A method for detecting platelet markers in non-small cell lung cancer

Abstract

The present invention relates to the field of biomedicine, and specifically is a method for detecting platelet markers of non-small cell lung cancer, comprising the steps of: A. acquiring a gene data source; B. preprocessing gene data; C. analyzing gene differences; D. screening prognostic genes; E. selecting prognostic characteristic genes; and F. displaying analysis results. The present invention can determine characteristic genes that are closely related to the prognosis of patients with non-small cell lung cancer: by analyzing differential genes between different subtypes, the present invention determines ten characteristic genes that are closely related to the prognosis of patients with non-small cell lung cancer, providing new clues for further prognosis evaluation.

Description

Technical Field

[0001] The invention relates to the field of biomedicine, and in particular to a method for detecting platelet markers of non-small cell lung cancer. Background Art

[0002] In the current study, there is evidence that BEST3 belongs to the Bestrophins family of chloride channels and is thought to play an important role in cell protection. However, although there have been some studies on BEST3 in cell protection mechanisms outside the brain, its role in platelets has not been fully explored.

[0003] The main disadvantage of the prior art in this regard is the lack of research and application of specific protective mechanisms for platelets. This limitation leads to deficiencies in the treatment of platelet-related diseases and the prevention and treatment of thrombosis. Therefore, the key to the present invention is to propose and develop a new technology by analyzing the significance of the key gene BEST3 in platelet subtypes in non-small cell lung cancer to solve the interaction between the BEST3 gene and platelets that the prior art cannot solve, thereby providing a new approach for the treatment and prevention of platelet-related diseases. Summary of the invention

[0004] The present invention provides a method and system for detecting platelet markers of non-small cell lung cancer, by determining characteristic genes closely related to the prognosis of patients with non-small cell lung cancer: by analyzing differential genes between different subtypes, characteristic genes closely related to the prognosis of patients with non-small cell lung cancer are determined, providing new clues for further prognosis evaluation.

[0005] The present invention provides a method for detecting platelet markers of non-small cell lung cancer, comprising the steps of:

[0006] A. Gene data source acquisition: Gene expression profile chip datasets of non-small cell lung cancer and normal control group and platelet-related gene ontology datasets were obtained from public databases;

[0007] B. Gene data preprocessing: Clean, format and standardize the obtained gene expression profile chip datasets of non-small cell lung cancer and normal control group and platelet-related gene ontology datasets;

[0008] C. Gene difference analysis: The Limma toolkit of R language was used to perform differential gene analysis on the processed gene expression profile chip data set and platelet-related gene ontology data set based on the variance analysis model algorithm, and non-small cell lung cancer mRNA with statistically significant expression differences between non-small cell lung cancer and normal control group was screened out; then the differential non-small cell lung cancer mRNA and platelet-related genes of the normal control group were subjected to set operations to obtain the non-small cell cancer characteristic platelet differential gene expression matrix; the R language clustering algorithm was used to cluster the non-small cell cancer characteristic platelet differential gene expression matrix to obtain different subtypes of non-small cell cancer characteristic platelet differential genes, and the stability and repeatability of different subtypes were tested to obtain different subtypes of platelet differential genes;

[0009] D. Prognostic gene screening: Use survival analysis to screen out subtype platelet differential genes related to patient survival time:

[0010] E. Selection of prognostic characteristic genes: The randomForestSRC toolkit of R language was used to adopt the random forest algorithm to select the first prognostic gene of the subtype platelet difference gene related to the patient's survival time. The glmnet tool of R language was used to adopt the LASSO-COX algorithm to screen the second prognostic gene of the subtype platelet difference gene related to the patient's survival time. The intersection operation of the first prognostic gene and the second prognostic gene was performed to obtain the prognostic characteristic gene.

[0011] F. Display analysis results: Use R language software package to draw visual volcano map and Venn diagram of prognostic characteristic genes.

[0012] Furthermore, in step A, the public databases include the NCBI-GEO public database and the MSigDB public database, and the gene expression profile chip dataset of non-small cell lung cancer and the gene ontology dataset related to platelets are obtained from the NCBI-GEO public database; the gene expression profile chip dataset and the gene ontology dataset related to platelets of the normal control group are obtained from the MSigDB public database.

[0013] Furthermore, the survival analysis method in step D is a univariate Cox regression analysis method.

[0014] Furthermore, the significance threshold of univariate Cox regression analysis was set at 0.01-0.02.

[0015] The beneficial effects of the present invention include:

[0016] 1. Identification of the diversity of platelets in non-small cell lung cancer: By analyzing clinical and genomic data, the present invention successfully identified three different platelet subtypes, revealing their significant differences in tumor tissue type and clinical stage.

[0017] 2. Determine the characteristic genes closely related to the prognosis of patients with non-small cell lung cancer: By analyzing the differential genes between different subtypes, the present invention has determined ten characteristic genes closely related to the prognosis of patients with non-small cell lung cancer, providing new clues for further prognosis evaluation.

[0018] 3. Study the abnormal expression of BEST3 gene in non-small cell lung cancer and its potential role: The present invention verified the abnormal expression of BEST3 gene in tumor patients through flow cytometric analysis and TCGA database, suggesting that it may become a potential clinical indicator for the progression and prognosis of non-small cell lung cancer, and explored its function and mechanism in tumor biology.

[0019] 4. Propose the association between platelet subtypes and prognosis of non-small cell lung cancer: The present invention found the association between different platelet subtypes and patient prognosis, especially the Cluster 1 subtype was associated with a poor prognosis, which provides a new direction for the prognosis assessment of non-small cell lung cancer.

[0020] 5. Provide new directions for individualized treatment strategies and prognosis assessment: By studying platelet diversity and abnormal expression of the BEST3 gene, the present invention provides a new direction for developing individualized treatment strategies and prognosis assessment, which is expected to help doctors better predict patients' disease progression and develop more effective treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a method for detecting platelet markers of non-small cell lung cancer.

[0022] Figure 2 (A) Volcano plot of differentially expressed platelet genes in the GSE89843 dataset.

[0023] Figure 3 This is a diagram for identifying platelet-related subtypes.

[0024] Figure 4 This is a validation diagram of BEST3 protein expression. DETAILED DESCRIPTION

[0025] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0026] like Figure 1 As shown, the technical solution 1 of the present invention is a method for detecting platelet markers of non-small cell lung cancer, which is characterized by comprising the steps of:

[0027] A. Gene data source acquisition: Gene expression profile chip datasets of non-small cell lung cancer and normal control group and platelet-related gene ontology datasets were obtained from public databases;

[0028] B. Gene data preprocessing: Clean, format and standardize the obtained gene expression profile chip datasets of non-small cell lung cancer and normal control group and platelet-related gene ontology datasets;

[0029] C. Gene difference analysis: The Limma toolkit of R language was used to perform differential gene analysis on the processed gene expression profile chip data set and platelet-related gene ontology data set based on the variance analysis model algorithm, and non-small cell lung cancer mRNA with statistically significant expression differences between non-small cell lung cancer and normal control group was screened out; then the differential non-small cell lung cancer mRNA and platelet-related genes of the normal control group were subjected to set operations to obtain the non-small cell cancer characteristic platelet differential gene expression matrix; the R language clustering algorithm was used to cluster the non-small cell cancer characteristic platelet differential gene expression matrix to obtain different subtypes of non-small cell cancer characteristic platelet differential genes, and the stability and repeatability of different subtypes were tested to obtain different subtypes of platelet differential genes;

[0030] D. Prognostic gene screening: Use survival analysis to screen out subtype platelet differential genes related to patient survival time;

[0031] E. Selection of prognostic characteristic genes: The randomForestSRC toolkit of R language was used to adopt the random forest algorithm to select the first prognostic gene of the subtype platelet difference gene related to the patient's survival time. The glmnet tool of R language was used to adopt the LASSO-COX algorithm to screen the second prognostic gene of the subtype platelet difference gene related to the patient's survival time. The intersection operation of the first prognostic gene and the second prognostic gene was performed to obtain the prognostic characteristic gene.

[0032] F. Display analysis results: Use R language software package to draw visual volcano map and Venn diagram of prognostic characteristic genes.

[0033] After analyzing the test data, Figure 2 Acquisition of differential platelet gene PRDEGs (A) Volcano plot of differential platelet genes in the GSE89843 dataset (log2|FC|>1, adj.P.Val<0.01), blue indicates down-regulation, red indicates up-regulation. (B) Volcano plot of differential genes in normal VS disease groups in the GTEx lung cancer dataset (log2|FC|>1, adj.P.Val<0.01), blue indicates down-regulation, red indicates up-regulation. (C) PRGs and NSCLC DEGs were intersected by Veen online drawing software to obtain PRDEGs. (D) Heat map showing 50 genes with significant differences.

[0034] Figure 3 Identification of platelet-related subtypes (A) Typing of differential platelet-related genes, and selecting the optimal number of clusters to divide into three subtypes. (B) Differences between different cancer grouping subtypes. The heat map shows the differential genes between the three subtypes, and the top genes upregulated between each subtype are selected to draw a heat map for display. (C) Comparison of survival prognosis among the three subtypes.

[0035] Figure 4 Verification of BEST3 protein expression in the blood. (AC) FCM analysis of single staining of BEST3 antibody. Plasma specimens were single stained with APC-coupled mouse Bestrophin 3 antibody. The groups were negative control group; healthy group; and cancer group. (B) FCM analysis of activated platelets. Plasma specimens were double stained with PE-coupled human CD62P antibody and APC-coupled mouse Bestrophin 3 antibody, respectively. The horizontal axis is Bestrophin 3 APC-A intensity, and the vertical axis is CD62PPE-A intensity. (C) Expression of BEST3 protein on platelets of tumor patients and healthy subjects, *: p <= 0.05. (D) Expression of BEST3 protein in activated platelets.

[0036] The present invention identifies the diversity of platelets in non-small cell lung cancer: By analyzing clinical data and genomic data, the present invention successfully identified three different platelet subtypes, revealing their significant differences in tumor tissue type and clinical stage, and determined the characteristic genes closely related to the prognosis of patients with non-small cell lung cancer: By analyzing the differential genes between different subtypes, the present invention determined ten characteristic genes closely related to the prognosis of patients with non-small cell lung cancer, providing new clues for further prognosis evaluation, and studying the abnormal expression of BEST3 gene in non-small cell lung cancer and its potential role: The present invention verified the abnormal expression of BEST3 gene in tumor patients through flow cytometry analysis and TCGA database Expression, suggesting that it may become a potential clinical indicator for the progression and prognosis of non-small cell lung cancer, and explored its function and mechanism in tumor biology, and proposed the association between platelet subtypes and the prognosis of non-small cell lung cancer: The present invention found the association between different platelet subtypes and patient prognosis, especially the Cluster1 subtype was associated with a poor prognosis, which provided a new direction for the prognosis evaluation of non-small cell lung cancer, and provided a new direction for individualized treatment strategies and prognosis evaluation: By studying platelet diversity and abnormal expression of the BEST3 gene, the present invention provides a new direction for the development of individualized treatment strategies and prognosis evaluation, which is expected to help doctors better predict patients' disease progression and formulate more effective treatment plans.

[0037] Furthermore, in step A, the public databases include the NCBI-GEO public database and the MSigDB public database, and the gene expression profile chip dataset of non-small cell lung cancer and the gene ontology dataset related to platelets are obtained from the NCBI-GEO public database; the gene expression profile chip dataset and the gene ontology dataset related to platelets of the normal control group are obtained from the MSigDB public database.

[0038] Furthermore, the survival analysis method in step D is a univariate Cox regression analysis method.

[0039] Furthermore, the significance threshold of univariate Cox regression analysis was set at 0.01-0.02.

[0040] The above-mentioned embodiments only express the specific implementation methods of the present application, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the protection scope of the present application. It should be pointed out that for ordinary technicians in this field, relevant deformations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for detecting platelet markers in non-small cell lung cancer, Its characteristics are: including the steps of: A. Gene data source acquisition: Gene expression profile chip datasets of non-small cell lung cancer and normal control group and platelet-related gene ontology datasets were obtained from public databases; B. Gene data preprocessing: Clean, format and standardize the obtained gene expression profile chip datasets of non-small cell lung cancer and normal control group and platelet-related gene ontology datasets; C. Gene difference analysis: The Limma toolkit of R language was used to perform differential gene analysis on the processed gene expression profile chip data set and platelet-related gene ontology data set based on the variance analysis model algorithm, and the non-small cell lung cancer mRNA with statistically significant expression differences between non-small cell lung cancer and the normal control group was screened out; the differential non-small cell lung cancer mRNA and the platelet-related genes of the normal control group were then subjected to set operations to obtain the non-small cell cancer characteristic platelet differential gene expression matrix; the R language clustering algorithm was used to cluster the non-small cell cancer characteristic platelet differential gene expression matrix to obtain different subtypes of non-small cell cancer characteristic platelet differential genes, cluster1, cluster2, and cluster3, and the stability and repeatability of different subtypes were tested to obtain different subtypes of platelet differential genes; D. Prognostic gene screening: Use survival analysis to screen out subtype platelet differential genes related to patient survival time; E. Selecting prognostic feature genes: using the randomForestSRC toolkit of R language and the random forest algorithm to select the first prognostic gene of the subtype platelet difference gene related to the patient's survival time, using the glmnet tool of R language and the LASSO-COX algorithm to screen the second prognostic gene of the subtype platelet difference gene related to the patient's survival time, performing the intersection operation of the first prognostic gene and the second prognostic gene to obtain the prognostic feature gene, and verifying the abnormal expression of the BEST3 gene in tumor patients through flow cytometry analysis and TCGA database; F. Display analysis results: Use R language software package to draw visual volcano map and Venn diagram of prognostic characteristic genes.

2. A method for detecting platelet markers in non-small cell lung cancer according to claim 1, characterized in that: In step A, public databases include NCBI-GEO public database and MSigDB public database. Gene expression profile chip datasets and platelet-related gene ontology datasets of non-small cell lung cancer are obtained from NCBI-GEO public database; and gene expression profile chip datasets and platelet-related gene ontology datasets of normal control group population are obtained from MSigDB public database.

3. A method for detecting platelet markers in non-small cell lung cancer according to claim 1, characterized in that: The survival analysis method in step D was univariate Cox regression analysis.

4. A method for detecting platelet markers in non-small cell lung cancer as claimed in claim 3, characterized in that: The significance threshold of univariate Cox regression analysis was set at 0.01-0.02.

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