Construction and application of lung cancer brain metastasis prognosis model based on plasma miRNA combined marker

Abstract

The invention discloses construction and application of a lung cancer brain metastasis prognosis model based on a plasma miRNA combined marker. A prognosis model of lung cancer brain metastasis is constructed based on plasma miRNA combined markers, the plasma miRNA combined markers comprise miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p, the prognosis model obtains a miRNA expression index according to the expression quantity of each plasma miRNA, and a predicted survival rate is obtained according to the miRNA expression index and clinical information and serves as a prognosis prediction result; the prognosis model is used for prognosis prediction of lung cancer brain metastasis. The prediction model and the prognosis prediction system both have high sensitivity and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of medical artificial intelligence technology, and specifically to the construction and application of a lung cancer brain metastasis prognosis model based on plasma miRNA combination markers. Background Art

[0002] Among patients with brain metastases from solid tumors, lung cancer brain metastases rank first among brain metastases with a metastasis rate of up to 40%, and the prognosis is extremely poor. Lung cancer brain metastases can be divided into brain parenchymal metastases and meningeal metastases, and their common clinical manifestations are mainly increased intracranial pressure and specific focal symptoms and signs. Currently, the diagnosis of brain metastases mainly relies on imaging technologies such as cranial magnetic resonance imaging. However, since the resolution range of cranial magnetic resonance imaging is in the millimeter range, while the size of tumor cells is in the micrometer range, this will lead to delayed diagnosis and treatment of tumors in the clinic.

[0003] Patients with brain metastases from lung cancer can currently choose comprehensive treatment techniques such as surgery, radiotherapy, and chemotherapy, which prolongs the survival of patients. However, the median survival of related patients is still short, averaging 12 to 15 months after treatment. Given the characteristics of brain metastases from lung cancer, which are insidious, rapid progression, poor prognosis, and short natural survival, there is an urgent need to screen new biomarkers and build prediction models for the diagnosis and prognosis of brain metastases from lung cancer.

[0004] Serum tumor markers related to lung cancer include carcinoembryonic antigen, cytokeratin fragment 19, and squamous cell carcinoma antigen. These serum biomarkers can assist in monitoring the disease changes and drug treatment effects of lung cancer patients. However, these indicators are easily affected by the microenvironment in the patient's body and have a certain false positive rate. In addition, although the development of imaging omics can provide more information for patients with brain metastases from lung cancer, including the number, volume, and location of lesions on imaging data, obtaining this information requires a large amount of computational workload and requires professional radiologists to set parameters and scan. In summary, there is currently no simple, efficient, and excellent predictive performance prognosis prediction system for brain metastases from lung cancer to accurately evaluate the prognosis of patients with brain metastases from lung cancer.

[0005] In recent years, the study of plasma or serum microRNA (miRNA) as a tumor prognosis biomarker has received continuous attention. miRNA is about 20-25bp long, affects target gene expression at the post-transcriptional level, and is widely involved in physiological processes such as tumor cell proliferation, differentiation, and apoptosis. In the field of lung cancer brain metastasis, many studies have shown that miRNA is involved in lung cancer invasion and migration, and miRNA as a new biomarker can guide the prognosis of patients with highly invasive lung cancer. The existing patent publication number CN108728543A discloses a miRNA combination and related kit for detecting lung cancer brain metastasis. This method detects three miRNA molecular markers in cerebrospinal fluid, and the risk discrimination of lung cancer brain metastasis based on this has a certain effectiveness, but it cannot be applied to the precise prognosis of patients with lung cancer brain metastasis.

[0006] Therefore, there is currently no potential simple, efficient and excellent predictive performance system and method for predicting the prognosis of lung cancer brain metastasis taking miRNA into consideration. Summary of the invention

[0007] In view of the problem that existing prediction systems and methods are not applicable to the accurate prognosis of patients with lung cancer brain metastasis, the present invention provides a construction and application of a lung cancer brain metastasis prognosis model based on plasma miRNA combination markers. The prognosis model provided by the present invention is constructed based on plasma miRNA combination markers and clinical information, and is used for the prognosis prediction of lung cancer brain metastasis.

[0008] The present invention adopts the following technical solution: 1. A plasma miRNA combination marker for prognostic prediction of brain metastasis of lung cancer The plasma miRNA combination marker is mainly composed of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p.

[0009] The plasma miRNA is the miRNA in an in vitro biological plasma sample.

[0010] Among the plasma miRNA combination markers, miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p and miR-452-3p are highly expressed in the ex vivo biological plasma samples of lung cancer brain metastasis. MiR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p are lowly expressed in the ex vivo biological plasma samples of lung cancer brain metastasis.

[0011] 2. Application of the above plasma miRNA combination marker The plasma miRNA combination marker is used to prepare a prognosis prediction drug or a prognosis prediction system for lung cancer brain metastasis.

[0012] The prognosis prediction drug includes primers, probes or fluorescent reagents that can specifically detect all plasma miRNA markers.

[0013] The prognosis prediction system includes a Cox regression prognosis prediction model.

[0014] 3. A method for constructing a prognostic model for lung cancer brain metastasis based on plasma miRNA combination markers The construction method comprises the following steps: 1) Obtaining sample data from at least one bioinformation database, performing differential expression analysis on the sample data obtained from each bioinformation database, and obtaining a differentially expressed miRNA combination corresponding to each bioinformation database; performing intersection extraction on the differentially expressed miRNA combinations corresponding to all bioinformation databases, and obtaining a common differentially expressed miRNA combination of all bioinformation databases; screening a significant common differentially expressed miRNA combination from the common differentially expressed miRNA combination according to a preset threshold, and using it as a candidate miRNA combination; Specifically, from the common differentially expressed miRNA combination, miRNAs with a fold change greater than a preset fold change threshold and a statistical significance less than a preset statistical significance threshold are screened as significant common differentially expressed miRNAs, namely candidate miRNAs.

[0015] 2) constructing the expression levels of each candidate miRNA in the pre-collected normal ex vivo biological plasma samples and the ex vivo biological plasma samples of lung cancer brain metastasis into a sampling data set, performing differential expression analysis on the sampling data set to obtain multiple differentially expressed miRNAs, and forming a plasma miRNA combination, wherein the plasma miRNA combination is used as a combined marker for prognosis prediction of lung cancer brain metastasis; using a Logistic regression method to construct a prediction model for the miRNA expression index according to the plasma miRNA combination and the expression levels of each plasma miRNA therein; In the step 2), pairwise association analysis is performed on the sampled data sets and after the colinear miRNAs are eliminated, differential expression analysis is performed; In the step 2), the plasma miRNAs are miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p; In step 2), the prediction model of miRNA expression index is specifically: g= 7.97-4.45 m 1 -2.85 m 2 +1.82 m 3 -3.38 m 4 -3.18 m 5 +1.13 m 6 +6.58 m 7 +4.67 m 8 +1.52 m 9 -7.73 m 10 In the formula, m 1 , m 2 , m 3 , m 4 , m 5 , m 6 , m 7 , m 8 , m 9 , m 10 They represent the expression levels of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p, respectively.

[0016] 3) The clinical information corresponding to all lung cancer brain metastasis ex vivo biological plasma samples was subjected to univariate Cox regression analysis and multivariate Cox regression analysis in turn to obtain multiple clinical variables to form a clinical variable combination; a Cox regression prognosis prediction model was constructed based on the clinical variable combination and the miRNA expression index using the Cox regression analysis method, and the Cox regression prognosis prediction model was optimized using the K-fold method to obtain the prognosis model.

[0017] In step 3), the clinical variable combination includes age, KPS and GPA.

[0018] In step 3), the prognostic model is specifically: h(t)=h 0 (t) exp( PI ) PI =1.43 X 1 -0.28 X 2 -22.27 X 3 +58.72 X 4 In the formula, h(t) represents the risk function, h 0 (t) represents the benchmark risk function, X 1 Indicates age, X 2 represents KPS, X 3 Indicates GPA, X 4 represents the miRNA expression index, t represents the predicted survival time, PI Represents the prognostic index.

[0019] 4. A prognostic prediction method for lung cancer brain metastasis based on the above plasma miRNA combination markers The prognosis prediction method comprises the following steps: obtaining the expression level of each plasma miRNA in an in vitro biological plasma sample and the clinical information corresponding to the in vitro biological plasma sample, inputting the expression levels of all plasma miRNAs and the clinical information into a prognosis model, the prognosis model obtains a miRNA expression index according to the expression levels of all plasma miRNAs, and then obtains a predicted survival rate according to the miRNA expression index and the clinical information and outputs it as a prognosis prediction result.

[0020] The predicted survival rate is obtained by the following formula: S(t) =exp(- ∫ t 0 h 0 (u)du ×exp( PI )) In the formula, S(t) Represents the predicted survival time t The predicted survival rate, t represents the predicted survival time, ∫t 0 h 0 (u)du Represents the predicted survival time t The cumulative risk within PI Represents the prognostic index.

[0021] Among them, the prognostic index PI It is obtained by the following formula based on the miRNA expression index and clinical information, where clinical variables include age, KPS and GPA: PI =1.43 X 1 -0.28 X 2 -22.27 X 3 +58.72 X 4 In the formula, X 1 Indicates age, X 2 represents KPS, X 3 Indicates GPA, X 4 Represents the miRNA expression index.

[0022] Among them, miRNA expression index X 4 Obtained by the following formula: g= 7.97-4.45 m 1 -2.85 m 2 +1.82 m 3 -3.38 m 4 -3.18 m 5 +1.13 m 6 +6.58 m 7 +4.67 m 8 +1.52 m 9 -7.73 m 10 In the formula, g represents the miRNA expression index, m 1 , m2 , m 3 , m 4 , m 5 , m 6 , m 7 , m 8 , m 9 , m 10 They represent the expression levels of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p, respectively.

[0023] 5. A prognosis prediction system applied to the above-mentioned prognosis prediction method The prognosis prediction system comprises: An input module is used to receive clinical information corresponding to the in vitro biological plasma sample to be predicted and the expression level of each plasma miRNA; the clinical variables in the clinical information include age, KPS and GPA; The prognostic model module is used to receive clinical information and the expression levels of all plasma miRNAs through the prognostic model, obtain the predicted survival rate and output it; The prognostic model is as follows: h(t)=h 0 (t) exp( PI ) PI =1.43 X 1 -0.28 X 2 -22.27 X 3 +58.72 X 4 In the formula, h(t) represents the risk function, h 0 (t) represents the benchmark risk function, X 1 Indicates age, X 2 represents KPS, X 3 Indicates GPA, X 4represents the miRNA expression index, t represents the predicted survival time, PI represents the prognostic index; The output module is used to output the predicted survival rate to the prognosis prediction system or to visualize the predicted survival rate.

[0024] Optionally, the visualization of the predicted survival rate is achieved through a nomogram.

[0025] The beneficial effects of the present invention are: 1. The present invention provides a group of plasma miRNAs, which are closely related to the prognostic effect of brain metastasis of lung cancer and can be used as prognostic markers for brain metastasis of lung cancer and for the prognosis prediction of brain metastasis of lung cancer.

[0026] 2. The present invention provides a method for constructing a prognostic model based on the expression level of plasma miRNA and clinical information using a regression analysis method. The prognostic model constructed using this method can be applied to the prognosis prediction of brain metastasis of lung cancer. Both the prediction model and the prognosis prediction method have high sensitivity and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of mining candidate miRNA combinations through public databases in an embodiment of the present invention; wherein A is a principal component analysis diagram, B is a volcano diagram, C is a schematic diagram of the intersection extraction process, D is a difference sorting diagram, E is a schematic diagram of the correlation between low expression of hsa-miR-1246 and low survival rate, and F is a schematic diagram of the correlation between high expression of hsa-miR-31-5p and low survival rate.

[0028] Figure 2 Schematic diagram of gene enrichment and pathway analysis in an embodiment of the present invention; wherein A is a pathway analysis result diagram, B is a gene enrichment analysis result diagram, C is a z-value analysis result diagram, and D is a Hub gene analysis result diagram.

[0029] Figure 3 Schematic diagram of differential analysis of in vitro biological plasma samples in an embodiment of the present invention; A is the plasma miRNA expression levels in two groups of samples, B is the Spearman pairwise correlation analysis result diagram of the plasma miRNA combination, and C is the Chord diagram of the plasma miRNA combination.

[0030] Figure 4 Schematic diagram of the prognostic model obtained in the comparative example of the present invention; A is a nomogram, B is a calibration curve diagram, and C is a receiver operating characteristic curve diagram.

[0031] Figure 5 Schematic diagram of the prognostic model obtained in the embodiment of the present invention; A is a nomogram, B is a calibration curve diagram, and C is a receiver operating characteristic curve diagram.

[0032] Figure 6 This is a technical roadmap used in the embodiments of the present invention. DETAILED DESCRIPTION

[0033] The present application is further described below in conjunction with the examples, but is not limited thereto. The materials, reagents, etc. used in the following examples, unless otherwise specified, can be obtained from commercial sources. In the present invention, for the convenience of description or in accordance with professional practices, the terms in some sentences are abbreviated and abbreviated in English, and the corresponding relationship is as follows, which will not be repeated later: BrM, Brain Metastasis; miRNA, microRNA, microRNA; AUC, Area Under Curve, area under the curve; ROC, Receiver Operator Characteristic Curve, subject characteristic diagnostic curve; NSCLC, Non-Small Lung Cancer Cell, Non-small cell lung cancer LUAD, Lung Adenocarcinoma; KPS, Karnofsky Performance Status, Karnofsky score; GPA, Graded Prognostic Assessment, graded prognostic assessment; RPM, Reads Per Million Mapped Reads, reads per million; z-score, z value; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus; ArrayExpress, ArrayExpress - Functional Genomics Data, functional genomics dataset; PI, Prognostic Index; PCA, Principal Component Analysis, principal component analysis.

[0034] The specific embodiments of the present invention are as follows: Example

[0035] like Figure 6 As shown, this embodiment constructs a prognostic model for brain metastasis of lung cancer through the following steps: 1) In this example, the candidate miRNA combinations are obtained by comprehensive analysis of miRNA sequencing data downloaded from three bioinformatics databases. The results of the comprehensive analysis are as follows: Figure 1 shown.

[0036] The comprehensive analysis is achieved through the following process: 1.1) Download miRNA expression data and clinical information of different samples from TCGA database, GEO database and ArrayExpress database respectively, merge the miRNA expression data from TCGA database, GEO database and ArrayExpress database, construct an expression matrix with miRNA number, counts and RPM as column vectors, normalize the expression data in the expression matrix to obtain a normalized matrix, and calculate the expression z value of each miRNA in different samples according to the normalized matrix; Among them, the expression z value of any miRNA in sample A is obtained by the following formula: z-score miRNA =(counts / RPM sample -counts / RPM average ) / (counts / RPM SD ) In the formula, z-score miRNA Indicates the expression z value of miRNA in sample A, counts / RPM sample Indicates the expression level of miRNA in sample A, counts / RPM average Represents the mean expression level of miRNA in all samples, counts / RPM SD Represents the standard deviation of miRNA in all samples.

[0037] 1.2) Combined with the group labels in the clinical information (lung cancer brain metastasis group and control group), the expression levels of all miRNAs were subjected to principal component analysis using the “FactoMineR” package and the “factoextra” package of the R language to obtain a differentially expressed miRNA combination between the two groups consisting of multiple differentially expressed miRNAs between the two groups.

[0038] In this example, the differentially expressed miRNA combinations between the two groups were analyzed using the "edgeR" and "limma" packages, and the data analysis results were visualized by drawing a volcano plot using the "ggplot2" package to present the statistical significance and fold changes of the differentially expressed miRNAs between the two groups. Figure 1 A and B.

[0039] 1.3) Using the “DESeq2” package, the differentially expressed miRNAs between the two groups were ranked according to statistical significance and fold change (for results, see Figure 1 D), from the differentially expressed miRNA combinations between the two groups, select genes that are differentially expressed in the three databases ( Figure 1 C), 73 miRNAs were differentially expressed between the lung cancer brain metastasis group and the control group in all three data sets, forming a common differentially expressed miRNA combination. Combined with the preset threshold, the top 60 miRNAs with large differences and statistical significance were selected from the common differentially expressed miRNA combination as the significant common differentially expressed miRNAs of all bioinformatics databases, forming a significant common differentially expressed miRNA combination for use as candidate miRNAs.

[0040] In this embodiment, the preset fold change threshold and the preset statistical significance threshold are respectively set to: |log2(FC)|>2 and p.adj<0.05 In the formula, |·| represents the absolute value of the parameter “·”, FC represents the fold change, and p.adj represents the adjusted P value, that is, statistical significance.

[0041] like Figure 1 As shown in D, the 73 significantly differentially expressed miRNAs all fall within the preset threshold range. Figure 1 A is the principal component analysis diagram of miRNA expression data from the GEO database, blue is the control group (primary), and red is the lung cancer brain metastasis group (metastasis). It can be seen that the control group and the lung cancer brain metastasis group can be preliminarily distinguished based on the miRNA expression data.

[0042] Figure 1B is a volcano map of miRNA expression data from the TCGA LUAD database. Red is the miRNA that is upregulated in the lung cancer brain metastasis group samples compared with the control group samples; blue is the miRNA that is downregulated in the lung cancer brain metastasis group samples compared with the control group samples. It can be seen that there are many differentially expressed miRNAs in the control group and the lung cancer brain metastasis group. In this embodiment, the number of miRNAs derived from the TCGA database is 527. Compared with the control group samples, 37 miRNAs are highly expressed in the lung cancer brain metastasis group samples (fold change <1 / 2, P value <0.05), and 31 miRNAs are underexpressed (fold change >2, P value <0.05).

[0043] Figure 1 D is the differential ranking diagram of miRNA. As shown in the figure, the differentially expressed miRNA combination between the two groups includes a total of 1292 miRNAs. Among the 60 miRNAs in the candidate miRNA combination, 27 miRNAs were highly expressed and 33 miRNAs were lowly expressed in the lung cancer brain metastasis group samples compared with the control group samples.

[0044] In this example, the clinical information of all samples was combined and the Kaplan–Meier survival analysis method was used to perform survival analysis on each candidate miRNA in the candidate miRNA combination. The survival analysis results are shown in Figure 1 E and F. Taking hsa-miR-1246 and hsa-miR-31-5p as examples, Figure 1 As shown in Figure E, low expression of hsa-miR-1246 was associated with poor survival (hazard ratio = 0.71, P value = 0.038 < 0.05). Figure 1 As shown in F, high expression of hsa-miR-31-5p was associated with poor survival rate (hazard ratio = 1.72, P value = 0.0013 < 0.005).

[0045] This example also uses the target gene enrichment method to predict the target genes of the candidate miRNA combination, obtains the target genes associated with each candidate miRNA, and then uses the GO (Gene Ontology) enrichment analysis method and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis method to perform gene enrichment and pathway analysis on the predicted target gene list to obtain the target gene function, so as to further verify the reliability of the candidate miRNA combination as a potential marker. The results of gene enrichment and pathway analysis can be found in Figure 2 .

[0046] Figure 2A is the result of pathway analysis, which shows that the candidate miRNA combination has functions in Wnt signaling pathway, cell migration, Ras signaling pathway, cell-cell junction, non-small cell lung cancer and brain metastasis of lung cancer. Figure 2 B is the result of gene enrichment analysis. It can be seen that GO functions are mainly enriched in functional pathways related to tumor occurrence and development, such as mitosis cell cycle and cell adhesion. Figure 2 C is a z-value analysis result diagram, which illustrates the z-values ​​corresponding to each GO functional pathway calculated by the "GOplot" package. A positive z-value indicates positive regulation, and a negative z-value indicates negative regulation.

[0047] Figure 2 D is the Hub Genes analysis result diagram, which further shows the association between candidate miRNAs and their associated target genes. It can be seen that candidate miRNAs are closely related to many genes related to tumor occurrence and development, such as Myc, GSK3β and PRKCA.

[0048] 2) Perform miRNA quantitative analysis on in vitro biological plasma samples collected from clinical cases of lung cancer brain metastasis and healthy controls to obtain quantitative analysis results for each candidate miRNA. Through secondary screening, 10 plasma miRNAs are determined from the candidate miRNA combination to form a plasma miRNA combination as a combined marker for prognosis prediction of lung cancer brain metastasis.

[0049] The 10 plasma miRNAs were miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p.

[0050] Secondary screening is achieved through the following process: 2.1) Collection of ex vivo biological plasma samples: Collect 2-3 mL of fasting blood from lung cancer brain metastasis patients or healthy controls in the morning into EDTA anticoagulant tubes, shake gently to mix to prevent blood coagulation, centrifuge at 4°C, 4000 rpm for 10 min in a low-temperature centrifuge within 1 hour after blood collection, take the light yellow transparent upper plasma and divide it into sterile and RNAase-free EP tubes, and immediately transfer to -80°C ultra-low temperature refrigerator for storage to obtain ex vivo biological plasma samples of lung cancer brain metastasis or normal ex vivo biological plasma samples. Transport to the laboratory for testing on dry ice.

[0051] In this embodiment, the number of the lung cancer brain metastasis ex vivo biological plasma samples and the normal ex vivo biological plasma samples are both 25.

[0052] 2.2) Extract RNA from in vitro biological plasma samples: Take out the in vitro biological plasma samples from the -80℃ ultra-low temperature refrigerator and immediately place them on ice. After they are completely dissolved, take 200μL of RNAase-free and add them to a 1.5mL centrifuge tube. Add 1mL of Trizol lysis buffer to the centrifuge tube on ice and mix thoroughly with a pipette. Add 1 / 5 volume of chloroform to the lysis buffer, shake vigorously for 15 seconds to form an emulsion, and let stand at 4℃ for 5min. After centrifugation at 12000g and 4℃ for 15min, the solution is divided into three layers: colorless aqueous phase (upper layer), white middle layer and red organic layer.

[0053] Carefully aspirate the upper aqueous phase and add it to a new centrifuge tube, then add an equal volume of isopropanol and mix by inverting, and let stand at 4°C for 10 minutes. After centrifugation at 12000g and 4°C for 10 minutes, a white precipitate can be seen. Use 1mL of 75% (V / V) ethanol (prepared with DEPC water) to resuspend the precipitate, centrifuge at 12000g and 4°C for 5 minutes, discard the supernatant, and dry in a clean environment. Add an appropriate amount of DEPC water to dissolve the precipitate to obtain a sample RNA solution. Use NanoDrop 2000 to measure the OD of the sample RNA solution. 260 / 280 ratio and concentration.

[0054] 2.3) Reverse transcription: After measuring the concentration of the sample RNA solution, dilute the sample RNA solution to 450 ng / μL, prepare a genomic DNA removal system, and react at 42°C for 2 min to remove the genomic DNA.

[0055] The composition of the genomic DNA removal system is shown in Table 1.

[0056] Table 1. Genomic DNA removal system Components volume <![CDATA[RNase-free ddH2O]]> 6μL 5 * gDNA Wiper Mix 2μL Total RNA extract 2μL (450ng / μL) Then, the stem-loop was used for miRNA reverse transcription, and the miRNA reverse transcription system was prepared. The amplification conditions were 25°C for 5 min, 50°C for 15 min, and 85°C for 5 min.

[0057] The composition of the miRNA reverse transcription system is shown in Table 2.

[0058] Table 2. miRNA reverse transcription system Components volume <![CDATA[RNase-free ddH2O]]> 5μL Previous step mixture 10μL Stem-loop primer (2 μM) 1μL 10*RT Mix 2μL HiScript II Enzyme Mix 2μL 2.4) Real-time fluorescence quantitative PCR: Prepare qPCR reaction system, amplification conditions: pre-denaturation at 95℃ for 3min, 40 cycles, each cycle includes 95℃ for 10s and 60℃ for 30s, melting curve (95℃ for 15s, 60℃ for 60s and 95℃ for 15s). Use U6 as miRNA internal reference, use relative quantification method to calculate relative expression of target gene, and obtain relative expression of each candidate miRNA.

[0059] The composition of the qPCR reaction system is shown in Table 3.

[0060] Table 3. qPCR reaction system Components volume 2*qPCR Master Mix 10μL F-Primer (10 μM) 0.4μL R-Primer (10 μM) 0.4μL Template DNA 2μL <![CDATA[ddH2O]]> 7.2μL The primer sequences used in the qPCR reaction system are shown in SEQ ID NO.1 to SEQ ID NO.21 in Table 4, respectively.

[0061] Table 4. Primer sequences used in qPCR reaction system ID Primer name Sequence (5'->3') SEQ ID NO.1 miR-1246-F GCGGCAATGGATTTTTGG SEQ ID NO.2 miR-1246-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACCCTGCT SEQ ID NO.3 miR-219-5p-F CGGCTGATTGTCCAAACGC SEQ ID NO.4 miR-219-5p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACAGAATT SEQ ID NO.5 miR-598-3p-F CGGCTACGTCATCGTTGTCA SEQ ID NO.6 miR-598-3p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACTGACGA SEQ ID NO.7 miR-9-5p-F GCGGCTCTTTGGTTATCTAGCT SEQ ID NO.8 miR-9-5p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACTCATAC SEQ ID NO.9 miR-31-5p-F GCAGGCAAGATGCTGGCA SEQ ID NO.10 miR-31-5p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACCAGCTA SEQ ID NO.11 miR-210-3p-F GGCCTGTGCGTGTGACAGC SEQ ID NO.12 miR-210-3p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACTCAGCC SEQ ID NO.13 miR-196a-5p-F CGCGGCTAGGTAGTTTCATGTT SEQ ID NO.14 miR-196a-5p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACCCCAAC SEQ ID NO.15 miR-130a-5p-F GCGGCTTCACATTGTGCTACT SEQ ID NO.16 miR-130a-5p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACGCAGAC SEQ ID NO.17 miR-1-3p-F CCGGCTGGAATGTAAAGAAGT SEQ ID NO.18 miR-1-3p-RT GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACATACAT SEQ ID NO.19 Common-R CAGTGCGTGTCGTGGAGT SEQ ID NO.20 U6-F CTCGCTTCGGCAGCACA SEQ ID NO.21 U6-R AACGCTTCACGAATTTGCGT

[0062] 2.5) The relative expression levels of candidate miRNAs in all in vitro biological plasma samples were constructed as a sampling data set, and Spearman pairwise correlation analysis was performed on the sampling data set to eliminate collinear miRNAs, followed by differential expression analysis, and 10 plasma miRNAs that were differentially expressed between the two groups were obtained to form a plasma miRNA combination, which was used as a combined marker for prognosis prediction of brain metastasis of lung cancer.

[0063] The plasma miRNAs were miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p.

[0064] like Figure 3 As shown in A, the expression levels of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p and miR-452-3p were significantly upregulated in the in vitro biological plasma samples of lung cancer brain metastasis (P<0.001), and the expression levels of miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p were significantly downregulated in the in vitro biological plasma samples of lung cancer brain metastasis (P<0.001).

[0065] Spearman pairwise correlation analysis was performed again on the plasma miRNA combination to further verify the correlation between plasma miRNAs.Figure 3 As shown in Figure B, the results of Spearman pairwise correlation analysis showed that the plasma miRNA combination was divided into two subgroups. The first subgroup consisted of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p and miR-452-3p, and the second subgroup consisted of miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p. The miRNAs in each subgroup were positively correlated, and the two subgroups were negatively correlated (P<0.05). This result is consistent with Figure 3 The results in A are consistent, further confirming the stability and reliability of plasma miRNA as a marker. Figure 3 As shown in Figure C, the Chord diagram further shows the pairwise correlation of the 10 plasma miRNAs, and the width and depth of the bands between the color blocks reflect the correlation between the two.

[0066] 2.6) Logistic regression method was used to construct a prediction model of miRNA expression index based on the plasma miRNA combination and the expression level of each plasma miRNA.

[0067] The specific prediction model of miRNA expression index is: g= 7.97-4.45 m 1 -2.85 m 2 +1.82 m 3 -3.38 m 4 -3.18 m 5 +1.13 m 6 +6.58 m 7 +4.67 m 8 +1.52 m 9 -7.73 m 10 In the formula, g represents the miRNA expression index, m 1 , m 2 , m 3 , m 4 , m 5 , m6 , m 7 , m 8 , m 9 , m 10 They represent the expression levels of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p, respectively.

[0068] 3) The clinical information corresponding to each lung cancer brain metastasis ex vivo biological plasma sample collected clinically was used to form a regression analysis data set. The regression analysis data set was subjected to univariate Cox regression analysis and multivariate Cox regression analysis in turn. According to the analysis results, age, KPS, and GPA that were significantly correlated with prognosis (P<0.05) were selected, and these clinical variables formed a clinical variable combination. The Cox regression analysis method was used to construct a Cox regression prognostic prediction model based on the clinical variable combination and miRNA expression index, and the K-fold method was used to optimize the Cox regression prognostic prediction model to obtain a prognostic model.

[0069] In this embodiment, the clinical information includes age, gender, smoking history, tumor TNM stage, pathological type, radiotherapy and chemotherapy history, targeted drug use history, KPS, tumor metastasis, EGFR, PD-L1, KRAS, ALK gene mutation site detection and survival status.

[0070] The prognostic model obtained in this embodiment is specifically: h(t)=h 0 (t) exp( PI ) PI =1.43 X 1 -0.28 X 2 -22.27 X 3 +58.72 X 4 In the formula, h(t) represents the risk function, h 0 (t) represents the benchmark risk function, X 1 Indicates age, X 2 represents KPS, X3 Indicates GPA, X 4 represents the miRNA expression index, t represents the predicted survival time, PI Represents the prognostic index.

[0071] 4) After the prognostic model is obtained, the nomogram is drawn using the R language "survival" package and the "nomogramFormula" package to visualize the prognostic model. The nomogram of the prognostic model obtained in this embodiment is shown in Figure 5 A, by calculating the prognostic index value PI Can predict the predicted survival time t Predicted survival rate within S(t) .

[0072] S(t) =exp(- ∫ t 0 h(u)du ) = exp(- ∫ t 0 h 0 (u)du ×exp( PI )) In the formula, S(t) is the survival function, that is, the individual survival time T Greater than predicted survival time t Probability P(T>t) , t is the predicted survival time in years. ∫ t 0 h 0 (u)du Represents the predicted survival time t The cumulative risk within.

[0073] This embodiment predicts the one-year survival rate through the above process. S(1)。 Figure 5 B is a calibration curve diagram of the prognostic model of this embodiment, in which the horizontal axis is the survival probability predicted by the model, the vertical axis is the actual observed survival probability, each point represents the survival probability predicted by the model and the actual observed survival probability, and the gray diagonal line is the ideal line. As shown in the figure, the 1-year survival rate (blue line) has a high degree of fit with the diagonal line, and the prediction effect is good. After calculation, the consistency index of the model is 0.82.

[0074] Subsequently, the receiver operating characteristic curve was plotted using the “pROC” package of the R language, and the area under the curve was calculated. The results are shown in Figure 5The C of the embodiment, the horizontal axis is 1-specificity, representing the false positive rate, the vertical axis is sensitivity, representing the true positive rate, the point closest to the upper left of the coordinate axis in the figure is the critical value with high sensitivity and specificity, the red color represents the curve of the prognostic model obtained in this embodiment, and the blue color represents the curve of the prognostic model obtained in the comparative example. The area under the curve of the prognostic model of this embodiment is 0.838.

[0075] Comparative Example This comparative example is based on the clinical variable combination (age, KPS and GPA) obtained in step 3) of the embodiment, uses the Cox regression analysis method to construct a Cox regression prognosis prediction model, and uses the K-fold method to optimize the Cox regression prognosis prediction model to obtain a prognosis model.

[0076] The prognostic model obtained in this comparative example is specifically: h(t)=h 0 (t) exp(1.50 X 1 -0.30 X 2 -6.09 X 3 ) PI =1.50 X 1 -0.30 X 2 -6.09 X 3 In the formula, h(t) represents the risk function, h 0 (t) represents the benchmark risk function, X 1 Indicates age, X 2 represents KPS, X 3 Indicates GPA, t Indicates the survival time, PI stands for prognostic index.

[0077] After obtaining the prognostic model, the R language "survival" package and "nomogramFormula" package were used to draw a nomogram to visualize the prognostic model. Figure 4 A. By calculating the prognostic index value PI To predict 1-year survival rate S(1) .

[0078] S(t) =exp(- ∫t 0 h(u)du ) = exp(- ∫ t 0 h 0 (u)du ×exp( PI )) In the formula, S(t) represents the survival function, that is, the individual survival time T Greater than predicted survival time t Probability P(T>t) , t Represents the predicted survival time in years. ∫ t 0 h 0 (u)du Represents the predicted survival time t The cumulative risk within.

[0079] Figure 4 Figure B is the calibration curve of the prognostic model of this comparison. The horizontal axis in the figure is the survival probability predicted by the model, and the vertical axis is the actual observed survival probability. Each point represents the survival probability predicted by the model and the actual observed survival probability. The gray diagonal line is the ideal line. As shown in the figure, the 1-year survival rate (blue line) has a certain degree of fit with the diagonal line, indicating that the model has a certain 1-year survival rate prognostic prediction effect. The consistency index (C-index) of this model was calculated to be 0.67.

[0080] Subsequently, the receiver operating characteristic curve was drawn using the “pROC” package in R language, and the area under the curve was calculated. Figure 4 C is the receiver operating characteristic curve of the comparative prognostic model of the present invention, the horizontal axis is 1-specificity, representing the false positive rate, and the vertical axis is sensitivity, representing the true positive rate. The point closest to the upper left of the coordinate axis in this figure is the critical value with high sensitivity and specificity. After calculation, the area under the curve is 0.669 (P<0.01).

[0081] It can be seen that after adding the miRNA expression index, the AUC value of the prognostic model was significantly improved (P<0.01), and the prediction effect was significantly improved. The plasma miRNA combination obtained by the present invention is used as a combined marker to construct a prognostic model for brain metastasis of lung cancer, which can effectively improve the accuracy of the prognostic model. The prognostic model obtained by the construction method of the present invention is used for the prognosis prediction of brain metastasis of lung cancer, and it also reflects a higher accuracy.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0083] The nucleotide sequences involved in the present invention are as follows: SEQ ID NO.1: Name: nucleotide sequence of miR-1246-F; DNA type: DNA (other DNA); Organism source: synthetic construct; GCGGCAATGGATTTTTGG; SEQ ID NO.2: Name: nucleotide sequence of miR-1246-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACCCTGCT; SEQ ID NO.3: Name: nucleotide sequence of miR-219-5p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; CGGCTGATTGTCCAAACGC; SEQ ID NO.4: Name: nucleotide sequence of miR-219-5p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACAGAATT; SEQ ID NO.5: Name: nucleotide sequence of miR-598-3p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; CGGCTACGTCATCGTTGTCA; SEQ ID NO.6: Name: nucleotide sequence of miR-598-3p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACTGACGA; SEQ ID NO.7: Name: nucleotide sequence of miR-9-5p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; GCGGCTCTTTGGTTATCTAGCT; SEQ ID NO.8: Name: nucleotide sequence of miR-9-5p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACTCATAC; SEQ ID NO.9: Name: nucleotide sequence of miR-31-5p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; GCAGGCAAGATGCTGGCA; SEQ ID NO.10: Name: nucleotide sequence of miR-31-5p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACCAGCTA; SEQ ID NO.11: Name: nucleotide sequence of miR-210-3p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; GGCCTGTGCGTGTGACAGC; SEQ ID NO.12: Name: nucleotide sequence of miR-210-3p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACTCAGCC; SEQ ID NO.13: Name: nucleotide sequence of miR-196a-5p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; CGCGGCTAGGTAGTTTCATGTT; SEQ ID NO.14: Name: nucleotide sequence of miR-196a-5p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACCCCAAC; SEQ ID NO.15: Name: nucleotide sequence of miR-130a-5p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; GCGGCTTCACATTGTGCTACT; SEQ ID NO.16: Name: nucleotide sequence of miR-130a-5p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACGCAGAC; SEQ ID NO.17: Name: nucleotide sequence of miR-1-3p-F; DNA type: DNA (other DNA); Organism source: synthetic construct; CCGGCTGGAATGTAAAGAAGT; SEQ ID NO.18: Name: nucleotide sequence of miR-1-3p-RT; DNA type: DNA (other DNA); Organism source: synthetic construct; GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACATACAT; SEQ ID NO.19: Name: Nucleotide sequence of Common-R; DNA type: DNA (other DNA); Organism source: synthetic construct; CAGTGCGTGTCGTGGAGT; SEQ ID NO.20: Name: Nucleotide sequence of U6-F; DNA type: DNA (other DNA); Organism source: synthetic construct; CTCGCTTCGGCAGCACA; SEQ ID NO.21: Name: nucleotide sequence of U6-R; DNA type: DNA (other DNA); Organism source: synthetic construct; AACGCTCACGAATTTGCGT.

Claims

1. A plasma miRNA combination marker for predicting the prognosis of brain metastasis of lung cancer, characterized in that: The plasma miRNA combination marker is mainly composed of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p.

2. The plasma miRNA combination marker according to claim 1, characterized in that: The plasma miRNA is the miRNA in an in vitro biological plasma sample.

3. A use of the plasma miRNA combination marker as claimed in claim 1 or 2, characterized in that: Used for preparing a prognosis prediction drug or a prognosis prediction system for lung cancer brain metastasis, wherein the prognosis prediction drug comprises a primer, a probe or a fluorescent reaction agent capable of specifically detecting all plasma miRNAs.

4. A method for constructing a prognostic model for lung cancer brain metastasis based on plasma miRNA combination markers, characterized in that: The construction method comprises the following steps: 1) obtaining sample data from at least one bioinformatics database, obtaining the common differentially expressed miRNA combinations of all bioinformatics databases through differential expression analysis, and screening the candidate miRNA combinations from the common differentially expressed miRNA combinations according to a preset threshold; 2) The expression levels of each candidate miRNA in the pre-collected normal ex vivo biological plasma samples and lung cancer brain metastasis ex vivo biological plasma samples were constructed as a sampling data set, and differential expression analysis was performed on the sampling data set to obtain the plasma miRNA combination. The Logistic regression method was used to construct a prediction model of the miRNA expression index based on the plasma miRNA combination and the expression level of each plasma miRNA; 3) The clinical information corresponding to all lung cancer brain metastasis ex vivo biological plasma samples was subjected to univariate Cox regression analysis and multivariate Cox regression analysis in turn to obtain a combination of clinical variables; a Cox regression prognostic prediction model was constructed based on the combination of clinical variables and the miRNA expression index using the Cox regression analysis method, and the Cox regression prognostic prediction model was optimized using the K-fold method to obtain the prognostic model.

5. The method for constructing a prognostic model for brain metastasis of lung cancer according to claim 4, characterized in that: The prediction model of the miRNA expression index is specifically: g= 7.97-4.45 m 1 -2.85 m 2 +1.82 m 3 -3.38 m 4 -3.18 m 5 +1.13 m 6 +6.58 m 7 +4.67 m 8 +1.52 m 9 -7.73 m 10 In the formula, g represents the miRNA expression index, m 1 , m 2 , m 3 , m 4 , m 5 , m 6 , m 7 , m 8 , m 9 , m 10 represent the expression levels of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p, and miR-210-3p, respectively; The clinical variable combination mainly consisted of age, KPS and GPA.

6. The method for constructing a prognostic model for lung cancer brain metastasis according to claim 5, characterized in that: The prognostic model is specifically: h(t)=h 0 (t) exp( PI ) PI =1.43 X 1 -0.28 X 2 -22.27 X 3 +58.72 X 4 In the formula, h(t) represents the risk function, h 0 (t) represents the benchmark risk function, X 1 Indicates age, X 2 represents KPS, X 3 Indicates GPA, X 4 represents the miRNA expression index, t represents the predicted survival time, PI Represents the prognostic index.

7. A method for predicting the prognosis of lung cancer brain metastasis based on the plasma miRNA combination marker as claimed in claim 1 or 2, characterized in that: The prognosis prediction system comprises the following steps: obtaining the expression level of each plasma miRNA in an in vitro biological plasma sample and the clinical information corresponding to the in vitro biological plasma sample, inputting the expression levels of all plasma miRNAs and the clinical information into a prognosis model, the prognosis model obtaining a miRNA expression index according to the expression levels of all plasma miRNAs, and then obtaining a predicted survival rate according to the miRNA expression index and the clinical information and outputting it as a prognosis prediction result.

8. The method for predicting the prognosis of lung cancer brain metastasis according to claim 7, characterized in that: The clinical variables in the clinical information include age, KPS and GPA, and the predicted survival rate is obtained by the following formula: S(t) =exp(- ∫ t 0 h 0 (u)du ×exp( PI )) PI =1.43 X 1 -0.28 X 2 -22.27 X 3 +58.72 X 4 In the formula, S(t) Represents the predicted survival time t The predicted survival rate, t represents the predicted survival time, ∫ t 0 h 0 (u)du Represents the predicted survival time t The cumulative risk within PI represents the prognostic index, X 1 Indicates age, X 2 represents KPS, X 3 Indicates GPA, X 4 Represents the miRNA expression index.

9. The method for predicting the prognosis of lung cancer brain metastasis according to claim 8, characterized in that: The miRNA expression index is obtained by the following formula: g= 7.97-4.45 m 1 -2.85 m 2 +1.82 m 3 -3.38 m 4 -3.18 m 5 +1.13 m 6 +6.58 m 7 +4.67 m 8 +1.52 m 9 -7.73 m 10 In the formula, g represents the miRNA expression index, m 1 , m 2 , m 3 , m 4 , m 5 , m 6 , m 7 , m 8 , m 9 , m 10 They represent the expression levels of miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p, respectively.

10. A prognosis prediction system applied to the prognosis prediction method according to any one of claims 7 to 9, characterized in that: The prognosis prediction system comprises: An input module, used for receiving clinical information corresponding to the in vitro biological plasma sample to be predicted and the expression level of each plasma miRNA; The prognostic model module is used to input clinical information and the expression levels of all plasma miRNAs into the prognostic model to obtain the predicted survival rate; The output module is used to output the predicted survival rate to the prognosis prediction system or to visualize the predicted survival rate.

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