Method, system and equipment for diagnosing osimertinib drug-resistant lung cancer patient, predicting drug treatment effect and screening drugs based on UQCC2 computer assistance

Abstract

The invention relates to the field of intelligent medical treatment, in particular to a method, system and equipment for diagnosing osimertinib drug-resistant lung cancer patients, predicting drug treatment effects and screening drugs based on UQCC2 computer assistance. According to the application, the remarkable high expression of UQCC2 in the osimertinib drug-resistant lung cancer is found for the first time, and the effect between UQCC2 and METTL3 is found, so that an efficient and rapid method is provided for diagnosis of osimertinib drug-resistant lung cancer patients, prediction of the drug treatment effect of the osimertinib drug-resistant lung cancer patients and screening of drugs based on targeting UQCC2 and METTL3; the method has important research significance on related disease prevention and treatment research.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent medical treatment and relates to a method, system and equipment for biomarker computer-aided diagnosis of osimertinib-resistant lung cancer patients, prediction of drug treatment effects and drug screening. Background Art

[0002] Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer and one of the most common malignancies, with extremely high morbidity and mortality worldwide. In NSCLC patients, gene mutations and alterations, especially L858R and T790M mutations of epidermal growth factor receptor (EGFR), are the most important major pathogenic factors. Therefore, targeted therapy with EGFR tyrosine kinase inhibitors (TKIs), such as gefitinib and erlotinib, has become the standard first-line treatment for patients with EGFR-mutated NSCLC. Osimertinib (AZD9291), as a third-generation EGFR-TKI, has made significant progress in the treatment of 34 patients with gefitinib or erlotinib resistance in non-small cell lung cancer. However, osimertinib resistance inevitably emerged, leading to treatment failure, and 36 patients died during clinical treatment. (A. Noronha, et al. Cancer Discov 2022, 12(11), 2666, https: / / doi.org / 10.1158 / 2159-8290.Cd-22-0111; JCMadukwe, Cell 2023, 186(8), 1515, https: / / doi.org / 10.1016 / j.cell.2023.03.019.). Therefore, it is urgent to elucidate the underlying mechanism of osimertinib resistance and discover new drugs to overcome resistance. Summary of the invention

[0003] In view of this, the object of the present invention is to provide a method, system and device for computer-aided diagnosis of osimertinib-resistant lung cancer patients, prediction of drug treatment effects and drug screening based on UQCC2.

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

[0005] A 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 comprising expression level data of UQCC2 in lung cancer patients;

[0007] The 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.

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

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

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

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

[0012] Data acquisition unit: UQCC2 expression level data of lung cancer patients;

[0013] 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;

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

[0015] 2) A device for computer-aided diagnosis of osimertinib-resistant lung cancer patients, the device comprising a memory and a processor, the memory being used to store program instructions; the processor being used to call the program instructions, and when the program instructions are executed, the method for diagnosing osimertinib-resistant lung cancer patients described in the first aspect of the present invention is implemented;

[0016] 3) A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for diagnosing osimertinib-resistant lung cancer patients described in the first aspect of the present invention.

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

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

[0019] 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;

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

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

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

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

[0024] 1) A computer-aided prediction system for drug treatment effects on osimertinib-resistant lung cancer patients, the system comprising:

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

[0026] 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;

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

[0028] 2) A device for computer-aided prediction of drug treatment effects on osimertinib-resistant lung cancer patients, the device comprising a memory and a processor, the memory being used to store program instructions; the processor being used to call program instructions, and when the program instructions are executed, the method for predicting drug treatment effects on osimertinib-resistant lung cancer patients described in the third aspect of the present invention is implemented;

[0029] 3) A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for predicting the drug treatment effect of osimertinib-resistant lung cancer patients described in the third aspect of the present invention.

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

[0031] Obtain UQCC2 protein and METTL3 protein data;

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

[0033] Through computer-assisted screening, candidate drugs that simultaneously target the binding sites of the two are obtained.

[0034] Further, the process of computer-assisted screening is:

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

[0036] Based on the spatial structure of the binding site of the UQCC2 protein and the METTL3 protein complex, small molecule compounds with similar structures were screened in the molecular database;

[0037] Perform molecular docking between the screened small molecule compounds and the METTL3 protein to calculate the affinity / binding energy of the target receptor to obtain a score, and rank them according to the score to obtain candidate drugs;

[0038] The scores are sorted, and the top n small molecules are selected to obtain candidate compounds, where n is a natural number greater than or equal to 1.

[0039] Furthermore, the computer-assisted screening process is: screening protein analogs / antibodies / RNA drugs through the binding sites to obtain candidate drugs.

[0040] Furthermore, computer-assisted drug screening can be performed to screen protein analogs / antibodies / RNA drugs based on the active site of the complex of METTL3 protein and UQCC2 protein; or protein / antibody / RNA drugs similar to METTL3 can be screened to obtain candidate drugs.

[0041] Furthermore, computer-assisted drug screening screens small molecule inhibitors or antibodies based on the active site or target of the complex of METTL3 protein and UQCC2 protein to obtain candidate drugs.

[0042] Furthermore, the computer-aided drug screening method based on UQCC2 and METTL3 performs inhibitory activity experimental tests on candidate small molecule drugs, calculates the inhibition rate after mixing the small molecule compounds with the UQCC2 protein solution, and screens out small molecule compounds with inhibitory effects.

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

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

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

[0046] Site determination unit: selecting the spatial structure of the UQCC2 protein and METTL3 protein complex, and determining the complex binding site as the binding site of the targeted drug;

[0047] Drug screening unit: using computer-assisted drug screening methods to obtain candidate drugs targeting the binding site;

[0048] 2) A computer-assisted drug screening device based on UQCC2 and METTL3, the device comprising a memory and a processor, the memory being used to store program instructions; the processor being used to call the program instructions, and when the program instructions are executed, the computer-assisted drug screening method based on UQCC2 and METTL3 described in the fifth aspect of the present invention is implemented;

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

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

[0051] Furthermore, the regulating autophagy is activating autophagy.

[0052] Furthermore, the method includes regulating autophagy-related markers.

[0053] Furthermore, the method includes increasing the expression level of autophagy-related markers.

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

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

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

[0057] Furthermore, the UQCC2 inhibitor 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] Furthermore, the inhibitors include nucleic acid inhibitors, protein inhibitors, proteolytic enzymes, protein binding molecules, and combinations thereof.

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

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

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

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

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

[0064] 1) Use of reagents for detecting UQCC2 in the preparation of products for diagnosing autophagy activation;

[0065] 2) Use of reagents that regulate UQCC2 in the preparation of products that regulate autophagy;

[0066] 3) Use of reagents for detecting UQCC2 in the preparation of products 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 described in 1) and 3) further include a detectable label.

[0069] Furthermore, the detectable labels include radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent moieties, magnetic particles, and bioluminescent moieties.

[0070] Furthermore, the products described in 1) and 3) include chips, test kits, test papers or nucleic acid membrane strips.

[0071] Furthermore, the chip includes a gene chip and a protein chip. The gene chip includes an oligonucleotide probe targeting the UQCC2 gene for detecting the transcription 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 transcription 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 comprises a buffer, a preservative or a protein stabilizer.

[0074] Furthermore, the kit also includes instructions.

[0075] Furthermore, 2) the regulating autophagy is activating autophagy.

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

[0077] Furthermore, the UQCC2 inhibitor 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.

[0078] Furthermore, the inhibitors include 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, and 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] A ninth aspect of the present invention provides any of the following products:

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

[0084] 2) A product for regulating autophagy, the product comprising an agent capable of regulating UQCC2;

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

[0086] Furthermore, the products described in 1) and 3) include chips, test kits, test papers 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, and immunoblotting method.

[0088] Furthermore, 2) the regulating autophagy is activating autophagy.

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

[0090] Furthermore, the UQCC2 inhibitor 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.

[0091] Furthermore, the inhibitors include 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, and CRISPR / Cas9.

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

[0094] Furthermore, 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 a protein encoded by it with a substance to be screened; and detecting the expression or activity of the UQCC2 gene or a protein encoded by it in the system; wherein, when the substance to be screened inhibits the expression level or activity of the UQCC2 gene or the protein encoded by it, the substance to be screened is a candidate drug for activating autophagy.

[0096] Advantages and beneficial effects of the present invention: This application discovered for the first time that UQCC2 is significantly overexpressed in osimertinib-resistant lung cancer, and discovered 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 effects of osimertinib-resistant lung cancer patients, and screening of drugs based on targeting UQCC2 and METTL3, which has important research significance for the research on the prevention and treatment of the diseases involved. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 is a flow chart 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 flowchart of a method for computer-aided prediction of drug treatment efficacy in osimertinib-resistant lung cancer patients;

[0101] Figure 5 A schematic diagram of a system for computer-aided prediction of drug treatment efficacy in osimertinib-resistant lung cancer patients;

[0102] Figure 6 It is a schematic diagram of a process of a method for computer-assisted drug screening based on UQCC2 and METTL3;

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

[0104] Figure 8The results of gene expression analysis of NCI-H1975 cells and osimertinib-resistant cells (Osim) treated with osimertinib are shown in Figure 2 by MeRIP-seq and RNA-seq.

[0105] Fig. 9 The protein expression of UQCC2 in NCI-H1975 cells and NCI-H1975 / OR cells (treated with Ctrl, Osim or Lomi alone) and the corresponding statistical analysis graphs were determined by western blot experiments, wherein 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 lomitripalide;

[0106] Fig.10 The expression of UQCC2 in non-small cell lung cancer parental cells (H1975) and osimertinib-resistant cells (Osim) treated with osimertinib was analyzed by IGV. 6 A abundance change results diagram;

[0107] Fig.11 It is the result of western blot experiment to determine the expression of each biomarker, among which, Fig.11 A is the effect of knocking down METTL3 on the expression of UQCC2, P62, and LC3 proteins and the corresponding statistical analysis chart, Fig.11 B is the effect of knocking down UQCC2 on the expression of METTL3, P62, and LC3 proteins and the corresponding statistical analysis graph;

[0108] Fig.12 This is the result of immunofluorescence experiment to determine the effect of knocking down UQCC2 on LC3 expression. DETAILED DESCRIPTION

[0109] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0110] In some of the processes described in the specification and claims of the present invention and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

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

[0113] S101: Receive input data, including UQCC2 expression level data of 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, methods for determining the protein expression level of UQCC2 include but are not limited to Western blotting, immunohistochemistry, immunofluorescence, enzyme-linked immunosorbent assay, mass spectrometry, and flow cytometry.

[0116] In one embodiment, the method for determining the nucleic acid expression level of UQCC2 includes but is not limited to RT-PCR, qRT-PCR, biochip detection, Southern blotting, Northern blotting, and gene chip.

[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 sample of UQCC2 of a lung cancer patient is selected from at least one of blood, tissue, feces, and urine.

[0120] S102: Applying the machine learning model to the input data to generate an indicator 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 a support vector machine learning model, a linear discriminant analysis model, a recursive feature removal model, a predictive analysis of a microarray model, a logistic regression model, a CART algorithm, a flextree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and a combination thereof.

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

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

[0124] (2) randomly dividing the acquired data into a training set and a test set, and performing block processing on the data;

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

[0126] In one 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 and their corresponding thresholds designed according to specific algorithms based on the above-mentioned performance indicators, etc., which are not limited in this application. In particular, when there are multiple application prediction models that meet the threshold requirements, the optimal application prediction model can be selected based on the prediction performance indicators concerned by different application scenarios. Thus, through the testing of the test set, it can be ensured that the trained application prediction model has strong robustness and generalization ability, which is not only applicable to samples in the training set, but also to new samples that have not participated in the training.

[0127] In one embodiment, when the expression level of UQCC2 of the input lung cancer patient is higher than the diagnostic threshold, the lung cancer patient is judged to be an osimertinib-resistant lung cancer patient.

[0128] In a specific embodiment, MeRIP-seq and RNA-seq were used to detect and analyze the gene expression of NCI-H1975 cells (Ctrl) and NCI-H1975 / OR cells (Osim), wherein 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), using TRIzol (Invitrogen) to separate total RNA, using NanoDrop to quantify, and using Bioanalyzer (Agilent) to perform integrity detection; concentration>50ng / μL, RIN value>7.0, OD260 / 280>1.8, total RNA>50μg meets the requirements of downstream experiments. Poly(A) RNA was purified with Dynabeads Oligo(dT), and the captured RNA was fragmented under high temperature conditions using a magnesium ion shearing kit. Then the RNA was separated from m 6 A-specific antibody (Synaptic Systems) bound to the partial RNA. IP RNA was reverse transcribed using SuperScript II, and then U-labeled second-strand DNA 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 shown in Figure 8 As shown, the expression level of UQCC2 in NCI-H1975 / OR cells (osim) was higher than that in NCI-H1975 cells (Ctrl).

[0129] In a specific embodiment, the protein expression of UQCC2 in NCI-H1975 cells and NCI-H1975 / OR cells (Ctrl) was determined by western blot experiments, wherein NCI-H1975 / OR cells (Ctrl) are osimertinib-resistant cells. 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. The significance was calculated using GraphPad Prism software. All statistical analyses were performed using data from at least three independent experiments. The averages of more than three samples were analyzed using two-way analysis of variance or one-way analysis of variance with Bonferroni correction, and the two averages were compared using an unpaired Student's t-test; *P<0.05 indicates that the difference is statistically significant. The experimental results are shown in the figure. Fig. 9 As shown, it was demonstrated 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 in an embodiment of the present application. Specifically, the system includes:

[0132] Data acquisition unit: UQCC2 expression level data of lung cancer patients;

[0133] Data evaluation unit: the evaluation unit comprises a stored reference and a data processor, the data processor having implemented an algorithm for comparing the expression level of UQCC2 detected by the detection unit with the stored reference;

[0134] Drug resistance identification unit: judging whether the lung cancer patient is an osimertinib-resistant lung cancer patient based on the UQCC2 expression level data of the lung cancer patient and the stored reference algorithm.

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

[0136] A memory and a processor, wherein 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] 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 diagnosing osimertinib-resistant lung cancer patients as described above is implemented.

[0138] Figure 4 The present invention provides a method for computer-aided prediction of drug treatment effects on osimertinib-resistant lung cancer patients, specifically, the method comprises the following steps:

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

[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, methods for determining the protein expression level of UQCC2 include but are not limited to Western blotting, immunohistochemistry, immunofluorescence, enzyme-linked immunosorbent assay, mass spectrometry, and flow cytometry.

[0142] In one embodiment, the method for determining the nucleic acid expression level of UQCC2 includes but is not limited to RT-PCR, qRT-PCR, biochip detection, Southern blotting, Northern blotting, and gene chip.

[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 collected sample of UQCC2 of a lung cancer patient is selected from at least one of blood, tissue, feces, and urine.

[0146] S402: 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;

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

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

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

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

[0151] In one embodiment, when the expressed data meets the judgment condition, the output prediction result 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 output prediction result 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, MeRIP-seq and RNA-seq were used to detect and analyze the gene expression of NCI-H1975 and NCI-H1975 / OR cells (Osim), wherein NCI-H1975 / OR cells (Osim) are osimertinib-resistant cells treated with osimertinib. The specific method is: the reads obtained by sequencing the IP library and the input library are aligned 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 area will be formed here, which is called a peak. Use exompeak software for differential Peak analysis and annotate with ANNOVAR software. The results are shown in the figure. Figure 8 As shown, the expression level of UQCC2 in NCI-H1975 / OR cells (Osim) was higher than that in parental NCI-H1975 cells.

[0154] In a specific embodiment, the protein expression of UQCC2 in NCI-H1975 / OR cells (Ctrl, Osim or Lomi treated alone) was determined by western blot experiment. 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 the significance. The experimental results are shown in Fig. 9 As shown, it was demonstrated 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 gene of interest from the differential peak results, the m on the target gene is analyzed by IGV software. 6 A methylation modification was visualized and analyzed. Fig.10 As shown, compared with the parental non-small cell lung cancer cells (H1975), UQCC2 m 6 A abundance changes in osimertinib-treated H1975 / OR cells (Osim), where UQCC2 m 6 A modification mainly changed the intronic region during the induction of osimertinib resistance.

[0157] Figure 5 : is a schematic diagram of a system for computer-aided prediction of drug treatment effects for osimertinib-resistant lung cancer patients provided in an embodiment of the present application. Specifically, the system comprises:

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

[0159] Data evaluation unit: the evaluation unit comprises a stored reference and a data processor, the data processor having 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 expression level data of UQCC2 after drug treatment of osimertinib-resistant lung cancer patients and the stored reference algorithm, it is determined whether the drug has a therapeutic effect on osimertinib-resistant lung cancer patients.

[0161] An embodiment of the present application provides a device for computer-aided prediction of drug treatment effects on patients with osimertinib-resistant lung cancer. Specifically, the device includes: a memory and a processor, the memory is used to store program instructions; the processor is used to call program instructions, and when the program instructions are executed, the method for predicting drug treatment effects on patients with osimertinib-resistant lung cancer 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 : is a flow chart of a method for computer-assisted drug screening based on UQCC2 and METTL3 provided in an embodiment of the present application. Specifically, the method comprises:

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

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

[0166] In a specific embodiment, the expression relationship between METTL3 and UQCC2 was studied by western blotting experiments after knocking down the gene expression of METTL3 or UQCC2 in H1975 / OR cells. siMETTL3 (5'-3'): sense chain GCACUUGGAUCUACGGAAU (dT) (dT); antisense chain AUUCCGUAGAUCC AAGUGC (dT) (dT), siUQCC2 (5'-3'): sense chain: GACUCCAUUCAAACUACUATT; antisense chain: UAGUAGUUUGAAUGGAGUCTT. The following antibodies were used in the western blotting experiment: 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. Fig.11 As shown in the results, it was demonstrated that knockdown of METTL3 led to downregulation of UQCC2, while knockdown of UQCC2 did not affect the expression of METTL3, suggesting that UQCC2 may be a new downstream target of METTL3.

[0167] S602: Determine the binding site of 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 part of a molecule that can form a stable interaction with a ligand. The binding site of a protein is usually formed by some mutually separated amino acid residues on a polypeptide chain being gathered together in space through the folding of the polypeptide chain to form a specific spatial arrangement.

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

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

[0171] In one embodiment, molecular docking is a method for drug design based on the characteristics of receptors and the interaction between receptors and drug molecules. It mainly studies the interaction between molecules (such as ligands and receptors) and predicts their binding mode and affinity. This method is widely used in the early stages of drug development and can help researchers quickly screen out compounds with potential efficacy.

[0172] The molecular docking method mainly focuses on spatial matching and energy matching. Spatial matching refers to the geometric 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, grid calculation, fragment growth and other methods are usually used, while energy calculation uses simulated annealing, genetic algorithm and other methods. 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 molecular conformation involved in the docking does not change during the calculation process, only the spatial position and posture of the molecule are changed. Semi-flexible docking allows some conformations to change during the calculation. Flexible docking allows more conformational changes.

[0173] In one embodiment, the molecular libraries used in virtual screening are mainly the following: ZINC, PubChem, DrugBank, ChEMBL, ChemDB, HMDB, BindingDB, SMPDB. 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.

[0174] In one embodiment, the process of computer-assisted screening is:

[0175] Perform molecular docking between the screened small molecule compounds and the METTL3 protein to calculate the affinity / binding energy of the target receptor to obtain a score, and rank them according to the score to obtain candidate drugs;

[0176] The scores are sorted, and the top n small molecules are selected to obtain candidate compounds, where n is a natural number greater than or equal to 1.

[0177] In one embodiment, the process of computer-assisted screening is:

[0178] The molecular structures of UQCC2 protein and METTL3 protein are obtained and input into the pharmacophore module library for matching. All action sites are clustered based on the interaction patterns with UQCC2 and METTL3 to obtain a pharmacophore model. The pharmacophore model is input into the molecular compound database for high-throughput screening to obtain candidate drugs.

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

[0180] In one embodiment, the computer-assisted screening process is: screening protein analogs / antibodies / RNA drugs through the binding sites to obtain candidate drugs.

[0181] In one embodiment, the process and method of designing protein analogs, antibodies and other drugs based on proteins can be summarized as follows:

[0182] Determine the target protein: First, you need to determine the target protein for drug design, that is, the target. The target can be a known disease-related protein, viral antigen or other biological molecule.

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

[0184] Determine drug-protein interactions: Study the interaction mechanism between the drug and the target protein, including binding sites, binding modes, and binding kinetics. This can be done through computer simulations, laboratory experiments, and other methods.

[0185] Drug design: Based on the interaction mechanism between the drug and the target protein, design drug molecules that can specifically bind to the target protein. This includes selecting the appropriate drug type, designing the chemical structure of the molecule, and optimizing the drug efficacy and pharmacokinetic properties of the molecule.

[0186] Synthesize and validate drugs: Prepare drug molecules through chemical synthesis and other methods, and validate 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 with similar structures and functions to certain proteins in the body. They can simulate the functions of proteins and thus play an important role in the treatment of diseases. Antibodies are immunoglobulins produced by plasma cells differentiated from B lymphocytes under antigen stimulation by the body's immune system and can specifically bind to corresponding antigens. The structure of antibodies is mainly divided into two parts: a constant region and a 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, thereby achieving the purpose of treating diseases.

[0188] In one embodiment, the small molecule compound is mixed with the UQCC2 protein solution, and the inhibition rate is calculated to screen out the small molecule compound with inhibitory effect.

[0189] In one embodiment, the general steps for performing an inhibitory activity experiment on a small molecule compound are as follows:

[0190] Select the appropriate target protein: According to the research purpose and disease target, select the appropriate target protein as the experimental object. Make sure that the target protein has potential interaction with the small molecule compound being studied.

[0191] Preparation of 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 assays 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), radioisotope labeling, and enzyme-linked immunosorbent assay (ELISA). Ensure that the assay 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, a control group and an experimental group can be set up to compare the inhibitory activity of different small molecule compounds.

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

[0195] Interpretation and discussion of results: Based on the experimental results, analyze the inhibitory activity of small molecule compounds on target proteins and discuss them in combination with their structures 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 drug candidates.

[0196] In one embodiment, the candidate drugs of protein analogs / antibodies / RNA drugs are experimentally tested for inhibitory activity, and immunoprecipitation analysis is used to perform unbiased screening on cells co-transfected with V5-tagged protein analogs / antibodies / RNA drugs and Flag-STING.

[0197] In one embodiment, co-immunoprecipitation (Co-IP) is a classic method for studying protein interactions based on the specific interaction between antibodies and antigens. It is an effective method for determining the physiological interaction between two proteins in intact cells. The principle is to use target protein-specific antibodies to indirectly capture proteins bound to specific target proteins to identify related protein-protein interactions in vivo. It is a classic method for studying protein interactions. Specific antibodies capture target proteins in samples to form antibody-target protein complexes, and then use beaded supports (such as Nanoab-Agarose) that can bind to antibodies to fix and precipitate the complexes. At the same time, proteins that interact with the target protein will also be precipitated. Finally, non-specific proteins bound to the target protein are washed away, and then analyzed by SDS-PAGE and 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, which is a common procedure for stable transfection. The physical process of co-transfection is the integration of two nucleic acid molecules into the same integration sequence and expression in the same transfected cell. It involves choosing the right co-transfection reagent and using advanced lipid nanoparticle technology to achieve excellent transfection performance and reproducible results.

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

[0200] In one embodiment, protein-small molecule docking refers to the computational simulation process of docking 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 drug design software with a high degree of automation that can achieve docking between small molecule ligands and biomacromolecule receptors. It uses a fragment-based scoring method to achieve fast and accurate docking. RosettaDock is a commonly used protein-protein docking software that uses a fragment-based scoring method to achieve fast and accurate protein docking. The software can achieve precise adjustment of side chain conformations during docking, and takes into account a variety of complex interactions, such as hydrogen bonds, ionic bonds, hydrophobic interactions, etc.

[0201] In one embodiment, protein-nucleic acid docking refers to the computational simulation process of docking 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 specially developed for nucleic acid structure prediction and design, which can achieve accurate modeling and docking of DNA or RNA molecules. NAflex software uses a fragment-based scoring method, which can achieve fast and accurate docking. It takes into account a variety of complex interactions, such as hydrogen bonds, ionic bonds, hydrophobic interactions, etc., and can achieve precise adjustment of side chain conformations during docking.

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

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

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

[0205] Drug screening unit: using computer-assisted drug screening methods to obtain candidate drugs targeting the binding site;

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

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

[0208] The present application embodiment provides a method for regulating autophagy, the method comprising administering a regulatory agent for UQCC2;

[0209] In one embodiment, the method includes regulating autophagy-related markers, wherein 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, Atg12, Beclin1; the lysosome markers include but are not limited to LAMP1 and LAMP2, cathepsins. As an embodiment of the present application, the autophagy-related markers are selected from LC3 and p62.

[0211] In one embodiment, the method includes increasing the expression level of autophagy-related markers, and the method for detecting the expression level of the autophagy-related markers includes Western Blot, immunofluorescence staining, RT-PCR, immunohistochemistry, and ELISA. As an embodiment of the present application, the detection method is 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 embodiment of the present application, the "inhibitor" is a substance that inhibits the expression level of UQCC2.

[0213] In one embodiment, the inhibitor comprises a nucleic acid inhibitor, a protein inhibitor, a proteolytic enzyme, a protein binding molecule, and a combination thereof.

[0214] In one embodiment, the nucleic acid inhibitor is selected from: an interfering molecule that uses UQCC2 or its transcript as a target sequence and can inhibit UQCC2 gene expression or gene transcription, including but not limited to shRNA, siRNA, ribozyme, antisense oligonucleotide, dsRNA, microRNA, zinc finger, CRISPR / Cas9, or a construct that can express or form the shRNA, siRNA, ribozyme, antisense oligonucleotide, dsRNA, microRNA, zinc finger, CRISPR / Cas9. Protein inhibitors are selected from substances that can inhibit UQCC2 protein. Proteolytic enzymes are selected from enzymes that can catalyze the hydrolysis of UQCC2 protein. Protein binding molecules are selected from substances that specifically bind to UQCC2 protein, such as antibodies or ligands that can inhibit the activity of UQCC2 protein.

[0215] In one embodiment, siRNA may include partially purified RNA, substantially pure RNA, synthetic RNA or recombinantly produced RNA and RNA that is different from natural RNA by adding, deleting, replacing and / or changing one or more nucleotides. The change may include adding non-nucleotide substances, such as adding to the end of siRNA or one or more internal nucleotides of siRNA; making siRNA resistant to nuclease digestion (e.g., using 2'-substituted ribonucleotides or modifying the sugar phosphate backbone); or replacing one or more nucleotides in siRNA with deoxyribonucleotides.

[0216] In one embodiment, ribozymes are a class of RNAs that can be engineered to enzymatically cleave and inactivate other RNA targets in a specific sequence-dependent manner. Ribozymes and methods of their delivery 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, thereby preventing the expression of the target gene. Ribozymes can be chemically synthesized in the laboratory and structurally modified to increase their stability and catalytic activity using methods known in the art. Alternatively, the ribozyme gene can be introduced into the cell by gene delivery mechanisms known in the art.

[0217] In one embodiment, the antisense oligonucleotide (antisense nucleic acid sequence) may include a nucleotide sequence that is complementary to a sense nucleic acid encoding a 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 to it. 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'): positive chain: GACUCCAUUCAAACUACUATT; antisense chain: UAGUAGUUUGAAUGGAGUCTT. The following antibodies were used in the Western blotting experiment: 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. The significance was calculated using GraphPad Prism software. The experimental results are shown in Figure 2. Fig.11 As shown, it was demonstrated that knockdown of UQCC2 led to upregulation of autophagosome markers LC3 and p62, further demonstrating that knockdown of 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 autophagosome marker LC3 after knocking down UQCC2 in H1975 / OR cells was analyzed by immunofluorescence experiment. siUQCC2 (5'-3'): positive chain: GACUCCAUUCAAACUACUATT; antisense chain: UAGUAGUUUGAAUGGAGUCTT. The cells were fixed with 4% formaldehyde, permeabilized with 0.2% Triton X-100, and the cells were blocked with 5% bovine serum albumin. Then incubated with the designated antibodies at -4°C overnight, stained with fluorescence-conjugated secondary antibodies and DAPI solution. The cells were photographed under a fluorescence microscope (Leica DM6B Thunder). Anti-LC3 (1:100; the antibody used was Proteintech, No. 81004-1-RR). The experimental results are as follows. Fig.12 As shown, it was demonstrated that knockdown of UQCC2 led to upregulation of the autophagosome marker LC3, further demonstrating that knockdown of UQCC2 significantly activated autophagy.

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

[0222] 1) Use of reagents for detecting UQCC2 in the preparation of products for diagnosing autophagy activation;

[0223] 2) Use of reagents that regulate UQCC2 in the preparation of products that regulate autophagy;

[0224] 3) Use of reagents for detecting UQCC2 in the preparation of products 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 binding agent that specifically binds to a 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 portion 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 a "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, binding agent refers to all or part of a protein (protein, protein-like or protein-containing) molecule that can be bound to a membrane protein using specific intermolecular interactions. The binding agent of an albumen is, for example, a receptor of a protein, a lectin that binds to a protein, an antibody to a protein, a peptide body (peptidebody), a bispecific dual binding agent or a bispecific antibody form. More specifically, the term "binding agent" refers to a polypeptide, more specifically a protein domain. Suitable protein domains are elements of overall protein structure, which are self-stabilizing and independent of the rest of the protein chain folding and are commonly referred to as "binding domains". The length of this binding domain varies from about 25 amino acids to 500 amino acids and more. Many binding domains can be classified as folding and are recognizable, identifiable, 3-D structures. Some foldings are very common in many different proteins, so that they are given specific names.

[0228] The reagents for detecting UQCC2 provided in the present application also include detectable labels. Detectable labels refer to compositions that can produce detectable signals indicating the presence of target polynucleotides in the assay sample. Suitable labels include, but are not limited to, radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent moieties, magnetic particles, bioluminescent moieties. Therefore, a label is any composition that can be detected by a device or method, including but not limited to spectral, photochemical, biochemical, immunochemical, electrical, optical, chemical detection devices or any other suitable device. In some embodiments, the label can be detected visually without the aid of a device.

[0229] In one embodiment, radioactive isotopes 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, and lanthanide phosphors.

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

[0231] In one embodiment, the chip, also referred to as an array, refers to a solid support comprising connected nucleic acid or peptide probes. The array typically comprises a plurality of different nucleic acid or peptide probes connected to the substrate surface according to different known positions. These arrays, also referred to as "microarrays", can typically be produced using mechanical synthesis methods or light-guided synthesis methods that incorporate a combination of photolithography and solid phase synthesis methods. The array can comprise a flat surface, or can be a nucleic acid or peptide on a bead, gel, polymer surface, fiber such as an optical fiber, glass, or any other suitable substrate. The array can be packaged in a certain manner to allow for diagnosis of a fully functional device or manipulation of other methods.

[0232] In one embodiment, the nucleic acid membrane strip includes a substrate and an oligonucleotide probe fixed on the substrate; the substrate can be any substrate suitable for fixing the oligonucleotide probe, such as a nylon membrane, a nitrocellulose membrane, a polypropylene membrane, a glass sheet, a silica gel wafer, a micro-magnetic bead, etc.

[0233] The kit described in the present application includes a reagent for detecting the UQCC2 gene or protein, and one or more substances selected from the following group: a container, instructions for use, 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, PET bottle, syringe or other container, in which a component can be placed, and preferably, appropriate aliquots can be made. When there are 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 may be contained in a vial. The kit of the present application will generally also include a container for containing reactants, sealed for commercial sale. Such a container may include an injection-molded or blow-molded plastic container, in which the required vials can be retained.

[0234] The kits described in the present application include, but are not limited to, qPCR kits, ELISA kits, immunoblotting detection kits, immunochromatography detection kits, immunohistochemistry detection kits, flow cytometry analysis kits, and electrochemiluminescence detection kits.

[0235] The present application may use any known method to detect the expression level of UQCC2 gene or protein, including but not limited to RT-PCR, qRT-PCR, biochip detection, Southern blotting, in situ hybridization, and immunoblotting.

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

[0237] A person 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 related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0238] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that for ordinary technicians in this field, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection 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; 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; Preferably, the lung cancer is selected from non-small cell lung cancer; Preferably, the expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

2. 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: judging whether the lung cancer patient is an osimertinib-resistant lung cancer patient based on the UQCC2 expression level data of the lung cancer patient and the stored reference algorithm; 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 claim 1 is implemented; 3) A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for diagnosing osimertinib-resistant lung cancer patients according to claim 1 is implemented.

3. 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; Preferably, the expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2; Preferably, the lung cancer is selected from non-small cell lung cancer; Preferably, the medicament comprises lomitapide.

4. Any of the following products: 1) A computer-aided prediction system for drug treatment effects on 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: judging whether the drug has a therapeutic effect on osimertinib-resistant lung cancer patients based on the UQCC2 expression level data after drug treatment of osimertinib-resistant lung cancer patients and the stored reference algorithm; 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 claim 3 is implemented; 3) A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and 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 in claim 3 is implemented.

5. A computer-aided drug screening method based on UQCC2 and METTL3, characterized in that: The method comprises: Obtain UQCC2 protein and METTL3 protein data; Determine the binding site of the UQCC2 protein and the METTL3 protein according to the data of the two proteins; Obtaining candidate drugs that simultaneously target the binding sites of the two drugs through computer-assisted screening; Preferably, the process of computer-assisted screening is: Obtain the binding site of UQCC2 protein and METTL3 protein complex; Based on the spatial structure of the binding site of the UQCC2 protein and the METTL3 protein complex, small molecule compounds with similar structures were screened in the molecular database; Perform molecular docking between the screened small molecule compounds and the METTL3 protein to calculate the affinity / binding energy of the target receptor to obtain a score, and rank them according to the score to obtain candidate drugs; Sorting the scores, selecting the top n small molecules to obtain candidate compounds, where n is a natural number greater than or equal to 1; Preferably, the computer-assisted screening process is: screening protein analogs / antibodies / RNA drugs through the binding sites to obtain candidate drugs; Preferably, the computer-assisted drug screening is based on the active site of the complex of METTL3 protein and UQCC2 protein to screen protein analogs / antibodies / RNA drugs; or screens protein / antibody / RNA drugs similar to METTL3 to obtain candidate drugs; Preferably, the computer-assisted drug screening screens small molecule inhibitors or antibodies based on the active site or target of the complex of METTL3 protein and UQCC2 protein to obtain candidate drugs; Preferably, the method for computer-assisted drug screening based on UQCC2 and METTL3 is characterized in that inhibitory activity experiments are performed on candidate small molecule drugs, the inhibition rate is calculated after the small molecule compounds are mixed with the UQCC2 protein solution, and small molecule compounds with inhibitory effects are screened.

6. Any of the following products: 1) A computer-aided drug screening system based on UQCC2 and METTL3, characterized in that: The system comprises: Data acquisition unit: obtain UQCC2 protein and METTL3 protein data; Site determination unit: selecting the spatial structure of the UQCC2 protein and METTL3 protein complex, and determining the complex binding site as the binding site of the targeted drug; Drug screening unit: using computer-assisted drug screening methods to obtain candidate drugs targeting the binding site; 2) A computer-assisted drug screening device based on UQCC2 and METTL3, 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 computer-assisted drug screening method based on UQCC2 and METTL3 according to claim 5 is implemented; 3) A computer-readable storage medium having a computer program thereon, characterized in that it includes: when the computer program is executed by a processor, the computer-assisted drug screening method based on UQCC2 and METTL3 as described in claim 5 is implemented.

7. A method for regulating autophagy, characterized in that: The method comprises administering a modulatory agent of UQCC2; Preferably, the regulating autophagy is activating autophagy; Preferably, the method comprises regulating autophagy-related markers; Preferably, the method comprises increasing the expression level of autophagy-related markers; Preferably, the autophagy-related markers include LC3 and p62; Preferably, the method comprises increasing the expression levels of nucleic acids and proteins of LC3 and p62; Preferably, the agent comprises an inhibitor of UQCC2; Preferably, the UQCC2 inhibitor includes any substance that can reduce the expression of a nucleic acid encoding UQCC2, reduce the level of UQCC2 protein, or inhibit the activity of UQCC2; Preferably, the inhibitor comprises a nucleic acid inhibitor, a protein inhibitor, a proteolytic enzyme, a protein binding molecule, and a combination thereof; Preferably, the nucleic acid inhibitors include siRNA, shRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, CRISPR / Cas9; Preferably, the nucleic acid inhibitor is siRNA; Preferably, the protein inhibitor comprises an antibody that neutralizes UQCC2; Preferably, the protein binding molecule is selected from substances that specifically bind to the UQCC2 protein.

8. Any of the following applications; 1) Use of reagents for detecting UQCC2 in the preparation of products for diagnosing autophagy activation; 2) Use of reagents that regulate UQCC2 in the preparation of products that regulate autophagy; 3) Use of reagents for detecting UQCC2 in the preparation of products for diagnosing osimertinib-resistant lung cancer; Preferably, 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; Preferably, the reagents described in 1) and 3) further include a detectable label; Preferably, the detectable label comprises a radioisotope, a nucleotide chromophore, an enzyme, a substrate, a fluorescent molecule, a chemiluminescent moiety, a magnetic particle, a bioluminescent moiety; Preferably, the products described in 1) and 3) include chips, test kits, test strips or nucleic acid membrane strips; Preferably, the chip comprises a gene chip and a protein chip, wherein the gene chip comprises an oligonucleotide probe targeting the UQCC2 gene for detecting the transcription level of the UQCC2 gene, and the protein chip comprises a specific binding agent for the UQCC2 protein; Preferably, the kit comprises a gene detection kit and a protein detection kit, wherein the gene detection kit comprises a reagent or a chip for detecting the transcription level of the UQCC2 gene, and the protein detection kit comprises a reagent or a chip for detecting the expression level of the UQCC2 protein; Preferably, the kit further comprises a buffer, a preservative or a protein stabilizer; Preferably, the kit further comprises instructions; Preferably, 2) the regulating autophagy is activating autophagy; Preferably, 2) the reagent comprises an inhibitor of UQCC2; Preferably, the UQCC2 inhibitor includes any substance that can reduce the expression of a nucleic acid encoding UQCC2, reduce the level of UQCC2 protein, or inhibit the activity of UQCC2; Preferably, the inhibitor comprises a nucleic acid inhibitor, a protein inhibitor, a proteolytic enzyme, a protein binding molecule, and a combination thereof; Preferably, the nucleic acid inhibitors include siRNA, shRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, CRISPR / Cas9; Preferably, the nucleic acid inhibitor is siRNA; Preferably, 3) the lung cancer is selected from non-small cell lung cancer.

9. Any of the following products: 1) A product for diagnosing autophagy activation, characterized in that: The product includes a reagent capable of detecting the expression level of UQCC2; 2) A product for regulating autophagy, characterized in that the product comprises an agent capable of regulating UQCC2; 3) A product for diagnosing osimertinib-resistant lung cancer, characterized in that the product comprises a reagent capable of detecting the expression level of UQCC2; Preferably, the products described in 1) and 3) include chips, test kits, test strips or nucleic acid membrane strips; Preferably, 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, and immunoblotting method; Preferably, 2) the regulating autophagy is activating autophagy; Preferably, 2) the reagent comprises an inhibitor of UQCC2; Preferably, the UQCC2 inhibitor includes any substance that can reduce the expression of a nucleic acid encoding UQCC2, reduce the level of UQCC2 protein, or inhibit the activity of UQCC2; Preferably, the inhibitor comprises a nucleic acid inhibitor, a protein inhibitor, a proteolytic enzyme, a protein binding molecule, and a combination thereof; Preferably, the nucleic acid inhibitors include siRNA, shRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, CRISPR / Cas9; Preferably, the nucleic acid inhibitor is siRNA; Preferably, 3) the lung cancer is selected from non-small cell lung cancer.

10. A method for screening a drug that activates autophagy in osimertinib-resistant lung cancer, characterized in that: The method comprises: treating a culture system expressing or containing the UQCC2 gene or a protein encoded by it with a substance to be screened; and detecting the expression or activity of the UQCC2 gene or a protein encoded by it in the system; wherein, when the substance to be screened inhibits the expression level or activity of the UQCC2 gene or a protein encoded by it, the substance to be screened is a candidate drug for activating autophagy.

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