Biomarkers for predicting the efficacy of anlotinib treatment in patients with lung cancer and their applications
By isolating and detecting the expression levels of miR-30a-3p and/or miR-362-5p in plasma small extracellular vesicles from the blood samples of lung cancer patients, a prediction model was established, which solved the problem that the prior art cannot effectively predict the efficacy of anlotinib treatment in lung cancer patients, and achieved efficient evaluation of the efficacy of targeted lung cancer treatment and accurate screening of the benefit population.
Patent Information
- Application Number
- CN202410563123.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-08
- Publication Date
- 2025-04-01
AI Technical Summary
The prior art cannot effectively predict the efficacy of anlotinib treatment in lung cancer patients, and there is a lack of biomarkers that can be used to screen targeted therapy benefit populations.
By isolating and detecting the expression levels of miR-30a-3p and/or miR-362-5p in plasma small extracellular vesicles from blood samples of patients with advanced non-small cell lung cancer, predictive models are established using small RNA sequencing technology and bioinformatic analysis to predict patients' response to anlotinib treatment.
It was found that the expression levels of miR-30a-3p and miR-362-5p can effectively predict the efficacy of anlotinib treatment in lung cancer patients, and screen patients who may benefit from this treatment plan, which improved the efficacy evaluation ability of targeted lung cancer therapy and the specificity of screening for benefit populations.
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Figure CN119736385A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of biotechnology and relates to biomarkers and applications thereof for predicting the therapeutic effect of lung cancer patients after anlotinib treatment. Background Art
[0002] Lung cancer includes small cell lung cancer and non-small cell lung cancer, of which non-small cell lung cancer (NSCLC) includes adenocarcinoma, squamous cell carcinoma and large cell carcinoma tissue subtypes, accounting for about 85% of all lung cancers. The early symptoms of lung cancer are extremely hidden, and most lung cancer patients are already in the advanced stage when they are first diagnosed. Therefore, reliable detection of biomarkers and treatment strategies are urgently needed to improve the survival and quality of life of lung cancer patients.
[0003] Small extracellular vesicles are rich in biological molecules such as nucleic acids, lipids and proteins. They are also called exosomes. They are double-layer membrane structures with a diameter of about 30-150nm that originate from cells and are released into the extracellular matrix. Exosomes exist in various types of body fluids and can participate in information transmission and material exchange, playing a pivotal role in the occurrence and development of diseases. At the same time, exosomes released from tumor cells have been used as biomarkers in the diagnosis, prognosis monitoring, graded management and personalized treatment of cancer patients. Studies have shown that the expression levels of plasma exosomes hsa-miR-320d, hsa-miR-320c and hsa-miR-320b in the PD group of patients with advanced EGFR / ALK-negative NSCLC after PD-1 / PD-L1 inhibitor immunotherapy were significantly increased compared with the PR group. These miRNAs can be used as potential biomarkers for predicting the efficacy of immunotherapy for advanced non-small cell lung cancer (PMID: 31959728). However, to date, there is still a lack of biomarker research that can predict the efficacy of targeted therapy for lung cancer and screen the beneficiary population.
[0004] In summary, currently, imaging techniques are widely used in clinical practice to evaluate the efficacy of lung cancer patients after surgery, radiotherapy, chemotherapy, targeted therapy, immunotherapy and combined therapy, and tumor markers are used in combination. However, these methods have low specificity and accuracy in detecting efficacy, and existing tumor markers cannot be used to predict the efficacy of lung cancer targeted therapy and screen the beneficiary population of lung cancer targeted therapy. Currently, there are no biomarkers that can assist conventional imaging techniques and tumor markers to improve the efficacy evaluation ability of lung cancer targeted therapy and the specificity of screening the beneficiary population. Therefore, it is of great significance to explore biomarkers that can be used to predict the efficacy of advanced non-small cell lung cancer patients after drug treatment and screen the beneficiary population of lung cancer. Summary of the invention
[0005] In view of the deficiencies of the prior art and actual needs, the present invention provides biomarkers for predicting the efficacy of lung cancer patients after anlotinib treatment and their applications, in order to improve the efficacy evaluation ability of lung cancer targeted therapy and the specificity of screening the beneficiary population.
[0006] To achieve the above object, the present invention adopts the following technical solutions:
[0007] In a first aspect, the present invention provides a biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment, wherein the biomarker comprises miR-30a-3p and / or miR-362-5p.
[0008] In the present invention, the blood of patients clinically diagnosed with advanced lung cancer is used as the research sample. The blood samples of the patients are collected before administration. After the patients are treated with anlotinib, they can be divided into a progression group and a response group according to imaging methods. Then, the plasma small extracellular vesicle miRNA is separated, and the expression of plasma small extracellular vesicle miRNA in the two groups of patients is detected by small RNA sequencing technology and bioinformatics analysis, so as to explore the characteristics of plasma small extracellular vesicle miRNA as the best biomarker for predicting the efficacy of advanced non-small cell lung cancer patients after anlotinib treatment and screening the lung cancer beneficiary population. In the training group and validation group, it was found that the expression of miR-30a-3p and miR-362-5p was significantly increased in the response group, that is, patients with high expression of miR-30a-3p and / or miR-362-5p would have stable disease after anlotinib targeted therapy and could benefit from the anlotinib targeted therapy; while patients with low expression of lung cancer plasma exosome markers miR-30a-3p and / or miR-362-5p would have disease progression after anlotinib targeted therapy and could not benefit from the anlotinib targeted therapy.
[0009] In a second aspect, the present invention provides the use of the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment and / or its detection product as described in the first aspect in the preparation of a product for predicting the efficacy of lung cancer patients after anlotinib treatment.
[0010] Preferably, the detection product includes a reagent and / or instrument for detecting the expression level of the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in claim 1.
[0011] It is understood that the products commonly used in the art for detecting miRNA levels in exosomes are applicable to the present invention, such as the commercially available exosome extraction reagent (EXO Quick-TC TM Exosome Isolation Reagent (System Biosciences, USA), etc.) as well as second-generation sequencing technology products, miRNA chips or third-generation sequencing technology products, etc.
[0012] Preferably, the reagents and / or instruments include reagents and / or instruments for small RNA sequencing.
[0013] The analysis methods used are the trimmed mean of M-values (TMM) and the limma-voom method in the limma analysis package to establish the model, etc., and can also be linear regression, support vector machine, least absolute shrinkage and selection operator (LASSO, The Least Absolute Shrinkage and Selectionator operator) and neural network, etc.
[0014] In a third aspect, the present invention provides a product for predicting the efficacy of lung cancer patients after anlotinib treatment, the product comprising a reagent and / or instrument for detecting the expression level of the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in the first aspect.
[0015] In a fourth aspect, the present invention provides a method for constructing a prediction model for the efficacy of lung cancer patients after anlotinib treatment, the method comprising: obtaining expression level data of a target marker in samples that benefit and do not benefit from anlotinib treatment as a training set, wherein the target marker is the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in the first aspect; and constructing a prediction model using a machine learning algorithm based on the classification information of the sample and the level data of the target marker.
[0016] Preferably, the machine learning algorithm includes the trimmed mean of M-values (TMM) and the limma-voom method in the limma analysis package.
[0017] In a fifth aspect, the present invention provides a prediction model for the therapeutic effect of lung cancer patients after anlotinib treatment, characterized in that the prediction model is obtained by the method for constructing a prediction model for the therapeutic effect of lung cancer patients after anlotinib treatment as described in the fourth aspect.
[0018] In a sixth aspect, the present invention provides a device for predicting the therapeutic effect of lung cancer patients after anlotinib treatment, the device comprising:
[0019] A data module, used to provide expression level data of a target marker of a sample to be tested, wherein the target marker is the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in the first aspect;
[0020] A scoring module, used to obtain a probability value through a prediction model based on the expression level data of the target marker of the sample to be tested; wherein the prediction model is the prediction model of the efficacy of lung cancer patients after anlotinib treatment as described in claim 7;
[0021] A judgment module is used to predict the therapeutic efficacy of anlotinib based on the probability value.
[0022] In a seventh aspect, the present invention provides an electronic device comprising one or more processors and a memory for storing executable instructions, wherein the one or more processors are configured to call the executable instructions stored in the memory to implement the function of the device for predicting the efficacy of lung cancer patients after anlotinib treatment as described in the sixth aspect.
[0023] In an eighth aspect, the present invention provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the functions of the device for predicting the therapeutic effect of lung cancer patients after anlotinib treatment as described in the sixth aspect.
[0024] Compared with the prior art, the present invention has the following beneficial effects:
[0025] The present invention uses the blood of patients clinically diagnosed with advanced lung cancer as research samples. Blood samples of patients are collected before administration. After the patients are treated with anlotinib, they can be divided into a progression group and a response group according to imaging methods. Then, plasma small extracellular vesicle miRNA is separated, and the expression of plasma small extracellular vesicle miRNA in the two groups of patients is detected by small RNA sequencing technology and bioinformatics analysis, so as to explore the characteristics of plasma small extracellular vesicle miRNA as the best biomarker for predicting the efficacy of patients with advanced non-small cell lung cancer after anlotinib treatment and screening the lung cancer beneficiary population. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 HE and IHC results of pathological tissues of lung cancer patients;
[0027] Figure 2 This is the electron microscopic morphological structure of small extracellular vesicles;
[0028] Figure 3 This is the particle size distribution diagram of plasma small extracellular vesicles NTA;
[0029] Figure 4 This is the expression diagram of plasma small extracellular vesicle miR-30a-3p between the response group and the progression group of anlotinib targeted therapy in the training group, with a p value of 0.0329;
[0030] Figure 5This is the expression diagram of plasma small extracellular vesicle miR-362-5p between the response group and the progression group of anlotinib targeted therapy in the training group, with a p value of 0.0467;
[0031] Figure 6 To verify the expression of plasma small extracellular vesicle miR-30a-3p between the response group and the progression group of anlotinib targeted therapy, the p value was 0.0235;
[0032] Figure 7 To verify the expression of plasma small extracellular vesicle miR-362-5p between the anlotinib targeted therapy response group and the progression group, the p value was 0.0383. DETAILED DESCRIPTION
[0033] To further illustrate the technical means and effects of the present invention, the present invention is further described below in conjunction with the embodiments and drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention, rather than to limit the present invention.
[0034] Example 1
[0035] This example explores biomarkers for predicting the efficacy of anlotinib targeted therapy for lung cancer.
[0036] (1) Study cohort and clinical information
[0037] This example includes 20 patients diagnosed with lung cancer by clinical examination. Blood samples were collected from these patients before drug treatment. Patients must meet the following conditions before enrollment: Patients were diagnosed with lung adenocarcinoma by pathology and were receiving anlotinib treatment for the first time. Pathological diagnosis results were available before anlotinib administration. HE and IHC results of representative patients are shown in Figure 1 After one month of treatment with anlotinib, 14 lung cancer patients showed stable disease (SD) and another 6 lung cancer patients showed progressive disease (PD).
[0038] (2) Characteristics of plasma small extracellular vesicles
[0039] 1) Blood Collection and Extraction of Small Extracellular Vesicles
[0040] Blood samples were collected from patients before drug treatment and stored in 10 mL vacuum blood collection tubes (REF367525, BD, USA). After being gently inverted several times, the tubes were placed vertically and processed using a two-step centrifugation method: first, centrifuged at 1600 g and 4 °C for 10 min. After completion, the plasma hemolysis grade was determined and recorded. If it was less than grade 4, it was included in the subsequent study; then the plasma supernatant obtained from the above centrifugation was centrifuged at 16000 g and 4 °C for 15 min, and the supernatant was carefully aspirated and dispensed into new 1.5 mL centrifuge tubes and stored in a -80 °C refrigerator for subsequent experiments. Plasma small extracellular vesicles were extracted using exosome separation reagents. The frozen plasma samples were taken out of the -80°C freezer and placed in a 37°C water bath until completely thawed. The precipitated impurities were removed by centrifugation at 12000g and 4°C for 10 min and the plasma supernatant was carefully collected. The supernatant was added to a 0.45 μm filter column (Costar, CLS8163-100EA, Corning, USA) and centrifuged at the same speed for 5 min to collect the plasma filtrate. The plasma was then filtered through a 0.22 μm filter column (Costar, CLS8161-100EA, USA) under the above conditions, and the filtered plasma was transferred to a new 1.5 mL centrifuge tube and the volume was measured. 1 / 4 volume of L-type exosome precipitant (L3525, 3DMed, Shanghai, China) was added to the above plasma, vortexed and placed in a 4°C refrigerator for 30 min, then centrifuged at 4700g and 4°C for 30 min, and then the supernatant was discarded, and the small extracellular vesicles accumulated at the bottom of the tube were resuspended by pipetting with 200 μL PBS (phosphate buffer saline).
[0041] 2) Characterization of plasma small extracellular vesicles
[0042] In this embodiment, the morphological structure of plasma exosomes was detected by transmission electron microscopy (TEM). The prepared 4% paraformaldehyde was added to the suspension of plasma small extracellular vesicles resuspended in PBS for fixation, and then added to the carbon-coated copper grid of the electron microscope. After washing twice with PBS, it was washed with PBS containing glycine (50mM) for 3 minutes and PBS containing 0.5% BSA for 10 minutes, and then stained with 2% uranyl acetate. The morphological characteristics of plasma small extracellular vesicles were detected using a transmission electron microscope (H-7650, Hitachi High-Technologies, Japan).
[0043] Nanoparticle Tracking Analysis (NTA) was used to detect the particle size distribution of plasma exosomes. The resuspended plasma exosomes were diluted to 1×10 7 / mL~1×10 9 / mL and pipet to mix. Turn on the NTA instrument (NanoSight NS300, Malvern, UK) and add the above sample to the sample chamber. Use the 488nm excitation module and set the camera lens parameters: shutter value 890, gain value 146, detection threshold 7. At least 200 complete tracks were analyzed and obtained for each video, and nanoparticle tracking data analysis of plasma small extracellular vesicles was performed according to the NTA analysis software (version 2.3).
[0044] Transmission electron microscopy results showed that the extracted small extracellular vesicles showed a typical "horseshoe-shaped" morphology (see Figure 2 The average particle size of the extracted plasma small extracellular vesicles by NTA analysis is consistent with the particle size range of small extracellular vesicles (see Figure 3 ).
[0045] (3) Extraction and expression detection of plasma extracellular vesicle miRNA
[0046] Small RNA sequencing technology was used to detect the expression level of plasma small extracellular vesicle miRNA in lung cancer patients.
[0047] 1) Extraction of miRNA from plasma extracellular vesicles of patients
[0048] The miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai, China) was used to extract plasma extracellular vesicle miRNA from lung cancer patients, and the total amount and fragment distribution of extracellular vesicle miRNA were detected using a 2100 analyzer and matching chips and reagents (5067-1548, Agilent, USA). The specific experimental operation process refers to the instruction manual of the kit.
[0049] 2) Detection of plasma small extracellular vesicle miRNA expression
[0050] In order to detect the expression level of plasma small extracellular vesicle miRNA in lung cancer patients, small RNA sequencing technology was used for detection. First, the plasma small extracellular vesicle miRNA library was prepared using NEBNext, Multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA), and the library was purified using the NucleoSpin Gel and PCRClean-up (740609.50, QIAGEN, Germany) kit. The specific operation procedures for library construction and purification refer to the operation manuals of the above two products. In brief, the total volume of miRNA loading does not exceed 6μL and the total amount is 100ng, and 3' adapter ligation, hybridization reverse transcription primer, 5' adapter ligation, reverse transcription, PCR amplification and PCR product purification are carried out in sequence. Finally, the concentration of the library was detected by using Qubit 4.0 fluorescence quantifier (Q32854, Thermo Fisher, USA) and the supporting reagent Qubit dsDNA HSAssay Kit (Q32854, Thermofisher, USA), and the peak shape of the library DNA was detected by using 2100 analyzer (5067-4626, Agilent, USA) and the supporting chip and reagent High Sensitivity DNAKit & Reagents (5067-4626, Agilent, USA). Finally, the sequencing was performed using the Illumina HiSeqX sequencing platform (sequencing strategy was PE150).
[0051] 3) Sequencing data analysis process
[0052] Based on small RNA sequencing detection technology, bioinformatics analysis calculates the expression of small extracellular vesicle miRNA in patient plasma. The sequencing data analysis process is as follows:
[0053] a) Sequencing data alignment: After removing the sequencing adapters of the Small RNA-seq data, the sequencing data were aligned to the human reference genome hg19 (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ) using BWA software (version: 0.7.12-r1039), and the number of reads aligned to miRNA was counted.
[0054] b) miRNA annotation: miRNAs were annotated using the Gencode v25 and miRBase v21 databases, and those annotated as known mature miRNAs were retained for subsequent analysis.
[0055] c) miRNA filtering. Mature miRNAs with a length of less than or equal to 30 nt and at least 2 reads per sample were retained for subsequent analysis;
[0056] d) Standardization of miRNA expression: The trimmed mean of M-values (TMM) and limma-voom methods in the limma analysis package in R language were used to standardize the miRNA expression of samples.
[0057] (4) Discovery of biomarkers
[0058] Based on the expression of plasma small extracellular vesicle miRNAs in lung cancer patients, the samples were grouped according to the imaging evaluation results after anlotinib targeted therapy, and statistical methods were used to discover plasma small extracellular vesicle miRNAs as biomarkers that can be used for lung cancer targeted therapy efficacy evaluation and benefit population screening. The process is as follows:
[0059] 1) Sample grouping. According to the imaging evaluation results after anlotinib targeted therapy, the patients were divided into two groups: progression group (PD) and response group (SD), and then divided into two cohorts: training group and validation group, including 10 cases in the training group (3 cases of PD and 7 cases of SD) and 10 cases in the validation group (3 cases of PD and 7 cases of SD).
[0060] 2) Candidate molecular markers. The linear model fitting (limma-voom) method in the limma analysis package of R language was used to analyze the miRNAs with significantly different expression levels between the progression group and the response group in the training cohort. The miRNAs with expression levels greater than 32 per million mapped reads (CPM, Counts Per Million), a change of more than 1.5 times between the two groups, and a P value less than or equal to 0.05 were selected as candidate molecular markers.
[0061] The expression level of plasma small extracellular vesicle miRNA is the average log of the second-generation sequencing results of the progress group miRNA in the training cohort (all vesicle miRNAs detected after library construction and sequencing and then using bioinformatics statistics). 2 CPM values are referenced, where miRNA expression levels are above the mean log 2 The patients with CPM values were in the high-level expression group, and the expression level of the marker was lower than the average log 2 Patients with CPM values were in the low-level expression group.
[0062] Based on the expression of miRNAs in plasma extracellular vesicles of lung cancer patients, the samples were grouped according to the imaging evaluation results after anlotinib targeted therapy. Two plasma extracellular vesicle miRNAs were analyzed in the training group using statistical methods, including miR-30a-3p ( Figure 4 , p value was 0.0329) and miR-362-5p( Figure 5 , p value was 0.0467) showed the most significant difference in expression between the two groups. Among them, miR-30a-3p and miR-362-5p were significantly increased in the response group. Further, in another validation cohort, miR-30a-3p ( Figure 6 , p value was 0.0235) and miR-362-5p( Figure 7 , p value is 0.0383) was significantly increased in the response group. It can be seen that patients with high expression of plasma small extracellular vesicle markers miR-30a-3p and / or miR-362-5p in lung cancer will have stable disease after anlotinib targeted therapy and can benefit from anlotinib targeted therapy; while patients with low expression of plasma exosome markers miR-30a-3p and miR-362-5p in lung cancer will have disease progression after anlotinib targeted therapy and cannot benefit from anlotinib targeted therapy. Therefore, any one or a combination of at least two of plasma exosome miR-30a-3p or miR-362-5p has the potential to become an exosome miRNA biomarker for predicting the efficacy of anlotinib targeted therapy for lung cancer.
[0063] In summary, the present invention is based on lung cancer patients as the research subject, and uses their blood-derived plasma small extracellular vesicle miRNA biomarkers to supplement the imaging results of lung cancer patients, promote the screening of lung cancer patients who can benefit from targeted drug therapy to improve the quality of life and survival of lung cancer patients; small RNA sequencing technology and bioinformatics analysis were used to find the differentially expressed plasma small extracellular vesicle miRNAs in the progression group and response group distinguished after anlotinib administration, which were miR-30a-3p and miR-362-5p respectively; it was found that plasma small extracellular vesicle miR-30a-3p and miR-362-5p can be used as biomarkers to predict the efficacy evaluation of lung cancer patients after anlotinib treatment and screen out lung cancer beneficiaries.
[0064] The applicant declares that the present invention illustrates the detailed method of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned detailed method, that is, it does not mean that the present invention must rely on the above-mentioned detailed method to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of various raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.
Claims
1. A biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment, characterized in that: The biomarkers include miR-30a-3p and / or miR-362-5p.
2. Use of the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment and / or its detection product according to claim 1 in the preparation of a product for predicting the efficacy of lung cancer patients after anlotinib treatment.
3. The use according to claim 2, characterized in that: The detection product comprises a reagent and / or instrument for detecting the expression level of the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in claim 1; Preferably, the reagents and / or instruments include reagents and / or instruments for small RNA sequencing.
4. A product for predicting the efficacy of lung cancer patients after anlotinib treatment, characterized in that: The product includes reagents and / or instruments for detecting the expression level of the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in claim 1.
5. A method for constructing a prediction model for the efficacy of lung cancer patients after anlotinib treatment, characterized in that: The method comprises: obtaining expression level data of a target marker in samples that benefit and do not benefit from anlotinib treatment as a training set, wherein the target marker is the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in claim 1; A prediction model is constructed using a machine learning algorithm based on the classification information of the sample and the level data of the target marker.
6. The method for constructing a prediction model for the therapeutic effect of lung cancer patients after anlotinib treatment according to claim 5, characterized in that: The machine learning algorithms include the M-value weighted truncated mean method and the limma-voom method in the limma analysis package.
7. A prediction model for the efficacy of lung cancer patients after anlotinib treatment, characterized in that: The prediction model is obtained by the method for constructing a prediction model for the therapeutic effect of lung cancer patients after anlotinib treatment as described in claim 5 or 6.
8. A device for predicting the therapeutic effect of lung cancer patients after anlotinib treatment, characterized in that: The device comprises: A data module, used to provide expression level data of a target marker of a sample to be tested, wherein the target marker is the biomarker for predicting the efficacy of lung cancer patients after anlotinib treatment as described in claim 1; A scoring module, used to obtain a probability value through a prediction model based on the expression level data of the target marker of the sample to be tested; wherein the prediction model is the prediction model of the efficacy of lung cancer patients after anlotinib treatment as described in claim 7; A judgment module is used to predict the therapeutic efficacy of anlotinib based on the probability value.
9. An electronic device comprising one or more processors and a memory for storing executable instructions, characterized in that: The one or more processors are configured to call the executable instructions stored in the memory to implement the function of the device for predicting the efficacy of lung cancer patients after anlotinib treatment as described in claim 8.
10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by the processor, the functions of the device for predicting the therapeutic effect of lung cancer patients after anlotinib treatment as claimed in claim 8 are realized.