Method, system and device for computer-aided diagnosis of osimertinib-resistant lung cancer patients, predicting drug treatment effects and screening drugs based on UQCC2

Abstract

This application relates to the field of intelligent medicine, and specifically to a method, system and device for computer-aided diagnosis of osimertinib-resistant lung cancer patients, predicting the drug treatment effect and screening drugs based on UQCC2. This application first discovers that UQCC2 is significantly highly expressed in osimertinib-resistant lung cancer, and discovers the interaction between UQCC2 and METTL3, providing an efficient and rapid method for the diagnosis of osimertinib-resistant lung cancer patients, predicting the drug treatment effect of osimertinib-resistant lung cancer patients and screening drugs based on targeting UQCC2 and METTL3, which has important research significance for the prevention and treatment research of related diseases.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent medicine and relates to methods, systems and devices for computer-aided diagnosis of osimertinib-resistant lung cancer patients, predicting drug treatment effects and screening drugs by using biomarkers. Background Art

[0002] Non-small cell lung cancer (NSCLC) is the main subtype of lung cancer and one of the most common malignant tumors, with extremely high incidence and mortality rates worldwide. Among NSCLC patients, gene mutations and alterations, especially the L858R and T790M mutations of the epidermal growth factor receptor (EGFR), are the most important main pathogenic factors. Therefore, tyrosine kinase inhibitors (TKIs) targeting EGFR, such as gefitinib and erlotinib, have become the standard first-line treatment regimens for treating EGFR-mutated NSCLC patients. As a third-generation EGFR-TKI, osimertinib (AZD9291) has made significant progress in treating 34 patients with gefitinib- or erlotinib-resistant non-small cell lung cancer. However, osimertinib resistance inevitably occurs, leading to treatment failure, and 36 patients died during the clinical treatment process. (A. Noronha, et al. Cancer Discov 2022, 12(11), 2666, https: / / doi.org / 10.1158 / 2159-8290.Cd-22-0111; J.C. Madukwe, Cell 2023, 186(8), 1515, https: / / doi.org / 10.1016 / j.cell.2023.03.019.). Therefore, it is urgently necessary to clarify the potential mechanism of osimertinib resistance and discover new drugs to overcome resistance. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide methods, systems and devices for computer-aided diagnosis of osimertinib-resistant lung cancer patients, predicting drug treatment effects and screening drugs based on UQCC2.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] The first aspect of the present invention provides a method for computer-aided diagnosis of osimertinib (Osim)-resistant lung cancer patients, the method comprising:

[0006] Receiving input data, the data including the expression level data of UQCC2 of lung cancer patients;

[0007] Applying a machine learning model to the input data to generate an indication of whether the lung cancer patient is an osimertinib-resistant lung cancer patient.

[0008] Further, the lung cancer is selected from non-small cell lung cancer.

[0009] Further, the expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

[0010] The second aspect of the present invention provides any one of the following products:

[0011] 1) A system for computer-aided diagnosis of osimertinib-resistant lung cancer patients, the system comprising:

[0012] A data acquisition unit: data on the expression level of UQCC2 in lung cancer patients;

[0013] A data evaluation unit: the evaluation unit includes a stored reference and a data processor, and the data processor has implemented an algorithm for comparing the acquired data on the expression level of UQCC2 with the stored reference;

[0014] A drug resistance identification unit: determining whether the lung cancer patient is an osimertinib-resistant lung cancer patient based on the algorithm of the expression level data of UQCC2 in the lung cancer patient and the stored reference;

[0015] 2) A device for computer-aided diagnosis of osimertinib-resistant lung cancer patients, the device comprising a memory and a processor, the memory for storing program instructions; the processor for calling the program instructions, and when the program instructions are executed, implementing the method for diagnosing osimertinib-resistant lung cancer patients according to the first aspect of the present invention;

[0016] 3) A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, implementing the method for diagnosing osimertinib-resistant lung cancer patients according to the first aspect of the present invention.

[0017] The third aspect of the present invention provides a method for computer-aided prediction of the drug treatment effect of osimertinib-resistant lung cancer patients, the method comprising:

[0018] Receiving input data, including data on the expression level of UQCC2 after drug treatment of osimertinib-resistant lung cancer patients;

[0019] Applying a machine learning model to the data to generate an indication of whether the drug has a therapeutic effect on osimertinib-resistant lung cancer patients;

[0020] Further, the expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

[0021] Further, the lung cancer is selected from non-small cell lung cancer.

[0022] Further, the drug includes lomitapide (Lomi).

[0023] The fourth aspect of the present invention provides any one of the following products:

[0024] 1) A system for computer-aided prediction of the therapeutic effect of drugs on patients with osimertinib-resistant lung cancer, the system comprising:

[0025] A data acquisition unit: data on the expression level of UQCC2 after drug treatment of patients with osimertinib-resistant lung cancer;

[0026] A data evaluation unit: the evaluation unit includes a stored reference and a data processor, and the data processor has implemented an algorithm for comparing the acquired expression level data of UQCC2 with the stored reference;

[0027] A data analysis unit: based on the algorithm of the expression level data of UQCC2 after drug treatment of patients with osimertinib-resistant lung cancer and the stored reference, determine whether the drug has a therapeutic effect on patients with osimertinib-resistant lung cancer;

[0028] 2) A device for computer-aided prediction of the therapeutic effect of drugs on patients with osimertinib-resistant lung cancer, the device comprising a memory and a processor, the memory is used for storing program instructions; the processor is used for calling the program instructions, and when the program instructions are executed, the method for predicting the therapeutic effect of drugs on patients with osimertinib-resistant lung cancer described in the third aspect of the present invention is implemented;

[0029] 3) A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for predicting the therapeutic effect of drugs on patients with osimertinib-resistant lung cancer described in the third aspect of the present invention is implemented.

[0030] The fifth aspect of the present invention provides a method for computer-aided screening of drugs based on UQCC2 and METTL3, the method comprising:

[0031] Obtain data on UQCC2 protein and METTL3 protein;

[0032] Determine the binding site between the two according to the data on UQCC2 protein and METTL3 protein;

[0033] Obtain candidate drugs that simultaneously target the binding sites of the two through computer-aided screening.

[0034] Furthermore, the process of the computer-aided screening is:

[0035] Obtain the binding site of the UQCC2 protein and the METTL3 protein complex;

[0036] Screen for small molecule compounds with similar structures in the molecular database based on the spatial structure of the binding site between the UQCC2 protein and the METTL3 protein complex;

[0037] Perform molecular docking calculations on the small molecule compounds obtained by screening and the METTL3 protein to obtain the affinity / binding energy of the target receptor, and sort according to the scores to obtain candidate drugs;

[0038] Sort the scores, select the top n small molecules to obtain candidate compounds, where n is a natural number greater than or equal to 1.

[0039] Furthermore, the process of computer-aided screening is as follows: screen for protein analogs / antibodies / RNA drugs through the binding site to obtain candidate drugs.

[0040] Furthermore, computer-aided drug screening is based on the active site of the METTL3 protein complexed with the UQCC2 protein to screen for protein analogs / antibodies / RNA drugs; or screen for proteins / antibodies / RNA drugs similar to METTL3 to obtain candidate drugs.

[0041] Furthermore, computer-aided drug screening is based on the active site or action target of the METTL3 protein complexed with the UQCC2 protein to screen for small molecule inhibitors or antibodies to obtain candidate drugs.

[0042] Furthermore, for the method of computer-aided drug screening based on UQCC2 and METTL3, perform inhibitory activity experimental tests on small molecule candidate drugs, mix the small molecule compounds with the UQCC2 protein solution respectively and calculate the inhibition rate, and screen for small molecule compounds with inhibitory effects.

[0043] The sixth aspect of the present invention provides any one of the following products:

[0044] 1) A computer-aided drug screening system based on UQCC2 and METTL3, the system includes:

[0045] Data acquisition unit: acquire UQCC2 protein and METTL3 protein data;

[0046] Site determination unit: select the spatial structure of the UQCC2 protein and METTL3 protein complex, and determine the complex binding site as the binding site of the target drug;

[0047] Drug screening unit: use the method of computer-aided drug screening to obtain candidate drugs targeting the binding site;

[0048] 2) A computer-aided drug screening device based on UQCC2 and METTL3, the device comprising a memory and a processor, the memory being used for storing program instructions; the processor being used for calling the program instructions, and when the program instructions are executed, implementing the computer-aided drug screening method based on UQCC2 and METTL3 described in the fifth aspect of the present invention;

[0049] 3) A computer-readable storage medium having a computer program thereon, including: when the computer program is executed by a processor, implementing the computer-aided drug screening method based on UQCC2 and METTL3 described in the fifth aspect of the present invention.

[0050] The seventh aspect of the present invention provides a method for regulating autophagy, the method comprising administering a regulatory reagent for UQCC2.

[0051] Further, the regulation of autophagy is to activate autophagy.

[0052] Further, the method comprises regulating autophagy-related markers.

[0053] Further, the method comprises increasing the expression levels of autophagy-related markers.

[0054] Further, the autophagy-related markers include LC3 and p62.

[0055] Further, the method comprises increasing the expression levels of nucleic acids and proteins of LC3 and p62.

[0056] Further, the reagent includes an inhibitor of UQCC2.

[0057] Further, the inhibitor of UQCC2 includes any substance that can reduce the expression of nucleic acid encoding UQCC2, reduce the level of UQCC2 protein or inhibit the activity of UQCC2.

[0058] Further, the inhibitor includes nucleic acid inhibitors, protein inhibitors, proteolytic enzymes, protein-binding molecules, and combinations thereof.

[0059] Further, the nucleic acid inhibitors include siRNA, shRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, CRISPR / Cas9.

[0060] Further, the nucleic acid inhibitor is siRNA.

[0061] Further, the protein inhibitor includes an antibody that neutralizes UQCC2.

[0062] Further, the protein-binding molecule is selected from substances that specifically bind to UQCC2 protein.

[0063] The eighth aspect of the present invention provides any of the following applications;

[0064] 1) The application of a reagent for detecting UQCC2 in the preparation of a product for diagnosing autophagy activation;

[0065] 2) The application of a reagent for regulating UQCC2 in the preparation of a product for regulating autophagy;

[0066] 3) The application of a reagent for detecting UQCC2 in the preparation of a product for diagnosing osimertinib-resistant lung cancer.

[0067] Furthermore, the reagents in 1) and 3) are selected from oligonucleotide probes that specifically recognize the UQCC2 gene, primers that specifically amplify the UQCC2 gene, or binding agents that specifically bind to the protein encoded by the UQCC2 gene.

[0068] Furthermore, the reagents in 1) and 3) further include a detectable label.

[0069] Furthermore, the detectable label includes a radioisotope, a nucleotide chromophore, an enzyme, a substrate, a fluorescent molecule, a chemiluminescent moiety, a magnetic particle, a bioluminescent moiety.

[0070] Furthermore, the products in 1) and 3) include a chip, a kit, a test strip, or a nucleic acid membrane strip.

[0071] Furthermore, the chip includes a gene chip and a protein chip. The gene chip includes oligonucleotide probes for the UQCC2 gene for detecting the transcriptional level of the UQCC2 gene, and the protein chip includes a specific binding agent for the UQCC2 protein.

[0072] Furthermore, the kit includes a gene detection kit and a protein detection kit. The gene detection kit includes a reagent or a chip for detecting the transcriptional level of the UQCC2 gene, and the protein detection kit includes a reagent or a chip for detecting the expression level of the UQCC2 protein.

[0073] Furthermore, the kit further includes a buffer, a preservative, or a protein stabilizer.

[0074] Furthermore, the kit further includes an instruction manual.

[0075] Furthermore, the regulation of autophagy in 2) is autophagy activation.

[0076] Furthermore, the reagent in 2) includes an inhibitor of UQCC2.

[0077] Furthermore, the inhibitor of UQCC2 includes any substance that can reduce the expression of the nucleic acid encoding UQCC2, reduce the UQCC2 protein level, or inhibit the activity of UQCC2.

[0078] Furthermore, the inhibitor includes nucleic acid inhibitors, protein inhibitors, proteolytic enzymes, protein-binding molecules, and combinations thereof.

[0079] Furthermore, the nucleic acid inhibitors include siRNA, shRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, CRISPR / Cas9.

[0080] Furthermore, the nucleic acid inhibitor is siRNA.

[0081] Furthermore, 3) the lung cancer is selected from non-small cell lung cancer.

[0082] The ninth aspect of the present invention provides any of the following products:

[0083] 1) A product for diagnosing autophagy activation, the product including a reagent capable of detecting the expression level of UQCC2;

[0084] 2) A product for regulating autophagy, the product including a reagent capable of regulating UQCC2;

[0085] 3) A product for diagnosing osimertinib-resistant lung cancer, the product including a reagent capable of detecting the expression level of UQCC2.

[0086] Furthermore, the products of 1) and 3) include chips, kits, test strips, or nucleic acid membrane strips.

[0087] Furthermore, the kit includes reagents for detecting the expression level of UQCC2 gene or protein by RT-PCR method, qRT-PCR method, biochip detection method, Southern blotting method, in situ hybridization method, immunoblotting method.

[0088] Furthermore, 2) the regulation of autophagy is autophagy activation.

[0089] Furthermore, the reagent of 2) includes an inhibitor of UQCC2.

[0090] Furthermore, the inhibitor of UQCC2 includes any substance that can reduce the expression of nucleic acid encoding UQCC2, reduce the protein level of UQCC2, or inhibit the activity of UQCC2.

[0091] Furthermore, the inhibitor includes nucleic acid inhibitors, protein inhibitors, proteolytic enzymes, protein-binding molecules, and combinations thereof.

[0092] Furthermore, the nucleic acid inhibitors include siRNA, shRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, CRISPR / Cas9.

[0093] Furthermore, the nucleic acid inhibitor is siRNA.

[0094] Further, 3) the lung cancer is selected from non-small cell lung cancer.

[0095] The tenth aspect of the present invention provides a method for screening drugs that activate autophagy in osimertinib-resistant lung cancer, the method comprising: treating a culture system expressing or containing the UQCC2 gene or its encoded protein with a substance to be screened; and detecting the expression or activity of the UQCC2 gene or its encoded protein in the system; wherein, when the substance to be screened inhibits the expression level or activity of the UQCC2 gene or its encoded protein, the substance to be screened is a candidate drug for activating autophagy.

[0096] Advantages and beneficial effects of the present invention: The present application first discovers that UQCC2 is significantly highly expressed in osimertinib-resistant lung cancer, and discovers the interaction between UQCC2 and METTL3, providing an efficient and rapid method for diagnosing osimertinib-resistant lung cancer patients, predicting the drug treatment effect of osimertinib-resistant lung cancer patients, and screening drugs based on targeting UQCC2 and METTL3, which has important research significance for the prevention and treatment research of related diseases. Description of the Drawings

[0097] Figure 1 is a schematic flowchart of a method for computer-aided diagnosis of osimertinib-resistant lung cancer patients;

[0098] Figure 2 is a schematic diagram of a system for computer-aided diagnosis of osimertinib-resistant lung cancer patients;

[0099] Figure 3 is a schematic diagram of a device for computer-aided diagnosis of osimertinib-resistant lung cancer patients;

[0100] Figure 4 is a schematic flowchart of a method for computer-aided prediction of the drug treatment effect of osimertinib-resistant lung cancer patients;

[0101] Figure 5 is a schematic diagram of a system for computer-aided prediction of the drug treatment effect of osimertinib-resistant lung cancer patients;

[0102] Figure 6 is a schematic flowchart of a method for computer-aided drug screening based on UQCC2 and METTL3;

[0103] Figure 7 is a schematic diagram of a system for computer-aided drug screening based on UQCC2 and METTL3;

[0104] Figure 8It is a figure showing the results of detecting and analyzing gene expression in NCI-H1975 cells and osimertinib-resistant cells (Osim) treated with osimertinib by MeRIP-seq and RNA-seq;

[0105] Figure 9 It is a figure showing the protein expression of UQCC2 in NCI-H1975 cells, NCI-H1975 / OR cells (treated with Ctrl, Osim, or Lomi alone) determined by western blot experiment and the corresponding statistical analysis. Among them, NCI-H1975 / OR cells (Ctrl) are osimertinib-resistant cells, NCI-H1975 / OR cells (Osim) are osimertinib-resistant cells treated with osimertinib, and NCI-H1975 / OR cells (Lomi) are osimertinib-resistant cells treated with lomitapide;

[0106] Figure 10 It is a figure showing the results of the change in the m 6 A abundance of UQCC2 analyzed by IGV in non-small cell lung cancer parental cells (H1975) and osimertinib-resistant cells (Osim) treated with osimertinib;

[0107] Figure 11 It is the result of determining the expression of each biomarker by western blot experiment. Among them, Figure 11 A is a figure showing the effect of knocking down METTL3 on the protein expression of UQCC2, P62, and LC3 and the corresponding statistical analysis; Figure 11 B is a figure showing the effect of knocking down UQCC2 on the protein expression of METTL3, P62, and LC3 and the corresponding statistical analysis;

[0108] Figure 12 It is a figure showing the result of determining the effect of knocking down UQCC2 on the expression of LC3 by immunofluorescence experiment. Detailed implementation manners

[0109] In order to enable those skilled in the art to better understand the solution of 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 in the embodiments of the present invention.

[0110] In some processes described in the specification, claims, and the above-mentioned drawings of the present invention, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0111] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0112] Figure 1 It is a schematic flowchart of a method for computer-aided diagnosis of osimertinib-resistant lung cancer patients provided by an embodiment of the present application. Specifically, the method includes the following steps:

[0113] S101: Receive input data, including the expression level data of UQCC2 in lung cancer patients

[0114] In one embodiment, the expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

[0115] In one embodiment, the methods for measuring the protein expression level of UQCC2 include but are not limited to Western blotting, immunohistochemistry, immunofluorescence, enzyme-linked immunosorbent assay, mass spectrometry, flow cytometry.

[0116] In one embodiment, the methods for measuring the nucleic acid expression level of UQCC2 include but are not limited to RT-PCR method, qRT-PCR method, biochip detection method, DNA blotting method, Northern blot hybridization method, gene chip method.

[0117] In one embodiment, the lung cancer is selected from at least one of small cell lung cancer and non-small cell lung cancer.

[0118] In one embodiment, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

[0119] In one embodiment, the collected samples of UQCC2 in lung cancer patients are selected from at least one of blood, tissue, feces, and urine.

[0120] S102: Apply a machine learning model to the input data to generate an indication of whether the lung cancer patient is an osimertinib-resistant lung cancer patient

[0121] In one embodiment, the machine learning model includes but is not limited to support vector machine learning model, linear discriminant analysis model, recursive feature elimination model, predictive analysis of microarray model, logistic regression model, CART algorithm, flextree algorithm, LART algorithm, random forest algorithm, MART algorithm, machine learning algorithm, penalized regression method and their combinations.

[0122] In a preferred embodiment, the method for constructing the machine learning model includes:

[0123] (1) Obtain the expression level data of UQCC2 in non-oxitinib-resistant lung cancer patients and oxitinib-resistant lung cancer patients;

[0124] (2) Randomly divide the obtained data into a training set and a test set, and perform chunking processing on the data;

[0125] (3) Use the expression level data of UQCC2 in the training set to adjust the parameters and train lung cancer patients to generate an oxitinib lung cancer diagnosis model; use the expression level data of UQCC2 in the test set to test the lung cancer detection model, optimize the oxitinib lung cancer diagnosis model, and determine the diagnostic threshold of the expression level of UQCC2.

[0126] In an embodiment, the diagnostic threshold includes, but is not limited to, the corresponding thresholds of performance indicators such as Acc, AUC, Sensitivity, TNR, PPV, NPV, etc., and may also be other performance indicators defined according to specific application scenarios, or composite performance indicators designed according to specific algorithms based on the above performance indicators and their corresponding thresholds, etc. The present application does not limit this. In particular, in the case of multiple application prediction models that meet the threshold requirements, the optimal application prediction model can be selected according to the prediction performance indicators concerned in different application scenarios. Thus, through the test of the test set, it can be ensured that the trained application prediction model has strong robustness and generalization ability, and is applicable not only to the samples in the training set, but also to new samples that have not participated in the training.

[0127] In an embodiment, when the expression level of UQCC2 in the input lung cancer patient is higher than the diagnostic threshold, it is determined that the lung cancer patient is an oxitinib-resistant lung cancer patient.

[0128] In a specific embodiment, the gene expression profiles of NCI-H1975 cells (Ctrl) and NCI-H1975 / OR cells (Osim) were detected and analyzed by MeRIP-seq and RNA-seq, where NCI-H1975 / OR cells (Osim) are osimertinib-resistant cells. The specific steps include: after extracting total RNA from H1975 parental cells and NCI-H1975 / OR cells (Osim), total RNA was isolated using TRIzol (Invitrogen), quantified using NanoDrop, and integrity was detected using Bioanalyzer (Agilent); a concentration > 50 ng / μL, RIN value > 7.0, OD260 / 280 > 1.8, and total RNA > 50 μg meet the requirements for downstream experiments. Poly(A) RNA was purified using Dynabeads Oligo(dT), and the captured RNA was fragmented using a magnesium ion fragmentation kit under high-temperature conditions. Then, the portion of RNA that binds to the m 6 A-specific antibody (Synaptic Systems) was isolated. The IP RNA was reverse-transcribed using SuperScript II, and then the second-strand DNA labeled with U was synthesized, and the ligation product was amplified by PCR. Finally, paired-end sequencing (PE150) was performed on the total RNA (input) and IP RNA libraries using the Illumina NovaSeq 6000 platform (LC-Bio Technology Co, Ltd, China). The results are as Figure 8 shown, the expression level of UQCC2 in NCI-H1975 / OR cells (osim) is higher than that in NCI-H1975 cells (Ctrl).

[0129] In a specific embodiment, the protein expression levels of UQCC2 in NCI-H1975 cells and NCI-H1975 / OR cells (Ctrl), where NCI-H1975 / OR cells (Ctrl) are osimertinib-resistant cells, were determined by western blot assay. The following antibodies were used in the western blotting experiment: anti-UQCC2 (1:1000, Proteintech, #25781-1-AP). Anti-GAPDH (1:50000, Proteintech, #60004-1-lg) was used as a loading control. GraphPad Prism software was used to calculate significance. All statistical analyses were performed using data from at least three independent experiments. For more than three means, two-way ANOVA or one-way ANOVA with Bonferroni correction was used, and for two means, an unpaired Student's t-test was used for comparison; *P<0.05 was considered statistically significant. The experimental results are as Figure 9 shown, demonstrating that the expression level of UQCC2 in NCI-H1975 / OR cells (Ctrl) was significantly higher than that in NCI-H1975 cells.

[0130] Note: *(p<0.05), **(p<0.01), ***(p<0.001)****(p<0.0001)

[0131] Figure 2 is a schematic diagram of a system for computer-aided diagnosis of osimertinib-resistant lung cancer patients provided by an embodiment of the present application. Specifically, the system includes:

[0132] A data acquisition unit: data on the expression level of UQCC2 in lung cancer patients;

[0133] A data evaluation unit: The evaluation unit includes a stored reference and a data processor that has implemented an algorithm for comparing the expression level of UQCC2 detected by the detection unit with the stored reference;

[0134] A drug resistance identification unit: Based on the algorithm of the expression level data of UQCC2 in lung cancer patients and the stored reference, it is determined whether the lung cancer patient is an osimertinib-resistant lung cancer patient.

[0135] Figure 3 is a schematic diagram of a device for computer-aided diagnosis of osimertinib-resistant lung cancer patients provided by an embodiment of the present application. Specifically, the device includes:

[0136] A memory and a processor, where the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method for diagnosing osimertinib-resistant lung cancer patients as described above is implemented.

[0137] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for diagnosing patients with osimertinib-resistant lung cancer as described above is implemented.

[0138] Figure 4 FIG. 6 is a schematic flowchart of a method for computer-aided prediction of the therapeutic effect of drugs on patients with osimertinib-resistant lung cancer provided by an embodiment of the present application. Specifically, the method includes the following steps:

[0139] S401: Receive input data, including the expression level data of UQCC2 after drug treatment of patients with osimertinib-resistant lung cancer;

[0140] In one embodiment, the expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

[0141] In one embodiment, the methods for measuring the protein expression level of UQCC2 include but are not limited to Western blotting, immunohistochemistry, immunofluorescence, enzyme-linked immunosorbent assay, mass spectrometry, flow cytometry.

[0142] In one embodiment, the methods for measuring the nucleic acid expression level of UQCC2 include but are not limited to RT-PCR method, qRT-PCR method, biochip detection method, DNA blotting method, Northern blot hybridization method, gene chip method.

[0143] In one embodiment, the lung cancer is selected from at least one of small cell lung cancer and non-small cell lung cancer.

[0144] In one embodiment, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

[0145] In one embodiment, the collection samples of UQCC2 from lung cancer patients are selected from at least one of blood, tissue, feces, and urine.

[0146] S402: Apply a machine learning model to the data to generate an indication of whether the drug has a therapeutic effect on patients with osimertinib-resistant lung cancer;

[0147] In one embodiment, the machine learning model includes but is not limited to support vector machine learning model, linear discriminant analysis model, recursive feature elimination model, predictive analysis of microarray model, logistic regression model, CART algorithm, flextree algorithm, LART algorithm, random forest algorithm, MART algorithm, machine learning algorithm, penalty regression method, and combinations thereof.

[0148] In a preferred embodiment, the method for constructing the machine learning model includes:

[0149] Receiving or inputting the expression level data of UQCC2 of patients with osimertinib-resistant lung cancer, processing the expression data through processing software, presetting a machine learning algorithm based on a generalized linear regression model, constructing a prediction model, and respectively predicting the sensitive prediction probability and the insensitive prediction probability of patients with osimertinib-resistant lung cancer;

[0150] Judging whether the expression data meets a preset judgment condition, where the judgment condition is that the sensitive prediction probability is greater than or equal to the insensitive prediction probability, so as to predict the therapeutic effect of the drug and output a prediction result.

[0151] In one embodiment, when the expressed data meets the judgment condition, the prediction result output is "the drug has a therapeutic effect on patients with osimertinib-resistant lung cancer"; when the expression data does not meet the judgment condition, the prediction result output is "the drug has no therapeutic effect on patients with osimertinib-resistant lung cancer".

[0152] In a specific embodiment, the drug is selected from lomitapide.

[0153] In a specific embodiment, the gene expression conditions of NCI-H1975 and NCI-H1975 / OR cells (Osim) are detected and analyzed by MeRIP-seq and RNA-seq, where NCI-H1975 / OR cells (Osim) are osimertinib-resistant cells treated with osimertinib. The specific method is: aligning the reads obtained by sequencing the IP library and the input library to the reference genome. Compared with the input library, the reads abundance and probability of the IP library falling on the genome are higher than those of the input library. Then, an obvious reads enrichment region will be formed here, which is called a peak. The differential Peak analysis is performed using the exompeak software and annotated using the ANNOVAR software. The results are as Figure 8 shown, the expression level of UQCC2 in NCI-H1975 / OR cells (Osim) is higher than that in parental NCI-H1975 cells.

[0154] In a specific embodiment, the protein expression of UQCC2 in NCI-H1975 / OR cells (treated with Ctrl, Osim, or Lomi alone) was determined by western blot assay. The western blotting assay used the following antibodies: anti-UQCC2 (1:1000, Proteintech, #25781-1-AP). Anti-GAPDH (1:50000, Proteintech, #60004-1-lg) was used as a loading control. GraphPad Prism software was used to calculate the significance. The experimental results are as Figure 9 shown, demonstrating that the protein expression of UQCC2 in NCI-H1975 / OR cells (osimertinib-resistant) was significantly decreased after lomitapide treatment.

[0155] Note: *(p < 0.05), **(p < 0.01), ***(p < 0.001)****(p < 0.0001)

[0156] In a specific embodiment, after selecting the target genes of interest from the differential peak results, the m 6 A methylation modification of the target genes was visualized by IGV software, and the results are as Figure 10 shown. Compared with the non-small cell lung cancer parental cells (H1975), the m 6 A abundance of UQCC2 changed in osimertinib-treated H1975 / OR cells (Osim), and the m 6 A modification of UQCC2 mainly changed the intron region during the induction of osimertinib resistance.

[0157] Figure 5 is a schematic diagram of a system for computer-aided prediction of the therapeutic effect of drugs on patients with osimertinib-resistant lung cancer provided by an embodiment of the present application. Specifically, the system includes:

[0158] Data acquisition unit: the expression level data of UQCC2 after drug treatment of patients with osimertinib-resistant lung cancer;

[0159] Data evaluation unit: the evaluation unit includes a stored reference and a data processor, and the data processor has implemented an algorithm for comparing the expression level of UQCC2 detected by the detection unit with the stored reference;

[0160] Data analysis unit: based on the algorithm of the expression level data of UQCC2 after drug treatment of patients with osimertinib-resistant lung cancer and the stored reference, it is determined whether the drug has a therapeutic effect on patients with osimertinib-resistant lung cancer.

[0161] An embodiment of the present application provides a device for computer-aided prediction of the drug treatment effect of osimertinib-resistant lung cancer patients. Specifically, the device includes: a memory and a processor, where the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method for predicting the drug treatment effect of osimertinib-resistant lung cancer patients as described above is implemented.

[0162] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for predicting the drug treatment effect of osimertinib-resistant lung cancer patients as described above is implemented.

[0163] Figure 6 It is a schematic flow diagram of a method for computer-aided drug screening based on UQCC2 and METTL3 provided by an embodiment of the present application. Specifically, the method includes:

[0164] S601: Obtain UQCC2 protein and METTL3 protein data

[0165] In one embodiment, the UQCC2 protein and METTL3 protein data include molecular structure and binding sites.

[0166] In a specific embodiment, after knocking down the expression of the METTL3 or UQCC2 gene in H1975 / OR cells, the expression relationship between METTL3 and UQCC2 was studied by western blotting experiments. siMETTL3 (5'-3'): sense strand GCACUUGGAUCUACGGAAU(dT)(dT); antisense strand AUUCCGUAGAUCC AAGUGC(dT)(dT), siUQCC2 (5'-3'): sense strand: GACUCCAUUCAAACUACUATT; antisense strand: UAGUAGUUUGAAUGGAGUCTT. The following antibodies were used in the western blotting experiments: anti-METTL3 (1:2000; #15073-1-AP; Proteintech), anti-UQCC2 (1:1000, Proteintech, 450#25781-1-AP). Anti-GAPDH (1:50000, Proteintech, #60004-1-lg) was used as a loading control. GraphPad Prism software was used to calculate the significance. The experimental results are as Figure 11 shown, which proved that knocking down METTL3 led to the downregulation of UQCC2, while knocking down UQCC2 did not affect the expression of METTL3, indicating that UQCC2 may be a new downstream target of METTL3.

[0167] S602: Determine the binding site between the UQCC2 protein and the METTL3 protein based on the data of the two proteins

[0168] In one embodiment, the binding site is a specific site in a molecule that can form a stable interaction with a ligand. The binding site of a protein is usually formed by some separated amino acid residues on the polypeptide chain that aggregate in space through peptide chain folding to form a specific spatial arrangement.

[0169] S603: Obtain candidate drugs that simultaneously target the binding sites of the two through computer-aided screening

[0170] In one embodiment, computer-aided drug screening is a technology that uses computer-aided drug design methods for drug screening. It can help researchers quickly screen out candidate drugs with strong binding ability to the target protein and potential drug efficacy from a large number of small molecule compounds.

[0171] In one embodiment, molecular docking is a method for drug design by the characteristics of the receptor and the interaction mode between the receptor and the drug molecule. It mainly studies the interaction between molecules (such as ligand and receptor) and predicts their binding mode and affinity, which is a theoretical simulation method. This method is widely used in the early stage of drug research and development and can help researchers quickly screen out compounds with potential drug efficacy.

[0172] The molecular docking method mainly focuses on spatial matching and energy matching. Spatial matching refers to the geometric shape complementarity between the drug molecule and the receptor protein, while energy matching refers to the minimization of the interaction between the drug molecule and the receptor protein. For the calculation of geometric matching, methods such as lattice calculation and fragment growth are usually used, while for energy calculation, methods such as simulated annealing and genetic algorithms are used. According to the degree and method of simplification, the molecular docking method can be divided into rigid docking, semi-flexible docking, and flexible docking. In the rigid docking method, the conformation of the molecules involved in docking does not change during the calculation, only the spatial position and orientation of the molecules are changed. Semi-flexible docking allows partial conformation changes during the calculation. Flexible docking allows more conformation changes.

[0173] In one embodiment, the molecular libraries used in virtual screening mainly include the following: ZINC, PubChem, DrugBank, ChEMBL, ChemDB, HMDB, BindingDB, SMPDB. In addition, there are also some commercial databases such as ChemDiv, Enamine, Lifechemicals, Specs, Chembridge, Maybridge, Microsource, Vitas-M, and Interbioscreen, etc., which are also often used in virtual screening.

[0174] In one embodiment, the process of computer-aided screening is as follows:

[0175] Perform molecular docking calculations on the screened small molecule compounds and METTL3 protein to obtain the affinity / binding energy of the target receptor and get a score, and sort according to the score to obtain candidate drugs;

[0176] Sort the scores, select the top n small molecules to obtain candidate compounds, where n is a natural number greater than or equal to 1.

[0177] In one embodiment, the process of computer-aided screening is as follows:

[0178] Obtain the molecular structures of UQCC2 protein and METTL3 protein and input the molecular structures into the pharmacophore module library for matching, and cluster all the binding sites based on the interaction modes with UQCC2 and METTL3 to obtain a pharmacophore model; input the pharmacophore model into the molecular compound database for high-throughput screening to obtain candidate drugs.

[0179] In one embodiment, the process of computer-aided screening is as follows: first obtain the molecular structure of a small molecule inhibitor that inhibits METTL3 and / or the molecular structure of a small molecule inhibitor that inhibits UQCC2; then screen a small molecule library with a similar structure based on the molecular structure of the small molecule inhibitor, and then perform molecular docking of the small molecule library with the binding site to obtain the score of the docked molecule, and finally sort to obtain candidate drugs.

[0180] In one embodiment, the process of computer-aided screening is as follows: screen protein analogs / antibodies / RNA drugs through the binding site to obtain candidate drugs.

[0181] In one embodiment, the processes and methods for designing drugs such as protein analogs and antibodies based on proteins can be summarized as the following aspects:

[0182] Determine the target protein: First, it is necessary to determine the target protein for which the drug is to be designed, that is, the target. The target can be a known disease-related protein, viral antigen, or other biomolecule.

[0183] Protein structure analysis: Perform structure analysis on the target protein to understand its three-dimensional conformation, surface configuration, subdomain structure and other characteristics. This can be obtained through techniques such as X-ray crystallography and nuclear magnetic resonance.

[0184] Determine the interaction between the drug and the protein: Study the interaction mechanism between the drug and the target protein, including the binding site, binding mode, and binding kinetics, etc. This can be accomplished through methods such as computer simulation and laboratory experiments.

[0185] Design of drugs: Based on the interaction mechanism between drugs and target proteins, design drug molecules that can specifically bind to target proteins. This includes selecting appropriate drug types, designing the chemical structure of the molecules, and optimizing the pharmacodynamic and pharmacokinetic properties of the molecules, etc.

[0186] Synthesis and verification of drugs: Prepare drug molecules through methods such as chemical synthesis and verify their biological activity, safety, and pharmacokinetic properties. This includes different stages such as cell experiments, animal experiments, and clinical trials.

[0187] In one embodiment, protein analogs refer to substances that have similar structures and functions to a certain protein in the body. They can mimic the functions of proteins and thus play an important role in disease treatment. Antibodies are immunoglobulins produced by plasma cells differentiated from B lymphocytes under the stimulation of antigens in the body's immune system and can specifically bind to the corresponding antigens. The structure of antibodies is mainly divided into two parts: the constant region and the variable region. RNA drugs are a class of drugs designed based on the characteristics of RNA and its mechanism of action in cells. RNA can act as messenger RNA (mRNA) in cells to guide protein synthesis or as microRNA (miRNA) to regulate gene expression. Therefore, rationally designed RNA drugs can be used to regulate gene expression in cells and thus achieve the purpose of treating diseases.

[0188] In one embodiment, after mixing a small molecule compound with UQCC2 protein solution, the inhibition rate is calculated to screen out small molecule compounds with inhibitory effects.

[0189] In one embodiment, the general steps for the inhibitory activity experiment of small molecule compounds are as follows:

[0190] Select a suitable target protein: According to the research purpose and disease target, select a suitable target protein as the experimental object. Ensure that the target protein has potential interactions with the small molecule compounds being studied.

[0191] Prepare small molecule compounds: Synthesize or purchase the required small molecule compounds and ensure their purity and structural accuracy. If necessary, the small molecule compounds can be modified or transformed to optimize their inhibitory activity.

[0192] Enzyme activity assay: Design appropriate enzyme activity assay methods to evaluate the inhibitory activity of small molecule compounds on target proteins. This can be achieved through techniques such as fluorescence resonance energy transfer (FRET), radioactive isotope labeling, enzyme-linked immunosorbent assay (ELISA), etc. Ensure that the assay method is reliable, sensitive, and can accurately reflect the interaction between small molecule compounds and target proteins.

[0193] Experimental operation: Mix small molecule compounds of different concentrations with the target protein, incubate for a period of time under appropriate conditions, and then measure the enzyme activity. According to the experimental design, control groups and experimental groups can be set up to compare the inhibitory activities of different small molecule compounds.

[0194] Data analysis: Through statistical analysis of the experimental data, determine the IC 50 value of the small molecule compound (i.e., the compound concentration required to inhibit 50% of the enzyme activity). The smaller the IC 50 value, the stronger the inhibitory activity of the small molecule compound. In addition, a dose-effect curve can be plotted to visually display the inhibitory activity of the small molecule compound.

[0195] Result interpretation and discussion: Based on the experimental results, analyze the inhibitory activity of the small molecule compound on the target protein, and discuss it in combination with its structure and properties. Explore possible binding modes, mechanisms of action, and comparisons with known inhibitors. In addition, the effects of small molecule compounds on cells or organisms can be further studied to evaluate their potential as drug candidates.

[0196] In one embodiment, an inhibitory activity experiment test is performed on a candidate drug of a protein analog / antibody / RNA drug, and an unbiased screening is carried out on cells co-transfected with a V5-labeled protein analog / antibody / RNA drug and Flag-STING using co-immunoprecipitation analysis.

[0197] In one embodiment, co-immunoprecipitation (Co-IP) is a classical method for studying protein interactions based on the specific interaction between an antibody and an antigen, and is an effective method for determining the physiological interaction between two proteins in intact cells. Its principle is to indirectly capture the protein bound to a specific target protein using a target protein-specific antibody to identify the relevant protein-protein interactions in vivo, which is a classical method for studying protein interactions. The specific antibody captures the target protein in the sample to form an antibody-target protein complex, and then a bead support (such as Nanoab-Agarose) that can bind to the antibody is used to immobilize and precipitate the complex. At the same time, the protein that interacts with the target protein will also be precipitated. Finally, the non-specific proteins bound to the target protein are washed away, and then analyzed by SDS-PAGE and detected by Western blot.

[0198] In one embodiment, co-transfection refers to the simultaneous transfection of two independent nucleic acid molecules, such as plasmid DNA and siRNA, and is a commonly used procedure for stable transfection. The physical process of co-transfection is to integrate two nucleic acid molecules into the same integration sequence and express them in the same transfected cells. It involves selecting the correct co-transfection reagent and using advanced lipid nanoparticle technology to achieve excellent transfection performance and reproducible results.

[0199] In one embodiment, screening drugs by computer-aided drug screening technology includes one or more of the following: protein-small molecule docking, protein-protein docking, and protein-nucleic acid docking.

[0200] In one embodiment, protein-small molecule docking refers to a computational simulation process that docks the structures of proteins and small molecules (such as drug molecules) together through certain algorithms and programs. This process can be used to study the interactions between proteins and small molecules, as well as possible biological functions. In protein-small molecule docking, software such as DOCK is usually used for computational simulation. DOCK is a highly automated drug design software that can achieve the docking between small molecule ligands and biomacromolecular receptors. It adopts a fragment-based scoring method and can achieve fast and accurate docking. RosettaDock is a commonly used protein-protein docking software. It adopts a fragment-based scoring method and can achieve fast and accurate protein docking. This software can precisely adjust the side chain conformation during the docking process and considers various complex interactions, such as hydrogen bonds, ionic bonds, and hydrophobic interactions.

[0201] In one embodiment, protein-nucleic acid docking refers to a computational simulation process that docks the structures of proteins and nucleic acids (such as DNA or RNA) together through certain algorithms and programs. This process can be used to study the interactions between proteins and nucleic acids, as well as possible biological functions. In protein-nucleic acid docking, software such as NAflex is usually used for computational simulation. NAflex is a software developed specifically for nucleic acid structure prediction and design that can achieve accurate modeling and docking of DNA or RNA molecules. The NAflex software adopts a fragment-based scoring method and can achieve fast and accurate docking. It considers various complex interactions, such as hydrogen bonds, ionic bonds, and hydrophobic interactions, and can precisely adjust the side chain conformation during the docking process.

[0202] Figure 7 It is a schematic diagram of a computer-aided drug screening system based on UQCC2 and METTL3 provided by an embodiment of the present application. Specifically, the system includes:

[0203] Data acquisition unit: Acquire UQCC2 protein and METTL3 protein data;

[0204] Site determination unit: Select the spatial structure of the UQCC2 protein and METTL3 protein complex, and determine the complex binding site as the binding site of the targeted drug;

[0205] Drug screening unit: Obtain candidate drugs targeting the binding site by using the method of computer-aided drug screening;

[0206] An embodiment of the present application provides a schematic diagram of a computer-aided drug screening device based on UQCC2 and METTL3. Specifically, the device includes: a memory and a processor, where the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the computer-aided drug screening method based on UQCC2 and METTL3 as described above is implemented.

[0207] The present application also provides a computer-readable storage medium with a computer program thereon, and when the computer program is executed by a processor, the computer-aided drug screening method based on UQCC2 and METTL3 as described above is implemented.

[0208] An embodiment of the present application provides a method for regulating autophagy, and the method includes administering a regulatory reagent for UQCC2;

[0209] In one embodiment, the method includes regulating autophagy-related markers, and the autophagy-related markers include autophagosome markers, autophagy-related genes and proteins, and lysosome markers.

[0210] Furthermore, the autophagosome markers include, but are not limited to, LC3 and p62; the autophagy-related genes and proteins include, but are not limited to, Atg5 and Atg12, Beclin1; the lysosome markers include, but are not limited to, LAMP1 and LAMP2, cathepsin. As an implementation scheme of the present application, the autophagy-related markers for regulation are selected from LC3 and p62.

[0211] In one embodiment, the method includes increasing the expression level of autophagy-related markers, and the methods for detecting the expression level of the autophagy-related markers include Western Blot, immunofluorescence staining, RT-PCR, immunohistochemistry, ELISA. As an implementation scheme of the present application, the detection methods are selected from Western Blot and immunofluorescence staining.

[0212] In one embodiment, the reagent includes an inhibitor of UQCC2, and the inhibitor refers to any substance that can inhibit the activity of UQCC2 protein, inhibit the stability of UQCC2 gene or protein, inhibit the expression level of UQCC2, inhibit the effective action time of UQCC2 protein, and inhibit the activity of UQCC2. As an implementation scheme of the present application, the "inhibitor" is a substance that inhibits the expression level of UQCC2.

[0213] In one embodiment, the inhibitor includes nucleic acid inhibitors, protein inhibitors, proteolytic enzymes, protein-binding molecules, and combinations thereof.

[0214] In one embodiment, the nucleic acid inhibitor is selected from: interfering molecules that target the UQCC2 or its transcript and are capable of inhibiting the expression or transcription of the UQCC2 gene, including but not limited to shRNA, siRNA, ribozyme, antisense oligonucleotide, dsRNA, microRNA, zinc finger, CRISPR / Cas9, or constructs capable of expressing or forming the shRNA, siRNA, ribozyme, antisense oligonucleotide, dsRNA, microRNA, zinc finger, CRISPR / Cas9. The protein inhibitor is selected from substances capable of inhibiting the UQCC2 protein. The proteolytic enzyme is selected from enzymes capable of catalyzing the hydrolysis of the UQCC2 protein. The protein-binding molecule is selected from substances that specifically bind to the UQCC2 protein, such as antibodies or ligands capable of inhibiting the activity of the UQCC2 protein.

[0215] In one embodiment, the siRNA may include partially purified RNA, substantially pure RNA, synthetic RNA, or recombinantly produced RNA, and altered RNA that differs from natural RNA by the addition, deletion, substitution, and / or alteration of one or more nucleotides. The alterations may include the addition of non-nucleotide substances, such as addition to the ends of the siRNA or to one or more internal nucleotides of the siRNA; modifications that render the siRNA resistant to nuclease digestion (e.g., using 2'-substituted ribonucleotides or modifying the sugar-phosphate backbone); or replacement of one or more nucleotides in the siRNA with deoxyribonucleotides.

[0216] In one embodiment, a ribozyme is a class of RNA that can be engineered to enzymatically cleave and inactivate other RNA targets in a sequence-specific manner. Ribozyme and its delivery methods are well known in the art (Hendry et al., BMC Chem. Biol., 4(1):1(2004); Grassi et al., Curr. Pharm. Biotechnol., 5(4):369-386(2004); Bagheri et al., Curr. Mol. Med., 4(5):489-506(2004); Kashani-Sabet M., Expert Opin. Biol. Ther., 4(11):1749-1755(2004)), each of which is incorporated herein by reference in its entirety. By cleaving the target RNA, ribozymes inhibit translation and thus prevent the expression of the target gene. Ribozyme can be chemically synthesized and structurally modified in the laboratory by methods known in the art to increase its stability and catalytic activity. Alternatively, the ribozyme gene can be introduced into cells by gene delivery mechanisms known in the art.

[0217] In one embodiment, the antisense oligonucleotide (antisense nucleic acid sequence) may comprise a nucleotide sequence complementary to the sense nucleic acid encoding the protein (e.g., complementary to the coding strand of a double-stranded cDNA molecule or complementary to UQCC2 mRNA). Antisense oligonucleotides and delivery methods are well known in the art (Goodchild, Curr. Opin. Mol. Ther., 6(2): 120-128 (2004); Clawson et al., Gene Ther., 11(17): 1331-1341 (2004)), which are incorporated herein by reference in their entirety. The antisense oligonucleotide may be complementary to the entire coding strand of the target sequence or only partially complementary thereto. The length of the antisense oligonucleotide may be, for example, about 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80 or more nucleotides.

[0218] In a specific embodiment, the effect of knocking down UQCC2 in H1975 / OR cells on the expression of autophagosome markers LC3 and p62 was studied by Western blotting experiments. siUQCC2 (5'-3'): sense strand: GACUCCAUUCAAACUACUATT; antisense strand: UAGUAGUUUGAAUGGAGUCTT. The following antibodies were used in the Western blotting experiments: anti-UQCC2 (1:1000, Proteintech, #25781-1-AP), anti-LC3 (1:2000; #81004-1-RR; Proteintech), anti-p62 (1:5000; #80294-1-RR; Proteintech). Anti-GAPDH (1:50000, Proteintech, #60004-1-lg) was used as a loading control. Significance was calculated using GraphPad Prism software. The experimental results are as Figure 11 shown, demonstrating that knocking down UQCC2 led to upregulation of autophagosome markers LC3 and p62, further demonstrating that knocking down UQCC2 significantly activated autophagy.

[0219] Note: *(p < 0.05), **(p < 0.01), ***(p < 0.001)****(p < 0.0001)

[0220] In a specific embodiment, the expression of the autophagosome marker LC3 after knocking down UQCC2 in H1975 / OR cells was analyzed by immunofluorescence experiments. siUQCC2 (5'-3'): sense strand: GACUCCAUUCAAACUACUATT; antisense strand: UAGUAGUUUGAAUGGAGUCTT. The cells were fixed with 4% formaldehyde, permeabilized with 0.2% Triton X-100, and blocked with 5% bovine serum albumin. Then they were incubated overnight with the specified antibody at -4°C and stained with a fluorescence-conjugated secondary antibody and DAPI solution. The cells were photographed under a fluorescence microscope (Leica DM6B Thunder). Anti-LC3 (1:100; the antibody used was Proteintech, catalog number 81004-1-RR). The experimental results are as Figure 12 shown, demonstrating that knocking down UQCC2 led to an upregulation of the autophagosome marker LC3, further demonstrating that knocking down UQCC2 significantly activated autophagy.

[0221] The embodiments of the present application provide any of the following applications:

[0222] 1) The application of a reagent for detecting UQCC2 in the preparation of a product for diagnosing autophagy activation;

[0223] 2) The application of a reagent for regulating UQCC2 in the preparation of a product for regulating autophagy;

[0224] 3) The application of a reagent for detecting UQCC2 in the preparation of a product for diagnosing osimertinib-resistant lung cancer.

[0225] In one embodiment, the reagent is selected from an oligonucleotide probe that specifically recognizes the UQCC2 gene, a primer that specifically amplifies the UQCC2 gene, or a binder that specifically binds to the protein encoded by the UQCC2 gene.

[0226] In one embodiment, a probe refers to a molecule that can bind to a specific sequence or subsequence or other part of another molecule. Unless otherwise indicated, the term "probe" generally refers to a polynucleotide probe that can bind to another polynucleotide (often referred to as the "target polynucleotide") through complementary base pairing. Depending on the stringency of the hybridization conditions, the probe can bind to a target polynucleotide that lacks complete sequence complementarity with the probe. The probe can be labeled directly or indirectly. Hybridization methods include, but are not limited to: solution phase, solid phase, mixed phase, or in situ hybridization assays.

[0227] In one embodiment, a binder refers to all or part of a protein (protein, protein-like or protein-containing) molecule that can bind to a membrane protein using specific intermolecular interactions. Binders of a protein are, for example, receptors for the protein, lectins that bind the protein, antibodies against the protein, peptidebodies against the protein, bispecific dual binders or bispecific antibodies. More specifically, the term "binder" refers to a polypeptide, more specifically a protein domain. A suitable protein domain is an element of the overall protein structure that is self-stabilizing and folds independently of the rest of the protein chain and is commonly referred to as a "binding domain". The length of such a binding domain varies from about 25 amino acids up to 500 amino acids and more. Many binding domains can be classified as having a folded and recognizable, identifiable, 3-D structure. Some folds are very common in many different proteins such that they are given specific names.

[0228] The reagent for detecting UQCC2 provided in this application further includes a detectable label. A detectable label refers to a composition that can generate a detectable signal indicating the presence of a target polynucleotide in a sample to be assayed. Suitable labels include, but are not limited to, radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent moieties, magnetic particles, bioluminescent moieties. Thus, a label is any composition that can be detected by a device or method, including but not limited to spectroscopic, photochemical, biochemical, immunochemical, electrical, optical, chemical detection devices or any other suitable device. In some embodiments, the label can be visually detected without the aid of a device.

[0229] In one embodiment, the radioisotopes include, but are not limited to 3 H, 14 C, 35 S, 125 I, 131 I. Enzymes include, but are not limited to, horseradish peroxidase, β-galactosidase, luciferase, alkaline phosphatase, acetylcholinesterase. Fluorescent molecules include, but are not limited to, FITC, rhodamine, lanthanide phosphors.

[0230] In one embodiment, the product includes, but is not limited to, a chip, a kit, a test strip or a nucleic acid membrane strip.

[0231] In one embodiment, a chip, also known as an array, refers to a solid support that contains linked nucleic acid or peptide probes. An array typically contains a variety of different nucleic acid or peptide probes that are linked to the surface of a substrate at different known positions. These arrays, also known as "microarrays", can generally be produced using mechanical synthesis methods or light-directed synthesis methods that incorporate a combination of photolithography methods and solid-phase synthesis methods. An array can include a flat surface or can be nucleic acid or peptide on beads, gels, polymer surfaces, fibers such as optical fibers, glass, or any other suitable substrate. The array can be packaged in such a way as to allow for the diagnosis or other manipulation of a fully functional device.

[0232] In one embodiment, a nucleic acid membrane strip includes a substrate and oligonucleotide probes immobilized on the substrate; the substrate can be any substrate suitable for immobilizing oligonucleotide probes, such as a nylon membrane, a nitrocellulose membrane, a polypropylene membrane, a glass slide, a silica wafer, a microbead, etc.

[0233] The kit described in the present application includes reagents for detecting the UQCC2 gene or protein, and one or more substances selected from the group consisting of: a container, an instruction manual, a positive control, a negative control, a buffer, an adjuvant, a solvent, a preservative, or a protein stabilizer. The components of the kit can be packaged in the form of an aqueous medium or in a lyophilized form. Suitable containers in the kit generally include at least one vial, test tube, flask, bottle, syringe, or other container in which a component can be placed and preferably can be appropriately aliquoted. When there is more than one component in the kit, the kit will generally also include a second, third, or other additional container in which the additional components are placed separately. However, different combinations of components can be included in one vial. The kit of the present application will generally also include a container for holding the reactants, sealed for commercial sale. Such a container can include a molded or blow-molded plastic container in which the required vials can be retained.

[0234] The kit described in the present application includes, but is not limited to, a qPCR kit, an ELISA kit, a Western blot detection kit, an immunochromatographic detection kit, an immunohistochemical detection kit, a flow cytometry analysis kit, an electrochemiluminescence detection kit.

[0235] Any known method can be used in the present application to detect the expression level of the UQCC2 gene or protein, and the methods include, but are not limited to, RT-PCR method, qRT-PCR method, biochip detection method, Southern blot method, in situ hybridization method, Western blot method.

[0236] In each embodiment of the present application, each functional unit may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0237] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0238] The description of the above embodiments is only for understanding the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present 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 computer-aided diagnosis of osimertinib-resistant lung cancer patients, characterized in that: The method comprises: Receiving input data, the data comprising expression level data of UQCC2 in lung cancer patients; A machine learning model is applied to the input data to generate an indication of whether the lung cancer patient is an osimertinib-resistant lung cancer patient, wherein the lung cancer is selected from non-small cell lung cancer.

2. The method according to claim 1, characterized in that The expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

3. Any of the following products: 1) A computer-aided diagnosis system for osimertinib-resistant lung cancer patients, characterized in that the system comprises: Data acquisition unit: acquiring the expression level data of UQCC2 in lung cancer patients; Data evaluation unit: the evaluation unit comprises a stored reference and a data processor, the data processor having implemented an algorithm for comparing the acquired expression level data of UQCC2 with the stored reference; Drug resistance identification unit: based on an algorithm comparing the UQCC2 expression level data of the lung cancer patient with the stored reference, determining whether the lung cancer patient is an osimertinib-resistant lung cancer patient; The lung cancer is selected from non-small cell lung cancer; 2) A device for computer-aided diagnosis of osimertinib-resistant lung cancer patients, characterized in that the device comprises a memory and a processor, the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method for diagnosing osimertinib-resistant lung cancer patients according to any one of claims 1 to 2 is implemented; 3) A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for diagnosing osimertinib-resistant lung cancer patients according to any one of claims 1 to 2 is implemented.

4. A computer-aided method for predicting the therapeutic effect of osimertinib-resistant lung cancer patients, characterized in that: The method comprises: Receive input data, including UQCC2 expression level data of osimertinib-resistant lung cancer patients after drug treatment; applying a machine learning model to the data to generate an indicator of whether the drug has a therapeutic effect on osimertinib-resistant lung cancer patients; The drug is selected from osimertinib, and the lung cancer is selected from non-small cell lung cancer.

5. The method according to claim 4, characterized in that The expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

6. Any of the following products: 1) A computer-aided prediction system for drug treatment efficacy of osimertinib-resistant lung cancer patients, characterized in that: The system comprises: Data acquisition unit: obtaining the expression level data of UQCC2 in osimertinib-resistant lung cancer patients after drug treatment; Data evaluation unit: the evaluation unit comprises a stored reference and a data processor, the data processor having implemented an algorithm for comparing the acquired expression level data of UQCC2 with the stored reference; Data analysis unit: based on an algorithm comparing the expression level data of UQCC2 in osimertinib-resistant lung cancer patients after drug treatment with a stored reference, determining whether the drug has a therapeutic effect on osimertinib-resistant lung cancer patients; The drug is selected from osimertinib, and the lung cancer is selected from non-small cell lung cancer; 2) A device for computer-aided prediction of drug treatment effects on osimertinib-resistant lung cancer patients, characterized in that the device comprises a memory and a processor, the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method for predicting drug treatment effects on osimertinib-resistant lung cancer patients according to any one of claims 4 to 5 is implemented; 3) A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for predicting the drug treatment effect of osimertinib-resistant lung cancer patients according to any one of claims 4 to 5 is implemented.

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