Methods, systems, and apparatus for computer-aided drug screening based on uqcc2 and mettl3

Abstract

The application relates to the field of intelligent medical treatment, in particular to a method, system and device for computer-aided screening of drugs based on UQCC2 and METTL3. It is found for the first time that UQCC2 is significantly highly expressed in osimertinib-resistant lung cancer, and the interaction between UQCC2 and METTL3 is found, which provides 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, and has important research significance for disease prevention and treatment research.

Description

[0001] This application is a divisional application of the application number: 2024113002085, the patent name: method, system and equipment for computer-aided diagnosis of osimertinib-resistant lung cancer patients based on UQCC2, prediction of drug treatment effect and screening of drugs, the application date: September 18, 2024. TECHNICAL FIELD

[0002] The application belongs to the field of intelligent medical treatment, and relates to a method, system and equipment for computer-aided screening of drugs based on UQCC2 and METTL3. BACKGROUND

[0003] Non-small cell lung cancer (NSCLC) is the main subtype of lung cancer and one of the most common malignant tumors, with extremely high morbidity and mortality worldwide. In NSCLC patients, genetic mutations and changes, especially L858R and T790M mutations of epidermal growth factor receptor (EGFR), are the most important major pathogenic factors. Therefore, tyrosine kinase inhibitors (TKIs) targeting EGFR, such as gefitinib and erlotinib, have become the standard first-line treatment for EGFR-mutant NSCLC patients. Osimertinib (AZD9291) as the third-generation EGFR-TKI has made significant progress in the treatment of 34 patients with NSCLC resistant to gefitinib or erlotinib. However, osimertinib resistance inevitably occurs, 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; J.C. Madukwe, Cell 2023, 186(8), 1515, https: / / doi.org / 10.1016 / j.cell.2023.03.019.) Therefore, it is urgent to elucidate the potential mechanisms of osimertinib resistance and discover new drugs to overcome drug resistance. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a method, system and equipment for computer-aided screening of drugs based on UQCC2 and METTL3.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] The first aspect of the present application provides a method for computer-aided diagnosis of osimertinib (Osim) resistant lung cancer patients, the method comprising:

[0007] receiving input data, the data comprising expression level data of UQCC2 of a lung cancer patient;

[0008] 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.

[0009] Further, the lung cancer is selected from non-small cell lung cancer.

[0010] Further, the expression level of UQCC2 comprises protein expression level, nucleic acid expression level of UQCC2.

[0011] A second aspect of the present application provides any one of the following products:

[0012] 1) A system for computer-aided diagnosis of an osimertinib-resistant lung cancer patient, the system comprising:

[0013] a data acquisition unit: expression level data of UQCC2 of a lung cancer patient;

[0014] a 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;

[0015] 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 of the lung cancer patient and the stored reference;

[0016] 2) An apparatus for computer-aided diagnosis of an osimertinib-resistant lung cancer patient, the apparatus comprising a memory and a processor, the memory for storing program instructions; the processor for invoking program instructions, when the program instructions are executed, implementing the method for diagnosing an osimertinib-resistant lung cancer patient according to the first aspect of the present application;

[0017] 3) A computer-readable storage medium, the computer-readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the method for diagnosing an osimertinib-resistant lung cancer patient according to the first aspect of the present application.

[0018] A third aspect of the present application provides a computer-aided method for predicting the drug treatment effect of an osimertinib-resistant lung cancer patient, the method comprising:

[0019] receiving input data, the data comprising expression level data of UQCC2 of an osimertinib-resistant lung cancer patient after drug treatment;

[0020] applying a machine learning model to the data to generate an indication of whether the drug has a therapeutic effect on the osimertinib-resistant lung cancer patient;

[0021] Further, the expression level of the UQCC2 includes a protein expression level, a nucleic acid expression level of the UQCC2.

[0022] Further, the lung cancer is selected from non-small cell lung cancer.

[0023] Further, the drug includes Lomi.

[0024] The fourth aspect of the present application provides any one of the following products:

[0025] 1) A system for computer-aided prediction of drug treatment effect on osimertinib-resistant lung cancer patients, comprising:

[0026] a data acquisition unit: expression level data of UQCC2 after drug treatment on osimertinib-resistant lung cancer patients;

[0027] a data evaluation unit: the evaluation unit contains 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;

[0028] a data analysis unit: based on the algorithm for comparing the expression level data of UQCC2 after drug treatment on osimertinib-resistant lung cancer patients with the stored reference, it is determined whether the drug has a therapeutic effect on osimertinib-resistant lung cancer patients;

[0029] 2) A device for computer-aided prediction of drug treatment effect on osimertinib-resistant lung cancer patients, comprising a memory and a processor, wherein the memory is used to store program instructions; and the processor is used to call the program instructions, and when the program instructions are executed, the method for predicting drug treatment effect on osimertinib-resistant lung cancer patients according to the third aspect of the present application is implemented.

[0030] 3) A computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method for predicting drug treatment effect on osimertinib-resistant lung cancer patients according to the third aspect of the present application.

[0031] The fifth aspect of the present application provides a computer-aided screening method based on UQCC2 and METTL3, comprising:

[0032] acquiring UQCC2 protein and METTL3 protein data;

[0033] determining the binding sites of the two proteins according to the UQCC2 protein and METTL3 protein data;

[0034] obtaining a candidate drug that targets the binding sites of the two proteins at the same time through computer-aided screening.

[0035] Further, the computer-aided screening process is as follows:

[0036] Obtaining the UQCC2 protein and METTL3 protein complex binding site;

[0037] Screening small molecule compounds with similar structures in a molecular database based on the spatial structure of the UQCC2 protein and METTL3 protein complex binding site;

[0038] Molecular docking calculation of the screened small molecule compounds and METTL3 protein targeting receptor affinity / binding energy to obtain a score, and sorting according to the score to obtain a candidate drug;

[0039] Sorting the scores, and selecting the top n small molecules to obtain a candidate compound, n is a natural number greater than or equal to 1.

[0040] Further, the computer-aided screening process is as follows: screening protein analogs / antibodies / RNA drugs through the binding site to obtain a candidate drug.

[0041] Further, the computer-aided screening drug is based on the active site of the METTL3 protein and the UQCC2 protein complex to screen protein analogs / antibodies / RNA drugs; or to screen proteins / antibodies / RNA drugs similar to METTL3 to obtain a candidate drug.

[0042] Further, the computer-aided screening drug is based on the active site or target of the METTL3 protein and the UQCC2 protein complex to screen small molecule inhibitors or antibodies to obtain a candidate drug.

[0043] Further, the computer-aided screening drug based on UQCC2 and METTL3 is a method for testing the inhibitory activity of small molecule candidate drugs. The small molecule compounds are mixed with UQCC2 protein solution to calculate the inhibition rate, and small molecule compounds with inhibitory effect are screened.

[0044] The sixth aspect of the present application provides any one of the following products:

[0045] 1) A system for computer-aided screening of drugs based on UQCC2 and METTL3, the system comprising:

[0046] Data acquisition unit: acquiring UQCC2 protein and METTL3 protein data;

[0047] Site determination unit: selecting the spatial structure of the UQCC2 protein and METTL3 protein complex to determine the complex binding site as the binding site of the targeted drug;

[0048] Drug screening unit: a method of computer-aided drug screening is used to obtain a candidate drug targeting the binding site;

[0049] 2) A computer-aided drug screening device based on UQCC2 and METTL3, comprising 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-aided drug screening method based on UQCC2 and METTL3 in the fifth aspect of the application is realized;

[0050] 3) A computer readable storage medium having a computer program thereon, comprising: the computer program is executed by the processor to realize the computer-aided drug screening method based on UQCC2 and METTL3 in the fifth aspect of the application.

[0051] The seventh aspect of the application provides a method for regulating autophagy, which comprises administering a regulatory agent of UQCC2.

[0052] Further, the regulation of autophagy is to activate autophagy.

[0053] Further, the method comprises regulating autophagy-related markers.

[0054] Further, the method comprises increasing the expression level of autophagy-related markers.

[0055] Further, the autophagy-related markers include LC3 and p62.

[0056] Further, the method comprises increasing the expression level of nucleic acid, protein of LC3 and p62.

[0057] Further, the agent comprises an inhibitor of UQCC2.

[0058] Further, 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.

[0059] Further, the inhibitor includes nucleic acid inhibitor, protein inhibitor, proteolytic enzyme, protein binding molecule, and combinations thereof.

[0060] Further, the nucleic acid inhibitor includes siRNA, shRNA, ribozyme, antisense oligonucleotide, dsRNA, microRNA, zinc finger, CRISPR / Cas9.

[0061] Further, the nucleic acid inhibitor is siRNA.

[0062] Further, the protein inhibitor includes an antibody that neutralizes UQCC2.

[0063] Further, the protein binding molecule is selected from the group consisting of a substance that specifically binds to the UQCC2 protein.

[0064] An eighth aspect of the present application provides any one of the following uses;

[0065] 1) use of a reagent for detecting UQCC2 in the preparation of a product for diagnosing autophagy activation;

[0066] 2) use of a reagent for regulating UQCC2 in the preparation of a product for regulating autophagy;

[0067] 3) use of a reagent for detecting UQCC2 in the preparation of a product for diagnosing osimertinib-resistant lung cancer.

[0068] Further, the reagent of 1) and 3) is selected from the group consisting of 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.

[0069] Further, the reagent of 1) and 3) further comprises a detectable label.

[0070] Further, 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.

[0071] Further, the product of 1) and 3) comprises a chip, a kit, a test paper, or a nucleic acid membrane strip.

[0072] Further, the chip comprises a gene chip comprising an oligonucleotide probe for UQCC2 gene for detecting the transcription level of UQCC2 gene, or a protein chip comprising a specific binding agent for UQCC2 protein.

[0073] Further, the kit comprises a gene detection kit comprising a reagent or a chip for detecting the transcription level of UQCC2 gene, or a protein detection kit comprising a reagent or a chip for detecting the expression level of UQCC2 protein.

[0074] Further, the kit further comprises a buffer, a preservative, or a protein stabilizer.

[0075] Further, the kit further comprises an instruction manual.

[0076] Further, the regulation of autophagy of 2) is activation of autophagy.

[0077] Further, the reagent of 2) comprises an inhibitor of UQCC2.

[0078] 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.

[0079] Further, the inhibitor includes nucleic acid inhibitor, protein inhibitor, proteolytic enzyme, protein binding molecule, and combinations thereof.

[0080] Further, the nucleic acid inhibitor includes siRNA, shRNA, ribozyme, antisense oligonucleotide, dsRNA, microRNA, zinc finger, CRISPR / Cas9.

[0081] Further, the nucleic acid inhibitor is siRNA.

[0082] Further, 3) the lung cancer is selected from non-small cell lung cancer.

[0083] A ninth aspect of the present application provides any one of the following products:

[0084] 1) a product for diagnosing autophagy activation, the product comprising a reagent capable of detecting the expression level of UQCC2;

[0085] 2) a product for regulating autophagy, the product comprising a reagent capable of regulating UQCC2;

[0086] 3) a product for diagnosing osimertinib-resistant lung cancer, the product comprising a reagent capable of detecting the expression level of UQCC2.

[0087] Further, the product of 1) and 3) comprises a chip, a kit, a test paper, or a nucleic acid membrane strip.

[0088] Further, the kit comprises a reagent 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.

[0089] Further, the regulation of autophagy of 2) is activation of autophagy.

[0090] Further, the reagent of 2) comprises an inhibitor of UQCC2.

[0091] 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.

[0092] Further, the inhibitor includes nucleic acid inhibitor, protein inhibitor, proteolytic enzyme, protein binding molecule, and combinations thereof.

[0093] Further, the nucleic acid inhibitor comprises siRNA, shRNA, ribozyme, antisense oligonucleotide, dsRNA, microRNA, zinc finger, CRISPR / Cas9.

[0094] Further, the nucleic acid inhibitor is siRNA.

[0095] Further, 3) the lung cancer is selected from non-small cell lung cancer.

[0096] A tenth aspect of the present application provides a method for screening a drug for activating autophagy of an osimertinib-resistant lung cancer, the method comprising: treating a culture system expressing or containing a UQCC2 gene or a protein encoded thereby with a substance to be screened; and detecting expression or activity of the UQCC2 gene or the protein encoded thereby in the system; wherein when the substance to be screened inhibits the expression level or activity of the UQCC2 gene or the protein encoded thereby, the substance to be screened is a candidate drug for activating autophagy.

[0097] Advantages and beneficial effects of the present application: the present application first discovers that UQCC2 is significantly highly expressed in osimertinib-resistant lung cancer, and discovers the interaction between UQCC2 and METTL3, thereby 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 disease prevention and treatment research. BRIEF DESCRIPTION OF DRAWINGS

[0098] Figure 1 is a flowchart of a method for computer-aided diagnosis of an osimertinib-resistant lung cancer patient;

[0099] Figure 2 is a schematic diagram of a system for computer-aided diagnosis of an osimertinib-resistant lung cancer patient;

[0100] Figure 3 is a schematic diagram of a device for computer-aided diagnosis of an osimertinib-resistant lung cancer patient;

[0101] Figure 4 is a flowchart of a method for computer-aided prediction of the drug treatment effect of an osimertinib-resistant lung cancer patient;

[0102] Figure 5 is a schematic diagram of a system for computer-aided prediction of the drug treatment effect of an osimertinib-resistant lung cancer patient;

[0103] Figure 6 is a flowchart of a method for computer-aided screening of a drug based on UQCC2 and METTL3;

[0104] Figure 7A system schematic diagram of a computer-aided drug screening system based on UQCC2 and METTL3;

[0105] Figure 8 A figure of gene expression results of NCI-H1975 cells and osimertinib-treated osimertinib-resistant cells (Osim) detected and analyzed by MeRIP-seq and RNA-seq;

[0106] Figure 9 A figure of protein expression of UQCC2 in NCI-H1975 cells, NCI-H1975 / OR cells (Ctrl, Osim or Lomi alone) and corresponding statistical analysis diagram determined by western blot experiment, wherein NCI-H1975 / OR cells (Ctrl) are osimertinib-resistant cells, NCI-H1975 / OR cells (Osim) are osimertinib-treated osimertinib-resistant cells, and NCI-H1975 / OR cells (Lomi) are lomustine-treated osimertinib-resistant cells;

[0107] Figure 10 A figure of m 6 A figure of change of abundance;

[0108] Figure 11 A figure of expression of each biomarker determined by western blot experiment, wherein, Figure 11 A is a figure of influence of knockdown of METTL3 on protein expression of UQCC2, P62 and LC3 and corresponding statistical analysis diagram, Figure 11 B is a figure of influence of knockdown of UQCC2 on protein expression of METTL3, P62 and LC3 and corresponding statistical analysis diagram;

[0109] Figure 12 A figure of influence of knockdown of UQCC2 on expression of LC3 determined by immunofluorescence experiment. DETAILED DESCRIPTION

[0110] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application.

[0111] In some of the flowcharts described in the description and claims of the present application and in the above-mentioned figures, a plurality of operations are included which occur in a specific order, but it should be clearly understood that these operations can be performed not in the order in which they appear herein or in parallel, and 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 flowcharts can include more or fewer operations, and the operations can be performed in sequence or in parallel.

[0112] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0113] Figure 1 is a flowchart of a method for computer-aided diagnosis of an osimertinib-resistant lung cancer patient provided by an embodiment of the present application. Specifically, the method comprises the following steps:

[0114] S101: receiving input data, including expression level data of UQCC2 of a lung cancer patient

[0115] In an embodiment, the expression level of UQCC2 includes the protein expression level and the nucleic acid expression level of UQCC2.

[0116] In an embodiment, the method for determining the protein expression level of UQCC2 includes but is not limited to Western blotting, immunohistochemistry, immunofluorescence, enzyme-linked immunosorbent assay, mass spectrometry, and flow cytometry.

[0117] In an 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 blot hybridization, and gene chip method.

[0118] In an embodiment, the lung cancer is at least one selected from small cell lung cancer and non-small cell lung cancer.

[0119] In an embodiment, the non-small cell lung cancer is at least one selected from squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

[0120] In an embodiment, the sample of the lung cancer patient UQCC2 is at least one selected from blood, tissue, stool, and urine.

[0121] S102: 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

[0122] 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 elimination model, a prediction 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 combinations thereof.

[0123] In a preferred embodiment, the machine learning model construction method comprises:

[0124] (1) obtaining expression level data of UQCC2 in non-osimertinib-resistant lung cancer patients and osimertinib-resistant lung cancer patients;

[0125] (2) randomly dividing the obtained data into a training set and a test set, and performing block processing on the data;

[0126] (3) using the expression level data of UQCC2 in the training set to perform parameter adjustment and training on lung cancer patients, generating an osimertinib lung cancer diagnosis model; using the expression level data of UQCC2 in the test set to test the lung cancer detection model, optimizing the osimertinib lung cancer diagnosis model, and determining the diagnosis threshold of the expression level of UQCC2.

[0127] In one embodiment, the diagnosis threshold includes, but is not limited to, the corresponding threshold of performance indicators such as Acc, AUC, Sensitivity, TNR, PPV, NPV, etc., and can 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 various performance indicators, etc., which are not limited by the present application. In particular, in the case of multiple application prediction models meeting the threshold requirements, the optimal application prediction model can be selected according to the prediction performance indicators concerned by 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, not only suitable for the samples in the training set, but also suitable for new samples that have not participated in the training.

[0128] In one embodiment, when the expression level of UQCC2 of a lung cancer patient is higher than the diagnosis threshold, the lung cancer patient is judged to be an osimertinib-resistant lung cancer patient.

[0129] In one specific embodiment, gene expression in NCI-H1975 cells (Ctrl) and NCI-H1975 / OR cells (Osim) was detected and analyzed using MeRIP-seq and RNA-seq, where NCI-H1975 / OR cells (Osim) were osimertinib-resistant cells. Specific steps included: extracting total RNA from H1975 parental cells and NCI-H1975 / OR cells (Osim), isolating total RNA using TRIzol (Invitrogen), quantifying it using NanoDrop, and performing integrity testing using Bioanalyzer (Agilent); concentration >50 ng / μL, RIN value >7.0, OD260 / 280 >1.8, and total RNA >50 μg were considered satisfactory for downstream experiments. Poly(A) RNA was purified using Dynabeads Oligo(dT), and the captured RNA was fragmented under high temperature using a magnesium fragmentation kit. Then, it was separated from m 6 Partial RNA was bound by an A-specific antibody (Synaptic Systems). IP RNA was reverse transcribed using SuperScript II, followed by the synthesis of U-labeled second-strand DNA, and ligation products were amplified by PCR. Finally, paired-end sequencing (PE150) of the total RNA (input) and IP RNA library was performed using the Illumina NovaSeq 6000 platform (LC-Bio Technology Co., Ltd., China). Results are as follows... Figure 8 As shown, the expression level of UQCC2 in NCI-H1975 / OR cells (osim) was higher than that in NCI-H1975 cells (Ctrl).

[0130] In one specific embodiment, the protein expression of UQCC2 in NCI-H1975 cells and NCI-H1975 / OR cells (Ctrl) was determined by Western blotting. NCI-H1975 / OR cells (Ctrl) were 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. GraphPad Prism software was used to calculate significance. All statistical analyses used data from at least three independent experiments. Means of three or more experiments were compared using ANOVA or one-way ANOVA with Bonferroni correction. Two means were compared using an unpaired Student's t-test; *P < 0.05 was considered statistically significant. Experimental results are as follows: Figure 9 As shown, the expression level of UQCC2 in NCI-H1975 / OR cells (Ctrl) was significantly higher than that in NCI-H1975 cells.

[0131] Note: * (p < 0.05), ** (p < 0.01), *** (p < 0.001), **** (p < 0.0001)

[0132] Figure 2 This is a schematic diagram of a computer-aided diagnosis system for osimertinib-resistant lung cancer patients provided in an embodiment of this application. Specifically, the system includes:

[0133] Data acquisition unit: Expression level data of UQCC2 in lung cancer patients;

[0134] Data evaluation unit: The evaluation unit includes 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;

[0135] Drug resistance identification unit: Based on the expression level data of UQCC2 in lung cancer patients and the stored reference, an algorithm is used to determine whether the lung cancer patient is an osimertinib-resistant lung cancer patient.

[0136] Figure 3 This is a schematic diagram of a computer-aided diagnostic device for osimertinib-resistant lung cancer patients provided in an embodiment of this application. Specifically, the device includes:

[0137] The system includes a memory for storing program instructions and a processor for invoking the program instructions, which, when executed, implement the previously described method for diagnosing osimertinib-resistant lung cancer patients.

[0138] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for diagnosing osimertinib-resistant lung cancer patients as described above.

[0139] Figure 4 This is a schematic flowchart of a computer-aided method for predicting the efficacy of osimertinib-resistant lung cancer treatment, provided in an embodiment of this application. Specifically, the method includes the following steps:

[0140] S401: Receive input data, including UQCC2 expression level data after drug treatment in osimertinib-resistant lung cancer patients;

[0141] In one embodiment, the expression level of UQCC2 includes the protein expression level and nucleic acid expression level of UQCC2.

[0142] In one embodiment, the method for determining the protein expression level of UQCC2 includes, but is not limited to, Western blotting, immunohistochemistry, immunofluorescence, enzyme-linked immunosorbent assay (ELISA), mass spectrometry, and flow cytometry.

[0143] 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, DNA blotting, Northern blotting hybridization, and gene chip method.

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

[0145] In one embodiment, non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

[0146] In one embodiment, the samples collected from lung cancer patient UQCC2 are selected from at least one of blood, tissue, feces, and urine.

[0147] S402: Apply a machine learning model to the data to generate indicators of whether a drug is effective in treating lung cancer patients resistant to osimertinib;

[0148] In one embodiment, the machine learning model includes, but is not limited to, support vector machine learning models, linear discriminant analysis models, recursive feature removal models, predictive analysis of microarray models, logistic regression models, CART algorithms, flextree algorithms, LART algorithms, random forest algorithms, MART algorithms, machine learning algorithms, penalized regression methods, and combinations thereof.

[0149] In a preferred embodiment, the method for constructing the machine learning model includes:

[0150] The system receives or inputs UQCC2 expression level data from osimertinib-resistant lung cancer patients, processes the expression data using processing software, and constructs a prediction model based on a machine learning algorithm with a generalized linear regression model to predict the sensitive and insensitive prediction probabilities of osimertinib-resistant lung cancer patients, respectively.

[0151] The system determines whether the expressed data meets a preset judgment condition, wherein the judgment condition is that the sensitive prediction probability is greater than or equal to the non-sensitive prediction probability, in order to predict the therapeutic effect of the drug and output the prediction result.

[0152] In one embodiment, when the expressed data meets the judgment condition, the output prediction result is "the drug has a therapeutic effect on osimertinib-resistant lung cancer patients"; when the expressed data does not meet the judgment condition, the output prediction result is "the drug does not have a therapeutic effect on osimertinib-resistant lung cancer patients".

[0153] In one specific embodiment, the drug is selected from lometapetine.

[0154] In one specific embodiment, gene expression in NCI-H1975 and NCI-H1975 / OR cells (Osim) was detected and analyzed using MeRIP-seq and RNA-seq. NCI-H1975 / OR cells (Osim) were osimertinib-resistant cells treated with osimertinib. Specifically, reads obtained from sequencing IP and input libraries were aligned to a reference genome. Compared to the input library, the IP library showed a higher abundance and probability of reads falling onto the genome, forming a distinct read-rich region, termed a peak. Differential peak analysis was performed using exompeak software, and annotation was performed using ANNOVAR software. The results are as follows: Figure 8 As shown, the expression level of UQCC2 in NCI-H1975 / OR cells (Osim) was higher than that in parental NCI-H1975 cells.

[0155] In one specific embodiment, the protein expression of UQCC2 in NCI-H1975 / OR cells (treated with Ctrl, Osim, or Lomi alone) was determined by Western blotting. 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. Significance was calculated using GraphPad Prism software. Experimental results are shown below. Figure 9 As shown, lometabolic treatment significantly reduced the protein expression of UQCC2 in NCI-H1975 / OR cells (resistant to osimertinib).

[0156] Note: * (p < 0.05), ** (p < 0.01), *** (p < 0.001), **** (p < 0.0001)

[0157] In one specific embodiment, after selecting target genes of interest from the differential peak results, the m-values ​​on the target genes are analyzed using IGV software. 6 A methylation modification status was visualized and analyzed, and the results are as follows: Figure 10 As shown, compared with the parental cells of non-small cell lung cancer (H1975), UQCC2 m 6 A abundance changed in osimertinib-treated H1975 / OR cells (Osim), with UQCC2 m 6 The A modification primarily alters the intronic region during osimertinib resistance induction.

[0158] Figure 5 This is a schematic diagram of a computer-aided system for predicting the efficacy of osimertinib-resistant lung cancer treatment, provided in an embodiment of this application. Specifically, the system includes:

[0159] Data Acquisition Unit: UQCC2 expression level data in osimertinib-resistant lung cancer patients after drug treatment;

[0160] Data evaluation unit: The evaluation unit includes 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;

[0161] Data Analysis Unit: Based on the expression level data of UQCC2 in osimertinib-resistant lung cancer patients after drug treatment and the stored reference algorithm, the unit determines whether the drug has a therapeutic effect on osimertinib-resistant lung cancer patients.

[0162] This application provides a computer-aided device for predicting the efficacy of drug treatment in osimertinib-resistant lung cancer patients. Specifically, the device includes a memory and a processor. The memory stores program instructions, and the processor invokes the program instructions. When the program instructions are executed, the method for predicting the efficacy of drug treatment in osimertinib-resistant lung cancer patients as described above is implemented.

[0163] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described above for predicting the efficacy of drug treatment in patients with osimertinib-resistant lung cancer.

[0164] Figure 6 This is a schematic flowchart of a computer-aided drug screening method based on UQCC2 and METTL3 provided in an embodiment of this application. Specifically, the method includes:

[0165] S601: Obtaining data on UQCC2 and METTL3 proteins

[0166] In one embodiment, the UQCC2 and METTL3 protein data include molecular structure and binding sites.

[0167] In one specific embodiment, the expression relationship between METTL3 and UQCC2 was investigated by western blotting after knocking down the expression of METTL3 or UQCC2 genes in H1975 / OR cells. 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 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. Significance was calculated using GraphPad Prism software. Experimental results are shown below. Figure 11 As shown, knocking down METTL3 leads to downregulation of UQCC2, while knocking down UQCC2 does not affect the expression of METTL3, suggesting that UQCC2 may be a new downstream target of METTL3.

[0168] S602: Determine the binding sites of the UQCC2 and METTL3 proteins based on the aforementioned data.

[0169] In one embodiment, the binding site is a specific location in the molecule where a ligand can form a stable interaction. Protein binding sites are typically formed by the spatial arrangement of separate amino acid residues on a polypeptide chain, folded together by the polypeptide chain.

[0170] S603: Using computer-aided screening, candidate drugs that simultaneously target the binding sites of both substances are obtained.

[0171] In one embodiment, computer-aided drug screening is a technique that uses computer-aided drug design methods to screen drugs. It can help researchers quickly screen a large number of small molecule compounds for candidate drugs that have strong binding affinity to target proteins and potential pharmacological effects.

[0172] In one embodiment, molecular docking is a method for drug design based on the characteristics of the receptor and the interaction between the receptor and drug molecules. It is a theoretical simulation method that primarily studies intermolecular interactions (such as ligands and receptors) and predicts their binding modes and affinities. This method is widely used in the early stages of drug development to help researchers quickly screen compounds with potential pharmacological effects.

[0173] The molecular docking methods described primarily focus 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. Geometric matching calculations typically employ methods such as grid-based computation and fragment growth, while energy calculations utilize methods such as simulated annealing and genetic algorithms. Based on the degree and method of simplification, molecular docking methods can be categorized into rigid docking, semi-flexible docking, and flexible docking. In rigid docking, the conformation of the docking molecules remains unchanged during the calculation process; only their spatial position and orientation are altered. Semi-flexible docking allows for partial conformational changes during the calculation. Flexible docking allows for even more conformational changes.

[0174] In one embodiment, the molecular libraries used in virtual screening mainly include the following: ZINC, PubChem, DrugBank, ChEMBL, ChemDB, HMDB, BindingDB, and SMPDB. In addition, there are some commercial databases such as ChemDiv, Enamine, Lifechemicals, Specs, Chembridge, Maybridge, Microsource, Vitas-M, and Interbioscreen, which are also commonly used for virtual screening.

[0175] In one embodiment, the computer-aided screening process is as follows:

[0176] The small molecule compounds selected from the screening are molecularly docked with the METTL3 protein to calculate the affinity / binding energy of the target receptor and obtain a score. Candidate drugs are then ranked according to the score.

[0177] 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.

[0178] In one embodiment, the computer-aided screening process is as follows:

[0179] The molecular structures of UQCC2 and METTL3 proteins were obtained and input into a pharmacophore module library for matching. Based on the interaction patterns with UQCC2 and METTL3, all action sites were clustered to obtain pharmacophore models. The pharmacophore models were then input into a molecular compound database for high-throughput screening to obtain candidate drugs.

[0180] In one embodiment, the computer-aided screening process is as follows: 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 for a small molecule library with similar structures based on the molecular structure of the small molecule inhibitor; then, perform molecular docking of the small molecule library with similar structures to the binding site to obtain the score of the docking molecules; and finally, sort them to obtain candidate drugs.

[0181] In one embodiment, the computer-aided screening process involves screening protein analogs / antibodies / RNA drugs through the binding sites to obtain candidate drugs.

[0182] In one embodiment, the process and method for designing protein analogs, antibodies, and other drugs based on proteins can be summarized as follows:

[0183] Identifying the target protein: The first step is to identify the target protein, or target site, for which the drug is to be designed. Target sites can be known disease-related proteins, viral antigens, or other biomolecules.

[0184] Protein structure analysis: This involves analyzing the structure of a target protein to understand its three-dimensional conformation, surface configuration, subdomain structure, and other characteristics. This can be obtained using techniques such as X-ray crystallography and nuclear magnetic resonance.

[0185] Determining drug-protein interactions: Investigating the interaction mechanisms between drugs and target proteins, including binding sites, binding modes, and binding kinetics. This can be accomplished through computer simulations, laboratory experiments, and other methods.

[0186] Drug design: Based on the interaction mechanism between drugs and target proteins, drug molecules that can specifically bind to the target protein are designed. This includes selecting appropriate drug types, designing the chemical structure of the molecule, and optimizing the molecule's pharmacodynamic and pharmacokinetic properties.

[0187] Drug synthesis and validation: This involves preparing drug molecules through methods such as chemical synthesis and validating their biological activity, safety, and pharmacokinetic properties. This includes different stages such as cell experiments, animal experiments, and clinical trials.

[0188] In one embodiment, protein analogs refer to substances that have similar structure and function to a protein in the body. They can mimic the function of proteins, thus playing an important role in disease treatment. Antibodies are immunoglobulins produced by plasma cells differentiated from B lymphocytes in response to antigen stimulation by the body's immune system. They can specifically bind to the corresponding antigens. The structure of an antibody 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. Intracellular RNA can act as messenger RNA (mRNA) to guide protein synthesis, or as microRNA (miRNA) to regulate gene expression. Therefore, well-designed RNA drugs can be used to regulate gene expression in cells, thereby achieving the purpose of treating diseases.

[0189] In one embodiment, the inhibition rate is calculated after mixing the small molecule compound with UQCC2 protein solution, and the small molecule compound with inhibitory effect is screened.

[0190] In one embodiment, the general steps for conducting inhibitory activity experiments on small molecule compounds are as follows:

[0191] Selecting appropriate target proteins: Based on research objectives and disease goals, select suitable target proteins as experimental subjects. Ensure that the target proteins have potential interactions with the small molecule compounds under study.

[0192] Preparation of small molecule compounds: Synthesize or purchase the desired small molecule compounds, ensuring their purity and structural accuracy. If necessary, the small molecule compounds can be modified or altered to optimize their inhibitory activity.

[0193] Enzyme activity assays: 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 the assay method is reliable, sensitive, and accurately reflects the interaction between the small molecule compound and the target protein.

[0194] Experimental procedure: Mix different concentrations of small molecule compounds with the target protein, incubate under appropriate conditions for a period of time, and then measure the enzyme activity. Depending on the experimental design, control and experimental groups can be set up to compare the inhibitory activities of different small molecule compounds.

[0195] Data Analysis: Statistical analysis of experimental data was performed to determine the IC50 of small molecule compounds. 50 IC50 value (i.e., the concentration of a compound required to inhibit 50% of enzyme activity). 50 The smaller the value, the stronger the inhibitory activity of the small molecule compound. Furthermore, dose-response curves can be plotted to visually demonstrate the inhibitory activity of the small molecule compound.

[0196] Results Interpretation and Discussion: Based on the experimental results, the inhibitory activity of the small molecule compounds on the target proteins was analyzed, and their structures and properties were discussed. Possible binding modes, mechanisms of action, and comparisons with known inhibitors were explored. Furthermore, the effects of the small molecule compounds on cells or organisms can be further investigated to assess their potential drug candidate status.

[0197] In one embodiment, inhibitory activity assays were performed on candidate protein analogs / antibodies / RNA drugs, and unbiased screening of V5-labeled protein analogs / antibodies / RNA drugs and Flag-STING co-transfected cells was conducted using immunoprecipitation analysis.

[0198] In one embodiment, co-immunoprecipitation (Co-IP) is a classic method for studying protein-protein interactions based on the specific interaction between antibodies and antigens. It is an effective method for determining the physiological interactions between two proteins within intact cells. The principle is to use target-protein-specific antibodies to indirectly capture proteins that bind to a specific target protein to identify related protein-protein interactions in vivo. Specific antibodies capture the target protein in the sample, forming an antibody-target protein complex. The complex is then immobilized and precipitated using a bead-like support (e.g., Nanoab-Agarose) that binds to the antibody. Simultaneously, proteins interacting with the target protein are also precipitated. Finally, non-specific proteins bound to the target protein are washed away, and the analysis is performed by SDS-PAGE and Western blot detection.

[0199] 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 involves integrating two nucleic acid molecules into the same integration sequence and expressing them in the same transfected cell. It involves selecting the correct co-transfection reagent and using advanced lipid nanoparticle technology to achieve superior transfection performance and reproducible results.

[0200] In one embodiment, the drug screening technology using computer-aided drug screening includes one or more of the following: protein-small molecule docking, protein-protein docking, and protein-nucleic acid docking.

[0201] 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 using specific algorithms and programs. This process can be used to study the interactions between proteins and small molecules, as well as their potential biological functions. Computational simulations of protein-small molecule docking are typically performed using software such as DOCK. DOCK is a highly automated drug design software capable of docking small molecule ligands with biomolecular receptors. It employs a fragment-based scoring method, enabling rapid and accurate docking. RosettaDock is a commonly used protein-protein docking software that also uses a fragment-based scoring method, enabling rapid and accurate protein docking. This software allows for precise adjustment of side chain conformations during docking and considers various complex interactions, such as hydrogen bonds, ionic bonds, and hydrophobic interactions.

[0202] In one embodiment, protein-nucleic acid docking refers to a computational simulation process that uses specific algorithms and procedures to dock the structures of proteins and nucleic acids (such as DNA or RNA). This process can be used to study the interactions between proteins and nucleic acids, as well as their potential biological functions. Computational simulations of protein-nucleic acid docking are typically performed using software such as NAflex. NAflex is a software specifically developed for nucleic acid structure prediction and design, capable of accurately modeling and docking DNA or RNA molecules. NAflex employs a fragment-based scoring method, enabling rapid 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.

[0203] Figure 7 This is a schematic diagram of a computer-aided drug screening system based on UQCC2 and METTL3 provided in an embodiment of this application. Specifically, the system includes:

[0204] Data acquisition unit: Acquires data on UQCC2 and METTL3 proteins;

[0205] 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;

[0206] Drug screening unit: Uses computer-aided drug screening methods to obtain candidate drugs targeting the binding site;

[0207] This 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. The memory is used to store program instructions, and the processor is used to call the program instructions. When the program instructions are executed, the computer-aided drug screening method based on UQCC2 and METTL3 as described above is implemented.

[0208] This application also provides a computer-readable storage medium having a computer program thereon, which, when executed by a processor, implements the computer-aided drug screening method based on UQCC2 and METTL3 as described above.

[0209] This application provides a method for regulating autophagy, the method comprising administering a regulating agent of UQCC2;

[0210] In one embodiment, the method includes regulating autophagy-related biomarkers, including autophagy markers, autophagy-related genes and proteins, and lysosomal biomarkers.

[0211] 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, and Beclin1; the lysosomal markers include, but are not limited to, LAMP1 and LAMP2, and cathepsins. As one embodiment of this application, the autophagy-related markers are selected from LC3 and p62.

[0212] In one embodiment, the method includes increasing the expression level of autophagy-related biomarkers. The methods for detecting the expression level of the autophagy-related biomarkers include Western blotting, immunofluorescence staining, RT-PCR, immunohistochemistry, and ELISA. As one embodiment of this application, the detection method is selected from Western blotting and immunofluorescence staining.

[0213] In one embodiment, the reagent includes an inhibitor of UQCC2. The inhibitor refers to any substance that can inhibit the activity of UQCC2 protein, inhibit the stability of the UQCC2 gene or protein, inhibit the expression level of UQCC2, inhibit the effective action time of UQCC2 protein, or inhibit the activity of UQCC2. As one embodiment of this application, the "inhibitor" is a substance that inhibits the expression level of UQCC2.

[0214] In one embodiment, the inhibitor includes nucleic acid inhibitors, protein inhibitors, proteolytic enzymes, protein-binding molecules, and combinations thereof.

[0215] In one embodiment, the nucleic acid inhibitor is selected from: interfering molecules that target UQCC2 or its transcripts and are capable of inhibiting UQCC2 gene expression or transcription, including but not limited to shRNA, siRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, CRISPR / Cas9, or constructs that can express or form said shRNA, siRNA, ribozymes, antisense oligonucleotides, dsRNA, microRNA, zinc fingers, or 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.

[0216] In one embodiment, siRNA may include partially purified RNA, substantially pure RNA, synthetic RNA, or recombinant RNA, as well as RNA modified to differ from native RNA by adding, deleting, substituting, and / or altering one or more nucleotides. Such alterations may include adding non-nucleotide substances, such as adding to the terminus of the siRNA or to one or more internal nucleotides of the siRNA; modifications that make the siRNA resistant to nuclease digestion (e.g., using 2'-substituted ribonucleotides or modifying the sugar phosphate backbone); or replacing one or more nucleotides in the siRNA with deoxyribonucleotides.

[0217] In one embodiment, a ribozyme is an RNA that can be engineered to enzymatically cleave and inactivate other RNA targets in a specific sequence-dependent manner. Ribozymes and their 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 incorporated herein by reference in its entirety. By cleaving the target RNA, the ribozyme inhibits translation, thereby preventing the expression of the target gene. Ribozymes can be chemically synthesized in the laboratory using methods known in the art and their structure modified to increase their stability and catalytic activity. Alternatively, the ribozyme gene can be introduced into the cell via gene delivery mechanisms known in the art.

[0218] In one embodiment, the antisense oligonucleotide (antisense nucleic acid sequence) may comprise a nucleotide sequence 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 to a portion thereof. The length of the antisense oligonucleotide may, for example, be about 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80 or more nucleotides.

[0219] In one specific embodiment, the effect of knockdown of UQCC2 in H1975 / OR cells on the expression of autophagy markers LC3 and p62 was investigated by Western blotting. siUQCC2 (5'-3'): sense strand: GACUCCAUCAAACUACUATT; antisense strand: 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), and 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. Experimental results are shown below. Figure 11 As shown, knocking down UQCC2 leads to upregulation of autophagosome markers LC3 and p62, further demonstrating that knocking down UQCC2 significantly activates autophagy.

[0220] Note: * (p < 0.05), ** (p < 0.01), *** (p < 0.001), **** (p < 0.0001)

[0221] In one specific embodiment, the expression of the autophagy marker LC3 in H1975 / OR cells after UQCC2 knockdown was analyzed by immunofluorescence assay. siUQCC2 (5'-3'): Sensitive strand: GACUCCAUUCAAACUACUATT; Antisense strand: UAGUAGUUUGAAUGGAGUCTT. Cells were fixed with 4% formaldehyde, infiltrated with 0.2% Triton X-100, and blocked with 5% bovine serum albumin. They were then incubated overnight at -4°C with the specified antibody and stained with fluorescently conjugated secondary antibody and DAPI solution. Cells were imaged under a fluorescence microscope (Leica DM6B Thunder). Anti-LC3 (1:100; antibody used was Proteintech, catalog number 81004-1-RR). Experimental results are as follows. Figure 12 As shown, knocking down UQCC2 leads to upregulation of the autophagosome marker LC3, further demonstrating that knocking down UQCC2 significantly activates autophagy.

[0222] The embodiments of this application provide any of the following applications:

[0223] 1) Application of reagents for detecting UQCC2 in the preparation of products for diagnosing autophagy activation;

[0224] 2) Application of reagents that regulate UQCC2 in the preparation of products that regulate autophagy;

[0225] 3) Application of reagents for detecting UQCC2 in the preparation of products for diagnosing osimertinib-resistant lung cancer.

[0226] In one embodiment, the reagent is 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.

[0227] In one embodiment, a probe refers to a molecule capable of binding to a specific sequence, subsequence, or other portion of another molecule. Unless otherwise specified, the term "probe" generally refers to a polynucleotide probe capable of binding to another polynucleotide (often called 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. Probes 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.

[0228] In one embodiment, a binder refers to all or part of a protein (protein, protein-like, or protein-containing) molecule capable of binding to a membrane protein using specific intermolecular interactions. Protein binders are, for example, protein receptors, protein-binding lectins, proteins-specific antibodies, protein-specific peptide bodies, bispecific dual binders, or bispecific antibody forms. More specifically, the term "binding agent" refers to a polypeptide, and more specifically, a protein domain. A suitable protein domain is an element of the overall protein structure; it is self-stabilizing and folds independently of the rest of the protein chain and is generally referred to as a "binding domain." The length of such binding domains varies from about 25 amino acids to 500 amino acids and more. Many binding domains can be classified as folds and are identifiable, identifiable, 3-D structures. Some folds are so common in many different proteins that they have been given specific names.

[0229] The reagents provided in this application for detecting UQCC2 also include detectable markers. A detectable marker is a composition capable of generating a detectable signal indicating the presence of a target polynucleotide in a sample. Suitable markers include, but are not limited to, radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent components, magnetic particles, and bioluminescent components. Therefore, a marker is any composition detectable by a device or method, including but not limited to spectroscopic, photochemical, biochemical, immunochemical, electrochemical, optical, chemical detection devices, or any other suitable device. In some embodiments, the marker can be detected visually without the aid of a device.

[0230] In one embodiment, the radioactive isotope includes, but is 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, and acetylcholinesterase. Fluorescent molecules include, but are not limited to, FITC, rhodamine, and lanthanide phosphors.

[0231] In one embodiment, the product includes, but is not limited to, chips, reagent kits, test strips, or nucleic acid membrane strips.

[0232] In one embodiment, a chip, also referred to as an array, refers to a solid support containing linked nucleic acid or peptide probes. Arrays typically contain a variety of different nucleic acid or peptide probes attached to a substrate surface at different known locations. These arrays, also known as “microarrays,” can typically be produced using mechanosynthesis or photoguided synthesis methods, which combine photolithography and solid-phase synthesis. Arrays can comprise flat surfaces or can be nucleic acids or peptides on beads, gels, polymer surfaces, fibers such as optical fibers, glass, or any other suitable substrate. Arrays can be packaged in a manner that allows for diagnostic or other manipulation of fully functional devices.

[0233] In one embodiment, the nucleic acid membrane strip includes a substrate and an oligonucleotide probe immobilized on the substrate; the substrate can be any substrate suitable for immobilizing the oligonucleotide probe, such as a nylon membrane, nitrocellulose membrane, polypropylene membrane, glass slide, silicone wafer, micro-magnetic beads, etc.

[0234] The kit described in this application includes reagents for detecting the UQCC2 gene or protein, and one or more substances selected from the group consisting of: containers, instructions for use, positive controls, negative controls, buffers, auxiliaries, solvents, preservatives, or protein stabilizers. The components of the kit may be packaged in an aqueous medium or in a lyophilized form. Suitable containers in the kit typically include at least one vial, test tube, long-necked flask, PET bottle, syringe, or other container in which one component can be placed, and preferably, appropriately aliquoted. When more than one component is present in the kit, the kit will also typically 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 single vial. The kit of this application will also typically include a container for containing the reactants, sealed for commercial sale. Such a container may include injection-molded or blow-molded plastic containers in which the desired vials can be held.

[0235] The kits described in this application include, but are not limited to, qPCR kits, ELISA kits, Western blot detection kits, immunochromatographic detection kits, immunohistochemical detection kits, flow cytometry analysis kits, and electrochemiluminescence detection kits.

[0236] This application may use any known method to detect the expression level of the UQCC2 gene or protein, including but not limited to RT-PCR, qRT-PCR, microarray detection, DNA blotting, in situ hybridization, and immunoblotting.

[0237] In the various embodiments of this application, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0238] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0239] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. A method for computer-aided screening of drugs based on UQCC2 and METTL3, characterized in that, The method comprises: obtaining UQCC2 protein and METTL3 protein data; determining the binding sites of the two according to the UQCC2 protein and METTL3 protein data; obtaining the candidate drug that targets the binding sites of the two through computer-aided screening; The process of the computer-aided screening is: obtaining the binding sites of the UQCC2 protein and METTL3 protein complex; screening small molecule compounds with similar structures in a molecular database based on the spatial structure of the binding sites of the UQCC2 protein and METTL3 protein complex; performing molecular docking calculation on the small molecule compounds screened to obtain the scores of the affinity / binding energy of the target receptors, and sorting the scores to obtain the candidate drug; sorting the scores, and selecting the top n small molecules to obtain the candidate compound, wherein n is a natural number greater than or equal to 1; The drug is a drug for treating osimertinib-resistant non-small cell lung cancer.

2. The method of claim 1, wherein, The process of the computer-aided screening is to screen protein analogs / antibodies / RNA drugs through the binding sites to obtain the candidate drug.

3. The method of claim 2, wherein, The computer-aided screening of the drug is based on the active sites of the METTL3 protein and the UQCC2 protein complex to screen protein analogs / antibodies / RNA drugs, or to screen proteins / antibodies / RNA drugs similar to METTL3 to obtain the candidate drug.

4. The method of claim 2, wherein, The computer-aided screening of the drug is based on the active sites or target sites of the METTL3 protein and the UQCC2 protein complex to screen small molecule inhibitors or antibodies to obtain the candidate drug.

5. The method of claim 4, wherein, The candidate drug of the small molecule type is tested by an inhibition activity experiment, the inhibition rate is calculated after mixing the small molecule compound with the UQCC2 protein solution, and the small molecule compound with an inhibitory effect is screened.

6. A system for computer-aided screening of drugs based on UQCC2 and METTL3, characterized by, The system comprises: a data acquisition unit that acquires UQCC2 protein and METTL3 protein data; a site determination unit that selects the spatial structure of the UQCC2 protein and METTL3 protein complex to determine the binding sites of the complex as the binding sites of the targeted drug; a drug screening unit that adopts a computer-aided drug screening method to obtain a candidate drug that targets the binding sites; The process of the computer-aided screening is: obtaining the binding sites of the UQCC2 protein and METTL3 protein complex; screening small molecule compounds with similar structures in a molecular database based on the spatial structure of the binding sites of the UQCC2 protein and METTL3 protein complex; performing molecular docking calculation on the small molecule compounds screened to obtain the scores of the affinity / binding energy of the target receptors, and sorting the scores to obtain the candidate drug; sorting the scores, and selecting the top n small molecules to obtain the candidate compound, wherein n is a natural number greater than or equal to 1; The drug is a drug for treating osimertinib-resistant non-small cell lung cancer.

7. A computer-aided drug screening device based on UQCC2 and METTL3, characterized by, The device comprises a memory for storing program instructions; and a processor for invoking the program instructions, which, when executed, implement the method for computer-aided screening of drugs based on UQCC2 and METTL3 according to any one of claims 1-5.

8. A computer readable storage medium having a computer program thereon, characterized in that The computer program comprises: The computer program, when executed by a processor, implements the method for computer-aided screening of drugs based on UQCC2 and METTL3 according to any one of claims 1-5.

9. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the method according to any one of claims 1-5.

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