Marker for olfactory neuroblastoma subtype classification and application thereof

Abstract

The invention relates to the field of disease diagnosis, and particularly discloses a marker for olfactory neuroblastoma subtype classification, and the marker comprises a substrate type marker, a nerve type marker and a mesenchymal type marker. The typing gene expression profile used in the invention is derived from high-precision single cell sequencing, has high accuracy and distinction degree, and can be projected to ordinary transcriptome sequencing with relatively simple sequencing steps and low cost to obtain a typing conclusion, so that verification and popularization within a certain range are facilitated, and the method has a wide application prospect. The kit is helpful for typing ONB patients in clinical work, and assists corresponding drug treatment selection according to molecular characteristics of each subtype.

Description

Technical Field

[0001] The present specification relates to the field of disease diagnosis, and in particular to a marker for classifying olfactory neuroblastoma subtypes and its application. Background Art

[0002] 1. Introduction to the clinical features of olfactory neuroblastoma

[0003] Olfactory neuroblastoma (ONB) is a malignant tumor that occurs in the olfactory region of the anterior skull base, accounting for approximately 3% to 6% of all nasal and paranasal sinus malignancies and 0.3% of all upper respiratory tract malignancies. The incidence of ONB is approximately 0.04 / 100,000 people per year, and the incidence of ONB has been on the rise in recent years. Although its exact location and mechanism of occurrence are not yet clear, ONB is generally believed to originate from sensory neurons in the olfactory epithelium.

[0004] In past studies, some scholars have discussed the staging of olfactory neuroblastoma. However, since ONB is a rare nasal skull base tumor, the relevant research progress has been slow due to the limited sample size, and there is no international unified standard for its clinical pathological grading system. At present, the modified Kadish staging (Foote staging) proposed by Kadish et al. in 1976 and modified by Foote et al. in 1993 is the most commonly used ONB staging system in clinical practice. In addition, Hyams et al. first reported the Hyams pathological grading system based on the histological characteristics of ONB in ​​1989 based on the differentiation level of ONB.

[0005] Existing literature reports show that the prognosis of ONB is related to its clinical stage and pathological grade. The 5-year survival rate of Kadish A stage tumors can be 75% to 90%, while the 5-year survival rate of Kadish C stage tumors is only 45%; the 5-year survival rate of Hyams high-grade tumors is only 25%, while that of low-stage tumors can be as high as 80%. There are significant differences in the prognosis of ONB cases under different pathological morphological classifications, indicating that olfactory neuroblastoma is a highly heterogeneous tumor type. Different tumor individuals under the same pathological diagnosis show differences in morphological characteristics, molecular pathological expression, invasion range, etc., which leads to large differences in the treatment mode, treatment efficacy, clinical outcomes, etc. of different types of ONB patients. This clinical situation reflects the lack of effective classification theory and recognized staging system for ONB, which leads to objective difficulties in the formulation of treatment plans and disease guidelines.

[0006] (II) Current status of research on the origin and classification of olfactory neuroblastoma

[0007] In the olfactory epithelium (OE), neurogenesis is a dynamic process that continues throughout adulthood. The OE contains a variety of cell types, including horizontal basal cells (HBCs), global basal cells (GBCs), immediate neuronal precursors (INPs), immature and mature olfactory receptor neurons (iORNs & mORNs), non-neuronal microvillar cells (MVs) cells, supporting cells (SUSs) and submucosal Bowman's glands (BGs) ducts. Olfactory neuroblastoma (ONB), a rare malignant tumor of the nasal skull base, has been generally believed to originate from the olfactory cell components of the OE in recent years, and has been preliminarily confirmed to express some marker genes of immature olfactory neurons.

[0008] In 1982, Trojanowski et al. used the then very advanced electron microscope technology to observe ONB tumor cells and observed the structure of neurofilaments. After publishing this result in the New England Journal of Medicine that year, researchers generally began to accept the hypothesis that ONB originates from olfactory epithelial neurons. However, although the hypothesis that ONB originates from the olfactory mucosa has been proposed for nearly half a century, there are relatively few studies that explore at what level of the development of olfactory neurons ONB may originate.

[0009] In 2018, Classe et al. reported a multi-omics study of whole exome sequencing (WES), bulk RNA seq sequencing, and DNA methylation sequencing data of tumor tissues from a cohort of 18 ONB cases. At the bulk mRNA seq data level, the authors used unsupervised clustering and other analytical techniques to distinguish ONB into the Neural type, which is well differentiated and expresses neural development marker molecules, and the Basal type, which is poorly differentiated and has upregulated expression of cell cycle-related genes. By comparing the bulk RNA seq data of olfactory neuroblastoma with the olfactory mucosal olfactory neuron subpopulations defined by single-cell sequencing, the researchers believed that the Basal subtype is similar to the normal olfactory mucosal globular basal cells GBCs in terms of RNA transcription gene expression, and the Neural type is more similar to the more mature direct neuronal precursors INPs, immature olfactory mucosal sensory neurons iORNs, and mature olfactory mucosal sensory neurons mORNs during the development of the olfactory mucosa. So far, this study has pointed out the high tumor heterogeneity of ONB for the first time through high-throughput sequencing technology and related analysis, and proposed the binary classification hypothesis of ONB for the first time.

[0010] In May 2024, Finlay et al. published the results of single-cell sequencing studies on ONB tumor heterogeneity and the similarities between ONB and small cell lung cancer (SCLC) in Cancer Cell, a subsidiary of Cell magazine. The study demonstrated that ONB originates from the GBC layer of the olfactory epithelium, and that ONB exhibits heterogeneity in cell fate and can mimic the normal GBC developmental trajectory. Through lineage tracing and scRNA transcriptomics studies, the authors determined that ONB is similar to SCLC, and that mouse and human ONB exhibit mutually exclusive NEUROD1 (neural differentiation) and pou2f3 (mesenchymal differentiation)-like states. This study further explored the cellular origins and molecular classification of ONB and proposed possible hypotheses. Summary of the invention

[0011] The present application provides a marker for subtype classification of olfactory neuroblastoma, wherein the marker includes a basal marker, a neural marker and a mesenchymal marker; the basal marker includes at least one of the following genes or fragments thereof: UBE2C, HMGB2, CENPF, NNAT, HIST1H4C, HIST1H1A, TUBA1B, PTTG1, TOP2A, NUSAP1, HIST1H1B, CCNB1, UBE2S, BIRC5, CKS1B, CKS2 , TPX2, H2AFZ, PCP4, CDK1, MFAP4, MKI67, PBK, MAD2L1, HMGB1, CKAP2, CCNB2, SMC4, CENPW, HIST1H1D, UBE2T, CDK N3, ASPM, TUBA1C, TYMS, DLGAP5, PCLAF, PRC1, NUF2, ARL6IP1, TMPO, CDC20, KPNA2, HMGN2, DTYMK, HIST1H1E, HIS T1H1C, MDK, HIST1H3B, H2AFX; the neurological markers include at least one of the following genes or fragments thereof: CRABP1, SCG5, PEG10, MAP1B, GNG8, CIB2, STMN2, PCSK1N, CHGB, RTN1, MIR7-3HG, SNRPN, EEF1A2, MEG3, GRP, SEC11C, NHLH1, KIF19, KCNK9, APMAP, TLCD3B, UNCX, CA LB2, BEX1, IRX2, PEBP1, RGS10, CIRBP, VAMP2, FXYD3, KLHL35, MAP1LC3A, BEX2, NCAM1, GADD45B, NEUROD1, FXYD6 , ATP6V0E2, C11orf96, CAMK2N1, C5orf38, CPE, RPS3, TMPRSS6, DUSP26, PDLIM1, STX1A, FABP6, KCTD17, SPOCK2;The mesenchymal markers include at least one of the following genes or fragments thereof: SLPI, S100A6, WFDC2, MT1X, MGP, CRYAB, S100A11, SAT1, CCN2, IGFBP7, VMO1, APOE, IER3, IGFBP5, S100A4, ANXA2, S100A10, VIM, MT2A, CD9, IFI27, LGALS1, LYPD2, TIMP1, IFITM3, ITGB1, LGALS3, CALD1, KRT18, SPARC, COL11A1, COL18A1, CEBPD, CSTB, CD59, APLP2, ID3, PTN, COL3A1, CCN1, KRT8, DST, TPM4, CYBA, ALDH1A1, CLU, IFITM2, CD63, TPM2, ZFP36L1. ;

[0012] The present application also provides the use of the above-mentioned markers or their detection products in the preparation of products for olfactory neuroblastoma subtype classification.

[0013] The present application also provides a product for classification of olfactory neuroblastoma subtypes, wherein the product comprises the genes and / or detection substances thereof in the above-mentioned markers.

[0014] The present application also provides an olfactory neuroblastoma subtype detection device, which includes the following modules: an information acquisition module, used to provide sequencing information of the target marker of the sample to be tested, and the target marker is the above-mentioned marker for classifying the subtype of olfactory neuroblastoma; a scoring module, used to score the subtype based on the sequencing information of the target marker of the sample to be tested; and a judgment module, used to judge whether the olfactory neuroblastoma subtype is basal, neural or mesenchymal based on the subtype score.

[0015] The present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, a method for detecting olfactory neuroblastoma subtypes is implemented, the method comprising: obtaining sequencing information of a target marker of a sample to be tested, wherein the target marker is the above-mentioned marker for classifying olfactory neuroblastoma subtypes; scoring the subtype based on the sequencing information of the target marker of the sample to be tested; and judging whether the olfactory neuroblastoma subtype is basal, neural or mesenchymal based on the subtype score.

[0016] The present application also provides an electronic terminal, which includes: a processor, a memory, an input / output interface and a communication port; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the method for detecting olfactory neuroblastoma subtypes described in the above-mentioned computer-readable storage medium.

[0017] The beneficial effects brought by this application include but are not limited to: This application first proposed a rare tumor, the concept of three-type olfactory neuroblastoma (ONB), from the perspective of gene expression and molecular typing, ONB cases are divided into basal type, neural type, and mesenchymal type, describing the gene expression spectrum of each subtype and the molecular functional pathway of each subtype, which promoted the understanding of ONB subtypes in related fields. In this process, this application newly discovered a mesenchymal type ONB that expresses mesenchymal cell-related characteristics, and successfully identified and characterized the molecular characteristics of mesenchymal ONB through single-cell sequencing technology. And through the molecular characteristics of NBM ONB obtained by single-cell sequencing, using existing open source R code and program packages, an algorithm was developed, which makes it possible to distinguish the ONB three-type system discovered by single-cell sequencing in ordinary transcriptome sequencing samples with low cost and less difficulty in promotion. The typing gene expression profile used in this technology is derived from high-precision single-cell sequencing, which has high accuracy and discrimination, and can be projected onto common transcriptome sequencing with relatively simple sequencing steps and low costs to draw typing conclusions. Therefore, it is conducive to verification and promotion within a certain range, which is helpful for typing ONB patients in clinical work and assisting in the selection of corresponding drug treatments based on the molecular characteristics of each subtype. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present application will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and include:

[0019] Figure 1In order to show the tumor heterogeneity of ONB according to some embodiments of the present application, a) UMAP diagram shows 7 subgroups after re-clustering of ONB malignant epithelial cells, and the cell cycle score of each cell is calculated using a gene list related to the cell cycle; b) UMAP describes the characteristic scores of classic basal and neural ONB in ​​tumor cells; c) Correlation between 40 NMF modules generated by 10 ONB tumor data sets; Hierarchical clustering identified 5 gene modules representing tumor commonality; d) Bar chart shows the representative GO pathways enriched in each NMF module; e) Violin plot shows the expression scores of 5 NMF modules in 7 tumor subgroups (from top to bottom: Basal, Neural, Mes-1, Mes-2, Ciliation); f) UMAP diagram shows the annotated phenotypes of 7 ONB tumor subgroups; g) Stacked bar chart shows the proportion of cells found in each tumor subgroup in each ONB sample; h) Heat map shows the representative GSVA pathways enriched in ONB tumor subgroups except ciliation subgroup.

[0020] Figure 2 To characterize the three subtypes of ONB based on scRNA-seq analysis as shown in some embodiments of the present application, a) the three-phase diagram depicts the NBM Scores (neural type / basal type / mesenchymal type scores) of each ONB tumor cell and the NBM scores of tumor cells in the 10 ONB tumor data sets involved in the study; b) the scRNA-seq NBM subtype features are projected into the ONB patient study set cohort containing 20 samples of the common transcriptome (bulk RNA seq), the upper heat map: the clinical characteristics of all ONB patients, including the modified Kadish stage, Dulguerov stage, Hyams grade and orbital invasion degree, represented by different colors, the lower heat map: a heat map showing the expression of each gene in the NBM ONBgene list marker gene set (Appendix 1) for each ONB patient; c) the scRNA-seq NBM subtype features are projected into the ONB patient validation set cohort containing 18 samples of the common transcriptome (bulk RNA seq) (GSE118995).

[0021] Figure 3To characterize the three subtypes of ONB based on scRNA-seq analysis as shown in some embodiments of the present application. a) Projecting the scRNA-seq NBM subtype features to the ONB patient study cohort containing 20 samples of the common transcriptome (bulk RNA seq), the lower heat map: shows the Neural / Basal / Mesenchymal type feature scores calculated by the AddModuleScore() function for the TPM matrix of 20 ONB patients; b) Flowchart of the ONB NBM tri-type method based on the gene expression profile obtained by single-cell sequencing.

[0022] Figure 4 The performance of the scoring system for predicting the three ONB subtypes according to some embodiments of the present application is shown in Figure 1. From left to right, the AUC values ​​of the scoring system for predicting the ONB subtypes are Basal Score, Neural Score, and Mesenchymal Score, which are 1, 0.96, and 0.95, respectively.

[0023] Figure 5 To explore the similarities and infer the origins between the lineages of olfactory epithelial (OE) cell subpopulations and ONB tumor cells as shown in some embodiments of the present application, a) UMAP diagram shows the distribution of OE cell subpopulations in normal adults, including horizontal basal cells HBCs, globular basal cells GBCs, direct neuronal precursor cells INPs, immature olfactory neurons iORNs, mature olfactory neurons mORNs, immature supporting cells iSus and mature supporting cells mSus subpopulations; b) violin plots focus on showing the expression levels of classic marker genes in olfactory neurons and supporting cell subpopulations; c) two-dimensional representation of single-cell gene expression profiles based on principal component analysis. Slingshot predicts early divergence of differentiation trajectories of neurons and supporting cells; d) Heatmap shows Pearson correlation between scRNA expression profiles of ONB malignant subpopulations (columns) and olfactory epithelial cells (rows), which are arranged in the order of differentiation to neuronal and supporting cell lineages; e) Heatmap represents the location of subpopulations enriched with NBM ONB features in normal OE subpopulations; f) Featureplot represents the location of subpopulations enriched with NBM ONB features in normal OE subpopulations.

[0024] Figure 6 It is a module diagram of an olfactory neuroblastoma subtype detection device according to some embodiments of the present application.

[0025] Figure 7 The present invention is a flowchart of a method for detecting olfactory neuroblastoma subtypes according to some embodiments of the present application.

[0026] Figure 8Schematic diagram of the architecture of an electronic terminal 700 according to some embodiments of the present application.

[0027] Fig. 9 These are differentially expressed genes among neural / basal / mesenchymal ONB tumor cells as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0028] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0029] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0030] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0031] The present application provides a marker for subtype classification of olfactory neuroblastoma, wherein the marker includes a basal marker, a neural marker and a mesenchymal marker; the basal marker includes at least one of the following genes or fragments thereof: UBE2C, HMGB2, CENPF, NNAT, HIST1H4C, HIST1H1A, TUBA1B, PTTG1, TOP2A, NUSAP1, HIST1H1B, CCNB1, UBE2S, BIRC5, CKS1B, CKS2 , TPX2, H2AFZ, PCP4, CDK1, MFAP4, MKI67, PBK, MAD2L1, HMGB1, CKAP2, CCNB2, SMC4, CENPW, HIST1H1D, UBE2T, CDK N3, ASPM, TUBA1C, TYMS, DLGAP5, PCLAF, PRC1, NUF2, ARL6IP1, TMPO, CDC20, KPNA2, HMGN2, DTYMK, HIST1H1E, HIS T1H1C, MDK, HIST1H3B, H2AFX; the neurological markers include at least one of the following genes or fragments thereof: CRABP1, SCG5, PEG10, MAP1B, GNG8, CIB2, STMN2, PCSK1N, CHGB, RTN1, MIR7-3HG, SNRPN, EEF1A2, MEG3, GRP, SEC11C, NHLH1, KIF19, KCNK9, APMAP, TLCD3B, UNCX, CA LB2, BEX1, IRX2, PEBP1, RGS10, CIRBP, VAMP2, FXYD3, KLHL35, MAP1LC3A, BEX2, NCAM1, GADD45B, NEUROD1, FXYD6 , ATP6V0E2, C11orf96, CAMK2N1, C5orf38, CPE, RPS3, TMPRSS6, DUSP26, PDLIM1, STX1A, FABP6, KCTD17, SPOCK2;The mesenchymal markers include at least one of the following genes or fragments thereof: SLPI, S100A6, WFDC2, MT1X, MGP, CRYAB, S100A11, SAT1, CCN2, IGFBP7, VMO1, APOE, IER3, IGFBP5, S100A4, ANXA2, S100A10, VIM, MT2A, CD9, IFI27, LGALS1, LYPD2, TIMP1, IFITM3, ITGB1, LGALS3, CALD1, KRT18, SPARC, COL11A1, COL18A1, CEBPD, CSTB, CD59, APLP2, ID3, PTN, COL3A1, CCN1, KRT8, DST, TPM4, CYBA, ALDH1A1, CLU, IFITM2, CD63, TPM2, ZFP36L1. ;

[0032] In some embodiments, the olfactory neuroblastoma subtypes include basal olfactory neuroblastoma, neuronal olfactory neuroblastoma, and mesenchymal olfactory neuroblastoma. In some embodiments, the basal olfactory neuroblastoma subgroup highly expresses cell proliferation and cell cycle pathways relative to the neuronal olfactory neuroblastoma subgroup and the mesenchymal olfactory neuroblastoma subgroup. In some embodiments, the neuronal olfactory neuroblastoma subgroup has the functions of neurogenesis and olfactory conduction, and the mesenchymal olfactory neuroblastoma subgroup has cell functions including angiogenesis, epithelial-mesenchymal transition, extracellular matrix remodeling, inflammatory response, and chemokine secretion.

[0033] The present application also provides the use of the above-mentioned markers or their detection products in the preparation of products for olfactory neuroblastoma subtype classification.

[0034] In some embodiments, the detection substance can be selected from any one or more of an antibody, a membrane strip, a chip, a probe or a primer pair.

[0035] The term "primer" as used herein refers to a naturally occurring oligonucleotide (e.g., a restriction fragment) or a synthetically produced oligonucleotide that is capable of serving as a starting point for the synthesis of a primer extension product that is complementary to a nucleic acid strand (a template or target sequence) when placed under appropriate conditions (e.g., buffer, salt, temperature, and pH) and in the presence of nucleotides and reagents for nucleic acid polymerization (e.g., a DNA-dependent or RNA-dependent polymerase). Typically, a set of primers will consist of at least two primers, an "upstream primer" and a "downstream primer" that together define an amplicon (a sequence to be amplified using the primers).

[0036] The term "probe" refers to any molecule that can selectively bind to a target biomolecule (e.g., a nucleic acid sequence that hybridizes to the probe). In some embodiments, the probe can be labeled, for example, with a fluorescent group and a quenching group. In some embodiments, the probe can be a Taqman probe with a fluorescent reporter group added to the 5' end and a fluorescent quenching group added to the 3' end.

[0037] Antibodies are proteins with protective effects produced by the body due to antigen stimulation. In some embodiments, antibodies can be used as detection reagents to detect gene expression.

[0038] The present application also provides a product for classification of olfactory neuroblastoma subtypes, wherein the product comprises the genes and / or detection substances thereof in the above-mentioned markers.

[0039] In some embodiments, the detection substance can be selected from any one or more of an antibody, a membrane strip, a chip, a probe or a primer pair.

[0040] The present application also provides a device for detecting subtypes of olfactory neuroblastoma, such as Figure 6 As shown, the olfactory neuroblastoma subtype detection device includes the following modules: an information acquisition module 610 , a scoring module 620 and a judgment module 630 .

[0041] The information acquisition module 610 can be used to provide sequencing information of target markers of the sample to be tested, wherein the target markers are the markers for olfactory neuroblastoma subtype classification described above. In some embodiments, the sequencing information may include single cell RNA sequencing information and / or common transcriptome RNA sequencing information.

[0042] In some embodiments, the sample to be tested may be a tumor tissue from a patient with olfactory neuroblastoma.

[0043] The term "sample" refers to any composition containing nucleic acid isolated from a subject. In some embodiments, the sample can be selected from at least one of blood, tissue, blood cells, bone marrow, ascites, fine needle biopsy samples, body fluids containing cells, free floating nucleic acids, sputum, saliva, urine, semen, cerebrospinal fluid, peritoneal fluid, pleural fluid, feces, lymph, skin swab, oral swab, nasal swab or lavage. In some embodiments, preferably, the sample can be a tumor tissue removed from a patient with olfactory neuroblastoma.

[0044] The scoring module 620 can be used to score the subtype based on the sequencing information of the target marker of the sample to be tested.

[0045] In some embodiments, the scoring module obtains the basal, neural, and mesenchymal tumor characteristic scores of olfactory neuroblastoma patients based on the sequencing information of the target markers of the sample to be tested.

[0046] In some embodiments, the scoring module calculates the gene expression level of the target marker based on the sequencing information of the target marker of the sample to be tested, and obtains a normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads of the gene expression level; further, each individual olfactory neuroblastoma case in the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads is uniformly scored to obtain the basal, neural, and mesenchymal tumor characteristic scores of a single olfactory neuroblastoma case.

[0047] Fragments Per Kilobase of exon model per Million mapped fragments is the number of fragments per kilobase of transcript per million mapped reads.

[0048] Reads Per Kilobase per Million mapped reads (abbreviated as RPKM) represents the number of reads per kilobase length from a gene per million reads. RPKM is the number of reads mapped to the gene divided by the total number of reads mapped to the genome (in million) and the length of the RNA (in KB).

[0049] In some embodiments, the "CreateSeuratObject()" function of the R language package "Seurat" can be used to convert the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments or Reads Per Kilobase per Million mapped reads into a data object for Seurat package operation. In some embodiments, the "AddModuleScore()" function of the Seurat package can be used to uniformly assign scores to each individual olfactory neuroblastoma case in the normalized expression matrix of the common transcriptome RNA sequencing information Transcripts Per Million, Fragments PerKilobase of exon model per Million mapped fragments or Reads Per Kilobase perMillion mapped reads. In some embodiments, the formula "Normalized Score = (x-min(x)) / (max(x)-min(x))" can be used to standardize the characteristic scores of basal, neural, and mesenchymal tumors, respectively, so that the discrete distribution of the assigned scores is concentrated in the range of 0 to 1.

[0050] In some embodiments, the sequencing information of the target marker can be subjected to necessary quality control, data cleaning, alignment and other conventional processes, and the gene expression amount can be calculated by the alignment results to obtain the TPM normalized expression matrix of the gene expression amount. The specific steps are as follows: ① Quality control: First, the original sequencing data (Fastq format) needs to be quality controlled. Usually, the R language package FastQC is used for preliminary quality inspection, and then the R language package Trimmomatic or other toolkits are used for quality trimming. ② Read segment alignment: Use the R language alignment toolkit (such as HISAT2, STAR, etc.) to align the quality trimmed read segments to the reference genome. ③ Data cleaning: Use R language packages Picard or samtools to mark and remove duplicate PCR reads, and use tools such as StringTie or Cufflinks to assemble the transcriptome of SAM / BAM files, generate transcripts, and estimate the expression amount. The sequencing method of ordinary transcriptome bulk RNA-Seq is fragmentary, so it is necessary to read the fragment information and compare and assemble it with the overall gene information of the genome to obtain the overall expression information of a single gene. The conventional bulk RNA sequencing process usually consists of extraction steps, reverse transcription steps, PCR steps, etc. All of the above steps will produce sequencing bias. The purpose of quality control and data cleaning is to eliminate the bias through statistical processing after the data is offline, so as to improve the data quality and ensure the accuracy and scientificity of subsequent analysis results.

[0051] The judgment module 630 can be used to judge whether the subtype of olfactory neuroblastoma is basal, neural or mesenchymal based on the subtype score.

[0052] In some embodiments, the judgment module 630 can determine the subtype of olfactory neuroblastoma according to the following steps: when the standardized basal tumor characteristic score is ≥0.3 points, the olfactory neuroblastoma is determined to be basal type; when the standardized basal tumor characteristic score is <0.3 points, and the standardized neural tumor characteristic score is ≥0.15 points, the olfactory neuroblastoma is determined to be neural type; when the standardized basal tumor characteristic score is <0.3 points, and the neural tumor characteristic score is <0.15 points, and the mesenchymal tumor characteristic score is >0 points, the olfactory neuroblastoma is determined to be mesenchymal type.

[0053] The present application also provides a computer-readable storage medium, wherein the storage medium stores computer instructions. When the computer instructions are executed by a processor, a method for detecting a subtype of olfactory neuroblastoma is implemented. The flowchart of the method is as follows: Figure 7In step S710, sequencing information of the target marker of the sample to be tested can be obtained, and the target marker is the above-mentioned marker for classification of olfactory neuroblastoma subtypes; in step S720, the subtype can be scored based on the sequencing information of the target marker of the sample to be tested; in step S730, the olfactory neuroblastoma subtype can be judged as basal, neural or mesenchymal based on the subtype score.

[0054] In some embodiments, the sequencing information may include single-cell RNA sequencing information and / or general transcriptome RNA sequencing information.

[0055] In some embodiments, the sample to be tested may be a tumor tissue from a patient with olfactory neuroblastoma.

[0056] In some embodiments, the basal, neural, and mesenchymal tumor characteristic scores of olfactory neuroblastoma patients can be obtained based on the sequencing information of the target markers of the sample to be tested.

[0057] In some embodiments, the gene expression of the target marker can be calculated based on the sequencing information of the target marker of the sample to be tested, and the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments or Reads Per Kilobase per Million mapped reads of the gene expression can be obtained. In some embodiments, further, each individual olfactory neuroblastoma case in the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments or Reads Per Kilobase per Million mapped reads can be uniformly scored to obtain the basal, neural, and mesenchymal tumor characteristic scores of a single olfactory neuroblastoma case.

[0058] In some embodiments, the “CreateSeuratObject()” function of the R language package “Seurat” can be used to convert the Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads normalized expression matrix into a data object operated by the Seurat package.

[0059] In some embodiments, the "AddModuleScore()" function of the Seurat package can be used to uniformly assign a score to each individual olfactory neuroblastoma case in the normalized expression matrix of common transcriptome RNA sequencing information Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads.

[0060] In some embodiments, the formula "Normalized Score = (x-min(x)) / (max(x)-min(x))" can be used to normalize the basal, neural, and mesenchymal tumor characteristic scores, respectively, so that the discrete distribution scores are concentrated in the range of 0 to 1.

[0061] The present application also provides an electronic terminal 800, which includes: a processor 810, a memory 820, an input / output interface 830 and a communication port 840; the memory 820 is used to store computer programs, and the processor 810 is used to execute the computer programs stored in the memory, so that the terminal executes the method for detecting olfactory neuroblastoma subtypes as described in the above-mentioned computer-readable storage medium.

[0062] The processor 810 can execute computing instructions (program code) and perform the functions of the detection device 600 described in this application. The computing instructions may include programs, objects, components, data structures, processes, modules and functions (functions refer to specific functions described in this application). For example, the processor 810 can process instructions for detecting olfactory neuroblastoma subtypes in the olfactory neuroblastoma subtype detection device 600. In some embodiments, the processor 810 may include a microcontroller, a microprocessor, a reduced instruction set computer (RISC), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physical processing unit (PPU), a microcontroller unit, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced RISC machine (ARM), a programmable logic device, and any circuits and processors capable of performing one or more functions, etc., or any combination thereof. For illustration only, Figure 8 Only one processor 810 is described, but it should be noted that the present application may include multiple processors.

[0063] The memory 820 can store data / information obtained from any component in the olfactory neuroblastoma subtype detection device 600. In some embodiments, the memory 820 may include a large capacity memory, a removable memory, a volatile read and write memory, and a read-only memory (ROM), etc., or any combination thereof. Exemplary large capacity memories may include magnetic disks, optical disks, and solid state drives, etc. Removable memories may include flash drives, floppy disks, optical disks, memory cards, USB flash drives, compressed disks, and mobile hard disks, etc. Volatile read and write memories may include random access memories (RAM). RAM may include dynamic RAM (DRAM), double rate synchronous dynamic RAM (DDRSDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and zero capacitance (Z-RAM), etc. ROM may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically erasable programmable ROM (EEPROM), compact disk ROM (CD-ROM), and digital versatile disk ROM, etc.

[0064] The input / output interface 830 can be used to input or output signals, data or information. In some embodiments, the input / output interface 830 can be used to implement the interactive behavior between the user (e.g., the person corresponding to the sample to be tested, the user of the olfactory neuroblastoma subtype detection device 600, etc.) and the processor 710. In some embodiments, the user can input the characteristic information of the person corresponding to the sample to be tested through the input / output interface 830. In some embodiments, the input / output interface 830 may include an input device and an output device. Exemplary input devices may include a keyboard, a mouse, a touch screen, a microphone, etc., or any combination thereof. Exemplary output devices may include a display device, a speaker, a printer, a projector, etc., or any combination thereof. Exemplary display devices may include a liquid crystal display (LCD), a display based on a light emitting diode (LED), a flat panel display, a curved display, a television device, a cathode ray tube (CRT), etc., or any combination thereof.

[0065] The communication port 840 can be connected to a network for data communication. The connection can be a wired connection, a wireless connection, or a combination of the two. The wired connection can include an electric cable, an optical cable, or a telephone line, etc., or any combination thereof. The wireless connection can include Bluetooth, WiFi, WiMax, WLAN, ZigBee, a mobile network (e.g., 3G, 4G, or 5G, etc.), etc., or any combination thereof. In some embodiments, the communication port 840 can be a standardized port, such as RS232, RS485, etc. In some embodiments, the communication port 840 can be a specially designed port.

[0066] The experimental methods in the following examples are conventional methods unless otherwise specified. The experimental materials used in the following examples are purchased from conventional biochemical reagent companies unless otherwise specified. The quantitative tests in the following examples are repeated three times, and the results are averaged.

[0067] Example 1—Study on the characteristics of ONB tumor cell subpopulations

[0068] Single-cell RNA sequencing (scRNA seq) technology can analyze relevant genetic information at the RNA expression level of a single cell and construct a cellular molecular expression map. In recent years, it has been widely used in the exploration of various tumor heterogeneity and the study of tissue development sources. In the study of this application, we attempted to use scRNA sequencing technology to explore the tumor heterogeneity and tumor typing of olfactory neuroblastoma (ONB).

[0069] To characterize the diversity of ONB tumor cells, 24,686 malignant cells from 10 ONB tumor tissues were screened and re-clustered into seven tumor subgroups ( Figure 1a). According to the multi-omics study of ONB published by Classe et al., the two known subtypes of ONB, namely basal and neural, can be verified in ONB single-cell data through the gene expression signatures of basal / neural markers provided in the study ( Figure 1 b); In addition, it was found that some ONB tumor subgroups (C4-C7) had a unique expression pattern that could not be characterized by known Neural / Basal subtype gene markers ( Figure 1 b. Extended data Figure 2 a).

[0070] Therefore, we performed non-negative matrix factorization (NMF) analysis to investigate the expression characteristics of tumor heterogeneity within ONB and found that ONB expressed 5 major gene modules ( Figure 1 c). Through GO enrichment pathway analysis, 5 gene modules were found to represent different functions ( Figure 1 d). Therefore, ONB tumor subgroups can be divided into 4 main functional categories ( Figure 1 e- Figure 1 f), namely, categories related to olfactory nerve development (module 1; CHGB / HES6 / NEUROD1) and cell cycle (module 2; MKI67 / TOP2A / UBE2C), which reflect the expression characteristics of known neuronal and basal ONB subtypes, respectively. Module 4 (CAPS / TPPP3 / PIFO) is associated with ciliary structure and movement. In addition, GO pathway enrichment analysis revealed that two additional NMF modules (and module 3, NRG1 / WNT10A, module 5, BMP7 / LAMA1) have mesenchymal cell characteristics.

[0071] The discovery of a “mesenchymal-like” ONB tumor subpopulation has triggered further exploration of the heterogeneity within ONB tumors ( Figure 1 g). Gene set variation analysis (GSVA, Figure 1 h), found that the basal ONB subgroup highly expressed cell proliferation and cell cycle pathways, the neural ONB subgroup had the functions of neurogenesis and olfactory conduction, and the newly defined mesenchymal ONB subgroup had multiple cell functions including angiogenesis, epithelial-mesenchymal transition (EMT), extracellular matrix (ECM) remodeling, inflammatory response and chemokine secretion, ( Figure 1 h).

[0072] Subsequently, the present application calculated the differentially expressed genes (DEGs) between neural / basal / mesenchymal (NBM) ONB tumor cells, and the results were as follows: Fig. 9 As shown, the top 50 upregulated DEGs were used to evaluate the tumor signature score in each ONB malignant cell (see Supplementary Table 1).

[0073] Example 2—Discovery of Neural / Basal / Mesenchymal Subtypes of ONB Tumors

[0074] Neural, Basal, and Mesenchymal ONB tumor cells may exist in a mixed state in each ONB tumor sample. In order to further clarify the distribution of this mixed cell state, we first used the top 50 gene sets of differentially expressed genes between neural / basal / mesenchymal (NBM) ONB tumor cells, a total of 150 genes, as a gene set that can represent the molecular characteristics of NBM ONB (NBM ONB genelist). The "AddModuleScore()" function of the Seurat package in the R language was used to calculate the Neural, Basal, and Mesenchymal type feature scores (Neural / Basal / MesenchymalScores) of each ONB tumor cell using this gene set. A three-phase diagram was then drawn, and each ONB tumor cell was placed in the diagram according to the relative value of its respective NBM Score. Different categories (neural / basal / mesenchymal cells) were marked with different colors ( Figure 2 a). Overall, the three-phase diagram confirmed the three-type structure of ONB tumor cell phenotype, and indicated that these 150 differentially expressed genes can be used for classification. Furthermore, when the three-phase diagram images of a single patient were displayed separately to observe the specific composition of NBM cells in each patient in detail, it was found that Neural, Basal, and Mesenchymal cells tended to be enriched in different tumor individuals, thus showing three tumor classification patterns that can be distinguished by naked eyes ( Figure 2a), it can be found that Basal tumor cells only exist in 5 cases numbered ONB-946, ONB-311, ONB-548, ONB-599, and ONB-983, and the distribution pattern of tumor cells in these patients always shows that Neural, Basal, and Mesenchymal tumor cells exist; while in 4 cases numbered ONB-764, ONB-288, ONB-199, and ONB-860, the distribution pattern of patient tumor cells shows that only Neural and Mesenchymal tumor cells exist; in particular, in the case numbered ONB-733, only Mesenchymal tumor cells exist. The above tumor cell distribution patterns suggest that there are three types of classification states in ONB tumor patients, which are mainly Basal, Neural, and Mesenchymal tumor cells, and the judgment of tumor type has the judgment priority of Basal type>Neural type>Mesenchymal type, which can be represented by the expression score of ONB NBM three types respectively.

[0075] To verify this finding, a bulk RNA-seq case cohort (HRA007322) consisting of 20 ONB patients who underwent surgical resection was used as a discovery cohort to further explore the clinical classification of ONB. The gene set (NBM ONB gene list) of the neural / basal / mesenchymal (NBM) ONB molecular characteristics of the 20 patients in the discovery cohort was pulled to form a matrix, and semi-supervised clustering was performed. The results showed that the 20 ONB patients could be divided into three major subgroups ( Figure 2 b). There are two groups of patients whose characteristics can be characterized by classic neural and basal gene signatures, respectively. However, after sorting the samples according to NBM ONB gene list expression, another group of tumors (N=4) was found to express mesenchymal tumor characteristics alone. This result suggests that there is a third subtype of ONB that expresses mesenchymal characteristics.

[0076] Subsequently, the bulk RNA-seq cohort (GSE118995) of 18 ONB patients published by Classe et al. (Classe M, Yao H, Mouawad R, et al. Integrated Multi-omic Analysis of Esthesioneuroblastomas Identifies Two Subgroups Linked to Cell Ontogeny [J]. Cell Rep, 2018; 25 (3): 811-21.e5.) was used as an external validation cohort, and the NBM ONB gene list was used as the marker gene for clustering. After semi-supervised clustering, the results were consistent with those observed in the study cohort, that is, there are three types of ONB tumor individuals, which mainly express the gene marker characteristics of neural / basal / mesenchymal (NBM) ONB. This step uses external data to verify the extensibility of the ONB trichotomy theory ( Figure 2 Finally, since the newly discovered ONB subtype was enriched with marker genes of mesenchymal-like cells, we named this previously unidentified ONB subtype Mesenchymal.

[0077] Example 3 - Establishment of a method for defining the Neural / Basal / Mesenchymal subtypes of ONB tumors: based on bulk RNA seq sequencing data

[0078] Single cell RNA sequencing (scRNA-seq) is a high-throughput experimental technology that uses RNA sequencing to quantify the gene expression profile of a specific cell population at the single-cell level. It can effectively analyze heterogeneous systems of early developmental tissues or complex tissues (such as tumor tissues, etc.), but it has disadvantages such as cumbersome sample delivery and sequencing processes and high costs. Conventional transcriptome RNA sequencing (bulk RNA seq) uses samples composed of cell mixtures for sequencing. Although it can only estimate the average expression level of genes in the cell population, the sequencing steps are relatively simple and the cost is low, which is conducive to verifying and promoting the conclusions of scRNA sequencing within a certain range.

[0079] Therefore, the present application further developed a method for projecting the NBM subtype scoring system for olfactory neuroblastoma established by single-cell scRNA sequencing analysis described above onto bulk RNAseq sequencing samples ( Figure 3a). In this part of the work, the sequencing data of 23 ONB bulk RNA seq cases in the study cohort were subsequently analyzed in the form of a TPM matrix after routine offline integration. The "CreateSeuratObject()" function of the R language package "Seurat" can be used to convert the TPM matrix into a data object that can be operated by the Seurat package. The NBM ONB gene list described above, which consists of 3 groups and 150 genes (50 genes in each group), was used as the gene set used for scoring. The "AddModuleScore()" function of the Seurat package was used to uniformly score each ONB case in the bulk RNA seq TPM matrix to obtain the Neural, Basal, and Mesenchymal tumor feature scores (Neural / Basal / Mesenchymal Scores, NBM Scores) of a single ONB case. In the study cohort, the expression pattern of the NBM Scores of the ONB bulk RNA seq cohort calculated using the above method was consistent with the NBM Score ternary diagram of the scRNA ONB single cell cohort ( Figure 2 a). The present application notes that most basal ONB samples are also enriched with higher neural and mesenchymal scores; patients with neural ONB may also have higher mesenchymal scores, but lower basal scores; and in patients with mesenchymal ONB, tumors show pure high mesenchymal score expression, and their basal and neural scores are both low. This indicates that mesenchymal ONB, a unique new subtype of ONB, is characterized by high mesenchymal feature scores and double negative expression of neural and basal features. The present application notes that the findings of the present application also change the previous understanding of the classic binary classification of neural / basal ONB, that is, the determination of basal ONB has priority over neural ONB. The results obtained by detecting the same sample using two methods (bulkRNA seq sequencing and scRNA-seq sequencing) are consistent.

[0080] In the above steps, the scoring rules for uniformly assigning scores to each ONB case individual in the bulk RNA seq TPM matrix are as follows:

[0081] The calculation principle of the AddModuleScore() function of the R language package "Seurat" is as follows: the genes of interest to the researcher are extracted from the expression matrix, and the average expression values ​​of these genes are calculated for each data individual. The average value of the background genes lies in finding the bin where each gene is located, and genes are randomly selected from the bin as the background. Finally, the average values ​​of all target genes are calculated uniformly, and the average values ​​of all background genes are calculated. The difference between the two is the Score value corresponding to the gene list input into the function.

[0082] Based on the findings from single-cell scRNA sequencing and bulk RNA-seq sequencing of common transcriptomes, this application established a classification flow chart to assist us in classifying bulk RNA seq ONB samples with calculated Neural / Basal / MesenchymalScore into three subtypes using a method suitable for clinical practice ( Figure 3 b). It is worth noting that according to the flowchart of the pattern recognition, the basal, neural, and mesenchymal ONB tumors derived from the NBM Score are not parallel to each other, but there is a priority in feature determination (i.e., basal type>neural type>mesenchymal type).

[0083] The formula "Normalized Score = (x-min(x)) / (max(x)-min(x))" was used to standardize the Basal Score, Neural Score, and Mesenchymal Score, respectively, so that the discrete distribution of the assigned values ​​was concentrated in the range of 0 to 1, so as to facilitate classification according to the score interval. ONB with a higher score of basal type characteristics was judged as basal type (if the Basal Score after standardization was ≥ 0.3 points, regardless of the high or low Neural / Mesenchymal Score, it was judged as basal type); if the expression of basal type characteristics was low (Basal Score after standardization < 0.3 points), but the expression of neural type characteristics was high (Neural Score after standardization ≥ 0.15 points), it was judged as neural type ONB (regardless of the high or low Mesenchymal Score). Finally, ONB that only highly expressed mesenchymal features were judged as mesenchymal type (i.e., after standardization, Basal Score < 0.3 points and Neural Score < 0.15 points, and Mesenchymal Score > 0 points were mesenchymal type).

[0084] So far, through the joint analysis of single-cell sequencing and general transcriptome RNA sequencing data of ONB, a rare anterior skull base malignant tumor, this application has successfully established the concept of three-type ONB and used external data sets for verification. The tumor sequencing data used in this application comes from the collection of tumor tissues from ONB patients who underwent endoscopic tumor resection at the Affiliated Eye, Ear, Nose and Throat Hospital of Fudan University from November 2019 to June 2021. 10×genomics single-cell sequencing and analysis were performed. The normal control data set for scRNA sequencing in this application was selected from 4 normal adult olfactory mucosa scRNA sequencing data (GSE139522) published by Durante et al. in the journal "Nature Neuroscience" in March 2020 (doi: 10.1038 / s41593-020-0587-9). The validation set data of bulk RNA seq sequencing used in this application comes from 18 ONB bulk RNA seq data (GSE118995) published by Classe et al. in the journal "Cell Reports" in October 2018 (doi: 10.1016 / j.celrep.2018.09.047). This study complies with the Provisions of the Declaration of Helsinki and has been approved by the Ethics Committee of the Affiliated Eye, Ear, Nose and Throat Hospital of Fudan University (Ethics Approval No.:

[2021] Lun Shen Zi No. 2021038). In this study, the informed consent documents of all ONB patients who underwent nasal endoscopic surgery in the Department of Otolaryngology, Affiliated Eye, Ear, Nose and Throat Hospital of Fudan University were signed and retained. For patients who obtained information and sequencing data from the GEO database, their informed consent was signed and retained by the researchers who published the data.

[0085] (I) Gene set used for scoring (NBM ONB gene list)

[0086] The present invention aims to derive a new three-classification method for olfactory neuroblastoma (ONB) through differential expression analysis of gene expression profiles. In the research related to the present invention, we named this new three-class ONB type as neural type, basal type, and mesenchymal type. The neural / basal / mesenchymal three-classification can be referred to as the "NBM three-classification" according to the abbreviation of its English name.

[0087] The specific genes included in the gene expression set related to the NBM tritype (NBM ONB gene list) are 150 in 3 groups, with 50 genes in each group representing the three ONB subtypes of neural / basal / mesenchymal. The specific genes are shown in the table attached below:

[0088] Schedule 1:

[0089]

[0090]

[0091] (II) Scoring objects and data nature input into the scoring system

[0092] Patients with pathological diagnosis of olfactory neuroblastoma (ONB) had tumor specimens obtained through biopsy or surgery, and then performed bulk RNA sequencing. After the raw data was downloaded from the machine and went through necessary routine processes such as quality control, data cleaning, and alignment, the gene expression was calculated based on the alignment results to obtain the TPM (Transcripts Per Million) normalized expression matrix of the gene expression.

[0093] TPM is a unit commonly used in the field of RNA sequencing data analysis, which represents the proportion of a transcript per million reads. The TPM value can be used to compare the expression abundance of different genes or different samples, and can standardize the RNA-Seq sequencing depth of the sample to eliminate the impact of the sequencing depth of the sample on the analysis of gene expression. Specifically, the calculation formula of the TPM value is: the number of reads of each gene (ReadCount) ÷ the number of species-specific transcripts (Effective Length) ÷ the total number of reads (TotalRead Count) × 1000000, where EffectiveLength represents the effective length of each transcript.

[0094] (III) Specific operation methods of the scoring system for scoring objects

[0095] The free, freely available, open-source statistical software “R” (version>4.0.0) was used for subsequent data analysis, and the R language package “Seurat” (version>3.2.0) dedicated to quality control, analysis, and exploration of single-cell RNA-seq data was loaded into the R environment.

[0096] ① After data processing of the ONB tumor sample common transcriptome RNA sequencing, the TPM matrix is ​​loaded into the R language environment, and the "CreateSeuratObject()" function of the Seurat package is used to convert the TPM matrix into a data object that the Seurat package can continue to operate on.

[0097] ② Use the ONB NBM gene list described in this section, which consists of 3 groups and 150 genes (50 genes in each group) as the gene set used for scoring, and use the "AddModuleScore()" function of the Seurat package to calculate the scores of the three ONB subtypes, neural / basal / mesenchymal, corresponding to each ONB patient data in the TPM matrix; in the output results of this step, each ONB patient will get a Neural Score, a Basal Score and a Mesenchymal Score.

[0098] ③ Use the formula “Normalized Score = (x-min(x)) / (max(x)-min(x))” to standardize the BasalScore, Neural Score, and Mesenchymal Score respectively, and concentrate the discrete distribution scores in the range of 0 to 1. The NBM Score calculated in the previous step is used to determine the ONB type: (1) ONB with a high score of basal-type feature expression is determined to be basal (if the Basal Score is ≥ 0.3 after standardization, regardless of the Neural / Mesenchymal Score, it is determined to be basal); (2) If the basal-type feature expression is low (Basal Score < 0.3 after standardization), and the neural-type feature expression is high (Neural Score ≥ 0.15 after standardization), it is determined to be neural-type ONB (regardless of the Mesenchymal Score); (3) ONB with only high expression of mesenchymal features is determined to be mesenchymal (i.e., if the Basal Score is < 0.3 and the Neural Score is < 0.15 after standardization, and the Mesenchymal Score is > 0, it is mesenchymal). (For specific steps, see the <Attached Figures> section of this application)

[0099] (IV) Statistical methods and statistical power for constructing scoring systems

[0100] The discovery process of the three-class NBM classification uses the K-means clustering method to perform unsupervised clustering on the ONB bulk RNA seq TPM matrix. According to the distribution of marker genes corresponding to the ONB NBM gene list in the clustering results, it is not difficult to conclude that when K=3, the case cohort in the matrix can be divided into three categories, which correspond to the Neural / Basal / Mesenchymal subtypes of ONB in ​​a biological sense. The calculation principle of the AddModuleScore() function of the R language package "Seurat" is: the genes of interest to the researcher are extracted from the expression matrix, and the average expression of these genes is calculated for each data individual. The average value of the background gene is to find the bin where each gene is located, and randomly extract genes in the bin as the background. Finally, all target genes are uniformly calculated and all background genes are calculated. The average value is subtracted from the average value to obtain the score value corresponding to the gene list input into the function. Based on this principle, we calculated the statistical power of this scoring method in the experimental set data set to score the Neural / Basal / Mesenchymal Score of the bulk RNA seq data matrix of ONB patients, and evaluated it with the ROC curve and the area under the curve (AUC) value. The scoring system involved in this patent predicts the AUC values ​​of BasalScore, Neural Score, and Mesenchymal Score of each ONB subtype, respectively, which is 1, 0.96, and 0.95, showing a relatively good statistical prediction efficiency ( Figure 4 ).

[0101] This technical invention proposed for the first time the concept of a three-type classification of a rare tumor, olfactory neuroblastoma (ONB). From the perspective of gene expression and molecular typing, ONB cases were divided into basal, neural, and mesenchymal types, and the gene expression spectrum and molecular functional pathways of each subtype were described, which promoted the understanding of ONB subtypes in related fields. In this process, we newly discovered a mesenchymal type ONB that expresses mesenchymal cell-related characteristics. Through single-cell sequencing technology, we successfully identified and characterized the molecular characteristics of mesenchymal ONB. And through the molecular characteristics of NBM ONB obtained by single-cell sequencing, we used existing open source R code and program packages to develop an algorithm that allows us to distinguish the ONB three-type system discovered by single-cell sequencing in common transcriptome sequencing samples with lower cost and less difficulty in promotion. The typing gene expression profile used in this technology is derived from high-precision single-cell sequencing, which has high accuracy and discrimination, and can be projected onto common transcriptome sequencing with relatively simple sequencing steps and low costs to draw typing conclusions. Therefore, it is conducive to verification and promotion within a certain range, which is helpful for typing ONB patients in clinical work and assisting in the selection of corresponding drug treatments based on the molecular characteristics of each subtype.

[0102] In addition, our previous research results related to the technology of the present invention show that different subtypes of ONB tumor cells may correspond to different sources of olfactory epithelial (OE) neural cell components. This means that there is a strong correlation between ONB subtypes and tumor differentiation status. In related studies, we used the scRNA dataset of adult OE (GSE139522) (Durante MA, Kurtenbach S, Sargi ZB, et al. Single-cell analysis of olfactory neurogenesis and differentiation in adult humans [J]. Nat Neurosci. 2020; 23 (3): 323-326.). Normal olfactory epithelial cell subpopulations were studied to further explore the potential origin cells of each ONB subtype ( Figure 5 a). Reference to known marker genes of multiple cell subsets in human and mouse OE ( Figure 5b), we identified six subpopulations corresponding to the developmental lineages of olfactory stem cells, including HBCs (TP63, KRT5), GBCs (ASCL1, HES6), supporting cells (including immature mSus and mature iSus; CXCL17, CYP2J2), direct neural precursor cells (INPs; LHX2, NEUROD1), and immature and mature olfactory neurons (iORNs and mORNs; GNG8, GNG13). Cell development trajectory analysis revealed a dichotomous branching structure, showing that supporting cells are directly transformed from HBCs, while the olfactory neuron cell lineage (INPs, iORNs and mORNs) is derived from the proliferation and differentiation of olfactory epithelial stem cells such as GBCs ( Figure 5 c).

[0103] Next, to search for the diverse origins of ONB subtypes, we compared transcriptome correlations between NBM tumor cell subsets and adult OE cells. We performed Pearson's correlation coefficient analysis between normal OE and ONB tumor subsets, and the results showed that olfactory epithelial stem cell GBCs were highly similar to basal ONB tumor characteristics, while the olfactory neuron lineage (INPs, iORNs and mORNs) was mainly highly associated with neural ONB in ​​marker expression. Mesenchymal ONB-related gene expression was observed in the supporting cell lineage ( Figure 5 d). The above results can be mutually confirmed with the scoring results of NBM marker expression scores in OE cell subsets ( Figure 5 e-5f).

[0104] The normal OE cell sources corresponding to basal, neural, and mesenchymal ONBs show a trend of gradual maturation in differentiation, which indicates that the degree of differentiation corresponding to basal, neural, and mesenchymal ONBs gradually increases. After the three-type NBM was obtained, the origin of normal OE cells corresponding to each subtype was explored. The related findings have strongly promoted our understanding of the origin of ONB and the occurrence and development of tumors; it also has positive significance for explaining the disease to patients in actual clinical work.

[0105] Example 4

[0106] Tumor tissues of 20 patients with olfactory neuroblastoma (ONB) who underwent endoscopic tumor resection at the Affiliated Eye, Ear, Nose and Throat Hospital of Fudan University from June 2018 to June 2021 were collected. The postoperative pathology of the patients was confirmed by the Pathology Department of the Eye, Ear, Nose and Throat Hospital to be ONB. The samples were stored at RNA later-80℃ and subjected to conventional transcriptome RNA sequencing (bulk RNA seq). After the output fastq raw data files were subjected to necessary routine processes such as quality control, data cleaning, and alignment, the gene expression levels were calculated by the alignment results, and the TPM (Transcripts Per Million) normalized expression matrix containing the gene expression levels of 20 ONB patients was obtained.

[0107] The TPM matrix can be converted into a data object that can be operated by the Seurat package using the "CreateSeuratObject()" function of the R language package "Seurat". The NBM ONB gene list described above, which consists of 3 groups and 150 genes (50 genes in each group), was used as the gene set used for scoring. The "AddModuleScore()" function of the Seurat package was used to uniformly score each ONB case in the bulkRNA seq TPM matrix, and the Neural, Basal, and Mesenchymal tumor characteristic scores (Neural / Basal / Mesenchymal Scores, NBM Scores, as shown in Appendix 2 below) of a single ONB case were obtained.

[0108] Schedule 2

[0109] No Name NBM-bulk Basal Score Neural Score Mesenchymal Score 1 ONB-063 Mesenchymal -97.36627823 -265.5406626 215.0700149 2 ONB-330 Mesenchymal -118.0457886 -294.4765003 354.004021 3 ONB-022 Mesenchymal -103.4366143 -291.2959331 157.7965598 4 ONB-402 Mesenchymal -110.52749 -294.8201694 108.2838608 5 ONB-261C Neural -80.40230591 -49.63135148 -252.8734017 6 ONB-764 Neural -92.05053688 23.7042222 -103.3546089 7 ONB-659 Neural -73.23184959 -178.842147 -287.8849566 8 ONB-288 Neural -63.5252671 -120.5806248 -108.0000482 9 ONB-412 Neural -113.1001711 -114.6875911 -223.1715457 10 ONB-643 Neural -83.62268485 -141.7809125 -368.0043795 11 ONB-698 Neural -112.1635174 -193.4778876 -333.3043504 12 ONB-142 Neural -113.5957678 -139.3795657 95.5089331 13 ONB-199 Neural -89.47341666 -179.8556563 94.08927546 14 ONB-365 Neural -88.36433841 -235.0279074 -18.6105357 15 ONB-993 Basal 21.21275459 -278.8758454 -156.9426674 16 ONB-983 Basal -29.26707562 -283.1032653 203.322637 17 ONB-559 Basal 102.8333504 -245.069349 100.7289626 18 ONB-520 Basal 15.92237351 -311.4835678 -264.6410963 19 ONB-126 Basal 27.52366581 -175.1659575 23.67911894 20 ONB-249 Basal -30.30686164 -236.9324703 112.3629879

[0110] The formula "Normalized Score = (x-min(x)) / (max(x)-min(x))" was used to standardize the Basal Score, Neural Score, and Mesenchymal Score, respectively, so that the discrete distribution scores were concentrated in the range of 0 to 1, so as to facilitate classification according to the score range (see Appendix 3).

[0111] Schedule 3

[0112]

[0113] According to the instructions for the typing steps in this technical method, ONB typing was determined: (1) ONB with a standardized BasalScore ≥ 0.3 was determined to be basal type, regardless of the Neural / Mesenchymal Score. According to this standard, 6 cases numbered ONB-993, ONB-983, ONB-559, ONB-520, ONB-126, and ONB-249 were determined to be basal type; (2) For the remaining 14 patients (all with standardized Basal Score < 0.3), those with standardized Neural Score ≥ 0.15 were determined to be neural type ONB, regardless of the Mesenchymal Score. According to this standard, 10 patients, ONB-261C, ONB-764, ONB-659, ONB-288, ONB-412, ONB-643, ONB-698, ONB-142, ONB-199, and ONB-365, can be identified as neurological type. (3) The remaining 4 patients all had Basal Score < 0.3 and Neural Score < 0.15 after standardization, and only expressed mesenchymal characteristics (Mesenchymal Score > 0), so they can be identified as mesenchymal ONB. So far, all ONB cases involved in this embodiment have completed the ONB NBM three-type subtype determination proposed by the present technical invention.

[0114] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0115] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0116] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0117] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A marker for subtype classification of olfactory neuroblastoma, the marker comprising a basal marker, a neural marker and a mesenchymal marker; The basal markers include at least one of the following genes or fragments thereof: UBE2C, HMGB2, CENPF, NNAT, HIST1H4C, HIST1H1A, TUBA1B, PTTG1, TOP2A, NUSAP1, HIST1H1B, CCNB1, UBE2S, BIRC5, CKS1B, CKS2, TPX2, H2AFZ, PCP4, CDK1, MFAP4, MKI67, PBK, MA D2L1, HMGB1, CKAP2, CCNB2, SMC4, CENPW, HIST1H1D, UBE2T, CDKN3, ASPM, TUBA1C, TYMS, DLGAP5, PCLAF , PRC1, NUF2, ARL6IP1, TMPO, CDC20, KPNA2, HMGN2, DTYMK, HIST1H1E, HIST1H1C, MDK, HIST1H3B, H2AFX; The neurogenic markers include at least one of the following genes or fragments thereof: CRABP1, SCG5, PEG10, MAP1B, GNG8, CIB2, STMN2, PCSK1N, CHGB, RTN1, MIR7-3HG, SNRPN, EEF1A2, MEG3, GRP, SEC11C, NHLH1, KIF19, KCNK9, APMAP, TLCD3B, UNCX, CALB2, BEX1, IRX2, PEBP1, RGS10, CIRBP, VAMP2, FXYD3, KLHL35, MAP1LC3A, BEX2, NCAM1, GADD45B, NEUROD1, FXYD6, ATP6V0E2, C11orf96, CAMK2N1, C5orf38, CPE, RPS3, TMPRSS6, DUSP26, PDLIM1, STX1A, FABP6, K CTD17, SPOCK2; the mesenchymal markers include at least one of the following genes or fragments thereof: SLPI, S100A6, WFDC2, MT1X, MGP, CRYAB, S100A11, SAT1, CCN2, IGFBP7, VMO1, APOE, IER3, IGFBP5, S100A4, ANXA2, S100A10, VIM, MT2A, CD9, IFI27, LGALS1 , LYPD2, TIMP1, IFITM3, ITGB1, LGALS3, CALD1, KRT18, SPARC, COL11A1, COL18A1, CEBPD, CSTB, CD59 , APLP2, ID3, PTN, COL3A1, CCN1, KRT8, DST, TPM4, CYBA, ALDH1A1, CLU, IFITM2, CD63, TPM2, ZFP36L1.

2. The marker according to claim 1, characterized in that The olfactory neuroblastoma subtypes include basal olfactory neuroblastoma, neuronal olfactory neuroblastoma and mesenchymal olfactory neuroblastoma. The basal olfactory neuroblastoma subgroup highly expresses cell proliferation and cell cycle pathways relative to the neuronal olfactory neuroblastoma subgroup and the mesenchymal olfactory neuroblastoma subgroup. The neuronal olfactory neuroblastoma subgroup has the functions of neurogenesis and olfactory conduction, and the mesenchymal olfactory neuroblastoma subgroup has cell functions including angiogenesis, epithelial-mesenchymal transition, extracellular matrix remodeling, inflammatory response and chemokine secretion.

3. Use of the marker or the detection substance thereof as claimed in claim 1 or 2 in preparing a product for olfactory neuroblastoma subtype classification.

4. The use according to claim 3, characterized in that The detection object is selected from any one or more of antibodies, membrane strips, chips, probes or primer pairs.

5. A product for classification of olfactory neuroblastoma subtypes, comprising genes and / or detection substances thereof in the markers as claimed in claim 1.

6. The product according to claim 5, characterized in that The detection object is selected from any one or more of antibodies, membrane strips, chips, probes or primer pairs.

7. A device for detecting olfactory neuroblastoma subtypes, characterized in that: The olfactory neuroblastoma subtype detection device comprises the following modules: An information acquisition module, used for providing sequencing information of a target marker of a sample to be tested, wherein the target marker is a marker for olfactory neuroblastoma subtype classification according to claim 1 or 2; A scoring module, used to score the subtype based on the sequencing information of the target marker of the sample to be tested; A judgment module is used to judge whether the subtype of olfactory neuroblastoma is basal, neural or mesenchymal based on the subtype score.

8. The olfactory neuroblastoma subtype detection device according to claim 7, characterized in that: Other features include: The sequencing information includes single-cell RNA sequencing information and / or common transcriptome RNA sequencing information; The sample to be tested is a tumor tissue of a patient with olfactory neuroblastoma; The scoring module obtains the basal, neural, and mesenchymal tumor characteristic scores of olfactory neuroblastoma patients based on the sequencing information of the target markers of the sample to be tested; The determination module determines the subtype of olfactory neuroblastoma according to the following steps: When the standardized basal tumor characteristic score was ≥0.3, olfactory neuroblastoma was judged to be basal type; When the standardized basal-type tumor characteristic score was <0.3 points and the standardized neural-type tumor characteristic score was ≥0.15 points, olfactory neuroblastoma was judged to be neural-type; When the standardized basal tumor characteristic score was <0.3 points, the neural tumor characteristic score was <0.15 points, and the mesenchymal tumor characteristic score was >0 points, olfactory neuroblastoma was judged to be mesenchymal type.

9. The olfactory neuroblastoma subtype detection device according to claim 8, characterized in that: The scoring module calculates the gene expression of the target marker based on the sequencing information of the target marker of the sample to be tested, and obtains a normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads of the gene expression; further, the quality of each individual olfactory neuroblastoma case in the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads is unified to obtain the basal, neural, and mesenchymal tumor characteristic scores of a single olfactory neuroblastoma case.

10. The olfactory neuroblastoma subtype detection device according to claim 9, characterized in that: Use the "CreateSeuratObject()" function of the R package "Seurat" to convert the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads into a data object operated by the Seurat package; And / or, use the "AddModuleScore()" function of the Seurat package to uniformly assign a score to each individual olfactory neuroblastoma case in the normalized expression matrix of common transcriptome RNA sequencing information Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads; And / or, the formula "Normalized Score = (x-min(x)) / (max(x)-min(x))" is used to standardize the characteristic scores of basal, neural, and mesenchymal tumors, respectively, so that the discrete distribution scores are concentrated in the range of 0 to 1.

11. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer instructions are executed by a processor, a method for detecting a subtype of olfactory neuroblastoma is implemented. The method includes: Obtaining sequencing information of a target marker of the sample to be tested, wherein the target marker is the marker for olfactory neuroblastoma subtype classification according to claim 1 or 2; Based on the sequencing information of the target markers of the sample to be tested, the subtype is scored; The olfactory neuroblastoma subtype was determined to be basal, neural, or mesenchymal based on the subtype score.

12. The computer-readable storage medium of claim 11, wherein: Other features include: The sequencing information includes single-cell RNA sequencing information and / or common transcriptome RNA sequencing information; The sample to be tested is a tumor tissue of a patient with olfactory neuroblastoma; Based on the sequencing information of the target markers of the samples to be tested, the basal, neural, and mesenchymal tumor characteristic scores of patients with olfactory neuroblastoma were obtained.

13. The computer-readable storage medium of claim 11, wherein: Based on the sequencing information of the target marker of the sample to be tested, the gene expression level of the target marker was calculated to obtain the normalized expression matrix of gene expression levels of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments or Reads Per Kilobase per Million mapped reads; further, each individual olfactory neuroblastoma case in the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model perMillion mapped fragments or Reads Per Kilobase per Million mapped reads was uniformly scored to obtain the basal, neural, and mesenchymal tumor characteristic scores of a single olfactory neuroblastoma case.

14. The computer-readable storage medium of claim 13, wherein: Use the "CreateSeuratObject()" function of the R package "Seurat" to convert the normalized expression matrix of Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads into a data object operated by the Seurat package; And / or, use the "AddModuleScore()" function of the Seurat package to uniformly assign a score to each individual olfactory neuroblastoma case in the normalized expression matrix of common transcriptome RNA sequencing information Transcripts Per Million, Fragments Per Kilobase of exon model per Million mapped fragments, or Reads Per Kilobase per Million mapped reads; And / or, the formula "Normalized Score = (x-min(x)) / (max(x)-min(x))" is used to standardize the characteristic scores of basal, neural, and mesenchymal tumors, respectively, so that the discrete distribution scores are concentrated in the range of 0 to 1.

15. An electronic terminal, characterized in that: The electronic terminal includes: a processor, a memory, an input / output interface and a communication port; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the method for detecting olfactory neuroblastoma subtypes as described in the computer-readable storage medium as described in any one of claims 11 to 13.

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