CN113512588B

CN113512588B - Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof

Info

Publication number: CN113512588B
Application number: CN202110661743.3A
Authority: CN
Inventors: 肖建如; 魏海峰; 周旺; 张�浩; 龚海熠; 王霆
Original assignee: Shanghai Changzheng Hospital; University of Shanghai for Science and Technology
Current assignee: Shanghai Changzheng Hospital; University of Shanghai for Science and Technology
Priority date: 2021-06-15
Filing date: 2021-06-15
Publication date: 2023-03-21
Anticipated expiration: 2041-06-15
Also published as: CN113512588A

Abstract

In the gene for osteosarcoma typing and osteosarcoma prognosis evaluation and the application thereof, aiming at tumor heterogeneity, the difference of cell dynamics and molecular characteristics between the conventional osteosarcoma and normal cancellous bone is compared, the obvious differentiation direction of osteosarcoma cells is detected based on a single cell RNA sequencing (scRNA-seq) technology or a gene detection kit, a gene detection chip technology or an immunohistochemical method, and possible interaction between each cell type in a tumor microenvironment is researched. Conventional osteosarcomas can be classified into three types according to the differentiation direction of osteosarcoma cells, and the microenvironment characteristics of each type of tumor are described, and the prognosis of each type is verified according to cancer genomic map (TCGA) data.

Description

Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof

Technical Field

The invention belongs to the technical field of biomedicine, and particularly relates to a gene, a detection kit, a detection chip and application thereof for osteosarcoma typing and osteosarcoma prognosis evaluation.

Background

Osteosarcoma (OS) is the most common primary bone malignancy. Surgical resection was the major treatment available to OS patients before chemotherapy was widely used in the 1970 s. The implementation of adjuvant chemotherapy significantly improves the prognosis for OS patients, with a five-year survival rate of non-metastatic patients that increases from 20% to over 60%. However, the prognosis of patients with metastatic or recurrent disease was not significantly improved, and the survival rates of both groups were only 20%. Subsequently, over the past forty years, the treatment pattern and prognosis of OS patients has not progressed much. Therefore, there is an urgent need to identify molecular mechanisms and new therapies that can improve OS management.

With the great success of targeted therapies in recent years, there has been a new promise for the treatment of osteosarcoma. Many clinical trials of targeted drugs have been conducted on osteosarcoma patients, including Tyrosine Kinase Inhibitors (TKIs), which is one of the most common targeted drugs in cancer treatment. In a series of TKIs reports, apatinib achieved the highest Partial Remission (PR) rate of 43% in advanced OS patients, while the toxic effects resulted in a dose reduction or discontinuation in 68% of patients. Given the limited clinical benefit observed with targeted therapy in non-selected patients with OS, it is necessary to discover molecular subtypes of OS, as subtype-based targeted therapy may gain clinical benefit in cancer therapy.

The existing clinical staging (TNM) and grading (pathological) diagnosis methods cannot well explain the heterogeneity of patient prognosis and well guide patients to carry out personalized treatment such as chemotherapy, targeted therapy and the like.

Disclosure of Invention

Based on this, the invention aims to provide a gene for typing osteosarcoma and evaluating the prognosis of osteosarcoma, which can well explain the heterogeneity of the prognosis of patients and is suitable for guiding patients to perform personalized treatment such as chemotherapy and targeted therapy. The specific technical scheme is as follows:

a gene set for use in typing osteosarcoma and assessing prognosis of osteosarcoma, wherein said gene group comprises 44 genes as shown in table 2.

In some embodiments, the 44 genes and their combinations with one or more related genes selected from the group consisting of: <xnotran> AC074351.1, AC091729.3, AC245595.1, ACAN, ACTA2, ACTG1, ACTN1, ACTR3B, ADIRF, AKR7A2, AL118516.1, ALKBH5, ALPL, ANKH, ANP32B, ANXA2, ANXA6, AOC2, APRT, ARF1, ARF4, ARGLU1, ARL1, ARL3, ARL4D, ARL6IP5, ASPM, ASS1, ATF3, ATP2B1, ATP5F1D, ATP5IF1, ATP5MC2, ATP5MC3, ATP5MD, ATP5ME, ATP5PF, ATPAF2, ATRAID, AURKB, B2M, B9D1, BAMBI, BASP1, BEX3, BGN, BIRC5, BMP3, BMP8B, BNIP3, BSG, BST2, BTF3, C12orf57, C12orf75, C19orf12, C19orf24, C19orf70, C1orf54, C1QBP, C1QTNF1, C1QTNF3, C4orf3, C5orf15, C7orf50, C8orf59, CA2, CADM1, CADM2, CALD1, CALM1, CALM2, CALU, CAMTA1, CAPZB, CARHSP1, CAVIN3, CBLN4, CBX5, CCDC80, CCNB1, CCNB2, CCND1, CCPG1, CCSER2, CCT2, CCT5, CCT6A, CD151, CD200, CD24, CD320, CD59, CD63, CD99, CDC20, CDC34, CDH11, CDK1, CDKN1C, CDKN2A, CDKN3, CEBPD, CENPA, CENPE, CENPF, CENPW, CERCAM, CETN2, CFH, CFL1, CGGBP1, CHCHD2, CHMP2A, CHN1, CIRBP, CKAP2, CKS1B, CKS2, CLEC11A, CLNS1A, CLSPN, CLU, CMSS1, CNN2, CNN3, CNPY2, COA4, COL10A1, COL11A1, COL18A1, COL1A1, COL1A2, COL3A1, COL5A2, COL6A1, COL6A2, COL8A1, COMMD4, COPS3, COPS9, COPZ1, COPZ2, COX20, COX6C, COX7A1, COX7C, CPE, CPQ, CRABP2, CRELD2, CRISPLD1, CRYAB, CRYGS, CSAG1, CSNK2B, CSRP1, CSRP2, CTGF, CTHRC1, CTNNAL1, CXCL14, CYB5A, CYB5R3, CYC1, CYP1B1, CYR61, CYTOR, DAD1, DAP, DBI, DCN, DCP1A, DDIT4, DDOST, DDT, DDX18, DDX24, DEK, DGCR6L, DGUOK, DNAJC15, DNAJC19, DNM3OS, DOHH, DPT, DSTN, DTYMK, DUT, DYNLL1, DYNLT1, DYNLT3, EBNA1BP2, EBPL, ECI1, EEF1A1, EEF1B2, EEF2, EFEMP2, EFHC1, EGFL6, EID1, EIF2S1, EIF3E, EIF3F, EIF3H, EIF3I, EIF4A1, EIF4A2, EIF6, ELOB, ELOC, EMP1, EMP2, EMP3, ENAH, ENO1, ENPP1, ENPP2, ENY2, EPB41L4A-AS1, EPDR1, EPSTI1, ERGIC3, ERH, ERLEC1, ERV3-1, ESF1, FAAP20, FAM114A1, FAM162A, FAP, FBLN1, FBN1, FDPS, FERMT2, FGF7, FGFR1, FGFR3, FHL1, FHL2, FIS1, FKBP10, FKBP11, FKBP3, FKBP7, FLOT1, FLRT3, FLYWCH2, </xnotran> <xnotran> FMOD, FN1, FNDC1, FOLR1, FOXK1, FSTL1, FUNDC1, FXYD1, FXYD6, GABARAP, GABPB1-AS1, GADD45B, GAL, GAMT, GAPDH, GAS6, GCSH, GGT5, GJA1, GLRX5, GLT8D2, GNAS, GNL1, GNPAT, GOLT1B, GPAA1, GPC1, GPX4, GPX8, GRP, GSTA1, GSTM3, GTSE1, GUK1, H2AFV, H2AFZ, HACD3, HADH, HAPLN1, HAS2, HCFC1R1, HES4, HILPDA, HIST1H1C, HIST1H2AC, HIST1H2BC, HIST1H2BN, HIST1H4E, HIST2H2BE, HLA-A, HLA-B, HLA-C, HMGB1, HMGB2, HMGB3, HMGN2, HMGN5, HNRNPA1, HNRNPAB, HP1BP3, HPF1, HSD17B10, HSP90AB1, HSPD1, HTRA1, IBSP, ID1, ID3, IER2, IER3, IER5L, IFI27, IFI27L1, IFI27L2, IFI35, IFI44L, IFI6, IFIT1, IFIT3, IFITM2, IFITM3, IFITM5, IGFBP2, IGFBP3, IGFBP4, IGFBP6, IGFBP7, IKBIP, ILF2, IMPDH2, INAFM1, INHBA, INSC, IRS1, IRX3, ISG15, ISLR, ITGA10, ITGA11, ITGAE, ITGBL1, JPT1, JUNB, KCNJ15, KDELR1, KDELR2, KDELR3, KIZ, KLF10, KLK4, KNOP1, LAGE3, LAMA4, LAMP1, LAPTM4A, LAPTM4B, LARP6, LAS1L, LDHA, LDHB, LENG8, LGALS1, LGALS3, LGALS3BP, LHFPL6, LIMA1, LINC00662, LMAN1, LMNA, LOXL1, LRRC1, LRRC15, LRRC17, LRRFIP2, LSM7, LTBP1, LUC7L3, LUM, LY6E, LY6K, LYPLA1, MAD2L1, MAGEA3, MAGED1, MAGED2, MAGEH1, MAP1B, MAP3K13, MARCKSL1, MCRIP1, MDH2, MDK, MED9, MEGF10, MELTF, METTL5, MFAP2, MFGE8, MFSD10, MGST3, MIF, MIR4458HG, MKI67, MLF2, MLLT11, MMP11, MMP13, MMP23B, MNS1, MORF4L1, MORF4L2, MPC1, MPG, MRC2, MT1E, MT1F, MT1G, MT1M, MT1X, MT2A, MTDH, MUC20-OT1, MXRA8, MYC, MYL12A, MYL12B, MYL6, MYL6B, MYL9, MYO6, MZT1, MZT2A, NACA, NASP, NBL1, NCBP2-AS2, NCLN, NCOR1, NDN, NDUFA4, NDUFA4L2, NDUFA6, NDUFB10, NDUFB11, NDUFB9, NDUFC2, NDUFS5, NEDD8, NELFE, NENF, NEU1, NFIC, NHP2, NIFK, NIPSNAP2, NME1, NME4, NNMT, NOP56, NOTCH3, NPB, NPM1, NPW, NQO2, NSMCE1, NSMCE3, NT5E, NTHL1, NTM, NUBP2, NUCB1, NUCKS1, NUDCD2, NUPR1, NUSAP1, OAS1, OAT, OCIAD1, OGN, OLFML3, OMD, OST4, OSTC, PA2G4, PABPC1, </xnotran> <xnotran> PALLD, PAXX, PCBP1, PCBP2, PCLAF, PCNA, PCOLCE, PCOLCE2, PDCD2, PDCD5, PDGFD, PDGFRL, PDLIM2, PDLIM4, PDLIM7, PDRG1, PFDN2, PFDN4, PHB, PHF19, PHGDH, PHLDA3, PHPT1, PIGL, PIGT, PITX2, PLAC9, PLAT, PLEKHA5, PLIN3, PLOD2, PLP2, PLS3, PMEPA1, PODNL1, POLR1D, POLR2F, POLR2K, POLR3K, POP4, POR, PPA1, PPFIBP1, PPIB, PPIC, PPP1CB, PRAF2, PRDX2, PRDX4, PRDX6, PRELP, PRKDC, PRPSAP2, PRRX1, PRRX2, PRSS23, PSIP1, PSMA2, PSMA3, PSMA4, PSMA7, PSMB1, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSME1, PSME2, PSMG3, PSMG4, PTGDS, PTGES, PTGES3, PTH1R, PTMS, PTN, PTS, PTTG1, PTTG1IP, PTX3, PVT1, PYCR1, QPCT, RAB2A, RAB31, RAB34, RABAC1, RAC3, RAD21, RAI1, RALY, RAMP1, RAN, RANBP1, RARRES3, RASD1, RBBP6, RBBP7, RBX1, RCN1, RCN3, REXO2, RFT1, RGCC, RGS3, RHOC, RHOD, RIDA, RNASEH2B, RNPS1, RPN2, RPS19BP1, RRBP1, RRM2, RSF1, RSL1D1, RSRP1, RSU1, RTRAF, RUNX2, S100A10, S100A11, S100A13, S100A16, S100A4, S100A6, SAP25, SAR1A, SDC2, SDF2L1, SEC11A, SEC61B, SEC61G, SELENOK, SELENOM, SELENOW, SEM1, SEPTIN11, SERBP1, SERF2, SERPINA1, SERPINE2, SERPINF1, SERPING1, SERPINH1, SEZ6L2, SF3B6, SFTA1P, SGCB, SHMT2, SIVA1, SLC25A39, SLC25A6, SLC29A1, SLIRP, SMC1A, SMC2, SMC3, SMC4, SMDT1, SMIM10, SMIM26, SMOC1, SNAI2, SNHG8, SNRNP25, SNRPA1, SNRPB, SNRPB2, SNRPD3, SNRPE, SNRPG, SNX3, SNX7, SOD1, SOX4, SPARC, SPATS2L, SPHK1, SPP1, SPRY1, SRM, SRPX, SRSF1, SSB, SSR3, ST13, STAT1, STMN1, STMP1, STXBP6, SUB1, SULF1, SUMF2, SUN1, SUPT16H, SURF4, SWI5, SYNGR1, TAF1D, TAF7, TAGLN, TAGLN2, TAX1BP3, TBCA, TCEA1, TCEAL3, TCEAL9, TECR, TERF1, TEX30, TFDP1, TFPI, TGFB1I1, TGFBI, THUMPD3-AS1, THY1, TIMM13, TIMP1, TIMP3, TKT, TM2D3, TMBIM6, TMED10, TMED2, TMED9, TMEM106C, TMEM14A, TMEM167A, TMEM258, TMEM259, TMEM45A, TMEM50A, TMEM98, TMSB15A, TNC, TNFAIP6, TNFRSF12A, TNFSF10, TOMM20, TOP2A, TPI1, TPM1, TPM2, TPM4, TPX2, TRAPPC1, </xnotran> TRIM16L, TRIR, TSPO, TUBA1A, TUBA1B, TUBA1C, TUBB2A, TUBB2B, TUBB4B, TUBB6, TUFM, TUSC3, TVP23B, TXN, TXNL1, TYMS, UACA, UBB, UBE2C, UBE2I, UBE2L3, UBE2L6, UBE2S, UBE2T, UBE2V2, UCHL1, UCHL3, UFD1, UBD 2, and the like UNC5B, UQCRB, UQCRC2, UQCRFS1, UQCRH, URI1, USP11, UTP11, VAMP5, VBP1, VCAN, VDR, VKORC1, VWA1, WDR18, WFDC1, WIPI2, XRCC6, YIF1A, YPEL3, YWHAE, YWHAQ, ZFHX4, ZNHIT1, ZRANB2, ZSCAN16-AS1, and ZSQUM 7.

In some of these embodiments, the gene cluster is a gene cluster consisting of 840 genes, as shown in table 1.

The invention also relates to an application of the gene set, and the specific technical scheme is as follows:

use of a gene set as described above in the preparation of a test kit for osteosarcoma typing and prognosis assessment, said test kit comprising: amplification of primers for evaluation of genes for osteosarcoma typing and evaluation of a prognostic gene set as described above; and/or

Probes that specifically bind to genes and/or their complementary sequences as assessed for osteosarcoma typing and for assessing a prognostic gene set as described above; and/or

Antibodies that specifically bind to proteins evaluated for osteosarcoma typing and for assessing gene expression of a prognostic gene set as described above.

In some of these embodiments, the primers comprise: the nucleotide sequence of the primer is shown as SEQ ID NO.1-SEQ ID NO. 88.

In some embodiments, the application is the application of the gene set in preparing a protein detection chip for osteosarcoma typing and prognosis evaluation, wherein the gene chip comprises a solid phase carrier and a detection antibody, and the capture antibody specifically binding to the genes of the gene set is immobilized on the solid phase carrier, and the detection antibody is connected with an antibody marker.

In some of these embodiments, the antibody label is: enzyme labeling, fluorescein labeling, isotope labeling or biotin labeling.

The invention also relates to a kit for osteosarcoma typing and osteosarcoma prognosis evaluation, and the specific technical scheme is as follows:

a kit for typing osteosarcoma and evaluating prognosis of osteosarcoma comprises

1) Amplification of primers for evaluation of genes for osteosarcoma typing and evaluation of a prognostic gene set as described above; and/or

2) Probes that specifically bind to genes and/or their complementary sequences as assessed for osteosarcoma typing and for assessing a prognostic gene set as described above; and/or

3) Antibodies that specifically bind to proteins evaluated for osteosarcoma typing and for gene expression in a prognostic gene set as described above.

In some of these embodiments, the test kit comprises: a total RNA extraction reagent, a reverse transcription reagent, and/or a sequencing reagent.

In some of these embodiments, the test kit comprises: dNTP solution and/or RNA reverse transcriptase.

In some embodiments, the sequencing reagent comprises a second generation sequencing reagent or a single cell sequencing reagent.

The invention also relates to a chip for osteosarcoma typing and osteosarcoma prognosis evaluation, and the specific technical scheme is as follows:

a gene detection chip for typing osteosarcoma and evaluating prognosis of osteosarcoma, said gene chip comprising a solid phase carrier on which a capture antibody specifically binding to genes of the gene set as described above is immobilized and a detection antibody linked to an antibody marker.

The invention also relates to a method for typing osteosarcoma and evaluating prognosis of osteosarcoma, which can be understood that the typing of osteosarcoma and the prognosis evaluation of osteosarcoma can be applied to medical diagnosis and treatment and scientific research, and can be specifically applied to development of related typing and evaluation products (such as development of kits and detection chips), establishment of typing and evaluation models and the like. The specific technical scheme claimed by the invention is as follows:

a method for typing osteosarcoma for a non-diagnostic and non-therapeutic purpose, comprising the steps of determining the presence of a gene set for osteosarcoma typing as described above in a sample and evaluating the prognosis of the osteosarcoma typing and the amount of the gene or protein expressed from the gene.

In some embodiments, the kit or the gene detection chip is used to detect the gene expression level in the sample, and the osteosarcoma typing result is obtained by data analysis.

In some embodiments, when any 2or more genes selected from ABCF1, ADAMTS1, ALDH3A2, ALKBH5, ATPAF2, C19orf12, CENPV, CPNE3, DRG2, EPN2, FLII, FSCN1, GID4, HIST1H2BD, LSM2, MPRIP, MYO15A, PEMT, PI15, PRAME, PRR3, TOM1L2, TTC19, VARS are highly expressed, the osteosarcoma is classified as type 1, which is a good prognosis for patients with osteosarcoma; and/or

When more than 2 genes of AEBP1, ASPN, C1R, C1S, COL12A1, COL5A1, COL6A3, OLFML2B, POSTN, SFRP4 and THBS2 are highly expressed, the osteosarcoma is classified into type 2, and the patient with osteosarcoma is still in advance; and/or

When more than 2 genes of any one of ATP1B3, CDK4, DNAJC3, GGH, HSPB11, LMO7, PFN2, SGO2 and UPF3A are highly expressed, the osteosarcoma is classified into type 3, and the prognosis of patients with the osteosarcoma is poor.

In some of these embodiments, when the ABCF1, ADAMTS1, ALDH3A2, ALKBH5, ATPAF2, C19orf12, CENPV, CPNE3, DRG2, EPN2, FLII, FSCN1, GID4, HIST1H2BD, LSM2, MPRIP, MYO15A, PEMT, PI15, PRAME, PRR3, TOM1L2, TTC19 and VARS genes are highly expressed, the osteosarcoma typing is type 1, which is a good prognosis for patients with osteosarcoma; and/or

When AEBP1, ASPN, C1R, C1S, COL12A1, COL5A1, COL6A3, OLFML2B, POSTN, SFRP4 and THBS2 genes are highly expressed, the osteosarcoma is classified into type 2, and the osteosarcoma patient with the type is still in the future; and/or

When the ATP1B3, CDK4, DNAJC3, GGH, HSPB11, LMO7, PFN2, SGO2 and UPF3A genes are highly expressed, the osteosarcoma is classified into 3 types, and the prognosis of patients with the osteosarcoma is poor.

Based on the technical scheme, the invention has the following beneficial effects:

in the invention, aiming at tumor heterogeneity, the difference of cellular dynamics and molecular characteristics between the conventional osteosarcoma and the normal cancellous bone is compared, the obvious differentiation direction of osteosarcoma cells is detected based on a single cell RNA sequencing (scRNA-seq) technology or a gene detection kit, a gene detection chip technology or an immunohistochemical method, and possible interaction between each cell type in a tumor microenvironment is researched. Conventional osteosarcomas can be classified into three types according to the differentiation direction of osteosarcoma cells, characteristics of microenvironment of each type are described, and prognosis of each type is verified according to cancer genome map (TCGA) data.

Drawings

Figure 1 is an identification of UMAP and all subpopulations from sequencing data of typical (osteogenic) osteosarcoma and normal cancellous bone samples.

FIG. 2 is a heat map display of the three subtype enrichment pathways A \ B \ C.

FIG. 3 is a representation of the patient clinical information in the Target-OS dataset together with the expression of the 44 signature gene sets.

FIG. 4 shows survival of different subtypes after case typing in the Target-OS dataset.

FIG. 5 is a graph plotting the expression level and survival time of 24A-type genes.

FIG. 6 is a graph plotting the correspondence between the expression level and the survival time of 9B-type genes.

FIG. 7 is a graph plotting the expression level and survival time of 11C-type genes.

FIGS. 8 to 10 are graphs plotting the correspondence between the expression level of 44 genes and the survival time, respectively.

Detailed Description

In order that the invention may be more readily understood, reference will now be made to the following more particular description of the invention, examples of which are set forth below. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete. It is understood that the experimental procedures in the following examples, where specific conditions are not noted, are generally in accordance with conventional conditions, or in accordance with conditions recommended by the manufacturer. The reagents used in the examples were commercially available.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The invention is described in detail below by way of examples:

example 1 Gene Screen for typing of osteosarcoma and assessing prognosis of osteosarcoma

Through screening single-cell high-throughput sequencing data in a GEO public database (which is called Gene Expression Omnibus and is a Gene Expression database created and maintained by the national center for Biotechnology information, NCBI), osteosarcoma 10x single-cell sequencing data with the number of GSE152048 is screened, and 6 cases of typical osteosarcoma (osteogenic osteosarcoma) data are selected for subsequent analysis. In addition, single cell sequencing data from cancellous bone samples from 9 degenerated patients who underwent surgery in Shanghai Yangtze Hospital were selected. And (4) integrating the two groups of data sets to carry out osteosarcoma molecular typing and prognosis analysis.

And obtaining the visual UMAP and different cell clusters through unsupervised dimension reduction and clustering analysis of the data set. UMAP (Uniform managed Approximation and project) is a new dimension-reducing Manifold learning technology, and is built on Riemann geometric and algebraic topological theory framework. UMAP is a very efficient visualization and scalable dimension reduction algorithm. By displaying the cell data characteristics for and visualization. Further, cell type annotation was performed for each cell cluster by a putative marker gene, and the tumor cell population was further subdivided using the single cell analysis Monocle3 algorithm, yielding a total of 28 cell types, and 3 subtypes with different differentiation directions, respectively defined as type A, B and C, in a typical osteosarcoma, as shown in FIG. 1.

Pathway enrichment of osteosarcoma subtypes of type a, type B and type C in3 different differentiation directions was calculated by genomic Variation Analysis (GSVA), and the most divergent and treatment-related pathway enrichment was shown using heatmaps, as shown in fig. 2.

By calculating the difference genes of the tumor cell populations of the 3 different subtypes, we obtained 840 gene sets related to prognosis and treatment of osteosarcoma by co-screening, see table 1 (wherein, the types in the table are 3 different differentiation directions possibly corresponding to the high expression of the genes).

TABLE 1 Gene sets relating to osteosarcoma prognosis and treatment

We use the FindMarker method to calculate the genes which are characterized and expressed in OS-A2, OS-B2 and OS-C2, respectively, take the TOP300 gene, remove the repeat gene, totally obtain 850 difference genes, draw the violin diagram of the expression of the 850 genes in the whole 28 cell groups, and screen out 44 genes which are characterized and highly expressed in the tumor groups OS-A2, OS-B2 and OS-C2.

The experimental results are as follows: a gene set of 44 genes associated with osteosarcoma prognosis and treatment was obtained, as shown in table 2.

TABLE 2 Gene sets relating to osteosarcoma prognosis and treatment

Serial number

Gene_Symbol

Gene_Ensembl

Typing

Serial number

Gene_Symbol

Gene_Ensembl

Typing

1

ABCF1

ENSG00000204574

A

21

PRR3

ENSG00000204576

A

2

ADAMTS1

ENSG00000154734

A

22

TOM1L2

ENSG00000175662

A

3

ALDH3A2

ENSG00000072210

A

23

TTC19

ENSG00000011295

A

4

ALKBH5

ENSG00000091542

A

24

VARS

ENSG00000204394

A

5

ATPAF2

ENSG00000171953

A

25

ATP1B3

ENSG00000069849

B

6

C19orf12

ENSG00000131943

A

26

CDK4

ENSG00000135446

B

7

CENPV

ENSG00000166582

A

27

DNAJC3

ENSG00000102580

B

8

CPNE3

ENSG00000085719

A

28

GGH

ENSG00000137563

B

9

DRG2

ENSG00000108591

A

29

HSPB11

ENSG00000081870

B

10

EPN2

ENSG00000072134

A

30

LMO7

ENSG00000136153

B

11

FLII

ENSG00000177731

A

31

PFN2

ENSG00000070087

B

12

FSCN1

ENSG00000075618

A

32

SGO2

ENSG00000163535

B

13

GID4

ENSG00000141034

A

33

UPF3A

ENSG00000169062

B

14

HIST1H2BD

ENSG00000158373

A

34

AEBP1

ENSG00000106624

C

15

LSM2

ENSG00000204392

A

35

ASPN

ENSG00000106819

C

16

MPRIP

ENSG00000133030

A

36

C1R

ENSG00000159403

C

17

MYO15A

ENSG00000091536

A

37

C1S

ENSG00000182326

C

18

PEMT

ENSG00000133027

A

38

COL12A1

ENSG00000111799

C

19

PI15

ENSG00000137558

A

39

COL5A1

ENSG00000130635

C

20

PRAME

ENSG00000185686

A

40

COL6A3

ENSG00000163359

C

41

OLFML2B

ENSG00000162745

C

43

SFRP4

ENSG00000106483

C

42

POSTN

ENSG00000133110

C

44

THBS2

ENSG00000186340

C

Example 2 TARGET-OS Classification and survival analysis

Osteosarcoma sequencing data were screened from a cancer genomic map (TCGA) database, validated in whole osteosarcoma public database samples. Standardized RNA sequencing FPKM and client files were downloaded from the TCGA data portal on 30/1/2021.

According to the expression level of 44 genes in each case sample in the Target-OS dataset, 85 cases of osteosarcoma are classified with clinical information, and the clinical cases in the Target-OS dataset can be divided into corresponding Cluster1, cluster2 and Cluster3. For example, a patient with high expression of type A gene, low expression of type B and type C genes is defined as Cluster1, a patient with high expression of type C gene, low expression of type B and type A genes is defined as Cluster2, and a patient with high expression of type B gene, low expression of type A and type C genes is defined as Cluster3. We show the clinical information and differential gene expression of the classification datasets by heatmap as shown in fig. 3.

Based on retrospective analysis of the cases, survival curves for patients of type 3 were generated to demonstrate the difference in survival among osteosarcoma patients for each gene and gene set. From the survival curves and 5-year survival, we found that the prognosis for patients with Cluster3 (type B) was the worst, the prognosis for patients with Cluster2 (type C) was slightly better than that for Cluster3, and the prognosis for patients with Cluster1 (type A) was the best, as shown in FIG. 4.

Example 3 analytical method

The single cell sample preparation, sequencing and analysis methods described in example 1 were as follows:

1. sample preparation and sequencing

All samples were cold-chain transported to the laboratory within 1 hour after sample isolation. According to the standard 10 × Genomics sample preparation method, tissue samples are first cut into 2-4 mm sized pieces, then digested with collagenase and incubated in a shaker at 37 ℃. The cell suspension after digestion and incubation was filtered through a sieve and the filtered contents were centrifuged to remove the enzyme. And taking the supernatant to count the cells, adjusting the cell density according to the result of the cell technology, and then performing single cell sequencing. Single Cell sequencing the Cell suspension of each sample was subjected to 3 'Single Cell RNA sequencing using the Single Cell A Chip Kit, single Cell 3' library and Gel Bead Kit V2, 10 Xgenomics, with a recovery number of target cells of 10,000.

2. Filtering and normalization of scRNA-seq data

The logarithm of the UMI count in the downloaded raw UMI matrix file is normalized to a similar TPM value and then 1 is added to each million records (TPM) using the log2 scale. We filtered the data for each sample, retaining the genes expressed in at least 3 cells, removing the cells expressing mitochondrial genes (over 20% of the total number of expressed genes), and cells with nFeature _ RNA less than 200 or over 5000, the remaining cells and gene expression matrix will be used for subsequent analysis.

3. Unsupervised dimension reduction and clustering of downloaded sc-RNA sequencing data

Integrating the filtered expression matrix obtained in the previous step using Seurat V3.2.2, performing data integration using FindIntegrationanchors and Integratedata functions, and performing UMAP visualization and cell clustering using RunUMAP and FindClusters functions. The cell types were annotated for each cell cluster using known marker genes.

4. Calculation and display of differential genes

The findall markers and FindMarker functions were used in the sourat software package to calculate genes specifically expressed for each cell subset. For epithelial and Clara cell populations subdivided by Monocle3, we mapped the grouping information for these cell subsets back to the saurta object and calculated the differential genes for the saurta object that overwritten the grouping information. From the calculation results, the heatmap, violin map and bubble map are visually displayed using the ggplot2 and heatmap packages.

5. Pathway enrichment (GSVA)

The expression matrices for all cells and genes were used as input and the enrichment of different metabolic pathways in each cell was calculated using the GSVA method (mainly GO and KEGG related pathways) and visualized by heat maps.

6. Analysis of epithelial cell differentiation Trace Monocle3

The cellular state changes of the cells in the cell population were calculated using the Monocle 3V 0.2.3.0 algorithm, by taking as input the gene-cell matrix of the determined cell subpopulation in the Seurat object, and using the new _ cell _ data _ set function to create the cds object, and using default parameters for dimensionality reduction, clustering and differentiation trajectory extrapolation.

Next, as described in example 2, survival verification was performed by the Target-OS dataset inside the TCGA as follows:

7. Target-OS dataset

Osteosarcoma sequencing data were screened against the cancer genomic map (TCGA) database. Standardized RNA sequence FPKM and client files were downloaded from the TCGA data portal on 30/1/2021. A total of 85 Target-OS datasets were obtained for osteosarcoma cases with clinical follow-up information.

8. Kaplan-Meier survival curve (characteristic gene set)

For the Target-OS data set downloaded in the previous step, it is integrated after normalization. From the integrated data set, we plotted the Kaplan-Meier Survival curves (including OS and RFS) for different subtype gene sets in the data set using the survivval software package. Specifically, overall Survival (OS) is calculated from diagnosis to death or last visit time. RFS (recurrence-free survival) refers to the time from complete remission (essentially about 1 month after diagnosis) in a patient to the patient's recurrence or follow-up expiration date.

The experimental results are as follows: the osteosarcoma patients can be classified into 3 types according to the detection result of osteosarcoma tissues of the patients, and different treatment suggestions are provided. Cluster type 1: the patient prognosis is best. Cluster type 2: the prognosis of the patient is good. Cluster type 3: the patient has the worst prognosis. The prediction result is consistent with the actual patient prognosis obtained by follow-up.

EXAMPLE 4 kit for typing osteosarcoma and assessing prognosis of osteosarcoma-IHC method (immunohistochemical staining)

The method adopts an immunohistochemical method to detect the expression level of the gene set of 44 genes in the osteosarcoma sample, analyzes the expression level to obtain the classification of osteosarcoma patients, and gives a suggestion on the treatment of osteosarcoma patients.

Tissue embedding

(1) Material taking: fresh tissue was fixed in 4% paraformaldehyde for over 24 h. Taking out the tissue from the fixing solution, flattening the tissue of the target part in a fume hood by using a scalpel, and placing the trimmed tissue and the corresponding label in a dehydration box.

(2) And (3) dehydrating: and (5) putting the dehydration box into a hanging basket, and dehydrating by sequentially gradient alcohol in a dehydrating machine. 75% alcohol 4h-85% alcohol 2h-90% alcohol 2h-95% alcohol 1 h-absolute ethanol I30 min-absolute ethanol II 30 min-alcohol benzene 5-10 min-xylene I5-10 min-xylene II 5-10 min-wax I1 h-wax II 1 h-wax III 1h.

(3) Embedding: embedding the tissues soaked with the wax in an embedding machine. Firstly, molten wax is put into an embedding frame, tissues are taken out from a dehydration box and put into the embedding frame according to the requirements of an embedding surface before the wax is solidified, and corresponding labels are attached. Cooling in a freezing table at-20 deg.C, solidifying the wax, taking out the wax block from the embedding frame, and trimming the wax block.

(4) Slicing: the trimmed wax block was sliced on a paraffin slicer to a thickness of 4 μm. The slices float on a spreading machine at 40 ℃ warm water to flatten the tissues, the tissues are taken out by a glass slide, and the slices are baked in a 60 ℃ oven. Taking out after water baking and wax baking and roasting for standby at normal temperature.

(II) immunohistochemical staining

(1) Dewaxing: the slices were thoroughly rehydrated with graded alcohol and water. The specific process comprises the following steps: xylene I5 min-xylene II 5 min-Anhydrous ethanol I30 sec-Anhydrous ethanol II 30 sec-95% ethanol I30 sec-95% ethanol II 30 sec-90% ethanol 30 sec-80% ethanol 30 sec-70% ethanol 30 sec-tap water wash-0.3% H2O2 methanol treated slices 10-20 min-water wash.

(2) Antigen retrieval, PBS wash 3 times, 1 min/time.

(3) Serum was added and incubated for 20 minutes.

(4) Spin-dry the serum and add primary antibody for 60 minutes. PBS wash 3 times, 2 min/time.

(5) Secondary antibody was added and incubated for 30 minutes. PBS wash 3 times, 2 min/time.

(6) ABC complex was added and incubated for 30 min. PBS wash 3 times, 2 min/time.

(7)DAB-H ₂ O ₂ Sections were incubated for 5-10 minutes. PBS washing and water washing.

(8) Harris hematoxylin stains the nucleus for 5-10 minutes. Washing with water, differentiating, bluing, dehydrating, clarifying and sealing.

Scoring by the pathologist the IHC histochemical staining results, the score being given as the score of the staining intensity: 0-12 points, the better the staining (higher the gene expression) the higher the score.

And performing weighted calculation according to the scoring results of the genotypic staining of different subtypes so as to type the stained sample.

EXAMPLE 5 kit for typing osteosarcoma and evaluating prognosis of osteosarcoma-RT-PCR method

The RT-PCR method is adopted to detect the expression level of the gene set of 44 genes described in example 2 in an osteosarcoma sample, and the typing of osteosarcoma patients is obtained through analysis and the treatment suggestion is given.

The RT-PCR method comprises the following specific steps:

(one) Total RNA extraction

The frozen tumor tissue is placed in a glass homogenizer, trizol reagent is added according to the proportion of 100g to 3ml, and the extraction is carried out according to the flow of the Trizol RNA extraction kit instruction.

(1) Trizol (3 ml/100mg tissue, little to no Ningduo) was added and homogenized in a glass homogenizer and ice-cooled for 10-15min.

(2) Transferred into a 1.5ml EP tube and centrifuged at 13000g for 10min at 4 ℃.

(3) The supernatant was transferred to another EP tube and allowed to stand at room temperature for 10-15min.

(4) 0.2ml of chloroform/1 ml of Trizol was added, and the mixture was shaken for 15 seconds and left at room temperature for 5min.

(5) Centrifuge at 12000g for 15min at 4 ℃.

(6) The upper aqueous phase was carefully pipetted off and transferred to a new EP tube.

(7) 0.5ml of isopropanol/1 ml of Trizol were added, shaken and left at room temperature for 10min.

(8) Then, the mixture was centrifuged at 12000g at 4 ℃ for 10min, and a white precipitate was observed at the bottom of the EP tube.

(9) The supernatant was discarded, dried with a paper towel, and 1ml of 75% ethanol was added thereto, followed by shaking and washing the precipitate sufficiently.

(10) Centrifuge at 11000g for 5min at 4 ℃.

(11) Sucking up ethanol, air drying for 10min (centrifugal drying can be accelerated, centrifugal liquid is sucked up as much as possible).

(12) When the RNA is semitransparent, the RNA is dissolved in 20ul of water containing the enucleating enzyme (the RNA can be evenly blown and stirred), and the RNA is frozen and stored at the temperature of 20 ℃ for later use.

(13) The extracted total RNA is analyzed for RNA content and purity by a nucleic acid protein analyzer, and the ratio of absorbance at 260/280nm of all samples is 1.8-2.0.

(14) The total RNA extracted was electrophoresed on a 1% agarose gel, showing two distinct rRNAs, 28s and 18 s.

(II) reverse transcription reaction

The system comprises the following components: DEPC water 9ul, dig primer 1ul,5 XBuffer 4ul,10M dNTPmix 2ul, RNase inhibitor 1ul, total RNA 2ul, reverse transcriptase 1ul, total 2ul, 42 ℃ 60min

(III) PCR reaction

The system comprises the following components: DEPC water 17.5ul,10 XTaq buffer 2.5ul, mgCl2.0ul, 10M dNTP Mix 0.5ul, upstream primer 0.5ul, downstream primer 0.5ul, tap enzyme (5 u/ul) 0.5ul, CDNA 1.0ul. A total of 25ul.

Setting parameters of the PCR instrument:

94℃ 5min℃

72℃ 30s 45s 2min

TABLE 3 Gene sets relating to osteosarcoma prognosis and treatment and their PCR-based upstream and downstream primer sequences

The expression of the 44 genes was calculated by the consensus cluster algorithm by the PCR method described above, and then the typing was obtained by calculation. The osteosarcoma patients can be classified into 3 types according to the detection result of osteosarcoma tissues of the patients, and different treatment suggestions are provided. Cluster type 1: the patient prognosis is best. Cluster type 2: the prognosis of the patient is good. Cluster type 3: the patient had the worst prognosis. The prediction result is consistent with the actual patient prognosis obtained by follow-up. Therefore, the osteosarcoma typing and prognosis method is accurate and reliable. The results are shown in FIGS. 5-7.

FIG. 5 is a graph plotting patient survival curves against data on the expression level and survival of type A gene, wherein the expression level of type A gene set was higher in 42 of the 85 patients and lower in 43 patients, and the survival rate was as high as 57.14% in 24 of the population with higher expression level after 50 months, while the survival rate was 39.53% in only 17 of the population with lower expression level after 50 months. As can be seen, the high gene expression level in the A-type gene set tends to lead osteosarcoma patients to have better prognosis and long survival time.

FIG. 6 is a graph plotting patient survival curves against data on expression levels and survival for 9B-type genes, with 48 of 85 patients expressing higher B-type gene sets and 37 patients expressing lower gene sets, and 24 of the patients with lower expression levels having a survival rate of 50% after 50 months, while only 17 of the patients with lower expression levels having a survival rate of 45.94% after 50 months. And according to the survival curve, osteosarcoma patients with low gene expression in B type gene set tend to have better prognosis and long survival time.

FIG. 7 is a graph of the expression level data of the C-type gene and survival rate of 85 patients, wherein 64 patients have higher expression level of the C-type gene set, 21 patients have lower expression level of the gene set, and 33 patients have survival rate of 51.56% after 50 months and 9 patients have survival rate of 14.06% after 100 months in the population with higher expression level. In contrast, only 8 of the population with low expression levels survived 50 months later, the survival rate was 38.09%, and only 1 patient survived 100 months later, and the survival rate was only 4.76%. And according to the survival curve, osteosarcoma patients with low gene expression in the C-type gene set tend to have better prognosis and long survival time.

EXAMPLE 6 validation of the Gene set for typing osteosarcoma and assessing the prognosis of osteosarcoma

The gene sets related to osteosarcoma prognosis and treatment and the upstream and downstream primer sequences of the PCR method thereof as shown in Table 3 are adopted, 85 osteosarcoma case data with clinical follow-up information in a cancer genome map (TCGA) database are adopted, and curves are respectively drawn according to the corresponding relation of expression level and survival time of 44 genes for effect verification, as shown in FIGS. 8-10. The relationship between the gene expression level of the gene set and the survival time thereof can be seen.

It can be seen that, among 24 genes in the a-type gene set, 23 genes including ABCF1, ADAMTS1, ALDH3A2, ALKBH5, ATPAF2, C19orf12, CENPV, CPNE3, EPN2, FLII, FSCN1, GID4, HIST1H2BD, LSM2, MPRIP, MYO15A, PEMT, PI15, PRAME, PRR3, TOM1L2, TTC19 and VARS are highly expressed and their prognosis survival rates are lower.

8 genes of 9 genes in the B type gene set, including ATP1B3, DNAJC3, GGH, HSPB11, LMO7, PFN2, SGO2 and UPF3A genes, have higher expression levels, and patients with low expression levels have poorer survival prognosis, while patients with low expression levels have higher survival rate.

Among 11 genes in the C-type gene set, 10 genes including AEBP1, ASPN, C1R, C1S, COL12A1, COL6A3, OLFML2B, POSTN, SFRP4 and THBS2 have higher expression levels, so that the survival prognosis of patients with lower expression levels is better, and the survival rate of patients with lower expression levels is lower.

Example 7 COX regression model analysis

COX regression model, also known as the proportional hazards regression model (COX model for short), is a semi-parametric regression model proposed by british statistician d.r.cox (1972). The model takes the survival outcome and the survival time as dependent variables, can simultaneously analyze the influence of a plurality of factors on the survival period, can analyze the data with the truncated survival time, and does not require to estimate the survival distribution type of the data. Due to the excellent properties, the model is widely applied to medical follow-up research since the advent and is the multi-factor analysis method which is most applied to survival analysis.

The main objective of survival analysis is to study the relationship between variable X and the observed result, i.e. the survival function (cumulative survival) S (t, X). When the survival function (cumulative survival rate) is affected by many factors, the conventional method is to consider the regression equation, i.e., the influence of the variables Xi on the survival function (cumulative survival rate).

Basic form of Cox regression model:

h(t，X)＝h0(t)exp(β1X1+β2X2+…+βmXm)

in the formula, beta 1, beta 2, \8230, beta m is a partial regression coefficient of an independent variable, which is a parameter needing to be estimated from sample data; h0 (t) is the baseline hazard rate for h (t, X) when the X vector is 0, which is the quantity to be estimated from the sample data.

By using the R algorithm and survivval package to perform Cox multifactor analysis, the influence of a certain gene on prognosis at different ages may be different, the influence of the gene on prognosis is large in the old and small in the young, and the influence of age and the gene on prognosis under the combined action can be obtained by the Cox multifactor analysis. Therefore, the prediction model obtained by Cox multi-factor analysis is more accurate. The analysis gave the cut-off value (cutpoint), P-value (P-value), HR value (Hazard Ratio) as follows:

	cutpoint	P-value	Hazard Ratio(95％CI)
				age at diagnosis of disease	11.23	0.21	1.7(0.75-3.8)
Type A gene set	3.37	0.056	2.4(0.98-5.7)
				B type gene set	3.98	0.05	0.72(0.28-1.9)
C type gene set	4.59	0.047	2.3(1-5.1)

From the above results, it can be seen that:

for a Target-OS data set, the expression values of all genes in an A type/B type/C type gene set are respectively averaged to be used as the expression values of the A type/B type/C type gene set, whether the 3 expression values are independent risk factors influencing the prognosis of patients in the data set is calculated, and the result proves that the genotyping model can accurately predict the survival of osteosarcoma patients.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the scope of the present description should be considered as being described in the present specification.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Sequence listing

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<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 69

ctctgccaaa cccttcttta gc 22

<210> 70

<211> 22

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 70

cgtgaatagc actgacatcc aa 22

<210> 71

<211> 20

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 71

ttccccaagc cttaccccaa 20

<210> 72

<211> 20

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 72

gctggaagac gagcttcacc 20

<210> 73

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 73

tttggcatgg gtttatgctg a 21

<210> 74

<211> 22

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 74

gggtgaagta gaggtgaatc cc 22

<210> 75

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 75

agctgaggca gacattgtgt t 21

<210> 76

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 76

cctcctttgt acggcaagtt t 21

<210> 77

<211> 19

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 77

gcccggatgt cgcttacag 19

<210> 78

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 78

aaatgcagac gcagggtaca g 21

<210> 79

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 79

atgaggaaac atcggcactt g 21

<210> 80

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 80

gggcatgagt tgtaggaaag c 21

<210> 81

<211> 19

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 81

aagcctcggc tgctagttc 19

<210> 82

<211> 19

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 82

gttgtccgcc tcgttttgc 19

<210> 83

<211> 22

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 83

ctcatagtcg tatcaggggt cg 22

<210> 84

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 84

acacagtcgt tttctgtcca c 21

<210> 85

<211> 19

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 85

cctggaacat cacgcggat 19

<210> 86

<211> 19

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 86

cggcttgata gggtcgtgc 19

<210> 87

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 87

gacacgctgg atctcaccta c 21

<210> 88

<211> 21

<212> DNA

<213> Artificial Sequence (Artificial Sequence)

<400> 88

gaagctgtct atgaggtcgc a 21

Claims

1. A specific amplification primer group for osteosarcoma typing and osteosarcoma prognosis evaluation is characterized in that the nucleotide sequence of the primer group is shown as SEQ ID NO.1-SEQ ID NO. 88.

2. A combination of nucleic acid fragments for typing osteosarcoma and assessing prognosis of osteosarcoma, wherein the combination of nucleic acid fragments is amplified by the primer set of claim 1, and consists of a gene set of 44 genes, wherein the 44 genes are:

gene name ABCF1, gene number ENSG00000204574;

the gene name ADAMTS1, gene number ENSG00000154734;

the gene name ALDH3A2, gene number ENSG00000072210;

the gene name ALKBH5 and the gene number ENSG00000091542;

the gene name ATPAF2, gene number ENSG00000171953;

the gene name C19orf12, gene number ENSG00000131943;

the gene name CENPV, gene number ENSG00000166582;

gene name CPNE3, gene number ENSG00000085719;

gene name DRG2, gene number ENSG00000108591;

the gene name EPN2, the gene number ENSG00000072134;

gene name FLII, gene number ENSG00000177731;

the gene name FSCN1, the gene number ENSG00000075618;

gene name GID4, gene number ENSG00000141034;

the gene name HIST1H2BD, gene number ENSG00000158373;

the gene name is LSM2, and the gene number is ENSG00000204392;

gene name MPRIP, gene number ENSG00000133030;

the gene name MYO15A, the gene number ENSG00000091536;

gene name PEMT, gene number ENSG00000133027;

the gene name PI15, gene number ENSG00000137558;

the gene name PRAME, the gene number ENSG00000185686;

gene name PRR3, gene number ENSG00000204576;

gene name TOM1L2, gene number ENSG00000175662;

the gene name TTC19, the gene number ENSG00000011295;

the gene name VARS, gene number ENSG00000204394;

the gene name ATP1B3, gene number ENSG00000069849;

the gene name CDK4, gene number ENSG00000135446;

the gene name DNAJC3, the gene number ENSG00000102580;

gene name GGH, gene number ENSG00000137563;

the gene name HSPB11, gene number ENSG00000081870;

gene name LMO7, gene number ENSG00000136153;

the name of the gene is PFN2, and the number of the gene is ENSG00000070087;

gene name SGO2, gene number ENSG00000163535;

gene name UPF3A, gene number ENSG00000169062;

the gene name AEBP1, gene number ENSG00000106624;

the gene name ASPN, gene number ENSG00000106819;

the gene name C1R, the gene number ENSG00000159403;

the gene name C1S, the gene number ENSG00000182326;

the gene name COL12A1, the gene number ENSG00000111799;

the gene name COL5A1, the gene number ENSG00000130635;

the gene name COL6A3, gene number ENSG00000163359;

the gene name OLFML2B, gene number ENSG00000162745;

the gene name POSTN, the gene number ENSG00000133110;

the gene name SFRP4, gene number ENSG00000106483;

gene name THBS2, gene number ENSG00000186340.

3. A detection material comprising the primer set according to claim 1.

4. The detection material according to claim 3, further comprising:

a probe set which specifically binds to the nucleic acid fragment set of claim 2 in combination with each gene and/or its complementary sequence; and/or

A set of antibodies that specifically bind to the nucleic acid fragment set according to claim 2, and to the proteins expressed by the respective genes.

5. A kit for typing osteosarcoma and evaluating prognosis of osteosarcoma, comprising the detection material according to any one of claims 3 to 4.

6. The kit of claim 5, wherein the kit comprises: total RNA extraction reagents, reverse transcription reagents, and/or sequencing reagents.

7. The kit of claim 5, wherein the kit comprises: dNTP solution and/or RNA reverse transcriptase.

8. The kit of claim 6, wherein the sequencing reagents comprise secondary sequencing reagents or single cell sequencing reagents.

9. Use of a product comprising the primer set of claim 1, the nucleic acid fragment combination of claim 2, the detection material of claim 3 or 4, or a combination thereof, in the preparation of a kit for typing osteosarcoma and assessing prognosis of osteosarcoma.