.0.1) for statistical analysis. The Shannon Index was used to assess microbial diversity, and the Wilcoxon rank-sum test was used to compare differences in microbial diversity between groups. Bray-Curtis distance was used to measure β-diversity, and its visualization was achieved using principal coordinate analysis (PCoA). Permanent ANOVA (also known as ADONISH) was used to assess statistical differences in β-diversity between groups. The Kruskal-Wallis rank-sum test was used to identify differences in microbial abundance at the genus level between groups, with a microbial filter condition set to relative abundance greater than 1%. The Spearman correlation between clinical characteristics and relative abundance at the genus level was calculated using the R package "cort.test," and all p-values ​​were corrected using the FDR (false discovery rate) method. Furthermore, the linear discriminant analysis effect size (LEfSe) method was applied to assess the statistical significance of relative microbial abundance between groups, where a p-value less than 0.05 was defined as statistically significant.

[0060] Experimental results This study, conducted between July 2021 and July 2022, included 75 biopsies of malignant lung tumors, from which tissue samples were successfully collected. Data from 21 non-small cell lung cancer (NSCLC) patients were fully followed up and included in the study analysis. Regarding driver gene testing, no ALK or ROS1 mutations were found in the NSCLC patients, but 8 patients tested positive for EGFR mutations and all received EGFR-targeted therapy. Of the remaining NSCLC patients, 5 received platinum-based dual-action chemotherapy, and 8 received platinum-based dual-action chemotherapy combined with immune checkpoint inhibitors. Due to grade IV adverse events, 1 patient receiving targeted therapy and 2 patients receiving chemotherapy combined with immune checkpoint inhibitors had their treatment regimens changed. Based on EGFR mutation status, patients were divided into EGFR mutation group A (8 cases) and EGFR wild-type group B (13 cases). According to the RECIST criteria (version 1.1), no patients achieved complete remission (CR). There were 11 patients with R0 in the effective group (including partial remission PR and stable disease SD) and 7 patients with R1 (progressive disease PD) in the ineffective group. According to the largest diameter of the tumor, there were 12 patients with BD in the large diameter group and 13 patients with SD in the small diameter group.

[0061] Analysis of the species-level composition of the intratumoral flora in the two groups showed that the top 20 bacterial species were generally similar between the two groups. However, the content of Bordetella pertussis was significantly higher in the effective treatment group than in the ineffective treatment group; conversely, the content of Staphylococcus alrighti in the ineffective treatment group was significantly higher than in the effective treatment group. Of particular note was that Staphylococcus alrighti appeared almost exclusively in the ineffective treatment sample. Figure 6 ).

[0062] Example 2: Staphylococcus alright drives resistance to chemotherapy and immunotherapy in lung cancer through metabolic reprogramming and T cell exhaustion. Materials and Methods Cell and bacterial sources: EGFR-mutant human lung cancer cells PC9, non-EGFR-mutant human lung cancer cells H23, and human acute T-cell leukemia cells Jurkat were all purchased from Qisai Company, and Staphylococcus aureus was purchased from Biobw Company.

[0063] Experiment on the effect of Staphylococcus aureus on lung cancer cell-T cell system 1. Experimental subjects and specific groupings: Group A: Lung cancer EGFR positive group; Group B: Lung cancer EGFR negative group; Group C: T cell (Jurkat-T) group; Group D: Lung cancer EGFR positive + T cell (Jurkat-T) group; Group E: Lung cancer EGFR negative + T cell (Jurkat-T) group; Group F: Staphylococcus alright + Lung cancer EGFR positive group; Group G: Staphylococcus alright + Lung cancer EGFR negative group; Group H: Staphylococcus alright + T cell (Jurkat-T) group; Group I: Staphylococcus alright + Lung cancer EGFR positive + T cell (Jurkat-T) group; Group J: Staphylococcus alright + Lung cancer EGFR negative + T cell (Jurkat-T) group.

[0064] 2. Apoptosis experiment After the required cell processing time, cells were collected by trypsin digestion, rinsed twice with PBS, and centrifuged at 1200 rpm for 5 min. Then, the Annexin V-FITC / PI apoptosis detection kit was used for flow cytometry analysis.

[0065] 3. Immunofluorescence experiment 3.1 Fixation of the slide: Fix the slide with 4% paraformaldehyde for 15 min, and wash the slide with PBST 3 times for 3 min each time.

[0066] 3.2 Cell permeability (this step is omitted for indicators of cell membrane localization): Permeabilize with 0.5% Triton X-100 (diluted with 1×PBS) at room temperature for 5 min, then wash the slides with PBST 3 times for 3 min each time.

[0067] 3.3 Serum blocking: Blot dry the smear with absorbent paper, add diluted normal goat serum, and block at room temperature for 30 minutes to reduce non-specific staining.

[0068] 3.4 Add primary antibody: Aspirate excess liquid without washing, then add the diluted primary antibody dropwise. After adding the primary antibody, incubate overnight (15h) at 4°C.

[0069] 3.5 Adding fluorescent secondary antibody: Wash the slides three times with PBST for 3 minutes each time. After blotting the slides with absorbent paper, add the diluted fluorescent secondary antibody CY3-goat anti-rabbit IgG. Incubate in a humidified chamber at 37°C for 1 hour. Wash the slides four times with PBST for 3 minutes each time. Note: This step and all subsequent steps should be performed in the dark as much as possible.

[0070] 3.6 Counterstaining the nucleus: Add DAPI and incubate in the dark for 5 min, then stain the nucleus of the slide. Wash away excess DAPI by PBST for 5 min × 4 times.

[0071] 3.7 Mounting: Place the anti-fluorescence quenching mounting medium on the slide, place the side of the slide with cells on it, first flatten one side, then gently place the other side down to avoid air bubbles, and let the mounted slide dry.

[0072] 3.8 Microscopic examination and photography: The dried slides can be observed or images can be acquired under a fluorescence microscope.

[0073] Methods for establishing a bacterial-lung cancer cell-T cell co-culture system as a cell model for drug intervention experiments 1. Drug concentration screening experiment 1.1 Cell treatment: ① Take cells in the logarithmic growth phase and in good growth condition, and use 3×10 3 Cells were seeded per well in a 96-well plate and incubated overnight at 37°C in a 5% CO2 incubator; (200 μL of sterile PBS was added to the wells surrounding the cells). ② Treat the cells according to the following groups and culture them in a 37 ℃, 5% CO2 incubator. Group A: Group a: PC9 cells + docetaxel (0, 0.01 μM, 0.05 μM, 0.1 μM, 0.5 μM, 1 μM, 2.5 μM, 5 μM, 10 μM, 20 μM).

[0074] Group b: H23 cells + docetaxel (0, 0.01 μM, 0.05 μM, 0.1 μM, 0.5 μM, 1 μM, 2.5 μM, 5 μM, 10 μM, 20 μM).

[0075] Group B: Group a: PC9 cells + gefitinib (0, 0.01 μM, 0.05 μM, 0.1 μM, 0.5 μM, 1 μM, 2.5 μM, 5 μM, 10 μM, 20 μM).

[0076] Group b: H23 cells + gefitinib (0, 0.01 μM, 0.05 μM, 0.1 μM, 0.5 μM, 1 μM, 2.5 μM, 5 μM, 10 μM, 20 μM).

[0077] ③ Duration of action: Different concentrations of docetaxel and gefitinib were used to treat cells for 12h, 24h, and 48h, respectively.

[0078] 1.2 Screening method for optimal concentrations of PD-1 and PD-L1 (1) Culture PC9 and H23 cells normally, change the medium, passage, and cryopreserve. Before the experiment, cryopreserve 3-5 strains of each cell type.

[0079] The cells were treated according to the following grouping: Group A: Group a: PC9 cells + PD-1 (0, 10 mg / mL, 12 mg / mL, 14 mg / mL, 16 mg / mL, 18 mg / mL, 20 mg / mL, 22 mg / mL, 24 mg / mL, 26 mg / mL).

[0080] Group b: H23 cells + PD-1 (0, 10 mg / mL, 12 mg / mL, 14 mg / mL, 16 mg / mL, 18 mg / mL, 20 mg / mL, 22 mg / mL, 24 mg / mL, 26 mg / mL).

[0081] Group B: Group a: PC9 cells + PD-L1 (0, 10 mg / mL, 12 mg / mL, 14 mg / mL, 16 mg / mL, 18 mg / mL, 20 mg / mL, 22 mg / mL, 24 mg / mL, 26 mg / mL).

[0082] Group b: H23 cells + PD-L1 (0, 10 mg / mL, 12 mg / mL, 14 mg / mL, 16 mg / mL, 18 mg / mL, 20 mg / mL, 22 mg / mL, 24 mg / mL, 26 mg / mL).

[0083] Treatment time: After the plate has been incubated overnight, add the corresponding concentration of drug and treat for 24 hours.

[0084] (2) Cell CCK8 detection: After the required cell culture time, 10 μL of CCK8 was added to each well and cultured at 37 ℃ for 1 h. The absorbance of each well was measured by OD 450 using an ELISA reader.

[0085] (3) IC50 calculation: Calculate the IC50 values ​​of PD-1 and PD-L1 for PC9 and H23 respectively. The smaller the IC50 value, the better the inhibition effect. Formula: Y=a / 1+be -cx .

[0086] Research on the mechanism of resistance to lung cancer cell-T cell system by Staphylococcus aureus: Cellular experimental methods 1. Effect of Staphylococcus aureus on cell survival rate of lung cancer cell-T cell system after antitumor drug treatment Cells were treated separately according to different grouping and cell treatment settings, and cultured in an incubator at 37 ℃ and 5% CO2. The groups are as follows: ① Lung cancer EGFR positive group PC9; ② Lung cancer EGFR negative group H23; ③ Staphylococcus alright + Lung cancer EGFR positive group; ④ Staphylococcus alright + Lung cancer EGFR negative group; ⑤ Lung cancer EGFR positive + T cell (Jurkat-T) group; ⑥ Lung cancer EGFR negative + T cell (Jurkat-T) group; ⑦ Staphylococcus alright + Lung cancer EGFR positive + T cell (Jurkat-T) group; ⑧ Staphylococcus alright + Lung cancer EGFR negative + T cell (Jurkat-T) group; ⑨ T cell (Jurkat-T) group.

[0087] Treatment time: Cells and bacterial strains were inoculated into the plate simultaneously and cultured for 24 hours to adhere to the plate. Then, the drug was added for another 24 hours of treatment.

[0088] Cell viability was assessed using CCK8 assay: After the required cell culture time, 10 μL of CCK8 was added to each well and the cells were incubated at 37 °C for 1–4 h.

[0089] The absorbance of each well was measured using an ELISA reader at OD 450.

[0090] 2. Effects of Staphylococcus aureus on effector proteins in lung cancer cell-T cell system after antitumor drug treatment 2.1 Study on drug resistance mechanisms using Western blotting (1) Experimental grouping: (T cells with the highest tumor cell killing rate were selected for subsequent experiments, and EGFR-negative H23 tumor cells were selected) ① Lung cancer EGFR negative group H23; ② Staphylococcus alright + lung cancer EGFR negative group; ③ Lung cancer EGFR negative + T cell (Jurkat-T) group; ④ Staphylococcus alright + lung cancer EGFR negative + T cell (Jurkat-T) group; ⑤ T cell (Jurkat-T) group.

[0091] Cells were treated with four drugs at optimal concentrations: docetaxel, gefitinib, PD1 antibody, and PDL1 antibody.

[0092] 2.2 Study on drug resistance mechanisms using ELISA kits (1) Grouping (25 groups in total, with no biological replicates) ① Lung cancer EGFR-negative group H23 blank; ② Staphylococcus aureus + lung cancer EGFR-negative group; ③ Lung cancer EGFR-negative + T-cell (Jurkat-T) group; ④ Staphylococcus aureus + lung cancer EGFR-negative + T-cell (Jurkat-T) group; ⑤ T-cell (Jurkat-T) group; ⑥ Lung cancer EGFR-negative group H23 docetaxel; ⑦ Staphylococcus aureus + lung cancer EGFR-negative group docetaxel; ⑧ Lung cancer EGFR-negative + T-cell (Jurkat-T) group docetaxel; ⑨ Staphylococcus aureus + lung cancer EGFR-negative + T-cell (Jurkat-T) group docetaxel; ⑩ T-cell (Jurkat-T) group docetaxel; 11 Lung cancer EGFR-negative group H23 gefitinib; 12 Staphylococcus aureus + lung cancer EGFR-negative group gefitinib; 13 Lung cancer EGFR-negative + T-cell (Jurkat-T) group gefitinib; 14 Gefitinib in the *Staphylococcus aureus* + EGFR-negative + T-cell (Jurkat-T) group; 15 Gefitinib in the T-cell (Jurkat-T) group; 16 H23PD1 antibody in the EGFR-negative lung cancer group; 17 PD1 antibody in the *Staphylococcus aureus* + EGFR-negative lung cancer group; 18 PD1 antibody in the EGFR-negative + T-cell (Jurkat-T) group; 19 PD1 antibody in the *Staphylococcus aureus* + EGFR-negative + T-cell (Jurkat-T) group; 20 PD1 antibody in the T-cell (Jurkat-T) group; 21 H23PDL1 antibody in the EGFR-negative lung cancer group; 22 PDL1 antibody in the *Staphylococcus aureus* + EGFR-negative lung cancer group; 23 PDL1 antibody in the EGFR-negative + T-cell (Jurkat-T) group; 24 PDL1 antibody in the *Staphylococcus aureus* + EGFR-negative + T-cell (Jurkat-T) group; 25 PDL1 antibody in the T cell (Jurkat-T) group.

[0093] (2) Sample addition: Set up blank wells (blank control wells do not contain sample or enzyme-labeled reagent, all other steps are the same), standard wells, and sample wells. Accurately add 50 μL of enzyme-labeled reagent to the enzyme-coated plate. Add 40 μL of sample diluent to the sample wells, and then add 10 μL of the sample to be tested (the final sample dilution is 5 times). When adding the sample, place it at the bottom of the well, avoiding touching the well wall as much as possible, and gently shake to mix.

[0094] (3) Incubation: After sealing the plate with sealing film, incubate at 37℃ for 30 minutes.

[0095] (4) Solution preparation: Dilute the 30-fold concentrated washing solution with distilled water.

[0096] (5) Washing: Carefully peel off the sealing film, discard the liquid, shake dry, fill each hole with washing liquid, let stand for 30 seconds and then discard, repeat this 5 times, and pat dry.

[0097] (6) Add enzyme: Add 50 μL of enzyme labeling reagent to each well, except for blank wells.

[0098] (7) Warming and washing.

[0099] (8) Color development: Add 50 μL of color developer A to each well first, then add 50 μL of color developer B, and gently shake. Mix well and develop color at 37°C in the dark for 10 minutes.

[0100] (9) Termination: Add 50 μL of stop solution to each well to terminate the reaction (at this time, the blue color will immediately turn yellow).

[0101] (10) Measurement: Zero the instrument with the blank well and measure the absorbance (OD value) of each well in sequence at a wavelength of 450 nM. The measurement should be performed within 15 minutes after adding the stop solution.

[0102] Animal experiments to study the mechanism of resistance to lung cancer cell-T cell system therapy by Staphylococcus aureus alright 1. Animal Information and Experimental Drug Preparation 1.1 Animal Information: Mice C57BL / 6 1.2 Drug Preparation PD-1: Tislelizumab: Take 100 μL of PD-1: Tislelizumab and dilute it to 5 mL with normal saline.

[0103] Docetaxel: Take 150 μL of docetaxel and dilute it to 5 mL with physiological saline to achieve a concentration of 6 mg / 10 mL.

[0104] 2. Animal model making steps 2.1 Animal Model Making (1) Animals were kept in an acclimatization environment for 7 days.

[0105] (2) The animals were randomly divided into 6 groups of 8 animals each: Group A: C57 mouse tumorigenesis group (CL); Group B: C57 mouse tumorigenesis + bacterial culture medium and cell group (CEXOL); Group C: C57 mouse tumorigenesis + bacterial and cell co-culture group (CSAL); Group D: C57 mouse tumorigenesis + docetaxel combined with PD1 monoclonal antibody group (CLTP); Group E: C57 mouse tumorigenesis + bacterial culture medium and cell co-culture + docetaxel combined with PD1 monoclonal antibody group (CEXOLTP); Group F: C57 mouse tumorigenesis + bacterial and cell co-culture + docetaxel combined with PD1 monoclonal antibody group (CSALTP).

[0106] (3) Inoculation: After wiping the subcutaneous skin on the right back with an alcohol swab for disinfection, inject 200 μl of cell suspension subcutaneously into the right back. After removing the needle, gently press the needle hole with your finger to confirm that there is no exudate, and then put it back into the cage for normal feeding.

[0107] (4) Observe the condition of the mice and the growth of the tumor every day. After the tumor grows out, weigh the mice every 3 days and measure the long diameter a and short diameter b of the tumor with calipers. Calculate the tumor volume V=a*b 2 / 2.

[0108] 2.2 Administration Once the tumor sizes in groups A and C were consistent, groups D, E, and F received a single intraperitoneal injection of the PD-1 monoclonal antibody InVivoMAb anti-mouse PD-1 (10 mg / kg). Docetaxel (6 mg / kg) was administered via tail vein starting the day after the InVivoMAb anti-mouse PD-1 injection, with injections every four days.

[0109] 2.3 Collection of materials (1) Mouse photography: After administration, mice were intraperitoneally injected with 2.5% tribromoethanol at a dose of 0.2 mL / 10 g. After anesthesia, they were neatly arranged in groups against a monochrome background and photographed with a ruler.

[0110] (2) 500-700 μL of anticoagulated whole blood was collected from the eyes of 3 mice in each group for subsequent testing. 500-700 μL of blood was collected from the eyes of 5 mice in each group. Half of the blood was left to stand at room temperature for 30 min, then centrifuged at 3500 r / min for 10 min. The supernatant serum was collected and frozen at -80℃. The other half of the blood was treated with anticoagulant, centrifuged at 3500 r / min for 10 min, and the supernatant plasma was collected and frozen at -80℃.

[0111] (3) Photographing the tumor: After anesthetizing the mice, open the abdomen and chest, cut open the right atrial appendage, and infuse 3 mL of physiological saline into the left ventricle. When the liver turns yellowish-brown, it indicates that the perfusion is complete. Remove the tumor, arrange them neatly in groups against a monochrome background, and take a picture with a ruler. (4) Sample preservation: Tumor tissue was divided into four parts, one part was fixed with 4% paraformaldehyde, and the other three parts were frozen. Lymph nodes, spleen, adjacent tissue and lung tissue were rinsed with physiological saline, half of which were fixed with 4% paraformaldehyde and the other half were frozen for subsequent testing.

[0112] (vi) Statistical methods All experiments in this study were repeated three times. All observational data were small sample data. SPSS 23.0 software was used for statistical analysis. One-way ANOVA was used for the two groups of continuous data, and the rank-sum test was used to compare the rates of the two groups.

[0113] Experimental results The ratio of co-culture components was determined to be bacteria:lung cancer-T cells = 200:1, with lung cancer cells:T cells = 1:2.

[0114] Staphylococcus alright had no significant effect on the proliferative and metabolic activity of lung cancer cells, but significantly increased the proliferative and metabolic activity of T cells, while significantly reducing the overall proliferative and metabolic activity of the lung cancer cell-T cell system. Figure 7 ).

[0115] Flow cytometry analysis showed that *Staphylococcus alrighti* significantly reduced T cell apoptosis and significantly increased apoptosis in various lung cancer cell-immune cell systems. Figure 8 This indicates that the bacteria activates immune cells in the system, promoting their ability to kill tumor cells.

[0116] From the selected concentrations, a concentration that was clearly effective without causing excessive cell damage was chosen. Based on the results of co-culturing PC9 and H23 cells with different concentrations of docetaxel at different time points, the optimal concentration of docetaxel was selected as 1 μM. Figure 9 ).

[0117] Based on the proliferation rate and IC50 value of PC9 (human lung cancer cells with high EGFR expression) and H23 (human lung cancer cells with low EGFR expression) cells treated with different concentrations of PD-1 and PD-L1, the optimal concentrations that were significantly effective without causing excessive cell damage (i.e., a cell inhibition rate of approximately 30%) were selected. Finally, the optimal concentration of PD-1 was determined to be 10 mg / mL; the optimal concentration of PD-L1 was determined to be 12.5 mg / mL. Figure 10 , Figure 11 ).

[0118] We selected H23 cells, which are most affected by T cells, for subsequent experiments. Following Staphylococcus aureus infection, docetaxel, PD-1 inhibitors, and PD-L1 inhibitors significantly downregulated NF-κB p65 expression in lung cancer cells. Figure 12 DTX: Docetaxel; PD-1: PD-1 inhibitor; PD-L1: PD-L1 inhibitor.

[0119] Following Staphylococcus aureus infection, docetaxel and PD-1 inhibitors significantly suppressed NF-κBp65 phosphorylation levels in lung cancer cells, while PD-L1 inhibitors had no significant effect on P-NF-κBp65 levels. Figure 13 DTX: Docetaxel; PD-1: PD-1 inhibitor; PD-L1: PD-L1 inhibitor.

[0120] Tumors were removed from C57 mice, arranged neatly in groups against a monochrome background, and photographed with a ruler attached. Figure 14 CEXOL: C57 mouse tumorigenesis + bacterial culture group; CSAL: C57 mouse tumorigenesis + bacterial co-culture group; CLTP: C57 mouse tumorigenesis + docetaxel combined with PD1 monoclonal antibody therapy group; CEXOLTP: C57 mouse tumorigenesis + bacterial culture + docetaxel combined with PD1 monoclonal antibody therapy group; CSALTP: C57 mouse tumorigenesis + bacterial infection + docetaxel combined with PD1 monoclonal antibody therapy group; bacteria: Staphylococcus aureus; PD1 monoclonal antibody: tislelizumab). Compared with the tumorigenesis group alone (A), tumor growth was inhibited in the immunotherapy group (D); while different bacterial intervention groups (B, C) may have changed the baseline tumor growth; most importantly, compared with the treatment group alone (D), the bacterial exosome combined therapy group and the bacterial combined therapy group (E, F) should show a weakened tumor growth inhibition effect, especially the bacterial treatment group (F) showed no significant difference in tumor volume compared with the bacterial non-treatment group (C), confirming that bacteria have a negative effect on efficacy. Figure 15 ).

[0121] To clarify the direct impact of harmful bacteria on the tumor immune microenvironment, we analyzed the infiltration of key CD4+ T cell subsets in tumor tissue by flow cytometry. The results showed that Staphylococcus alright and its culture medium significantly altered the ratio of Tregs to Th cells, driving the immune microenvironment towards a suppressive state. (1) SA bacteria promote the expansion of immunosuppressive Treg cells. By detecting CD4+FoxP3+ Treg cells, we found that compared with the tumorigenic control group (CL), the proportion of Tregs in both the bacterial co-culture group (CSAL) and the bacterial culture medium group (CEXOL) was significantly increased. Figure 16 This result indicates that both the live bacteria themselves and their secreted components can effectively induce or recruit Treg cells, establishing a strong immunosuppressive barrier within the tumor.

[0122] Example 3: Staphylococcus alright drives immunochemotherapy resistance in lung cancer through dendritic cell activation, macrophage M2 polarization, and NK cell dysfunction. Materials and Methods (I) Steps for establishing a co-culture system of lung cancer cells and macrophages / NK cells 1. Cell and bacterial sources: PC9 cells (human lung cancer cells with high EGFR expression), H23 cells (human lung cancer cells with low EGFR expression), NK-92 natural killer cells from human patients with malignant non-Hodgkin lymphoma, human monocyte / macrophage THP-1, and Staphylococcus aureus were purchased from Biobw. DC2.4 (mouse bone marrow-derived dendritic cells) were purchased from Procell.

[0123] 2. Screening of cell co-culture ratios 2.1 Induction of macrophage differentiation THP-1 cells were treated with 100 ng / mL PMA (phorbol 12-myristate 13-acetate) for 24 hours to induce their differentiation into a macrophage-like phenotype.

[0124] THP-1 cells were cultured in RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin / streptomycin. (Cultured at 37°C in a 5% CO2 incubator until the cell concentration reached approximately 1 × 10⁻⁶ cells / year). 6 (cells / mL). Seed THP-1 cells in culture dishes (e.g., 6-well or 12-well plates) to allow the cells to attach before adding PMA. Continue culturing after seeding until the cell concentration reaches approximately 1 × 10⁻⁶ cells / mL. 6 Cells / mL, and confirm that the cells are in good condition. Dissolve PMA in a suitable solvent (such as DMSO) to ensure a final concentration of 100 ng / mL. Add PMA to the culture medium and treat the cells for 24 hours. At this time, PMA will induce THP-1 cells to differentiate into a macrophage-like phenotype by activating the protein kinase C pathway.

[0125] 2.2 Other cell culture and preparation: PC9, H23, THP-1, and NK-92 cells were routinely cultured in a medium containing 10% FBS and 1% antibiotics at 37°C and 5% CO2.

[0126] 2.3 Establishment of a co-cultivation system PC9 or H23 cells were co-cultured with THP-1 macrophages / NK-92 cells at different ratios (1:1, 1:2, 1:5, 1:10, 1:20) in 96-well plates, with a total of 5 × 10³ cells per well, and cultured for 24 h.

[0127] 3.4 Cell viability assay Add 10 μL of CCK-8 solution to each well, incubate at 37°C for 2 h, and measure the absorbance at OD 450 nm using a microplate reader to calculate the cell proliferation rate.

[0128] 2.5 Results and Optimization Conditions The CCK-8 results showed that the immunosuppressive effect was best when the ratio of lung cancer cells to immune cells was 1:2, so this ratio was used in subsequent experiments.

[0129] 3. Screening of bacterial co-culture concentrations 3.1 Bacterial preparation: SA bacterial solution preparation and concentration adjustment.

[0130] 3.2 Co-culture and bacterial intervention: PC9 or H23 cells were co-cultured with THP-1 / NK-92 cells at a ratio of 1:2, and different concentrations of SA were added (cell:bacterial ratio of 1:50 to 1:1000) for 24 h.

[0131] 3.3 Cell viability assay: Total cell proliferation activity was detected by CCK-8 assay.

[0132] 3.4 Results and Optimization Conditions The results showed that the minimum effective concentration was SA:cell = 200:1, and this concentration was used in all subsequent experiments.

[0133] (II) Research methods for SA on dendritic cell function and co-culture system mechanism 1. Effects of SA on dendritic cell (DC) viability 1.1 DCs culture and treatment DC2.4 cells were isolated and cultured, and co-cultured with live SA bacteria or their sterile supernatant for 12 h, 24 h, and 48 h.

[0134] 1.2 CCK-8 assay for DC activity.

[0135] 2. SA's role in the maturation and functional activation of DCs 2.1 Flow cytometry was used to detect the expression of surface markers CD80, CD86 and HLA-DR in DCs.

[0136] 3. Effects of SA on macrophage phenotype 3.1 Flow cytometry detection of CD68 in co-culture system + CD86 + (M1) and CD68 + CD206 + (M2) Macrophage ratio.

[0137] 4. Effects of SA on apoptosis and metabolism in co-culture systems 4.1 Apoptosis detection: Annexin V-FITC / PI double staining method, flow cytometry analysis.

[0138] 4.2 LDH release assay: The concentration of LDH in the co-culture supernatant was detected to assess tumor cell damage.

[0139] 5. Protein Expression and Signaling Pathway Analysis 5.1 Western blotting was used to detect the expression of proteins such as PD-1, PD-L1, p65, and p-p65.

[0140] 5.2 The experimental steps include protein extraction, BCA quantification, SDS-PAGE, membrane transfer, blocking, primary / secondary antibody incubation, and ECL imaging.

[0141] (III) Research methods for SA interference with the efficacy of antitumor drugs 1. Drug susceptibility testing 1.1 IC50 assay of docetaxel and gefitinib: CCK-8 assay was used to detect the viability of PC9 and H23 cells at concentrations of 0–20 μM.

[0142] 1.2 PD-1 / PD-L1 antibody IC50 assay: Cell viability was detected at concentrations of 0–26 mg / mL using the CCK-8 assay.

[0143] 2. Drug intervention in co-culture system 2.1 Establishment of co-culture system: PC9 / H23 and THP-1 / NK-92 were co-cultured at a ratio of 1:2, and SA (MOI=200) was added for pretreatment for 24 h.

[0144] 2.2 Drug treatment: Docetaxel (1 μM), gefitinib (2.5 μM), PD-1 (10 mg / mL), and PD-L1 (12.5 mg / mL) were added for 24 h respectively.

[0145] 2.3 Cell viability was detected by the CCK-8 assay.

[0146] (iv) In vivo verification methods for SA's treatment resistance mechanism 1. Animal Information and Experimental Drug Preparation 1.1 Animal information: BALB / c nude mice (female, 4-6 weeks old) were used.

[0147] 1.2 Drug Preparation: Docetaxel: Dilute with saline to working concentration.

[0148] Anti-PD-1 antibody (tislelizumab): Dilute with physiological saline to working concentration.

[0149] 2. Animal model making steps 2.1 Animal Model Making (1) Animals were kept in an acclimatization environment for 7 days.

[0150] (2) Randomly divide the animals into 6 groups, with 5 animals in each group: LL: Nude mouse tumorigenesis group; LEXO: Nude mouse tumorigenesis + bacterial culture medium group; LSAL: Nude mouse tumorigenesis + bacterial co-culture group; LLTP: Nude mouse tumorigenesis + docetaxel + anti-PD-1 treatment group; LEXOLTP: LEXO + treatment group; LSALTP: LSAL + treatment group.

[0151] (3) Inoculation: 200 μL of H23 cell suspension was injected subcutaneously into the right back.

[0152] (4) Observation: Measure tumor volume and body weight every 3 days.

[0153] 2.2 Administration Once the tumors reached a uniform size, the treatment group received an intraperitoneal injection of anti-PD-1 antibody (10 mg / kg) and a tail vein injection of docetaxel (6 mg / kg) every 4 days.

[0154] 2.3 Collection of materials (1) After anesthesia, the eyeballs were enucleated to collect blood, and the serum and plasma were separated.

[0155] (2) Remove the tumor, weigh it and take a picture.

[0156] (3) The tumor tissue was partially fixed with 4% paraformaldehyde and partially frozen at -80°C.

[0157] 3. Flow cytometry analysis of tumor tissue 3.1 Preparation of single-cell suspension.

[0158] 3.2 Antibodies used: anti-mouse F4 / 80, CD86, CD206.

[0159] 3.3 Flow cytometry was used to detect the ratio of M1 / M2 macrophages.

[0160] (v) Statistical methods All experiments were independently repeated three times. Data are expressed as mean ± standard deviation. Statistical analysis was performed using SPSS 23.0. Independent samples t-tests were used for comparisons between two groups, and one-way ANOVA was used for comparisons among multiple groups. A p-value < 0.05 was used as the significance threshold.

[0161] Experimental results To eliminate the toxic effects of SA on immune cells themselves, we separately evaluated the effect of SA on the viability of DCs (dendritic cells). The CCK-8 assay showed that after 48 hours of co-culture, live SA significantly inhibited the proliferation of DCs, while the sterile supernatant culture medium showed no significant effect. Figure 17 Culture medium: culture medium.

[0162] Flow cytometry analysis further confirmed that live SA bacteria strongly induced the maturation and activation of dendritic cells (DCs), significantly upregulating the expression of surface maturation markers (CD80, CD86) and key antigen-presenting molecules (HLA-DR). In contrast, bacterial culture medium only induced mild activation. Figure 18 This indicates that the activation of DCs by SA mainly depends on the direct action of live bacteria.

[0163] Mechanistic experiments were conducted using co-culture conditions of bacteria:cells = 200:1 and lung cancer cells:immune cells = 1:2.

[0164] In systems containing macrophages or NK cells, SA itself significantly enhances the metabolic activity of these two types of immune cells, but at the same time reduces the overall metabolic activity of the entire co-culture system. Figure 19 Mø: THP-1 macrophages).

[0165] In systems containing dendritic cells, SA also showed an inhibitory effect on the proliferation of the system within 24-48 hours. Figure 20 ,3LL: Lewis lung carcinoma 3LL lung cancer cell line).

[0166] Flow cytometry analysis showed that SA significantly reduced the apoptosis rate of macrophages and NK cells, but simultaneously increased the overall apoptosis rate of the entire co-culture system. Figure 21 This indicates that SA indirectly enhances the killing of tumor cells by activating and protecting immune cells.

[0167] The results showed that in the lung cancer-dendritic cell system, live SA bacteria also increased the total apoptosis rate of lung cancer cells and the co-culture system, while the effect of its metabolites was not significant. Figure 22 ).

[0168] CCK-8 testing revealed that the addition of SA generally reduced the overall proliferative activity in the docetaxel (chemotherapy), gefitinib (targeted therapy), and PD-1 / PD-L1 inhibitor (immunotherapy) treatment groups. Figure 23 ).

[0169] The addition of SA specifically enhanced the inhibitory effect of the PD-1 inhibitor on the system, while having no significant effect on other drug groups. Figure 24 ).

[0170] The effect of Staphylococcus alright on treatment resistance was verified using a nude mouse xenograft tumor model. Figure 25 LL: Nude mouse tumorigenesis group; LEXO: Nude mouse tumorigenesis + bacterial culture medium and cell group; LSAL: Nude mouse tumorigenesis + bacterial and cell co-culture group; LLTP: Nude mouse tumorigenesis + docetaxel combined with PD1 monoclonal antibody; LEXOLTP: Nude mouse tumorigenesis + bacterial culture medium and cell co-culture + docetaxel combined with PD1 monoclonal antibody; LSALTP: Nude mouse tumorigenesis + bacterial and cell co-culture + docetaxel combined with PD1 monoclonal antibody; SA bacteria: Staphylococcus alright.

[0171] In vivo experimental results showed that, compared with the treatment-only group (LLTP), the mouse group simultaneously inoculated with live SA bacteria and treated (LSALTP) experienced less tumor volume reduction and a greater endpoint tumor weight. This indicates that SA can indeed attenuate the efficacy of docetaxel combined with PD-1 inhibitors in vivo and induce treatment resistance. Flow cytometry analysis of tumor tissue revealed that, in the absence of treatment, SA colonization promoted macrophage conversion to the M1 type (F4 / 80). + CD86 + ) polarization. However, in the context of treatment, SA significantly promoted macrophages to the M2 type (F4 / 80) polarization. + CD206 + )polarization( Figure 26 , Figure 27 This crucial polarization shift reveals the in vivo mechanism by which SA shapes the immunosuppressive microenvironment under therapeutic stress, thereby driving drug resistance.

[0172] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. A method for predicting the treatment effect of lung cancer, characterized in that, The method is computer-based and specifically includes: Obtain bacterial abundance data in the sample to be tested; Extract the abundance data of the target species from the bacterial abundance data, wherein the target species is Staphylococcus alright. Based on the abundance data of the target bacterial species, classification prediction is performed to obtain a classification result of whether the lung cancer treatment of the test sample is effective. If the abundance of Staphylococcus albus is low, the lung cancer treatment of the test sample is effective; if the abundance of Staphylococcus albus is high, the lung cancer treatment of the test sample is ineffective. Preferably, the lung cancer is non-small cell lung cancer; Preferably, the treatment includes one or more of targeted therapy, chemotherapy, or immunotherapy.

2. A computer-aided drug screening method based on Staphylococcus aureus, characterized in that, The method includes: Obtain the genomic / proteomic information of Staphylococcus aureus; Extracting target proteins from Staphylococcus aureus; Substances that inhibit Staphylococcus aureus were identified through computer-aided screening as candidate drugs to enhance the drug sensitivity of lung cancer. Preferably, the lung cancer is non-small cell lung cancer; Preferably, the enhancement of lung cancer drug sensitivity refers to enhancing the sensitivity of lung cancer to one or more of docetaxel, gefitinib, PD-1, and PD-L1.

3. A system for predicting the treatment effect of lung cancer, characterized in that, The system includes: Acquisition Unit: Acquires bacterial abundance data in the sample to be tested; Extraction unit: Extracts the abundance data of the target species from the bacterial abundance data, wherein the target species is Staphylococcus alright. Prediction Unit: Based on the abundance data of the target bacterial species, classification prediction is performed to obtain a classification result of whether the lung cancer treatment of the test sample is effective. If the abundance of Staphylococcus albus is low, a classification result of whether the lung cancer treatment of the test sample is effective is obtained; if the abundance of Staphylococcus albus is high, a classification result of whether the lung cancer treatment of the test sample is ineffective is obtained. Preferably, the lung cancer is non-small cell lung cancer; Preferably, the treatment includes one or more of targeted therapy, chemotherapy, or immunotherapy.

4. A computer-aided drug screening system based on Staphylococcus aureus, characterized in that, The system includes: Acquisition Unit: Acquire genomic / proteomic information of Staphylococcus aureus; Extraction unit: Extraction of target proteins from Staphylococcus aureus; Screening Unit: Substances that inhibit Staphylococcus aureus were identified through computer-aided screening as candidate drugs to enhance the drug sensitivity of lung cancer. Preferably, the lung cancer is non-small cell lung cancer; Preferably, the enhancement of lung cancer drug sensitivity refers to enhancing the sensitivity of lung cancer to one or more of docetaxel, gefitinib, PD-1, and PD-L1.

5. A computer device, characterized in that, The computer device includes: Memory: The memory is used to store program instructions; Processor: The processor is used to invoke program instructions, which, when executed, are used to perform the steps of the method described in claim 1 or 2.

6. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method described in claim 1 or 2.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 1 or 2.

8. A method for screening candidate drugs that enhance drug sensitivity in lung cancer, characterized in that, The method includes: treating a culture system containing Staphylococcus alrightis with a substance to be screened; and detecting the abundance of Staphylococcus alrightis in the system; wherein, when the substance to be screened reduces the abundance of Staphylococcus alrightis, the substance to be screened is a candidate drug for promoting the sensitivity of lung cancer drugs; Preferably, the lung cancer is non-small cell lung cancer; Preferably, the enhancement of lung cancer drug sensitivity refers to enhancing the sensitivity of lung cancer to one or more of docetaxel, gefitinib, PD-1, and PD-L1.

9. A method for increasing the activity of T cells, macrophages, or NK cells / decreasing T cell, macrophage, or NK cell apoptosis / increasing dendritic cell activation, characterized in that, The method includes the application of Staphylococcus aureus.

10. Application of Staphylococcus aureus in the preparation of drugs that enhance the activity of T cells, macrophages or NK cells / decrease the apoptosis of T cells, macrophages or NK cells / enhance the activation of dendritic cells.

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