Biomarker combination for auxiliary diagnosis of lung adenocarcinoma and risk early warning of lung adenocarcinoma complicated with ischemic stroke and application of biomarker combination

Abstract

The invention discloses a biomarker combination for auxiliary diagnosis of lung adenocarcinoma and risk early warning of lung adenocarcinoma complicated with ischemic stroke and application of the biomarker combination. The biomarker combination comprises an SLC25A39 gene, an NME4 gene, an LDHA gene and an SLC7A5 gene. Reagents for detecting the expression levels of the SLC25A39 gene, the NME4 gene, the LDHA gene and the SLC7A5 gene in the biomarker combination can be applied to preparation of products for auxiliary diagnosis of the lung adenocarcinoma and risk early warning of the lung adenocarcinoma complicated with ischemic stroke, and the effect of one-detection dual-energy is achieved.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a combination of biomarkers for the auxiliary diagnosis of lung adenocarcinoma and for early warning of the risk of ischemic stroke in lung adenocarcinoma, and their application. Background Technology

[0002] Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, with high incidence and mortality rates. Clinical studies have shown that patients with malignant tumors generally have a hypercoagulable state, and the hypoxic metabolic characteristics of the tumor microenvironment are closely related to vascular lesions, which significantly increases the risk of ischemic stroke (IS) in patients with lung adenocarcinoma.

[0003] Currently, the clinical diagnosis of lung adenocarcinoma mainly relies on imaging and pathology, while the risk assessment of ischemic stroke depends on traditional coagulation indicators or imaging examinations. Existing diagnostic methods usually treat these two conditions as independent diseases, lacking a universal biomarker that can simultaneously reflect the malignant characteristics of lung adenocarcinoma and its associated risk of ischemic stroke at the molecular level. Summary of the Invention

[0004] Objective: To overcome the shortcomings of the existing technology, the present invention provides a combination of biomarkers for the auxiliary diagnosis of lung adenocarcinoma and the early warning of the risk of lung adenocarcinoma complicated with ischemic stroke, and the application thereof. The combination of biomarkers is used to indicate the risk of lung adenocarcinoma and its complication with ischemic stroke.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a combination of biomarkers for the auxiliary diagnosis of lung adenocarcinoma and for early warning of the risk of ischemic stroke in lung adenocarcinoma, the combination of biomarkers including the SLC25A39 gene, the NME4 gene, the LDHA gene and the SLC7A5 gene.

[0007] In some embodiments, the biomarker combination consists of the SLC25A39 gene, the NME4 gene, the LDHA gene, and the SLC7A5 gene.

[0008] In a second aspect, the present invention provides the use of reagents for detecting the expression levels of SLC25A39, NME4, LDHA and SLC7A5 genes in the biomarker combination described in the first aspect in the preparation of products for the auxiliary diagnosis of lung adenocarcinoma and for early warning of the risk of ischemic stroke in lung adenocarcinoma.

[0009] In some embodiments, the product includes a kit or reagent.

[0010] Thirdly, the present invention provides a product for the auxiliary diagnosis of lung adenocarcinoma and for early warning of the risk of ischemic stroke in lung adenocarcinoma, the product comprising a detection reagent for detecting the expression levels of SLC25A39 gene, NME4 gene, LDHA gene and SLC7A5 gene.

[0011] In some embodiments, the product includes a kit.

[0012] In some embodiments, the detection reagent includes primer pairs or probes specifically designed for the mRNA sequences of the SLC25A39 gene, NME4 gene, LDHA gene, and SLC7A5 gene.

[0013] In some embodiments, the detection reagent includes antibodies that specifically bind to the proteins encoded by the SLC25A39 gene, NME4 gene, LDHA gene, and SLC7A5 gene.

[0014] Beneficial Effects: The biomarker combination provided by this invention achieved an AUC of 0.962 in the TCGA lung adenocarcinoma (LUAD) training set, and AUCs of 0.924 and 0.992 in two independent external validation sets (GSE31210 and GSE30219), respectively. This demonstrates that the biomarker combination can indicate the occurrence of lung adenocarcinoma with extremely high accuracy and robustness. The reagents used to detect the expression levels of SLC25A39, NME4, LDHA, and SLC7A5 genes have the application of auxiliary diagnosis of lung adenocarcinoma and early warning of the risk of ischemic stroke associated with lung adenocarcinoma. In the ischemic stroke (IS) dataset (GSE146882) of the Gene Expression Comprehensive Database (GEO), the AUC reached 0.940, demonstrating its potential for risk warning. Monitoring the expression levels of SLC25A39, NME4, LDHA, and SLC7A5 genes in lung adenocarcinoma patients can not only assess tumor status but also reflect the patient's potential risk of ischemic stroke, achieving "dual functionality in one test." This invention combines differential analysis, PPI network topology analysis, and cross-validation of two machine learning algorithms (SVM-RFE and Random Forest) to ensure the high reliability of the biomarkers. Attached Figure Description

[0015] Figure 1 This is a diagram for identifying differentially expressed genes between normal and diseased tissues in an embodiment of the present invention; wherein, A and B are volcano diagrams of differential analysis of the TCGA LUAD and GSE146882 datasets, respectively; C and D are heatmaps of the top 10 differentially expressed genes in the two datasets, respectively; E and F are Venn diagrams of up- and down-regulated genes.

[0016] Figure 2This is a diagram of protein-protein interaction (PPI) network analysis and core gene identification in an embodiment of the present invention; wherein, A is a PPI network diagram and B is a key gene network diagram screened based on Cytoscape.

[0017] Figure 3 The graph shows the performance of feature selection and classification based on machine learning in this embodiment of the invention; where A and B are the SVM-RFE feature selection accuracy curves; C and D are the Random Forest feature importance ranking and ROC curves; and E is the UpSet graph, which shows the four common core genes that were finally determined.

[0018] Figure 4 This is a risk warning value assessment diagram of biomarker combinations in the TCGA LUAD and GSE146882 datasets and cell experiments in the embodiments of the present invention; wherein, A and B are gene expression box plots; C is a qRT-PCR analysis result of the expression of LDHA, NME4, SLC25A39 and SLC7A5 mRNA in normal bronchial epithelial cell line (BEAS-2B) and lung cancer cell lines (HCC827 and NCI-H460); D and E are single-gene ROC curves; F and G are multi-gene combined risk warning ROC curves.

[0019] Figure 5 This is a risk warning value verification diagram of the biomarker combination in the external independent validation sets (GSE31210 and GSE30219) in the embodiments of the present invention; wherein, A and B are the single-gene ROC curves of each core gene in the GSE31210 and GSE30219 datasets, respectively; C and D are the ROC curves of the multi-gene joint risk warning model in the GSE31210 and GSE30219 datasets, respectively. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use.

[0021] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may include different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0022] The present invention will be further described below with reference to the embodiments.

[0023] Example 1: Screening and determination of biomarker combinations for auxiliary diagnosis of lung adenocarcinoma and early warning of the risk of ischemic stroke in lung adenocarcinoma.

[0024] I. Data Collection and Preprocessing

[0025] 1. Data Source

[0026] Lung adenocarcinoma (LUAD) training set: from the Cancer Genome Atlas (TCGA) LUAD dataset, containing 59 normal samples and 539 tumor samples.

[0027] Ischemic stroke (IS) dataset: from the Gene Expression Comprehensive Database (GEO) GSE146882 dataset, containing 10 normal samples and 10 IS samples.

[0028] Lung adenocarcinoma (LUAD) validation set: Additional LUAD expression datasets were obtained from the GEO database, including the GSE31210 dataset (20 normal samples and 226 tumor samples) and the GSE30219 dataset (14 normal samples and 85 tumor samples).

[0029] 2. Analytical Methods

[0030] Differential expression analysis was performed on the raw count matrix of TCGA LUAD using DESeq2 (version 1.36.0). Differential analysis was performed on the GEO microarray data using the limma package (version 3.52.2). The selection criteria for differentially expressed genes (DEGs) were: |log2 fold change| > 0.58 and P < 0.05.

[0031] 3. Results

[0032] like Figure 1 As shown in Figure A, in the TCGA LUAD dataset, compared with the normal control group, 5,547 genes were upregulated and 3,151 genes were downregulated in tumor tissue. Figure 1 As shown in Figure B, in the GSE146882 dataset, compared with the normal control group, 417 genes were upregulated and 498 genes were downregulated in IS tissues. The heatmaps of the top 10 differentially expressed genes from both datasets are shown below. Figure 1 As shown in C and D. Perform Venn analysis, as follows... Figure 1 As shown in Figure F, a total of 54 genes were continuously downregulated in both datasets; Figure 1 As shown in Figure E, there are a total of 85 genes that are continuously upregulated in both datasets. These 85 genes are candidate genes for subsequent analysis.

[0033] II. Screening and Identification of Core Genes

[0034] 1. PPI Network Construction

[0035] PPI analysis was performed on 85 candidate genes using the STRING online database, such as... Figure 2 As shown in Figure A, 49 genes were retained after removing isolated nodes. Visualization analysis based on Cytoscape ( Figure 2 According to the data in Figure B, the top 10 genes ranked by "stress" centrality are: CDC34, RPS18, CCNB1, RPL36A, TXN, SNRPE, LDHA, DSG4, PI3, and HMBS.

[0036] 2. Feature Selection in Machine Learning

[0037] Two algorithms were used to screen the above 49 genes.

[0038] SVM-RFE analysis: 22 optimal features were identified in TCGA LUAD, with an accuracy of 0.9995. Figure 3 (A); 25 optimal features were identified in GSE146882, with an accuracy of 0.96 ( Figure 3 (B)

[0039] Random forest analysis: AUC reached 0.9971 in TCGA LUAD ( Figure 3 In GSE146882, the AUC reached 0.93 (C). Figure 3 (D).

[0040] 3. Intersection filtering

[0041] UpSet graph analysis identified genes that were consistently selected by both algorithms in both datasets, such as... Figure 3As shown in Figure E, four core genes (HubGenes) were finally identified: SLC25A39, NME4, LDHA, and SLC7A5. The biomarker combination for the auxiliary diagnosis of lung adenocarcinoma and the early warning of the risk of ischemic stroke in lung adenocarcinoma includes the SLC25A39 gene, NME4 gene, LDHA gene, and SLC7A5 gene.

[0042] Example 2: Risk Prediction Value Assessment of Biomarker Combinations

[0043] 1. Verification of expression level

[0044] Wilcoxon rank-sum test ( Figure 4 As shown in Figures A and B, the expression levels of SLC25A39, NME4, LDHA, and SLC7A5 in tumor / disease tissues of the TCGA LUAD and GSE146882 datasets were significantly higher than those in the normal control group (P<0.05).

[0045] 2. ROC curve analysis

[0046] TCGA LUAD (Lung Adenocarcinoma): such as Figure 4 As shown in Figure E, the AUC of the multivariate logistic regression model (combining 4 genes) was as high as 0.962 (95% CI: 0.946-0.978). Figure 4 As shown in Figure C, the AUCs of the single genes are: LDHA (AUC=0.926), NME4 (AUC=0.893), SLC25A39 (AUC=0.937), and SLC7A5 (AUC=0.919).

[0047] Ischemic stroke (IS) dataset GSE146882: such as Figure 4 As shown in Figure F, the AUC of the multivariate logistic regression model is as high as 0.940 (95% CI: 0.841–1.000). Figure 4 As shown in Figure D, the AUC of single genes is above 0.820.

[0048] 3. Cellular experimental verification

[0049] Cell culture methods: Cells were purchased from iCellBioscience Inc. (Shanghai, China) and included the human bronchial epithelial cell line BEAS-2B and the human lung cancer cell lines HCC827 and NCI-H460. BEAS-2B cells were cultured in Dulbecco modified Eagle medium (DMEM; Gibco, USA); HCC827 and NCI-H460 cells were maintained in RPMI 1640 medium (Gibco). All media were supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin-streptomycin solution (P / S; Gibco). All cells were incubated at 37°C in a CO2-containing humidified incubator.

[0050] RNA extraction and quantitative real-time PCR (qRT-PCR): Total RNA was isolated from cultured cells using RNAiso Easy (Takara, Japan) according to the manufacturer's instructions. The concentration and purity of the extracted RNA were determined using a NanoDrop 2000 spectrophotometer (ThermoFisher Scientific, USA). Subsequently, PrimeScript was used... TM Complementary DNA (cDNA) was synthesized using the RTReagent Kit (Takara). TB Green was used. ® Premix Ex Taq TM (Takara) Real-time quantitative PCR (qRT-PCR) was performed on a Gentier 96 real-time quantitative PCR system (Tianlong, China). The primers used are shown in Table 1. Thermal cycling conditions were set according to the kit instructions. β-actin was used as an internal control, and a 2... −ΔΔCt The relative mRNA expression levels of LDHA, nucleoside diphosphate kinase 4 (NME4), solute carrier family 25 member 39 (SLC25A39), and solute carrier family 7 member 5 (SLC7A5) were calculated.

[0051] Table 1 Primer sequences used for qRT-PCR

[0052] like Figure 4 As shown in Figure C, compared with the normal human bronchial epithelial cell line BEAS-2B, the expression levels of LDHA, NME4, SLC25A39 and SLC7A5 in lung cancer cell lines HCC827 and NCI-H460 were significantly increased (P<0.05). The combination of biomarkers composed of SLC25A39, NME4, LDHA and SLC7A5 genes showed risk warning efficacy.

[0053] Example 3: Validation of biomarker combinations in a multicenter independent cohort

[0054] To demonstrate the clinical universality and robustness of the biomarker combination, this embodiment further validates it on two independent external LUAD datasets (GSE31210 and GSE30219).

[0055] 1. GSE31210 Validation Results

[0056] Single gene efficacy: such as Figure 5 As shown in Figure A, LDHA (AUC=0.861), NME4 (AUC=0.877), SLC25A39 (AUC=0.892), and SLC7A5 (AUC=0.776).

[0057] Joint risk early warning effectiveness: such as Figure 5 As shown in Figure C, in the multivariate logistic regression model that includes all core genes, the AUC = 0.924 (95% CI: 0.886-0.962), demonstrating extremely high accuracy in risk warning.

[0058] 2. GSE30219 Validation Results

[0059] Single gene efficacy: such as Figure 5 As shown in Figure B, LDHA (AUC=0.977), NME4 (AUC=0.893), SLC25A39 (AUC=0.894), and SLC7A5 (AUC=0.874).

[0060] Joint risk early warning effectiveness: such as Figure 5 As shown in Figure D, in the multivariate logistic regression model that includes all core genes, the AUC = 0.992 (95% CI: 0.980-1.000).

[0061] 3. Conclusion

[0062] The above multicenter validation results demonstrate that the biomarker combination consisting of SLC25A39, NME4, LDHA, and SLC7A5 genes exhibits extremely stable high-risk early warning efficacy in lung adenocarcinoma samples from different sources and platforms. Combined with its high expression characteristics in stroke datasets, this combination can serve as a powerful tool for early warning of lung adenocarcinoma risk and to indicate the concurrent risk of ischemic stroke.

[0063] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A combination of biomarkers for the auxiliary diagnosis of lung adenocarcinoma and for early warning of the risk of ischemic stroke in lung adenocarcinoma, characterized in that, The biomarker combination includes the SLC25A39 gene, NME4 gene, LDHA gene, and SLC7A5 gene.

2. The biomarker combination for the auxiliary diagnosis of lung adenocarcinoma and the early warning of the risk of ischemic stroke in lung adenocarcinoma as described in claim 1, characterized in that, The biomarker combination consists of the SLC25A39 gene, NME4 gene, LDHA gene, and SLC7A5 gene.

3. The application of reagents for detecting the expression levels of SLC25A39, NME4, LDHA and SLC7A5 genes in the biomarker combination as described in claim 1 or 2 in the preparation of products for the auxiliary diagnosis of lung adenocarcinoma and for early warning of the risk of ischemic stroke in lung adenocarcinoma.

4. The application according to claim 3, characterized in that, The products include kits or reagents.

5. A product for the auxiliary diagnosis of lung adenocarcinoma and for early warning of the risk of ischemic stroke in lung adenocarcinoma, characterized in that, The product includes detection reagents for detecting the expression levels of the SLC25A39 gene, NME4 gene, LDHA gene, and SLC7A5 gene.

6. The product according to claim 5, characterized in that, The product includes a reagent kit.

7. The product according to claim 5, characterized in that, The detection reagents include primer pairs or probes specifically designed for the mRNA sequences of the SLC25A39, NME4, LDHA, and SLC7A5 genes.

8. The product according to claim 5, characterized in that, The detection reagent includes antibodies that specifically bind to the proteins encoded by the SLC25A39, NME4, LDHA, and SLC7A5 genes.

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