CN113881768B - Gene for osteosarcoma typing and assessing osteosarcoma prognosis and application thereof - Google Patents
Gene for osteosarcoma typing and assessing osteosarcoma prognosis and application thereof Download PDFInfo
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Abstract
In the present invention, we compared the differences in cell dynamics and molecular characteristics between conventional osteosarcoma and normal cancellous bone for tumor heterogeneity, examined the apparent differentiation direction of osteosarcoma cells based on single cell RNA sequencing (scRNA-seq) technology or gene detection kit, gene detection chip technology or immunohistochemical method, and studied the possible interactions between each cell type in tumor microenvironment. Conventional osteosarcomas can be classified into three types according to the differentiation direction of osteosarcoma cells, each type of tumor microenvironment is characterized, and prognosis of each type is verified according to cancer genomic profile (TCGA) data.
Description
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 bone sarcoma typing and bone sarcoma prognosis evaluation.
Background
Osteosarcoma (OS) is the most common primary bone malignancy. Prior to the widespread use of chemotherapy in the 1970 s, surgical resection was the primary treatment modality available to OS patients. The implementation of adjuvant chemotherapy significantly improves the prognosis of OS patients, with five-year survival rates of non-metastatic patients increased from 20% to over 60%. However, the prognosis of patients with metastatic or recurrent disease is not significantly improved, with survival rates of only 20% for both groups. Subsequently, in the last forty years, the treatment pattern and prognosis of OS patients did not develop much. Thus, 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, the treatment of osteosarcomas has new promise. Many clinical trials of targeted drugs have been conducted for osteosarcoma patients, including Tyrosine Kinase Inhibitors (TKIs), one of the most common targeted drugs in cancer treatment. In a series of TKIs reports, apatinib achieved the highest 43% Partial Remission (PR) rate in advanced OS patients, while toxic effects resulted in 68% patient dose reduction or interruption. Given the limited clinical benefit observed in unselected OS patients with targeted therapy, it is necessary to discover molecular subtypes of OS, as subtype-based targeted therapies may have clinical benefit in cancer treatment.
The existing clinical stage (TNM) and stage (pathological) diagnosis methods cannot well explain the heterogeneity of patient prognosis and cannot well guide patients to carry out personalized treatments such as chemotherapy, targeted therapy and the like.
Disclosure of Invention
Based on this, the present invention aims to provide a gene for osteosarcoma typing and assessing osteosarcoma prognosis which can well explain heterogeneity of patient prognosis and is suitable for guiding patients to conduct personalized treatments such as chemotherapy, targeted therapy, etc. The specific technical scheme is as follows:
a gene set for use in osteosarcoma typing and assessing osteosarcoma prognosis, wherein said gene set comprises 44 genes as shown in table 2.
In some of these embodiments, the 44 genes and their combination with one or more related genes selected from the group consisting of: AC, ACAN, ACTA2, ACTG1, ACTN1, ACTR 3A2, AL, ALKBH5, 32, ANXA6, AOC2, APRT, ARF1, ARF4, ARGLU1, ARL3, ARL4 IP5, ASPM, ASS1, ATF3, ATP2B1, ATP5F1 IF1, ATP5MC2, ATP5MC3, ATP5MD, ATP5ME, ATP5PF, ATPAF2, 2D 1, BAMBI, BASP1, BEX3, BGN, BIRC5, BMP3, BMP8, BSG, BST2, BTF3, C12orf57, C12orf75, C19orf12, C19orf24, C19orf70, C1orf54, C1orf, C1QTNF1, BIRC 1C 1QTNF3, C4orf3, C5orf15, C7orf50, C8orf59, CA2, CADM1, CADM2, CALD1, CALM2, CALU, CAMTA1, CAPZB, CARHSP1, CAVIN3, CBLN4, CBX5, CCDC80, CCNB1, CCNB2, CCND1, CCPG1, CCSER2, CCT5, CCT6 151, CD200, CD24, CD320, CD59, CD63, CD99, CDC20, CDC34, CDH11, CDK1, CDKN 12 3, 2, CFH, CFL1, CGGBP1, CHCHD2, CHMP 21, CIRBP, CKAP2, CKS1, CKEC 11 1, CNN2, CNN3, CIRBP 1, CKAP2, CKEP 1 CNPY2, COA4, COL10A1, COL11A1, COL18A1, COL1A2, COL3A1, COL5A2, COL6A1, COL6A2, COL8A1, COMMD4, COPS3, COPS9, COPZ1, COPZ2, COX20, COX 6A1, COX 72, CRELD2, CRISPLD1, CSNK 21, CSRP2, CTGF, CTHRC1, CTNNAL1, CXCL14, CYB5R3, CYC1, CYB 1B1, CYR61, CYTOR, DAD1, 14, 18, DDX24, DEK, CR 6A 15, DNAJC19, DNM 31, DYNLT3, EBNA1BP2, PL, ECI1, EEF1A1 EEF1B2, EEF2, EFEMP2, EFHC1, EGFL6, EID1, EIF2S1, EIF 33 33 4A1, EIF4A2, EIF6, 1, EMP2, EMP3, ENAH, ENO1, ENPP2, EPB41L4A-AS1, EPPR 1, EPSTI1, ERGIC3, ERH, ERLEC1 ERV3-1, ESF1, FAAP20, FAM114A1, FAM162 1, FBN1, FDPS, FERM 2, FGF7, FGFR1, FGFR3, FHL1, FHL2, FIS1, FKBP10, FKBP11, FKBP3, FKBP7, FLOT1, FLRT3, FLYWCH2, FMOD, FN1, FNDC1, FOLR1, FKBP10, FKBP11, FKBP3, FLT 1, FLRT3, FLYWCH2, FMOD, FN1, FNDC1, FOLR1, FWD 1, F, FOXK1, FSTL1, candc 1, FXYD6, GABARAP, GABPB-AS 1, 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, HIST H2AC, HIST1H2BC, HIST1H2BN, HIST1H4E, HIST H2BE, HLA-A, HLA-B, HLA-C, HMGB1, HMGB2, HMGB3, HMGN2, HMGN5, HNRNPA1, HNRNPAB, HP1BP3, HPF1, HSD17B10, HSP90AB1, HSP1 HAPLN1, HAS2, HCFC1R1, HES4, HILPDA, HIST1H1C, HIST H2AC, HIST1H2BC, HIST1H2BN, HIST1H4E, HIST H2BE, HLA-A HLA-B, HLA-C, HMGB1, HMGB2, HMGB3, HMGN2, HMGN5, HNRNPA1, HNRNPAB, HP1BP3, HPF1, HSD17B10, HSP90AB1 LIMA1, LINC C, HMGB, LMAN1, LMNA, LOXL1, LRRC15, LRRC17, LRRFIP2, LSM7, LTBP1, LUC7L3, LUM, LY 6C, HMGB 6C, HMGB1, MAD2L1, MAGEA3, MAGED1, MAGED2, MAGEH1, MAP 1C, HMGB K13, MARGKSL 1, MCRIP1, MDH2, MDK, MED9, MEGF10, MAG 1, MAG 2, MAG 3K13, MAG 1, MEG 2, MEG 10, L1, L3, L6, L5, L1, L3, L1, L2, L9, L1, L G L and L that L MELTF, METTL5, MFAP2, MFGE8, MFSD10, MGST3, MIF, MIR4458HG, MKI67, MLF2, MLLT11, MMP13, MMP 23C, HMGB1, MORF4L2, MPC1, MPG, MRC2, MT 1C, HMGB 1C, HMGB 1C, HMGB 2C, HMGB 20-OT1, MXRA8, MYC MELTF, METTL5, MFAP2, MFGE8, MFSD10, MGST3, MIF, MIR4458HG, MKI67, MLF2, MLLT11, MMP13 MMP 23C, HMGB, MORF4L1, MORF4L2, MPC1, MPG, MRC2, MT 1C, HMGB 1C, HMGB 1C, HMGB 1C, HMGB 1C, HMGB 2C, HMGB-OT 1, MXRA8, MYC, 2, PDCD5, 2, PDLIM4, PDLIM7, PDRG1, PFDN2, PFDN4, PHB, PHF19, PHGDH, PHLDA3, PHPT1, 2, PLAC9, PLAT, PLEKHA5, PLIN3, PLOD2, PLP2, PLS3, PMEPA1, PODNL1, POLR 12 2 23 4, POR, PPA1, PPFIBP1, 1CB, PRAF2, PRDX4, PRDX6, 2, PRRX1, PRX 2, PRSS23, PSIP1, PSMA2, PSMA3, PSMA4, PSMA7, PSMB1, PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSME1, PSME2, PSMG3, PSMG4, PSMB 3, PSMB8, PSMB9, PSME 3, PSME 4 PTH1, PTTG1IP, PTX3, PVT1, PYCR1, QPCT, RAB2 31, RAB34, RABAC1, RAC3, RAD21, RAI1, RALY, RAMP1, RAN, RANBP1, RARRES3, RASD1, RBBP6, RBBP7, RBX1, RCN3, REXO2, RFT1, RGCC, RABAC1, RABX 1, RABY 1, RARRES 1, RARRED 1, RASD1, RBBP6, RBX1, RCN3, REXO2, RFT1, RGCC, RABX 1, RABY 1, RAREBY 1, RABY 1 RGS3, 21, RPN2, RPS19BP1, RRBP1, RRM2, RSF1, RSL1D1, RSRP1, RSU1, RTRAF, RUNX2, S100A10, S100A11, S100A13, S100A16, S100A4, S100A6, SAP25, SAR 12, SDF2L1, SEC11 61 1, S100A11, S100A1, S100A4, S100A6, SAP25, SAR 12, SEC11 61 1 SEPTIN11, SERBP1, SERF2, SERPINA1, SERPINE2, SERPINF1, SERPING1, SERPINH1, SEZ6L2, SF3B6, SFTA 12, SIVA1, SLC25A39, SLC25A6, SLC29A1, SLIRP, SMC 12, SMC3, SMC4, SMDT1, SMIM10, SMIM26, SMOC1, SNAI2, SNHG8, SNRNP25, SNRPA1, SNRPB2, SNRPD3, SNX7, SOD1, SOX4, SPARC, SPASTS 21, SPP1, SPRY1, SSB, SSR3, ST13, STAT1, STMN1, STMP1, STXBP6, SESUB 1, SULF1 SUMF2, SUN1, SUPT16, SWI5, SYNGR1, TAF1, TAGL, TAGLN2, TAX1BP3, TBCA, TCEA1, TCEAL3, TCEAL9, TECR, TEF 1, TEX30, TFDP1, TFPI, TGFB1I1, TGFBI, THUMPD3-AS1, THY1, TIMM13, TIMP1, TIMP3, TKT, TM2D3, TMBIM6, TMED10, TMED2, TMED9, TMEM106 14 167 258, TMEM259, TMEM45 BP3, TMSB15 6, TNFRSF12 10, TOMM20, TOP 21, TPM2, TPM4, TPX2, TRAPPC1, TRIM16 11 12 2 24, TKT 6, TUFM, TUSC3, TVP23B, TXN, TXNL1, TYMS, UACA, UBB, UBE2C, UBE2I, UBE L3, UBE2L6, UBE2S, UBE2T, UBE V2, UCHL1, UCHL3, UFD1, UNC5B, UQCRB, UQCRC2, UQCURFS 1, UQCRH, URI1, USP11, UTP11, VAMP5, VBP1, VCAN, VDR, VKORC1, VWA1, WDR18, WFDC1, WIPI2, XRCC6, YIF1A, YPEL3, YWHAE, YWHAQ, ZFHX4, ZNHIT1, ZRANB2, ZCAN 16-AS1 and ZWIM 7.
In some of these embodiments, the population of genes is a population of genes consisting of 840 genes, as shown in table 1.
The invention also relates to application of the gene set, and the specific technical scheme is as follows:
use of a gene set as described above for the preparation of a test kit for osteosarcoma typing and assessing prognosis, said test kit comprising: amplification of primers evaluating genes for osteosarcoma typing and evaluating the gene set for prognosis as described above; and/or
Probes specifically binding to genes and/or their complementary sequences evaluated for osteosarcoma typing and evaluating the prognostic gene set as described above; and/or
Antibodies that specifically bind to proteins evaluated for osteosarcoma typing and evaluating gene expression of the prognostic gene set as described above.
In some of these embodiments, the primer comprises: the nucleotide sequence is shown as SEQ ID NO.1-SEQ ID NO. 88.
In some embodiments, the use is the use of the gene set in the preparation of a protein detection chip for osteosarcoma typing and assessing prognosis, the gene chip comprising a solid support on which a capture antibody that specifically binds to a gene of the gene set as described above is immobilized and a detection antibody linked to an antibody label.
In some of these embodiments, the antibody marker is: enzymatic, fluorescein, isotope, or biotin labels.
The invention also relates to a kit for parting and assessing osteosarcoma prognosis, which comprises the following specific technical scheme:
a kit for genotyping and assessing osteosarcoma prognosis comprises
1) Amplification of primers evaluating genes for osteosarcoma typing and evaluating the gene set for prognosis as described above; and/or
2) Probes specifically binding to genes and/or their complementary sequences evaluated for osteosarcoma typing and evaluating the prognostic gene set as described above; and/or
3) Antibodies that specifically bind to proteins evaluated for osteosarcoma typing and evaluating gene expression of the prognostic gene set as described above.
In some of these embodiments, the detection kit comprises: total RNA extraction reagents, reverse transcription reagents and/or sequencing reagents.
In some of these embodiments, the detection 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.
In some of these embodiments, the primer comprises: the nucleotide sequence is shown as SEQ ID NO.1-SEQ ID NO. 88.
The invention also relates to a chip for parting and assessing osteosarcoma prognosis, which comprises the following specific technical scheme:
a gene test chip for use in the genotyping and assessment of osteosarcoma prognosis, the gene chip comprising a solid support on which is immobilized a capture antibody that specifically binds to a gene of a gene set as described above and a detection antibody linked to an antibody label.
The invention also relates to a method for typing and assessing prognosis of osteosarcoma, which can be understood that the typing and assessing prognosis of osteosarcoma can be applied not only in medical diagnosis and treatment, but also in scientific research, in particular, in development of related typing and assessing products (such as development of a kit and a detection chip), establishment of typing and assessing models, and the like. The specific technical scheme of the invention is as follows:
a method for genotyping osteosarcoma for non-diagnostic, non-therapeutic purposes, characterized by detecting in a sample the gene-concentrated osteosarcoma genotyping as described above and assessing the prognostic gene content or the protein content expressed by said gene.
In some of these embodiments, the level of gene expression in the sample is detected using a kit as described above or a gene detection chip as described above, and osteosarcoma typing results are obtained by data analysis.
In some embodiments, when any 2or more genes in ABCF1, ADAMTS1, ALDH3A2, alk bh5, ATPAF2, C19orf12, CENPV, CPNE3, DRG2, EPN2, FLII, FSCN1, GID4, HIST1H2BD, LSM2, mpri, MYO15A, PEMT, PI, PRAME, PRR3, TOM1L2, TTC19, VARS are expressed, the osteosarcoma is typed as type 1, which has a good prognosis for osteosarcoma patients; and/or
When any more than 2 genes in AEBP1, ASPN, C1R, C1S, COL A1, COL5A1, COL6A3, OLFML2B, POSTN, SFRP4 and THBS2 are expressed highly, the osteosarcoma is typed as type 2, and the prognosis of the osteosarcoma patient is still acceptable; and/or
When any 2or more genes selected from ATP1B3, CDK4, DNAJC3, GGH, HSPB11, LMO7, PFN2, SGO2, and UPF3A are highly expressed, the osteosarcoma type is 3, and the prognosis of the osteosarcoma patient is poor.
In some of these embodiments, the abdf 1, ADAMTS1, ALDH3A2, alk bh5, ATPAF2, C19orf12, CENPV, CPNE3, DRG2, EPN2, FLII, FSCN1, GID4, HIST1H2BD, LSM2, mpri, MYO15A, PEMT, PI, PRAME, PRR3, TOM1L2, TTC19, and VARS genes are highly expressed, which form a type 1 osteosarcoma with a good prognosis for patients; and/or
AEBP1, ASPN, C1R, C1S, COL A1, COL5A1, COL6A3, OLFML2B, POSTN, SFRP4 and THBS2 genes are highly expressed, the osteosarcoma type is type 2, and the prognosis of the osteosarcoma patient is still acceptable; and/or
ATP1B3, CDK4, DNAJC3, GGH, HSPB11, LMO7, PFN2, SGO2 and UPF3A genes are highly expressed, and the osteosarcoma type is 3, and the prognosis of the osteosarcoma patient is poor.
Based on the technical scheme, the invention has the following beneficial effects:
in the present invention, we compared the differences in cell dynamics and molecular characteristics between conventional osteosarcoma and normal cancellous bone for tumor heterogeneity, examined the apparent differentiation direction of osteosarcoma cells based on single cell RNA sequencing (scRNA-seq) technology or gene detection kit, gene detection chip technology or immunohistochemical method, and studied the possible interactions between each cell type in tumor microenvironment. Conventional osteosarcomas can be classified into three types according to the differentiation direction of osteosarcoma cells, each type of microenvironment characteristics are described, and each type of prognosis is verified according to cancer genomic profile (TCGA) data.
Drawings
Figure 1 is the identification of UMAP and all subgroups of sequencing data for classical (osteogenic) osteosarcoma and normal cancellous bone samples.
FIG. 2 is a thermal diagram display of the enrichment pathway of three subtypes A\B\C.
FIG. 3 is a representation of patient clinical information in the Targert-OS dataset and expression of 44 signature gene sets.
FIG. 4 shows the survival of different subtypes after case typing in the Target-OS dataset.
FIG. 5 is a graph showing the correspondence between the expression levels and survival times of 24 genes of type A.
FIG. 6 is a graph showing the correspondence between the expression levels and survival times of 9B-type genes.
FIG. 7 is a graph showing the correspondence between the expression levels and survival times of 11 genes of type C.
FIGS. 8-10 are graphs showing the correspondence between the expression levels and the survival times of 44 genes.
Detailed Description
In order that the invention may be understood more fully, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended claims. 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 should be understood that the experimental methods in the following examples, in which specific conditions are not noted, are generally performed under conventional conditions or under conditions suggested by the manufacturer. The various reagents commonly used in the examples are all commercially available products.
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. The term "and/or" as used herein 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 screening for osteosarcoma typing and assessing osteosarcoma prognosis
We screened one 10x single cell sequencing data of osteosarcoma number GSE152048 by screening single cell high throughput sequencing data in GEO public database (collectively Gene Expression Omnibus, a gene expression database created and maintained by NCBI, national center for biotechnology information), 6 of which were selected for subsequent analysis. In addition single cell sequencing data from cancellous bone samples from 9 patients with degeneration who underwent surgery in Shanghai long sign hospitals were selected. And (3) performing bone and sarcoma molecular typing and prognosis analysis by combining the two data sets.
And obtaining the visualized UMAP and different cell clusters through unsupervised dimension reduction and cluster analysis of the data set. UMAP (Uniform Manifold Approximation and Projection) is a new dimension-reducing manifold learning technique, which is built on the theoretical framework of Riemann geometry and algebraic topology. UMAP is a very efficient visualization and scalable dimension reduction algorithm. By displaying the cell data characteristics for and for visualization. Further, each cell cluster was annotated with cell types by putative marker genes, and tumor cell populations were further subdivided using a single cell analysis Monocle3 algorithm, yielding a total of 28 cell populations of different cell types, and 3 different differentiation-directed subtypes of classical osteosarcoma, defined as type a, type B, and type C, respectively, as shown in fig. 1.
Pathway enrichment was calculated for the 3 different differentiation-directed osteosarcoma subtypes type a, type B and type C by genomic variation analysis (GSVA, gene Set Variation Analysis) and shown using a heat map to demonstrate the most differential and treatment-related pathway enrichment, as shown in figure 2.
By calculating the differential genes of the 3 different subtypes of tumor cell populations, we screened a total of 840 gene sets associated with prognosis and treatment of osteosarcoma, see table 1 (wherein the genotypes in the table are 3 different differentiation directions that might be corresponding to high expression of the genes).
TABLE 1 Gene sets associated with prognosis and treatment of osteosarcoma
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We calculated the genes expressed characteristically in OS-A2, OS-B2 and OS-C2 using FindMarker method, each taking TOP300 gene, removing the repeated genes to obtain 850 differential genes altogether, drawing violin map of the expression of these 850 genes in the whole 28 cell groups, screening 44 genes characteristically expressed highly in tumor groups OS-A2, OS-B2 and OS-C2.
Experimental results: a gene set of 44 genes associated with prognosis and treatment of osteosarcoma was obtained, as shown in Table 2.
TABLE 2 Gene sets associated with prognosis and treatment of osteosarcoma
| Sequence number | Gene_Symbol | Gene_Ensembl | Parting type | Sequence number | Gene_Symbol | Gene_Ensembl | Parting type |
| 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
The osteosarcoma sequencing data was screened from the cancer genomic profile (TCGA) database, validated in the global osteosarcoma public database sample. Standardized RNA sequencing FPKM and Clinic files were downloaded from the TCGA data portal at 30 months 1 in 2021.
According to the expression level of 44 genes in each case sample in the Target-OS data set, 85 osteosarcoma cases and clinical information are classified, and the clinical cases in the Target-OS data set can be classified into corresponding Cluster1, cluster2 and Cluster3. For example, patients with high expression of type A genes, low expression of type B and type C genes are defined as Cluster1, patients with high expression of type C genes, low expression of type B and type A genes are defined as Cluster2, and patients with high expression of type B genes, low expression of type A and type C genes are defined as Cluster3. We showed clinical information and differential gene expression of the classification dataset by thermogram as shown in figure 3.
According to retrospective analysis data of cases, a survival curve of the parting patient in3 is prepared for proving survival difference of each gene and gene set in osteosarcoma patients. Based on the survival curves and 5 year survival, as shown in FIG. 4, we found that Cluster3 (type B) patients had the worst prognosis, cluster2 (type C) patients had slightly better prognosis than Cluster3, and Cluster1 (type A) patients had the best prognosis.
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 transported to the laboratory cold chain within 1 hour after separation of the samples. According to standard 10x Genomics sample preparation methods, tissue samples are first cut into pieces of 2-4 mm in size, then digested with collagenase and incubated in a shaker at 37 ℃. The digested and incubated cell suspension was filtered through a sieve and the filtered contents were centrifuged to remove enzymes. The supernatant was taken to count cells, cell density was adjusted according to the results of the cell technology, and single cell sequencing was then performed. Single Cell sequencing the Cell suspension of each sample was subjected to 3 'Single Cell RNA sequencing using 10xGenomics company Single Cell A Chip Kit, single Cell 3' library and Gel head Kit V2, the number of target Cell recovery was 10,000.
2. Filtering and normalization of scRNA-seq data
The log 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 entries (TPM) using the log2 scale. We filtered the data for each sample, retained genes expressed in at least 3 cells, removed cells expressing mitochondrial genes (more than 20% of total expressed genes), and where nfeature_rna is less than 200 or more than 5000 cells, the remaining cells and expression matrices of genes would be used for subsequent analysis.
3. Unsupervised dimension reduction and clustering of downloaded sc-RNA sequencing data
The filtered expression matrix obtained in the previous step was integrated using the setup V3.2.2, data integration was performed using findsegregannschors and IntegrateData functions, and UMAP visualization and cell clustering were performed using RunUMAP and FindClusters functions. Cell types were annotated with known marker genes for each cell cluster.
4. Calculation and display of differential Gene
Genes specifically expressed for each cell subset were calculated in the setup software package using findalmarkers and findammers functions. For epithelial cells and Clara cell populations subdivided by Monocle3, we mapped grouping information of these cell subsets back to the setup object and calculated the differential genes of the setup object that rewritten the grouping information. According to the calculation result, the heat map, the violin map, and the bubble map are visually displayed using the ggplot2 and the hematmap package.
5. Pathway enrichment (GSVA)
The expression matrices of all cells and genes were used as inputs and the enrichment of the different metabolic pathways in each cell (mainly the GO and KEGG related pathways were calculated) was calculated using the GSVA method and visualized by heat maps.
6. Analysis of epithelial cell differentiation trajectory Monocle3
The cell state changes of cells in this 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 setup object and creating the cds object using the new_cell_data_set function and performing dimension reduction, clustering and differentiation trajectory inference using default parameters.
Next, as described in example 2, survival verification was performed by the Target-OS dataset within the TCGA, as follows:
7. Target-OS data set
The cancer genomic profile (TCGA) database was screened for osteosarcoma sequencing data. Standardized RNA sequence FPKM and Clinic files were downloaded from the TCGA data portal at 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)
The Target-OS dataset downloaded in the previous step is integrated after normalization. With the integrated dataset, we plotted Kaplan-Meier Survival curves (including OS and RFS) for different subtype gene sets in the dataset using the survivinal software package. Specifically, the total survival rate (OS) is calculated from diagnosis to death or last follow-up time. RFS (relapse free survival) refers to the time from complete remission of the patient (essentially about 1 month after diagnosis) to the date of relapse or follow-up expiration of the patient.
Experimental results: the patients with osteosarcoma can be classified into 3 types according to the detection result of the osteosarcoma tissue of the patients, and different suggestions are made for the treatment of the osteosarcoma patients. Cluster type 1: the prognosis of the patient is best. Cluster type 2: the prognosis of the patient is still viable. Cluster type 3: the patient prognosis is the worst. The predicted outcome is consistent with the actual patient prognosis obtained by follow-up.
EXAMPLE 4 kit for typing and assessing osteosarcoma prognosis-IHC (immunohistochemical staining)
The level of expression of the gene set of 44 genes described in example 2 in osteosarcoma samples was examined by immunohistochemical method, and the classification of osteosarcoma patients was analyzed and advice was given to the treatment thereof.
Tissue embedding
(1) Drawing materials: fresh tissue was fixed to 4% paraformaldehyde for more than 24 h. And taking out the tissue from the fixing solution, trimming the tissue of the target part in a fume hood by using a surgical knife, and placing the trimmed tissue and a corresponding label in a dehydration box.
(2) Dehydrating: and placing the dehydration box into a basket, and sequentially carrying out gradient alcohol dehydration in a dehydrator. 75% alcohol 4h-85% alcohol 2h-90% alcohol 2h-95% alcohol 1 h-absolute alcohol I30 min-absolute alcohol 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 wax-soaked tissue in an embedding machine. Firstly, putting melted wax into an embedding frame, taking out tissues from a dehydration box before the wax is solidified, putting the tissues into the embedding frame according to the requirement of an embedding surface, and attaching corresponding labels. Cooling at-20deg.C, solidifying, removing the wax block from the embedding frame, and trimming.
(4) Slicing: the trimmed wax block was placed on a paraffin microtome for slicing to a thickness of 4 μm. The slices float on warm water at 40 ℃ of a slice spreading machine to flatten the tissues, the tissues are fished up by using glass slides, and the slices are put into a 60 ℃ oven to be baked. And taking out the water after the water is baked to dry the wax and bake the wax, and preserving the wax at normal temperature for standby.
(II) immunohistochemical staining
(1) Dewaxing: the sections were thoroughly rehydrated by dewaxing with xylene and with gradient alcohol and water. The specific flow is as follows: xylene I5 min-xylene II 5 min-absolute alcohol I30 sec-absolute alcohol II 30 sec-95% alcohol I30 sec-95% alcohol II 30 sec-90% alcohol 30 sec-80% alcohol 30 sec-70% alcohol 30 sec-tap water washing-0.3% H2O2 methanol treatment slices 10-20 min-water washing.
(2) Antigen retrieval, PBS wash 3 times, 1 min/time.
(3) Serum was added and incubated for 20 minutes.
(4) The serum was spun dry and primary antibody was added for 60 minutes. PBS was washed 3 times, 2 min/time.
(5) The secondary antibody was added for 30 minutes. PBS was washed 3 times, 2 min/time.
(6) ABC complex was added and incubated for 30 min. PBS was washed 3 times, 2 min/time.
(7)DAB-H 2 O 2 The sections were incubated for 5-10 minutes. PBS wash, water wash.
(8) Harris hematoxylin stained nuclei for 5-10 minutes. Washing with water, differentiating, bluing, dewatering, transparent and sealing.
Scoring by pathologist by scoring IHC histochemical staining results was determined as a score of staining intensity: the better the staining effect (higher gene expression) the higher the score of 0-12.
And (5) weighting calculation is carried out according to the scoring results of the genomic staining of the different subtypes, so that the stained sample is typed.
EXAMPLE 5 kit for typing and assessing osteosarcoma prognosis-RT-PCR method
The RT-PCR method is adopted to detect the expression level of the gene set of 44 genes described in the example 2 in the osteosarcoma sample, and the genotyping of the osteosarcoma patient is obtained by analysis, and the treatment of the osteosarcoma patient is suggested.
The RT-PCR method comprises the following specific steps:
total RNA extraction
The liquid nitrogen frozen tumor tissue is placed in a glass homogenizer, and Trizol reagent is added according to the proportion of 100g to 3ml, and the preparation method is strictly carried out according to the flow of the Trizol RNA extraction kit instruction.
(1) Trizol (3 ml/100mg tissue, ningduo or Duo) was added to the solution, and the mixture was homogenized in a glass homogenizer, followed by ice bath for 10-15min.
(2) Into a 1.5ml EP tube, centrifuged at 13000g for 10min at 4 ℃.
(3) The supernatant was transferred to another EP tube and left at room temperature for 10-15min.
(4) 0.2ml chloroform/1 ml Trizol was added, and the mixture was shaken for 15 seconds and allowed to stand at room temperature for 5 minutes.
(5) Centrifuge at 12000g for 15min at 4 ℃.
(6) The upper aqueous phase was carefully aspirated and transferred to a new EP tube.
(7) 0.5ml of isopropanol/1 ml of Trizol was added, and the mixture was shaken and allowed to stand at room temperature for 10 minutes.
(8) A white precipitate was seen at the bottom of the EP tube by centrifugation at 12000g for 10min at 4 ℃.
(9) The supernatant was discarded, the towel was blotted dry, 1ml of 75% ethanol was added, and the pellet was washed thoroughly with shaking.
(10) Centrifugation is carried out at 11000g for 5min at 4 ℃.
(11) Ethanol is absorbed completely, and the mixture is dried for 10min (centrifugation can be carried out to accelerate drying, and centrifugal liquid is absorbed as much as possible).
(12) When semitransparent, RNA is dissolved in 20ul of water without nuclease (can be blown and mixed uniformly), and frozen at 20 ℃ for standby.
(13) The total RNA extracted is analyzed by a nucleic acid protein analyzer for RNA content and purity, and the ratio of absorbance of all samples at 260/280nm is 1.8-2.0.
(14) The total RNA extracted was electrophoresed on a 1% agarose gel, showing clear 28s and 18s rRNA.
(II) reverse transcription reaction
The system comprises the following components: DEPC water 9ul,dig primer 1ul,5 Xbuffer 4ul,10M dNTPmix 2ul,RNA enzyme inhibitor 1ul, total RNA 2ul, reverse transcriptase 1ul, total 20ul, 60min at 42 ℃
(III) PCR reaction
The system comprises the following components: DEPC water 17.5ul,10×Taq buffer 2.5ul,MgCl2 2.0ul,10M dNTP Mix 0.5ul, upstream primer 0.5ul, downstream primer 0.5ul, tap enzyme (5 u/ul) 0.5ul, CDNA 1.0ul. And total 25ul.
Parameter setting of a PCR instrument:
94℃5min℃
72℃30s 45s 2min
TABLE 3 Gene sets associated with prognosis and treatment of osteosarcoma and PCR method upstream and downstream primer sequences thereof
By the above-described PCR method, the expression of these 44 genes was calculated by consensus cluster algorithm, and then the typing was calculated. The patients with osteosarcoma can be classified into 3 types according to the detection result of the osteosarcoma tissue of the patients, and different suggestions are made for the treatment of the osteosarcoma patients. Cluster type 1: the prognosis of the patient is best. Cluster type 2: the prognosis of the patient is still viable. Cluster type 3: the patient prognosis is the worst. The predicted outcome is consistent with the actual patient prognosis obtained by follow-up. Therefore, the osteosarcoma parting and prognosis method is accurate and reliable. The results are shown in FIGS. 5-7.
FIG. 5 is a graph showing the survival curves of patients drawn according to the expression level data and survival of the type A genes, wherein the expression level of the type A gene set is higher in 42 patients, the expression level of the gene set is lower in 43 patients, 24 patients survive after 50 months in the group with higher expression level, the survival rate is up to 57.14%, and only 17 patients survive after 50 months in the group with lower expression level, and the survival rate is 39.53%. As can be seen, patients with osteosarcoma with high gene expression in the A-type gene set tend to have better prognosis and longer survival time.
FIG. 6 is a graph showing the survival curves of patients plotted against the expression amount data and survival of 9 type B genes, wherein the expression amount of the type B gene set is higher in 48 patients among 85 patients, the expression amount of the gene set is lower in 37 patients, 24 patients in the group with lower expression amount survive after 50 months, the survival rate is as high as 50%, and only 17 patients in the group with lower expression amount survive after 50 months, the survival rate is 45.94%. And according to the survival curve, the osteosarcoma patients with low gene expression in the B-type gene set tend to have better prognosis and survival time.
FIG. 7 shows the expression level data and survival curves of the patients plotted for the expression level data of the C-type gene, wherein the expression level of the C-type gene set is higher in 85 patients, the expression level of the C-type gene set is lower in 21 patients, and the survival rate is up to 51.56% after 50 months in 33 patients and 14.06% after 100 months in 9 patients in the group with higher expression level. In contrast, only 8 cases of the population with lower expression level survived after 50 months, the survival rate was 38.09%, and only 1 case of the population survived after 100 months, the survival rate was only 4.76%. And according to the survival curve, the patients with osteosarcoma with low gene expression in the C-type gene set tend to have better prognosis and survival time.
Example 6 efficacy validation of Gene sets for typing and assessing osteosarcoma prognosis
The gene sets and the upstream and downstream primer sequences of the PCR method thereof related to the prognosis and treatment of the osteosarcoma 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 for the corresponding relation between the expression level and the survival time of 44 genes and used for effect verification, and are shown in figures 8-10. The relation between the gene expression level of the gene set and the lifetime thereof can be seen.
As can be seen, 23 of the 24 genes in the A-type gene set, 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, were high in the corresponding patient prognosis survival, while low in the expression level.
8 of the 9 genes of the B-type gene set, including ATP1B3, DNAJC3, GGH, HSPB11, LMO7, PFN2, SGO2 and UPF3A genes, had poor prognosis and low survival rate.
Of the 11 genes in the C-type gene set, 10 genes including AEBP1, ASPN, C1R, C1S, COL A1, COL6A3, OLFML2B, POSTN, SFRP4 and THBS2 have better prognosis and patients with low expression have lower prognosis survival rate.
EXAMPLE 7COX regression model analysis
The COX regression model, also called "proportional-risk regression model (proportional hazards model, simply COX model)", was a semi-parametric regression model proposed by the uk collectist d.r.cox (1972). The model takes survival ending and survival time as dependent variables, can analyze the influence of a plurality of factors on the survival time at the same time, can analyze the data with the truncated survival time, and does not require to estimate the survival distribution type of the data. Because of the excellent properties, the model has been widely applied in medical follow-up study since the advent of the model, and is the most widely applied multi-factor analysis method in survival analysis so far.
The main objective of survival analysis is to study the relationship between the variable X and the observed results, i.e. the survival function (cumulative survival) S (t, X). When the survival function (cumulative survival) is affected by many factors, the conventional approach is to consider the regression equation, i.e., the effect of the variables Xi on the survival function (cumulative survival).
Basic form of Cox regression model:
h(t,X)＝h0(t)exp(β1X1+β2X2+…+βmXm)
wherein β1, β2, … βm are partial regression coefficients of the independent variables, which are parameters to be estimated from the sample data; h0 (t) is the reference risk of h (t, X) when the X vector is 0, which is the quantity to be estimated from the sample data.
Through the R algorithm, the survivinal package is used for Cox multifactor analysis, the influence of a certain gene on prognosis is possibly different under different ages, the influence of the gene on prognosis is large in the old and small in the young, and at the moment, the influence of age and the gene on prognosis under the combined action can be obtained through Cox multifactor analysis. Therefore, the prediction model obtained by Cox multi-factor analysis is more accurate. Analysis gave a cut-off value (cut point), a P value (P-value), and an HR value (Hazard Ratio) as follows:
| cutpoint | P-value | Hazard Ratio(95％CI) | |
| age of disease diagnosis | 11.23 | 0.21 | 1.7(0.75-3.8) |
| A-type 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 the Target-OS data set, taking the average value of the expression values of all genes in the A type/B type/C type gene set as the expression values of the A type/B type/C type gene set, and calculating whether the 3 expression values are independent risk factors influencing the prognosis of patients in the data set, wherein the result proves that the genotyping model can accurately predict the survival of patients with osteosarcoma.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Sequence listing
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<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 38
tccgctgaat ctaattgtgc tt 22
<210> 39
<211> 19
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 39
agcctttgac gggagacac 19
<210> 40
<211> 22
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 40
gagttcttcc gtaaatccag ca 22
<210> 41
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 41
tagaggccca attcggagag g 21
<210> 42
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 42
gagccttcga gggtttctct g 21
<210> 43
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 43
ctgcaaagtg aggattggac g 21
<210> 44
<211> 20
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 44
cttcagggct cgaatggcat 20
<210> 45
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 45
gcgagccaag ttgagcatta t 21
<210> 46
<211> 19
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 46
gcgagacgaa gagcgtcat 19
<210> 47
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 47
cgactagcag gactcccttt c 21
<210> 48
<211> 19
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 48
cggcgtaact gacccactg 19
<210> 49
<211> 19
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 49
aacccgacca ccggagaat 19
<210> 50
<211> 22
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 50
tgagagtctg aagcataacc ca 22
<210> 51
<211> 22
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 51
atggctacct ctcgatatga gc 22
<210> 52
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 52
cattggggac tctcacactc t 21
<210> 53
<211> 20
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 53
ggctcggtat tccccttcct 20
<210> 54
<211> 23
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 54
agtagccctc cgataataag caa 23
<210> 55
<211> 23
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 55
ggctggatct tacagagaaa gac 23
<210> 56
<211> 22
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 56
actctccact aatcagcagt ga 22
<210> 57
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 57
ctctgaaggg tccgaagtga t 21
<210> 58
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 58
attcctgtgg tggtccaaaa c 21
<210> 59
<211> 22
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 59
gtctacagtt ccgtcaagaa gg 22
<210> 60
<211> 22
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 60
tctgaaggat aagttgctcc ct 22
<210> 61
<211> 22
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 61
gtggataacc tgatgtgcga tg 22
<210> 62
<211> 19
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 62
ctgcccagac gtatttggc 19
<210> 63
<211> 23
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 63
acccaaaaat caggaataga cca 23
<210> 64
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 64
tcaacactcc atgaaaggcc a 21
<210> 65
<211> 19
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 65
gctgtcggcc ctagaagtg 19
<210> 66
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 66
gaactcgaag tagtcgtgtg c 21
<210> 67
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 67
acccacactg gactacaatg a 21
<210> 68
<211> 21
<212> DNA
<213> Artificial sequence (Artificial Sequence)
<400> 68
gttggggatc acgtaaccat c 21
<210> 69
<211> 22
<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 (11)
1. A gene combination for assessing prognosis of osteosarcoma, characterized in that the gene combination consists of the following 23 genes: ABCF1, ADAMTS1, ALDH3A2, ALKBH5, ATPAF2, C19orf12, CENPV,
CPNE3, EPN2, FLII, FSCN1, GID4, HIST1H2BD, LSM2, MPRIP, MYO15A, PEMT, PI15, PRAME, PRR3, TOM1L2, TTC19 and VARS.
2. Use of a combination of genes as defined in claim 1 for the preparation of a test kit for assessing osteosarcoma prognosis, said test kit comprising: amplifying primers for each gene in the gene combination for assessing osteosarcoma prognosis according to claim 1; and/or
A probe that specifically binds to each gene of the gene combination for osteosarcoma prognosis according to claim 1 and/or the complementary sequence thereof; and/or
An antibody that specifically binds to a protein expressed by each gene in the gene combination for assessing osteosarcoma prognosis according to claim 1.
3. The use according to claim 2, wherein the primer comprises: the nucleotide sequence is shown as SEQ ID NO.1-SEQ ID NO.16 and SEQ ID NO.19-SEQ ID NO. 48.
4. Use of the gene combination according to claim 1 for the preparation of a protein detection chip for assessing osteosarcoma prognosis, characterized in that the protein detection chip comprises a solid support on which a capture antibody that specifically binds to each gene in the gene combination according to claim 1 is immobilized and a detection antibody linked to an antibody label.
5. The use according to claim 4, wherein the antibody markers are: enzymatic, fluorescein, isotope, or biotin labels.
6. A kit for assessing osteosarcoma prognosis, comprising
1) Amplifying primers SEQ ID NO.1-SEQ ID NO.16 and SEQ ID NO.19-SEQ ID NO.48 for each gene of the gene combination of claim 1; and/or
2) A probe that specifically binds to each gene and/or its complement in the gene combination of claim 1; and/or
3) An antibody that specifically binds to a protein expressed by each gene in the gene combination of claim 1.
7. The kit of claim 6, wherein the kit comprises: total RNA extraction reagents, reverse transcription reagents and/or sequencing reagents.
8. The kit of claim 6, wherein the kit comprises: dNTP solution and/or RNA reverse transcriptase.
9. The kit of claim 7, wherein the sequencing reagent comprises a second generation sequencing reagent or a single cell sequencing reagent.
10. The kit of any one of claims 6-9, wherein the primer comprises: the nucleotide sequence is shown as SEQ ID NO.1-SEQ ID NO.16 and SEQ ID NO.19-SEQ ID NO. 48.
11. A protein detection chip for assessing osteosarcoma prognosis, comprising a solid support on which a capture antibody that specifically binds to each gene in the gene combination of claim 1 is immobilized and a detection antibody linked to an antibody label.
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