Biomarker of glioblastoma invasion frontier region and application of biomarker

Abstract

The invention relates to a biomarker of a glioblastoma invasion leading edge region and application of the biomarker, and belongs to the technical field of biological detection. In order to solve the technical problems of unknown tumor invasion mechanism, scarcity of treatment targets and inaccurate prognosis evaluation caused by lack of a GBM invasion leading edge region specific marker in the prior art, 12 GBM invasion leading edge region specific high-expression genes, namely CREG2, KIF5A, CELF4, TMEM132D, KHDRBS2, SNAP25, SYT1, ASIC2, PAK3, RBFOX1, STMN2 and CNTNAP2, are analyzed and screened through space transcriptomics. A COX multi-factor regression risk assessment system is developed and constructed based on the markers. According to the invention, a molecular basis is provided for accurate recognition of a GBM invasion range, patient layering and prognosis prediction, targeted therapy development and curative effect monitoring.

Description

Technical Field

[0001] The present invention belongs to the field of biological detection technology and relates to biomarkers of the invasion front zone of glioblastoma and applications thereof. Background Art

[0002] Glioblastoma (GBM) is the most aggressive malignant tumor in the central nervous system. Its clinicopathological features include a highly heterogeneous tumor cell population, disordered tissue architecture, and extensive vascular proliferation. Malignant invasion is one of the core characteristics of GBM, and its pathological manifestations are tumor cells breaking through the blood-brain barrier and infiltrating healthy brain tissue on a large scale. GBM cells not only proliferate rapidly at the primary site, but can also spread along white matter nerve fiber bundles, blood vessels, and matrix, and even invade the contralateral hemisphere through the corpus callosum. This high degree of invasiveness makes GBM difficult to completely resect, extremely prone to recurrence, and has become one of the key factors affecting patient prognosis.

[0003] The GBM invasive leading edge (iLE) is a critical region for tumor spread to surrounding normal brain tissue, and its biological characteristics differ significantly from those of the tumor core. Tumor cells in the iLE region typically have unique gene expression profiles, metabolic plasticity, and proliferation and invasion characteristics, demonstrating a stronger dynamic invasive ability to spread along nerve fiber and axon pathways. However, current treatment strategies mainly focus on the tumor core, while research on characteristic biomarkers of the invasive front is relatively scarce, making it difficult to effectively control the invasiveness of GBM. Therefore, identifying and characterizing characteristic biomarkers of the GBM invasive front is of great significance for revealing the invasion mechanism, predicting patient prognosis, and developing new anti-invasive treatment strategies.

[0004] From the perspective of clinical application, the development of biomarkers at the invasion front has multiple values. First, it can be used as a target for imaging omics or liquid biopsy to achieve early non-invasive assessment of the extent of invasion, thereby assisting in the formulation of individualized surgical resection boundaries and radiotherapy targets; second, by analyzing the invasion front-specific signaling pathways (such as EMT, hypoxia metabolism or immune escape-related pathways), new therapeutic targets can be screened and patients' responses to targeted therapy or immunotherapy can be predicted; finally, dynamic monitoring of the expression changes of invasion front markers is expected to become a molecular tool for assessing the risk of recurrence and efficacy. In addition, combining multi-omics technology to analyze the spatiotemporal heterogeneity of the GBM invasion front will promote the transformation of precision diagnosis and treatment strategies from the "lesion center" to the "invasive edge", providing a scientific basis for improving the quality of life of patients. Summary of the Invention

[0005] In view of this, one of the objects of the present invention is to provide a group of biomarkers of the invasion front zone of glioblastoma, and the second object is to provide the application of the biomarkers of the invasion front zone of glioblastoma in the diagnosis, prediction, prognosis evaluation, treatment effect and target screening of glioblastoma.

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

[0007] The present invention collects GBM samples containing tumors and invasion fronts, performs spatial transcriptome sequencing, and uses hematoxylin and eosin staining to annotate spatial regions. Spatial transcriptome data are used to define the GBM invasion front and integrate multiple samples. Machine learning is used to screen for signatures of highly expressed genes in the GBM invasion front and identify candidate biomarkers.

[0008] Method for constructing a binary classification model for the GBM invasion front using biomarkers:

[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 based on the previously screened candidate biomarkers for GBM invasion in the ivyGAP data and evaluated the model effects using AUC.

[0013] C. Association between biomarkers and prognosis

[0014] COX multivariate regression analysis was used to evaluate the risk of GBM invasion frontier biomarkers, and TCGA GBM samples 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 group of biomarkers for the invasion front zone of glioblastoma, wherein the biomarker is at least one of CREG2, KIF5A, CELF4, TMEM132D, KHDRBS2, SNAP25, SYT1, ASIC2, PAK3, RBFOX1, STMN2, and CNTNAP2 genes;

[0016] Furthermore, the use of biomarkers of the glioblastoma invasion front zone in the preparation of a kit for diagnosing glioblastoma;

[0017] Preferably, 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 next-generation sequencing technology;

[0018] Preferably, the use of biomarkers of the glioblastoma invasion front in stratification screening of glioblastoma;

[0019] Preferably, the use of biomarkers of the glioblastoma invasion front zone in the prognostic assessment of glioblastoma;

[0020] Preferably, the biomarkers of the glioblastoma invasion front zone are used to evaluate the therapeutic effect of glioblastoma;

[0021] Preferably, the biomarkers of the glioblastoma invasion front zone are used to screen therapeutic targets for glioblastoma.

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

[0023] 1. Improved diagnostic accuracy

[0024] The present invention detects the expression levels of 12 iLE-specific markers, including CREG2 and KIF5A, and combines them with machine learning algorithms such as random forest (RF) and support vector machine (SVM) to construct a predictive model. In the test of distinguishing GBM invasive front (iLE) from non-invasive areas (Others), the area under the receiver operating characteristic (ROC) curve (AUC) reached above 0.95. The model, trained on expression data from 459,424 iLE cells from 14 spatial transcriptome samples, can accurately identify tumor invasion margins, significantly outperforming the subjective limitations of traditional histopathological assessment.

[0025] 2. Optimization of treatment guidance

[0026] Spatial transcriptomics was used to identify the molecular characteristics of the iLE region, providing the following clinical guidance:

[0027] Surgical boundary planning: Rapidly detect marker expression levels during surgery to assist in determining the extent of tumor invasion and reduce postoperative residual risk;

[0028] Radiotherapy target area setting: combining imaging omics with iLE marker positioning to accurately define the radiotherapy irradiation area to avoid excessive damage to normal brain tissue;

[0029] Recurrence risk control: Reduce the probability of tumor recurrence in situ by eliminating invasive cell populations expressing genes such as CREG2 and KIF5A.

[0030] 3. Innovation in prognostic assessment

[0031] Based on the COX multivariate regression analysis of 178 GBM samples in the TCGA database, an iLE marker risk assessment model was constructed:

[0032] Patients were divided into iLEhigh (84 patients) and iLElow (94 patients) groups. Kaplan-Meier survival curves showed a significant difference in median survival between the two groups. The risk score was strongly correlated with overall survival (HR>2), predicting disease progression earlier than traditional histological grading, providing data support for personalized treatment strategies, such as the timing of intensive adjuvant chemotherapy.

[0033] 4. Target development support

[0034] Twelve iLE-specific genes (such as STMN2, which regulates cell migration, and SNAP25, which is involved in synaptic transmission) have been shown to be directly associated with the GBM invasion phenotype. Gene function enrichment analysis showed that the marker set was significantly enriched in pathways such as "axon guidance" and "cell migration regulation," suggesting that they could serve as intervention nodes for anti-invasive therapies. This provides clear targets for the development of small molecule inhibitors or gene therapies.

[0035] 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

[0036] 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:

[0037] Figure 1 The number of cells detected in each sample of 14 idle samples in our hospital, the X axis represents different samples, and the Y axis represents the logarithm (log 10 ) The number of cells after conversion;

[0038] Figure 2 The number of genes detected in cells of each sample in 14 idle samples of our hospital. The X-axis represents different samples and the Y-axis represents the logarithm (log 10 ) The number of genes after conversion;

[0039] Figure 3 The expression levels of 12 GBM invasion front biomarkers in different spatial structures: GBM-iLE: GBM invasion front; GBM-Others: other areas of GBM samples excluding the invasion front.

[0040] Figure 4 This is the multi-model receiver operating characteristic curve (ROC Curve) of 12 GBM invasion front biomarkers in various models. LR: logistic regression;

[0041] Figure 5 The risk assessment of 12 GBM invasion front biomarkers is associated with GBM prognosis. The figure below represents the sample distribution at different time points in different groups. DETAILED DESCRIPTION

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Example 1 Acquisition and preliminary processing of GBM spatial transcriptome data

[0046] 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 ).

[0047] Example 2 Identification of GBM Invasion Front

[0048] Hotspot was used to analyze the spatial expression patterns of genes, 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 spatial domains with similar expression patterns in different samples were grouped into the same cluster using unsupervised clustering. 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, 459,424 cells were identified from 1,876,195 cells as being in the GBM invasion front.

[0049] Example 3 Screening of candidate markers for GBM invasion front

[0050] 1,129 highly expressed genes in the GBM invasion front area 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 a GBM invasion front feature gene set, laying the foundation for subsequent functional research and clinical prediction model development. Based on the above steps, 12 biomarker genes (as shown in Table 1) were finally screened out from the 1,129 genes highly expressed in the GBM invasion front area and used as biomarkers for the GBM invasion front area ( Figure 3 ).

[0051] Table 1: 12 biomarkers (UCSC Genome Browser database)

[0052]

[0053]

[0054] Example 4 Construction of GBM invasion front prediction model

[0055] The transcriptome data of different spatial regions of GBM were downloaded from the IvyGAP database, and the data were divided into invasion front group and other regional groups according to the sample annotation information. Subsequently, a variety of machine learning algorithms were used to construct a prediction model for the GBM invasion front area, combining the invasion front feature gene set screened out by spatial transcriptome analysis. The main algorithms used for modeling were RF, LR, Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). 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 the GBM invasion front area, and providing an important reference for subsequent tumor invasion mechanism research and clinical stratification diagnosis. Based on the above steps, the AUC of the prediction models constructed by various algorithms were all greater than 0.95 ( Figure 4 ).

[0056] The LR model parameters are:

[0057] log(P(PE=1) / (1-P(PE=1))=-1.646949946+0.028563246·ASIC2+0.026032476·CELF4+

[0058] 0.017095496·CNTNAP2+0.025747375·CREG2+0.004881766·KHDRBS2+

[0059] 0.012071207·KIF5A+0·PAK3+0.036269265·RBFO1+0.012548652·SNAP25+0·STMN2+

[0060] 0.020559381·SYT1+0.023381435·TMEM132D

[0061] Example 5: Correlation between GBM invasion front biomarkers and clinical prognosis

[0062] 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 GBM-iLE with 71 samples. high and GBM-iLE with 76 samples low Kaplan-Meier survival analysis was performed according to GBM sample classification to evaluate the prognostic value of these 12 GBM invasion front zone biomarkers ( Figure 5 ), providing potential targets for personalized treatment.

[0063] 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 biomarker for the invasion front of glioblastoma, characterized by: The biomarker is at least one of CREG2, KIF5A, CELF4, TMEM132D, KHDRBS2, SNAP25, SYT1, ASIC2, PAK3, RBFOX1, STMN2, and CNTNAP2 genes.

2. Use of the biomarker of the glioblastoma invasion front zone according to claim 1 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 biomarker of the glioblastoma invasion front zone according to claim 1 in glioblastoma stratification screening.

5. Use of the biomarker of the glioblastoma invasion front zone according to claim 1 in the prognosis assessment of glioblastoma.

6. Use of the biomarker of the glioblastoma invasion front zone according to claim 1 in evaluating the therapeutic effect of glioblastoma.

7. Use of the biomarker of the glioblastoma invasion front zone according to claim 1 in screening therapeutic targets for glioblastoma.

Images