Method for analyzing molecular difference in glioblastoma and application thereof

Abstract

The invention provides a method for analyzing molecular difference in glioblastoma and application of the method, and belongs to the technical field of biology. Specific molecular characteristics related to pseudofence-like necrosis (PAN) and microvascular hyperplasia (MVP) are identified by utilizing differential gene expression analysis, spatial transcriptomics analysis, deconvolution analysis, single cell RNA sequencing data and immunofluorescence, and differences between the tissue structures are disclosed. Results show that PAN and MVP have spatial expression of specific marker genes and enrich endothelial cells of different phenotypes; the single cell trajectory deduces a pseudo time trajectory of differentiation of glioblastoma stem cells (GSC) to EC. The invention discloses different roles played by endothelial cells in PAN and MVP regions of GBM, highlights the complexity and diversity of tumor microenvironment, and provides a new insight for differentiation of subgroups of endothelial cells in GBM and GSCs to endothelium.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to an analysis method for molecular differences in glioblastoma and an application thereof. Background Art

[0002] Glioblastoma (GBM) is a highly lethal malignant intracranial tumor with histopathological features including pseudopalisading cells surrounding necrosis (PAN) and microvascular proliferation (MVP). These features distinguish GBM from low-grade gliomas and have been recognized as markers of poor prognosis. PAN refers to high-density cellular areas surrounding necrotic areas in GBM, while MVP is a form of angiogenesis characterized by the proliferation of new vascular endothelial cells. PAN and MVP are considered indicators of the high malignancy and invasiveness of GBM.

[0003] A generally accepted theory holds that the overgrowth of GBM cells leads to intratumoral vascular damage, which in turn triggers endothelial damage and exacerbates tissue hypoxia. Malignant cells migrate out of the hypoxic environment and form a wave-like structure that moves toward the periphery, appearing as pseudoapoptotic cells under a microscope. In addition, tumor cells in hypoxic areas secrete vascular endothelial growth factor (VEGF) and IL-8, which promote microvascular proliferation and accelerate the outward expansion of tumor cells accompanied by the formation of new blood vessels. Although considerable progress has been made in the molecular mechanisms of GBM tumorigenesis over the past few decades, the molecular and cellular heterogeneity of these landmark areas and their spatial characteristics have not been fully elucidated.

[0004] Angiogenesis is essential for tumor growth and progression. Glioma, including GBM, is a highly vascularized tumor. Endothelial cells (ECs) that constitute blood vessels are the most critical, forming capillaries that supply blood to glioma cells. Studies have shown that ECs are heterogeneous in GBM patients and models of different tumor sites. GBM contains a large number of non-tumor components, such as stromal cells, blood vessels, cytokines, immune cells, and fibroblasts, which constitute a complex tumor microenvironment (TME) with many anatomically different regions. Necrotic cores surrounding hypoxic and perivascular areas within the GBM TME are considered to be standard morphological indicators of GBM invasiveness. However, the pathogenesis of these areas in malignant GBM is not fully understood. Therefore, elucidating the molecular characteristics of specific areas and targeting therapies based on these characteristics may become a promising therapeutic strategy.

[0005] The Ivy Glioblastoma Atlas Project (Ivy GAP) collects genomic and transcriptomic data from anatomical regions within tumors, such as PAN, MVP, and cellular tumors (CT). Therefore, this database can help understand the molecular diversity associated with histological heterogeneity. However, few studies have revealed the spatial heterogeneity of molecular features in different cellular states in PAN and MVP. The development of spatial transcriptomics (SRT) technology has enabled us to elaborate the correlation between characteristic gene expression profiles while retaining the information of spatial context and tissue architecture within tumors. Here, we integrated RNA sequencing, SRT, and single-cell RNA sequencing (scRNA-seq) data to characterize the differences between GBM structures, reveal cellular heterogeneity and molecular stratification within GBM tissue architecture, and identify prognostic and predictive markers corresponding to GBM localization. However, the molecular and cellular heterogeneity of these pathological features and their spatial properties have not been fully elucidated. Comprehensive characterization of histological features in a spatial context can help improve the diagnosis and treatment of GBM. Single-cell transcriptomic technology has revealed valuable heterogeneity insights and potential therapeutic molecular targets. SRT can classify adjacent points into histological regions, thus enabling the identification of spatial heterogeneity between patients.

[0006] Intraoperative necrosis, PET (positron emission tomography) tracer flumisonidazole (FMISO) imaging, and histological vascular pathology have highlighted hypoxia as a unique pathological feature of GBM that promotes its invasiveness. The hypoxia-regulated gene N-myc downstream regulatory gene-1 (NDRG1) in GBM plays an important role in regulating migration, angiogenesis, and drug resistance in GBM. Studies have shown that overexpression of NDRG1 can reduce angiogenesis and tumor growth, and the study of Yanming Ren et al. also highlighted the enrichment of CH3CL1 in the hypoxic and invasive glioma environment. However, the biological significance of these differentially expressed genes still requires further functional verification, and this cautious attitude is essential for scientific research, especially when exploring potential therapeutic targets. Summary of the invention

[0007] The purpose of the present invention is to provide a method for analyzing molecular differences in glioblastoma and its application, to provide new insights into the subpopulations of endothelial cells in GBM and the differentiation of GSCs into endothelial cells, and to clarify the molecular characteristics of specific regions, which can be used to guide the targeted treatment of glioblastoma.

[0008] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0009] The present invention provides a method for analyzing molecular differences in glioblastoma, comprising the following steps:

[0010] (1) Based on the Ivy-GAP dataset, we extracted gene expression profiles of glioblastoma cell tumor CT, pseudo-palisading cells PAN around necrosis, and microvascular proliferation MVP areas, and performed differential gene expression analysis to identify PAN and MVP marker genes;

[0011] (2) Based on the spatial transcriptomic dataset of GBM samples without IDH mutations, spatial transcriptomic analysis was performed to identify spatially variable genes in pseudopalisading cells PAN and microvascular proliferation MVP areas around necrosis, and to achieve spatial localization of PAN and MVP marker genes;

[0012] (3) using the spatial transcriptomics dataset of GBM samples, different cell types were identified through deconvolution analysis;

[0013] (4) Single-cell RNA sequencing data analysis was performed based on the scRNA-seq dataset to obtain the gene expression of PAN and MVP marker genes in each cell type, and pseudo-time analysis was performed on all cell types;

[0014] (5) Based on the heterogeneity of endothelial cells, unsupervised cluster analysis was performed to determine the different roles played by endothelial cells in the PAN and MVP areas of GBM.

[0015] Preferably, the Ivy-GAP dataset in step (1) is an RNA-seq dataset of different anatomical regions of primary human glioblastoma derived from the Ivy Glioblastoma Atlas Project;

[0016] The steps of differential gene expression analysis are: converting the RNA-seq data sets of different anatomical regions of primary human glioblastoma into log2 (n+1) format, and then performing quality control and differential expression analysis on the paired data of different anatomical regions through the limma package; using the UPsetR package to perform intersection query, and converting the overlapping gene IDs from the SYMBOL format to the ENTREZID format through the clusterProfiler package, and performing gene ontology functional enrichment analysis and KEGG enrichment analysis on biological processes, molecular functions and cellular components to obtain differentially expressed gene data of different anatomical regions of primary human glioblastoma, and determine PAN and MVP marker genes.

[0017] Preferably, the spatial transcriptomics analysis step in step (2) is as follows:

[0018] The spatial transcriptomics dataset of GBM samples without IDH mutations was filtered, and the gene expression at each position was normalized using the SCTransform function. Then, principal components were extracted from highly variable genes, and resolution parameters were set for clustering. Seurat and SpaGCN toolkits were used to identify robust spatially variable genes in the 10X Visium dataset, and Seurat was used to integrate points in the same cluster in each sample into pseudo-blocks to achieve spatial localization of PAN and MVP marker genes.

[0019] The filtering conditions are: the number of genes is between 500 and 8000, the number of transcripts is less than 30000, and the proportion of mitochondrial genes is less than 30%; the number of principal components extracted is 30, and the resolution parameter is set to 0.5.

[0020] Preferably, in step (3), the deconvolution analysis step comprises:

[0021] Using GBM's spatial transcriptome data as a reference dataset, the "Harmony" method was used to integrate the scRNA-seq dataset from GSE131928 to obtain a merged spatial transcriptome dataset. The integrated data were visualized by UMAP dimensionality reduction and unsupervised clustering, and annotated and manually proofread by SingleR. Using RCTD, the merged spatial transcriptome dataset was used for deconvolution analysis to obtain possible constituent cell types, and the deconvolution ratios of each cell type were visualized using the Stdeconvolve and ggplot2 packages.

[0022] Preferably, in step (4), the single-cell RNA sequencing data analysis step includes: using the Seurat4.0 package to analyze the count table of the scRNA-seq data set, following the standard workflow and default settings; using the InferCNV package to infer the copy number variation CNV in the scRNA-seq data, with cutoff = 1; then combining the CNV classification with the UMAP- and gene set-based classification to define GSCs and GBM-associated macrophages / microglia, using the CellPhoneDB-R 2.1.7 algorithm to infer intercellular communication in the GSC environment, and performing pseudo-time analysis on all cell types.

[0023] The present invention also provides an application of a method for analyzing molecular differences in glioblastoma in guiding targeted therapy of glioblastoma.

[0024] The beneficial effects of the present invention compared with the prior art are:

[0025] (1) This application uses differential gene expression analysis, spatial transcriptomics analysis, deconvolution analysis, single-cell RNA sequencing data, and immunofluorescence to identify specific molecular features associated with PAN and MVP, and reveal differences between these tissue structures, providing new insights into the subpopulations of endothelial cells in GBM and the differentiation of GSCs into endothelial cells, and elucidating the molecular features of specific regions, which can be used to guide targeted therapy for glioblastoma. The results showed that PAN and PNZ showed similar expression due to histological similarities, while HBV and MVP highly expressed genes that promote angiogenesis, such as IL-8, ADM, VEGFA, and NDRG1. Compared with other regions, HBV and MVP showed high expression of endothelial cells and stemness-related molecules, such as ESM1 and CRIP1. In addition, MVP also showed high expression of tumorigenesis and migration-related molecules, such as HIGD1B and MMP9.

[0026] (2) Differential expression analysis showed that PAN-associated endothelial cells highly expressed CHI3L1, SNRPD3, CLU, LAMTOR4, and AQP1, while MVP-associated endothelial cells expressed MGP, FABP5, collagen, and modifying enzymes such as COL1A2, COL3A1, COL4A1, and COL4A2. Functionally, MVP endothelial cells were particularly active in cell adhesion, angiogenesis, and cytoplasmic translation pathways. These insights revealed the different roles played by endothelial cells in the PAN and MVP regions of GBM, highlighting the complexity and diversity of the tumor microenvironment, and providing new insights into the subpopulations of endothelial cells and the differentiation of GSCs into endothelial cells in GBM. Moreover, NDRG1 and EPAS1 were specifically expressed in the PAN and MVP regions, revealing that two different phenotypes of endothelial cells were enriched in the MVP and PAN regions, respectively. Single-cell trajectory inference showed the differentiation trajectory of glioblastoma stem cells (GSCs) into endothelial cells, providing a new perspective for further understanding the pathogenesis of GBM.

[0027] (3) Studies have shown that GSCs can differentiate into pericyte-like cells that support vascular and tumor growth. GSCs are located in the perivascular environment close to endothelial cells. Here, GSCs secrete pro-angiogenic VEGF, which drives the proliferation, survival, migration and permeability of ECs. The receptor PTPRZ1 is preferentially expressed in GSCs, and inhibition of PTPRZ1 can inhibit GBM growth and prolong survival. PTN secreted by TAMs has been shown to stimulate PTPRZ1 on GSCs and promote tumor malignancy. The results of the present invention indicate that PTN / PTPRZ1 and MDK / PTPRZ1 may also play a role in the communication between endothelial cells and GSCs. This suggests that PTN and MDK not only promote malignant transformation by stimulating PTPRZ1 on GSCs, but also play a key role in the interaction between endothelial cells and GSCs, thereby further promoting tumor progression. Furthermore, live cell imaging showed that GSCs can also transdifferentiate into endothelial progenitor cells and endothelial cells, which may explain the poor efficacy of anti-angiogenic glioma treatment.

[0028] (4) The present invention found that NDRG1 is highly expressed in the hypoxic PAN region but not in the MVP. MVP endothelial cells exhibit angiogenesis and high collagen expression, while PAN endothelial cells show activation of the hypoxic pathway. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0030] Figure 1The present invention is an analysis of different molecular features of intratumoral expression heterogeneity in glioblastoma in Example 1, wherein A is a schematic diagram of multi-level RNA sequencing data processing and integration, RNA-seq dataset (n=41), spatial transcriptomics dataset (SRT, n=28) and single-cell RNA-seq dataset (n=28, GSE131928); B is a heat map of differentially expressed genes (rows) in different anatomical regions (columns), comparing the expression differences (>2-fold change, FDR<0.05) between the perivascular region (including hyperplastic blood vessels [HBV] and microvascular hyperplasia [MVP]) and the cellular tumor (CT) and the peri-necrotic region (perinecrotic zone [PNZ], pseudo-palisading cells around necrosis [PAN]) and CT, IT, invasive tumor; LE, leading edge zone; identification of PAN and MVP-related marker genes, wherein C is a Venn diagram representing the significantly upregulated genes in the Ivy GAP data and the spatial variant genes (SVGs) of Seurat and SpaGCN in PAN, and D is Ivy Overlap of significantly upregulated genes in GAP data with those in Seurat and SpaGCN spatially variant genes (SVGs) in MVP; E is the expression of PAN-related marker genes in GBM patients, F is the expression of MVP-related marker genes in GBM patients, and the bar on the right indicates the number of spatially variant expressions of each gene in patients; color indicates expression level;

[0031] Figure 2 A is a Venn diagram analysis of genes down-regulated in the MVP region and down-regulated in the PAN region and genes up-regulated in the MVP region and up-regulated in the PAN region in Example 1 of the present invention; B is a Gene Ontology (GO) biological process network diagram of the MVP region (down-regulated) (left) and a bubble diagram of the top 5 pathways enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (right), the X-axis is the enrichment score, the bubble size indicates the number of genes included in the project, and the bubble color from purple to red indicates the P value; C is the GO and KEGG enrichment analysis of the PAN region (down-regulated); D and E are the GO and KEGG enrichment analysis of the MVP region (up-regulated) and the PAN region (up-regulated);

[0032] Figure 3 The spatial cluster identification of GBM patient tissue in Embodiment 1 of the present invention;

[0033] Figure 4 The spatial localization of PAN and MVP marker genes in GBM in Example 1 of the present invention, wherein A is a representative section of different regions based on H&E staining and spatially variable genes (SVGs); B is a gene characteristically expressed in each region; C is the expression of 8 genes in a detailed spatial location set of patient UKF423;

[0034] Figure 5This is a comparison of the expression levels of PAN and MVP marker genes in LGG and GBM patients in the TCGA database in Example 1 of the present invention, wherein A is a marker gene for the PAN region; B is a marker gene for the MVP region;

[0035] Figure 6 This is the Kaplan-Meier survival analysis of low-expression and high-expression genes in glioma patients in Example 1 of the present invention, the blue line represents low-expression patients, and the yellow line represents high-expression patients;

[0036] Figure 7 The expression of NDRG1 and SPAS1 in GBM in Example 1 of the present invention, wherein A is an in situ hybridization (ISH) image in the Ivy GAP database showing the expression pattern of NDRG1 in PAN (left) and the expression pattern of EPAS1 in MVP (right) in GBM tissue; B is a representative multiple immunofluorescence image of NDRG1 (green), HIF2A (green), CD31 (pink), and CD44 (red) in GBM sections;

[0037] Figure 8 It is a UMAP visualization of human GBM SRT clusters and cell type annotations in Example 1 of the present invention, wherein A is a UMAP map of GBM spatial transcriptome data of 16 patients, and cells are color-coded according to the defined clusters; B is a UMAP map of 16 patients, and cells are color-coded according to the patient source; C is the proportion of each cell subset in the 16 patients; D is a heat map showing the expression of PAN (upper) and MVP (lower) regional characteristic genes in different cell types;

[0038] Fig. 9 The different states of endothelial cells in the PAN and MVP regions in Example 1 of the present invention; wherein A is the composition of different types of cells in the PAN region; B is the composition of different types of cells in the MVP region; C is the UMAP map of PAN glioma endothelial cells; D is the UMAP map of MVP glioma endothelial cells; E is a heat map of the top 10 differentially expressed genes in PAN and MVP related endothelial cells; F is a heat map of the top 10 enriched GO terms of different endothelial cells based on differentially expressed genes between PAN (748) and MVP (746);

[0039] Fig.10The figure is the differentiation of GSCs into endothelial cells in Example 1 of the present invention; wherein A and B are violin plots showing the standardized expression levels of marker genes in different cell types; C is a dot plot showing the intercellular ligand and receptor pairs between endothelial cells and other cell types; D is a spatial heat map of PTN and PTPRZ1 in patient UKF423; E is a t-SNE map of 9 cell types identified in GBM; F is a pseudo-time analysis of single-cell RNA-seq data; G is a correlation heat map showing the relationship between pairs of infiltrating cell types in GBM;

[0040] Fig.11 This is the cell type identification of the single-cell sequencing data in the GBM microenvironment in Example 1 of the present invention, wherein A is the UMAP clustering analysis of 9 cell types in GBM; and B is the proportion of different cell types in patients. DETAILED DESCRIPTION

[0041] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0042] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0043] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0044] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to the skilled artisan. The present invention description and examples are exemplary only.

[0045] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0046] Example 1

[0047] In order to resolve the heterogeneity of MVP and PAN anatomical structures hidden in large-scale analysis and explore the unique molecular features associated with GBM malignancy, the present invention conducted a comprehensive bioinformatics analysis. This analysis combined the transcriptional profiles of specific tissue structures, spatial transcriptome datasets, and single-cell transcriptome datasets. The specific methods are as follows:

[0048] (1) Data Collection

[0049] An RNA-seq dataset of 41 primary human glioblastoma (GBM) samples from different anatomical regions was collected from the Ivy Glioblastoma Atlas Project (Ivy-GAP). In addition, a spatial transcriptomic (SRT) dataset of 16 GBM samples without IDH mutations was obtained from data provided by Ravi et al. and analyzed using Visium technology (10XGenomics) (https: / / doi.org / 10.5061 / dryad.h70rxwdmj).

[0050] Smart-seq2 glioblastoma single-cell RNA sequencing (scRNA-seq) dataset (n=28, GSE131928) was downloaded from the GEO database. The Cancer Genome Atlas (TCGA) GBM dataset (n=155), including RNA sequencing and clinical data, was obtained from the UCSC Xena browser (https: / / xenabrowser.net / ). RNA sequencing data and clinical data were used for the expression and survival validation analysis of PAN and MVP signature gene sets.

[0051] (2) Differential gene expression analysis

[0052] Gene expression profiles of cellular tumor (CT), pseudopalisading cells around necrosis (PAN), and microvascular proliferation (MVP) regions were extracted from the Ivy-GAP dataset. To normalize gene expression values, the counts in Fragments PerKilobase Million (FPKM) format were log2(n+1) transformed using the dplyr package (R 1.0.10). Quality control and differential expression analysis (logFoldChange cutoff=1) of paired data between the three groups were performed using the limma package (R 3.54.0). Intersection queries were performed using the UPsetR package (R 1.4.0), and overlapping gene IDs were converted from SYMBOL format to ENTREZID format using the clusterProfiler package (R4.6.0), and gene ontology functional enrichment analysis was performed in biological process (BP), molecular function (MF), and cellular component (CC). KEGG enrichment analysis was also performed, and the results are shown in Figure 2. Figure 1 , 2 shown.

[0053] Figure 1 , 2 The results show that in the Ivy-GAP dataset, the present invention compares the features of 270 laser microdissected GBM tissues, including 111 CT (cellular tumor), 22 HBV (hypervascularization), 40 PAN, 28 MVP, 26 PNZ (perinecrotic zone), 24 IT (invasive tumor) and 19 LE (leading edge zone) ( Figure 1 (B). Pairwise comparisons of expression differences between perivascular areas (HBV and MVP) and CT, and between perinecrotic areas (PNZ and PAN) and CT were performed (>2-fold change, FDR<0.05). As expected, PAN and PNZ showed similar expression due to their histological similarities, while HBV and MVP highly expressed genes that promote angiogenesis, such as IL-8, ADM, VEGFA, and NDRG1. HBV and MVP showed high expression of endothelial cell- and stemness-related molecules, such as ESM1 and CRIP1, compared to other areas. In addition, MVP also showed high expression of tumorigenesis and migration-related molecules, such as HIGD1B and MMP9. The differentially expressed genes were more significant in PAN and MVP than in PNZ and HBV.

[0054] The Venn diagram of differentially expressed genes showed that 312 genes were specifically upregulated in PAN and 1048 genes were upregulated in MVP ( Figure 2 ). Gene ontology and KEGG analysis of MVP differentially expressed genes showed that they were related to amoeba migration and PI3K-Akt and focal adhesion pathways ( Figure 1Middle C). PAN differentially expressed genes are enriched in hypoxia response and HIF-1 signaling ( Figure 1 Among them, 151 down-regulated genes overlapped in PAN and MVP, accounting for about one-third of the down-regulated genes in PAN. These genes are involved in nervous system development and cell adhesion ( Figure 2 ), suggesting that cells in PAN and MVP are highly malignant and that reduced intercellular adhesion promotes permeability and invasion.

[0055] (3) Spatial transcriptional analysis

[0056] The present invention follows the standard procedures recommended by Seurat (R 4.0) for spatial transcriptomics analysis. Based on the spatial transcriptomics dataset of GBM samples without IDH mutations, the transcriptome spatial data of 16 GBM patients were merged, and the filtering parameters included: the number of genes was between 500 and 8000, the number of transcripts was less than 30,000, and the proportion of mitochondrial genes was less than 30%. The gene expression at each position was normalized using the SCTransform function. Then, the present invention extracted the top 30 principal components from the highly variable genes and set the resolution parameter to 0.5 for clustering. Seurat and SpaGCN toolkits were also used to identify robust spatially variable genes (SVGs) in the 10X Visium dataset. The present invention integrated the points of the same cluster in each sample into pseudo blocks ( Figure 3 ), the results are shown in Figures 3 to 7 .

[0057] 1000 and 524 SVGs were identified by Seurat and SpaGCN, respectively. Overlap with PAN and MVP upregulated genes yielded 63 PAN and 53 MVP candidate marker genes ( Figure 4 The genes were ranked by frequency in the patient's SVGs. The top genes for PAN included IGFBP5, MTIX, CHI3L1, NDRG1, and VEGF. The top genes for MVP included IGFBP, SPARC, IFITM3, IGFBP3, and A2M ( Figure 4 Most genes related to migration, invasion, stemness, and angiogenesis were consistent with the histological features and functions of PAN and MVP. TCGA RNA sequencing data showed that most marker genes were more highly expressed in GBM than in low-grade glioma (LGG) ( Figure 5 In addition, high expression is associated with poor prognosis ( Figure 6 ), including IGFBP5, IGFBP3, IGFBP7, and CHI3L1. This suggests that the spatial upregulation of these genes in PAN and MVP may promote the malignancy of GBM.

[0058] In order to clarify whether PAN and MVP marker genes have specific spatial expression patterns, the present invention further compared their spatial expression in GBM patients. The present invention selected UFK243 as a typical sample, which has obvious necrosis and vascular proliferation. The points of UFK243 were clustered into 6 regions by Seurat and SpaGCN. The present invention observed that APOC1 and NDRG1 were expressed more in region 0, EPAS1 and VEGFA were expressed more in region 1, and IGFBP7, CHI3L1 and TAGLN were expressed more in region 5. PAN genes such as IGFBP5, MT1X, CHI3L1 and NDRG1 had similar spatial patterns, mainly concentrated in regions 0 and 5. At the same time, IGFBP7, IFITM3, A2M and EPAS1 were scattered in regions 1, 3 and 5. PAN and MVP genes also showed different spatial patterns in UFK243. Ivy GAP in situ hybridization further verified the increase of NDRG1 in PAN and the increase of EPAS1 in MVP ( Figure 7 Immunofluorescence also confirmed this difference, with high expression of NDRG1 in PAN and high expression of HIF2A in vascular-rich areas ( Figure 7 These results confirmed the accuracy of the obtained region-specific marker genes.

[0059] (4) Deconvolution analysis

[0060] The complex glioma microenvironment includes non-tumor cell types such as endothelial cells, pericytes, microglia, macrophages and fibroblasts. In order to fully characterize these cell types, the present invention uses Bhaduri (Bhaduri A, DiLullo E, Jung D, et al. Outer Radial Glia-like Cancer Stem Cells Contribute to Heterogeneity of Glioblastoma. Cell Stem Cell.) people and Neftel (Neftel C, Laffy J, Filbin MG, et al. An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell) et al. GBM scRNA-seq data sets (spatial transcriptomics data sets of GBM samples) as reference data sets. The scRNA-seq data sets from GSE131928 were integrated using the "Harmony" method to obtain a merged spatial transcriptome data set. The integrated reference data set includes 40791 cells, 22346 genes and 20 cell types. Cell types with less than 1000 cells were filtered out. The integrated data were visualized by UMAP dimensionality reduction and unsupervised clustering ( Figure 8 Through SingleR annotation and manual proofreading, the present invention identified 9 major cell types: astrocytes, macrophages / microglia, neurons, oligodendrocytes, OPCs, fibroblasts, endothelial cells, T cells and adipocytes ( Figure 8 Middle C).

[0061] The merged spatial transcriptome dataset was used as input for subsequent deconvolution analysis. The transcriptome of each point was deconvolved to obtain possible constituent cell types using RCTD (v1.1.0, https: / / github.com / dmcable / spacexr). The deconvolved proportions of individual cell types were visualized using the Stdeconvolve and ggplot2 packages.

[0062] The module scores of PAN and MVP signature features also showed relatively independent distributions ( Fig. 9 In A and B, it is shown that the endothelial cells in these regions have different expression patterns and functional states. In order to understand the heterogeneity of endothelial cells, the present invention performed unsupervised cluster analysis ( Fig. 9). Differential expression analysis showed that PAN-associated endothelial cells highly expressed CHI3L1, SNRPD3, CLU, LAMTOR4, and AQP1, whereas MVP-associated endothelial cells expressed MGP, FABP5, collagen, and modifying enzymes such as COL1A2, COL3A1, COL4A1, and COL4A2 ( Fig. 9 Middle E). Functionally, MVP endothelial cells are particularly active in cell adhesion, angiogenesis, and cytoplasmic translation pathways. These insights reveal distinct roles played by endothelial cells in PAN and MVP regions of GBM, highlighting the complexity and diversity of the tumor microenvironment.

[0063] (5) Single-cell RNA sequencing data analysis

[0064] The scRNA-seq dataset (GSE131928) contains 6863 GBM cells and 1067 normal cells from 28 patients. The count table was analyzed using the Seurat 4.0 package, following the standard workflow and default settings. Subsequently, the InferCNV package (R 1.3.3) was used to infer copy number variations (CNVs) in the scRNA-seq data, with cutoff = 1. The present invention then combines CNV classification with UMAP- and gene set-based classification to define GSCs and GBM-associated macrophages / microglia. Marker genes for macrophages, T cells, and oligodendrocytes are from Neftel et al. (GSE131928). In order to study the molecular interaction network between each cell subtype, the present invention uses the CellPhoneDB (R2.1.7) algorithm to infer intercellular communication in the GSC environment.

[0065] The above results establish the connection between PAN / MVP-related signature genes and different functional states of endothelial cells in GBM. Due to the lack of single-cell resolution in spatial transcriptomics (SRT), the present invention analyzed 5 GBM single-cell RNA sequencing (scRNA-seq) datasets, including 2 paired SRT datasets. Different cell types were identified by Seurat: T cells, microglia / macrophages, oligodendrocytes, astrocytes, monocytes, endothelial cells, and B cells ( Fig.11 ). It is noteworthy that many PAN / MVP signature genes are highly or specifically expressed in endothelial cells, such as AKAP12. In addition, some genes such as MT1X, MT2A, SPARC, EPAS1, and THY1 are highly expressed in both GSCs and endothelial cells ( Fig.10 (A, B).

[0066] To explore intercellular communication, the present invention visualized the ligand-receptor pairs between each cell type. Multiple pairs involved endothelial cells, among which the PTN / PTPRZ1 ligand-receptor pair was a key regulator between GSCs and endothelial cells ( Fig.10 Middle C). Spatial heatmap shows that PTN and PTPRZ1 have similar localization ( Fig.10 Middle D). Pseudo-temporal analysis reveals the trajectory of the transition from GSCs to endothelial cells ( Fig.10 Correlation analysis confirmed the strong correlation between GSC-endothelial cells ( Fig.10 Middle G).

[0067] (6) Immunofluorescence

[0068] The present invention obtains clinical glioma paraffin sections from Wuhan Union Hospital. All samples were collected after signing the informed consent form according to the requirements of the internal review and ethics committee of Wuhan Union Hospital. The experimental procedures were approved by the Ethics Committee of Wuhan Union Hospital. After dewaxing and rehydration, the sections were boiled in Tris-EDTA antigen retrieval buffer (pH = 8.0) for 10 minutes. Next, the sections were treated with 3% hydrogen peroxide for 10 minutes to bleach endogenous peroxidase. After blocking with donkey serum in PBS for 30 minutes, the sections were incubated with primary antibodies at 4 ° C overnight. Glioma sections were probed with primary antibodies for the following proteins: CD44 (eBioscience, San Diego, USA), HIF2A (Signalway Antibody, Greenbelt, USA), NDRG1 and CD31 (Cell Signaling Technology, MA, USA). The sections were then washed with PBS and incubated with DyLight 488 / Fluor™ Plus 647-conjugated donkey anti-rabbit, mouse or rat secondary antibodies (Invitrogen) at room temperature in the dark for 2 hours, and then stained with DAPI for 10 minutes. After washing, the sections were mounted with anti-fluorescence quenching sealant and fluorescence was observed using an Olympus VS200 slide scanner.

[0069] This was verified by double immunofluorescence, which showed colocalization of the GSC marker CD44 with the endothelial cell marker CD31 ( Figure 7 B) This result is consistent with the evidence that GSCs differentiate into tumor vascular endothelium.

[0070] Ethical approval and informed consent: All glioma samples were collected after signing informed consent according to the requirements of the Internal Review and Ethics Committee of Wuhan Union Hospital. The experimental procedures were approved by the Ethics Committee of Wuhan Union Hospital.

[0071] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for analyzing molecular differences in glioblastoma, characterized in that: The steps include: (1) Based on the Ivy-GAP dataset, we extracted gene expression profiles of glioblastoma cell tumor CT, pseudo-palisading cells PAN around necrosis, and microvascular proliferation MVP areas, and performed differential gene expression analysis to identify PAN and MVP marker genes; (2) Based on the spatial transcriptomic dataset of GBM samples without IDH mutation, spatial transcriptomic analysis was performed to identify spatially variable genes in the pseudopalisading cells PAN and microvascular proliferation MVP areas around necrosis, and to achieve spatial localization of PAN and MVP marker genes; (3) Using the spatial transcriptomics dataset of GBM samples, different cell types were identified through deconvolution analysis; (4) Single-cell RNA sequencing data analysis was performed based on the scRNA-seq dataset to obtain the gene expression of PAN and MVP marker genes in various cell types, and pseudo-time analysis was performed on all cell types; (5) Based on the heterogeneity of endothelial cells, unsupervised cluster analysis was performed to determine the different roles played by endothelial cells in the PAN and MVP areas of GBM.

2. A method for analyzing molecular differences in glioblastoma according to claim 1, characterized in that: The Ivy-GAP dataset in step (1) is an RNA-seq dataset of different anatomical regions of primary human glioblastoma derived from the Ivy Glioblastoma Atlas Project; The steps of differential gene expression analysis are as follows: converting the RNA-seq data sets of different anatomical regions of primary human glioblastoma into log2(n+1) format, and then performing quality control and differential expression analysis on the paired data of different anatomical regions using the limma package; using the UPsetR package for intersection query, and converting the overlapping gene IDs from the SYMBOL format to the ENTREZID format using the clusterProfiler package, and performing gene ontology functional enrichment analysis and KEGG enrichment analysis of biological processes, molecular functions and cellular components to obtain differentially expressed gene data of different anatomical regions of primary human glioblastoma, and determine PAN and MVP marker genes.

3. A method for analyzing molecular differences in glioblastoma according to claim 1, characterized in that: The steps of spatial transcriptomics analysis in step (2) are: The spatial transcriptomics dataset of GBM samples without IDH mutations was filtered, and the gene expression at each position was normalized using the SCTransform function. Then, principal components were extracted from highly variable genes, and resolution parameters were set for clustering. Seurat and SpaGCN toolkits were used to identify robust spatially variable genes in the 10X Visium dataset, and Seurat was used to integrate points in the same cluster in each sample into pseudo-blocks to achieve spatial localization of PAN and MVP marker genes. The filtering conditions are: the number of genes is between 500 and 8000, the number of transcripts is less than 30000, and the proportion of mitochondrial genes is less than 30%; the number of principal components extracted is 30, and the resolution parameter is set to 0.

5.

4. A method for analyzing molecular differences in glioblastoma according to claim 1, characterized in that: In step (3), the deconvolution analysis step includes: Using GBM's spatial transcriptome data as a reference dataset, the "Harmony" method was used to integrate the scRNA-seq dataset from GSE131928 to obtain a merged spatial transcriptome dataset. The integrated data were visualized by UMAP dimensionality reduction and unsupervised clustering, and annotated and manually proofread by SingleR. Using RCTD, the merged spatial transcriptome dataset was used for deconvolution analysis to obtain possible constituent cell types, and the deconvolution ratios of each cell type were visualized using the Stdeconvolve and ggplot2 packages.

5. A method for analyzing molecular differences in glioblastoma according to claim 1, characterized in that: In step (4), the single-cell RNA sequencing data analysis step includes: The scRNA-seq datasets were analyzed for count tables using the Seurat 4.0 package, following the standard workflow and default settings. Copy number variation (CNV) in scRNA-seq data was inferred using the InferCNV package with a cutoff of 1. The CNV classification was then combined with UMAP- and gene set-based classification to define GSCs and GBM-associated macrophages / microglia. CellPhoneDB-R 2.1.7 algorithm was used to infer intercellular communication in the GSC environment, and pseudo-time analysis was performed for all cell types.

6. Use of the method for analyzing molecular differences in glioblastoma according to any one of claims 1 to 5 in guiding targeted therapy of glioblastoma.

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