Novel marker for glioblastoma tumor transition region and application of novel marker

Abstract

The invention relates to a novel marker for a glioblastoma tumor transition region and application of the novel marker, and belongs to the technical field of biological detection. In order to solve the technical problems that in the prior art, tumor invasion leading edge markers cannot be effectively recognized, and postoperative residues and prognosis are difficult to accurately evaluate, 19 gene markers with high specificity expression in a tumor transition region, such as SELENOP, SHTN1 and CTNNA3, are screened out by combining space transcriptome analysis with a machine learning algorithm. The expression level of the marker is detected through digital imaging, nucleic acid sequencing and other technologies, a logistic regression prediction model with the AUC value larger than 0.95 is constructed, and the logistic regression prediction model is applied to diagnostic kit preparation, tumor layering screening and prognosis evaluation. According to the invention, precise recognition of the tumor transition region is realized, and a molecular basis is provided for postoperative recurrence monitoring, individualized treatment scheme making and novel treatment target screening.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological detection, and relates to a novel marker of a glioblastoma tumor transition zone and an application thereof. Background Art

[0002] Glioblastoma (GBM) is a highly aggressive malignant brain tumor characterized by diffuse infiltration, making complete surgical resection difficult and highly resistant to chemoradiotherapy, resulting in a high recurrence rate and extremely poor prognosis. The transition zone between the tumor and normal tissue is a critical area for GBM invasion and spread. Located between the main tumor and normal brain tissue, it serves as a key interface for tumor cells to break through tissue barriers and spread outward. The GBM tumor transition zone (TZZ) has unique microenvironmental features, including pro-invasive signals (CXCL12 / CXCR4 axis, matrix metalloproteinases-mediated matrix degradation factors), immunosuppressive factors (TGF-β, PD-L1), and microvascular proliferation. It shares certain similarities with the distal invasive front (IF) in molecular characteristics and invasion patterns. Systematic analysis of the molecular mechanisms of the GBM-TZZ is crucial for understanding GBM invasion patterns and optimizing clinical intervention strategies.

[0003] Identifying GBM-TZZ biomarkers not only helps reveal the mechanisms of tumor invasion but also plays an important role in clinical applications. GBM-TZZ-related markers can be used to monitor residual disease after surgery and, combined with liquid biopsy or imaging analysis, improve the ability to predict recurrence. Furthermore, these molecules are closely associated with patient prognosis and can be used to construct multi-gene prognostic models for accurate classification and risk assessment. Furthermore, GBM-TZZ-related molecules may become novel therapeutic targets, such as integrins, matrix metalloproteinases (MMPs), or AC-like cell-related pathways, providing new therapeutic strategies for inhibiting GBM invasiveness. Therefore, in-depth exploration of GBM transition zone-related biomarkers will provide important support for early diagnosis, recurrence monitoring, and personalized treatment of GBM.

[0004] Due to the high similarity in biological characteristics between GBM-TZZ and invasive zones, predictive models based on invasive zones can be applied to the study of GBM transition zones, further enhancing the assessment of invasive potential. For example, multigene models based on co-expressed genes in GBM-TZZ and invasive zones can more accurately predict patient prognosis. Integrating spatial omics data can reveal the dynamic evolution of GBM-TZZ and distal invasive zones, providing a basis for the development of more clinically valuable invasion prediction systems. These studies will help optimize the clinical management of GBM and ultimately improve patient outcomes. Summary of the Invention

[0005] In view of this, one of the objects of the present invention is to provide a set of novel markers of the glioblastoma tumor transition zone, and the second object is to use the novel markers of the glioblastoma tumor transition zone in the diagnosis, evaluation, stratification screening and target screening of glioblastoma.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] This method collects GBM samples containing both tumor and invasion front zones, performs spatial transcriptome sequencing, and uses hematoxylin and eosin staining to annotate spatial regions. Spatial transcriptome data are used to define GBM transition zones and integrate multiple samples. Machine learning is used to identify signatures of highly expressed GBM-TZZ genes and identify candidate biomarkers.

[0008] Methods for constructing a binary classification model of GBM transition zone using these markers:

[0009] A. Public data download

[0010] GBM gene expression matrix, clinical information, and spatial location annotation information of samples were downloaded from IvyGAP and TCGA, respectively;

[0011] B. Construction and evaluation of binary classification models

[0012] Using machine learning methods, we constructed models for GBM-TZZ candidate biomarkers identified in the early screening phase using IvyGAP data and evaluated the model performance using AUC.

[0013] C. Association between biomarkers and prognosis

[0014] COX multivariate regression analysis was used to evaluate the risk of GBM-TZZ biomarkers, and GBM samples from TCGA were classified according to the risk score. Finally, survival curve analysis was performed to evaluate the association between biomarkers and the prognosis of GBM patients.

[0015] The present invention provides a set of novel markers for the glioblastoma tumor transition zone, wherein the novel markers are selected from at least one of SELENOP, SHTN1, CTNNA3, TF, CNTNAP4, ENPP2, MAL, PCSK6, FGFR2, PLP1, ST18, TMEM125, TMEM144, UGT8, CNTN2, MAG, UNC5C, ERMN, and MAN2A1 genes;

[0016] Furthermore, the novel marker is used in the preparation of a kit for diagnosing glioblastoma;

[0017] Preferably, the application includes detecting the expression level of the novel marker gene in the sample by one or more of digital imaging technology, protein immunoassay technology, dye technology, nucleic acid sequencing technology, nucleic acid hybridization technology, chromatography technology, mass spectrometry technology, and next-generation sequencing technology;

[0018] Preferably, the application of novel markers of the glioblastoma tumor transition zone in glioblastoma stratification screening;

[0019] Preferably, the application of novel markers of the glioblastoma tumor transition zone in the prognostic assessment of glioblastoma;

[0020] Preferably, the novel marker of glioblastoma tumor transition zone is used to evaluate the therapeutic effect of glioblastoma;

[0021] Preferably, the novel markers of the glioblastoma tumor transition zone are used to screen therapeutic targets for glioblastoma.

[0022] The beneficial effects of the present invention are:

[0023] 1. High sensitivity detection

[0024] Breakthrough in spatial transcriptome technology: Using SAW spatiotemporal omics analysis software, spatial transcriptome sequencing was performed on 14 GBM samples. An average of 134,013 cells were detected per sample, covering 312 genes, and the cell capture efficiency was improved compared to traditional single-cell sequencing.

[0025] Data comprehensiveness guaranteed: Using Hotspot spatial structure domain analysis technology, 459,424 TZZ region cells were precisely located from 1,876,195 cells, achieving a single-cell spatial positioning error of <5μm.

[0026] Technical verification: HE staining corresponds to the idle data space to ensure that the test results are highly consistent with the pathological characteristics (.

[0027] 2. High clinical precision

[0028] LR model specificity: The logistic regression model constructed based on 19 TZZ markers had an AUC>0.95, which was 40% more accurate than traditional MRI imaging diagnosis (average AUC 0.75).

[0029] Cross-dataset validation: Using an independent dataset from the IvyGAP database for validation, the model achieved a classification accuracy of 92.7% in infiltrated areas versus other areas, demonstrating generalization capabilities.

[0030] Technical reproducibility: Through 10-fold cross-validation, the average standard deviation of the model is <0.03, and the overfitting risk is reduced by 60% after hyperparameter optimization.

[0031] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0033] Figure 1 The number of cells detected in each of the 14 idle samples in our hospital is shown in Figure 2. The X-axis represents different samples, and the Y-axis represents the number of cells after logarithmic (log10) transformation.

[0034] Figure 2 The number of genes detected in cells from each of the 14 idle samples in our hospital is shown in Figure 2. The X-axis represents different samples, and the Y-axis represents the number of genes after logarithmic (log10) transformation.

[0035] Figure 3 The expression levels of 19 GBM-TZZ biomarkers in different spatial structures, GBM-TZZ: GBM transition zone, GBM-Others: other regions of GBM samples excluding the transition zone;

[0036] Figure 4 The multi-model receiver operating characteristic curve (ROCCurve) of 19 GBM-TZZ biomarkers in the LR model, LR: Logistic Regression (LR);

[0037] Figure 5 The risk assessment of 19 GBM-TZZ biomarkers is associated with GBM prognosis. The figure below represents the sample distribution at different time points in different groups. DETAILED DESCRIPTION

[0038] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0039] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0040] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0041] Example 1 Acquisition and preliminary processing of GBM spatial transcriptome data

[0042] Fourteen fresh GBM tissue samples were collected prospectively and unbiased in our hospital. Pathologists obtained GBM tissue samples containing iLE areas and performed serial sections to complete HE staining and spatial transcriptome sequencing. The spatiotemporal omics analysis software SAW was used to complete spatial transcriptome quantitative analysis, obtain spatial location information and gene expression matrix, and correspond the idle data with HE images to facilitate subsequent observation of features and regional identification. A total of 1,876,195 cells and 24,142 genes were identified in the 14 idle samples, of which an average of 134,013 cells and 312 genes were identified per sample ( Figure 1 and Figure 2 ).

[0043] Example 2 Identification of GBM Transition Region

[0044] Hotspot analysis of gene spatial expression patterns was performed, and spatial domains were analyzed for each sample. Single cells were classified based on the domain scores. Subsequently, Seurat was used to calculate the gene expression matrix for each sample in different spatial domains. The resulting spatial expression matrix was normalized, and unsupervised clustering was used to group spatial domains with similar expression patterns across different samples into the same cluster. Specifically highly expressed genes in different clusters were calculated and functional enrichment analysis was performed. Clusters were regionally annotated based on gene function, spatial distribution characteristics, and the location of corresponding HE-stained sections. Ultimately, 246,952 cells from 1,876,195 cells were identified as being in GBM-TZZ.

[0045] Example 3 Screening of candidate markers for GBM transition zone

[0046] 718 highly expressed genes in GBM-TZZ were extracted, and feature screening was performed using machine learning methods, including Random Forest (RF) and Logistic Regression (LR) algorithms. Feature selection was performed on these gene sets, and model evaluation was performed in combination with 10-fold cross-validation to reduce the impact of overfitting and improve the stability of the screening results. Subsequently, the feature importance of each gene in different training and test set partitions was calculated through 20 rounds of traversal, and ranked according to contribution. Finally, genes that appeared stably in all traversal results were screened out to construct the GBM-TZZ feature gene set, laying the foundation for subsequent functional research and clinical prediction model development. Based on the above steps, 19 candidate genes (as shown in Table 1) were finally screened out from the 718 genes highly expressed in the GBM transition zone and used as biomarkers for GBM-TZZ ( Figure 3 ).

[0047] Table 1: 19 biomarkers (UCSC Genome Browser database)

[0048] Biomarkers SELENOP SHTN1 CTNNA3 TF CNTNAP4 ENPP2 MAL PCSK6 FGFR2 PLP1 ST18 TMEM125 TMEM144 UGT8 CNTN2 MAG UNC5C ERMN MAN2A1

[0049] Example 4 GBM-TZZ prediction model construction

[0050] The transcriptome data of different spatial regions of GBM were downloaded from the IvyGAP database, and the data were divided into infiltration group and other regional groups according to the sample annotation information. Subsequently, the GBM-TZZ characteristic gene set screened out by spatial transcriptome analysis was combined with a variety of machine learning algorithms to construct a prediction model for GBM-TZZ. The LR algorithm was mainly used for modeling. During the model training process, the generalization ability of the model was evaluated by hyperparameter optimization and 10-fold cross-validation, and the area under the curve (AUC) under the receiver operating characteristic curve (ROC Curve) was used as the main performance indicator to measure the classification ability of the model. Finally, the optimal model was selected according to the stability and average performance of the AUC value, thereby improving the recognition accuracy of GBM-TZZ, and providing an important reference for subsequent research on tumor invasion mechanisms and clinical stratification diagnosis. Based on the above steps, the prediction model finally constructed had an AUC greater than 0.95 ( Figure 4 ).

[0051] The LR model parameters are:

[0052] log(P(PE=1) / (1-P(PE=1))=

[0053] -4.5243-0.5498·CNTN2+2.8835·CNTNAP4+0.3137·CTNNA3-0.4005·ENPP2+1.0449·ERMN-1.7132·FGFR2-1.7676·MAG+1.9779·MAL-1. 1456·MAN2A1-1.2615·PCSK6+1.3098·PLP1+0.4999·ST18+1.4671·TF-0.4275·TMEM125-0.1186·TMEM144-0.8256·UGT8+0.1304·UNC5C

[0054] Example 5 Correlation between GBM-TZZ and clinical prognosis

[0055] The gene expression matrix and clinical information of GBM were downloaded from the TCGA database, including 147 GBM samples with clinical and prognostic information. The risk of candidate gene sets was assessed using COX multivariate regression analysis, and GBM samples were grouped according to risk scores, including the GBM-risk group with 70 samples. high and GBM-risk of 77 samples low Kaplan-Meier survival analysis was performed according to GBM sample classification to evaluate the prognostic value of these 19 GBM-TZZ biomarkers ( Figure 5 ), providing potential targets for personalized treatment.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A novel marker for the glioblastoma tumor transition zone, characterized by: The biomarker is selected from at least one of SELENOP, SHTN1, CTNNA3, TF, CNTNAP4, ENPP2, MAL, PCSK6, FGFR2, PLP1, ST18, TMEM125, TMEM144, UGT8, CNTN2, MAG, UNC5C, ERMN, and MAN2A1 genes.

2. The novel marker of the glioblastoma tumor transition zone according to claim 1, characterized in that: Application in the preparation of a kit for diagnosing glioblastoma.

3. The use according to claim 2, characterized in that: The application includes detecting the expression level of the biomarker gene in the sample by one or more of digital imaging technology, protein immunoassay technology, dye technology, nucleic acid sequencing technology, nucleic acid hybridization technology, chromatography technology, mass spectrometry technology, and second-generation sequencing technology.

4. Use of the novel marker of the glioblastoma tumor transition zone according to claim 1 in glioblastoma stratification screening.

5. Use of the novel marker of the glioblastoma tumor transition zone according to claim 1 in the prognosis assessment of glioblastoma.

6. Use of the novel marker of the glioblastoma tumor transition zone according to claim 1 in evaluating the therapeutic effect of glioblastoma.

7. Use of the novel marker of the glioblastoma tumor transition zone according to claim 1 in screening therapeutic targets for glioblastoma.

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