CN113881768A - Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof - Google Patents
Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof Download PDFInfo
- Publication number
- CN113881768A CN113881768A CN202110663031.5A CN202110663031A CN113881768A CN 113881768 A CN113881768 A CN 113881768A CN 202110663031 A CN202110663031 A CN 202110663031A CN 113881768 A CN113881768 A CN 113881768A
- Authority
- CN
- China
- Prior art keywords
- osteosarcoma
- typing
- gene
- artificial sequence
- genes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
- G01N33/54306—Solid-phase reaction mechanisms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57407—Specifically defined cancers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
- G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/112—Disease subtyping, staging or classification
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/118—Prognosis of disease development
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Immunology (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Molecular Biology (AREA)
- Urology & Nephrology (AREA)
- Hematology (AREA)
- Biomedical Technology (AREA)
- Analytical Chemistry (AREA)
- Pathology (AREA)
- Biochemistry (AREA)
- Biotechnology (AREA)
- Microbiology (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Cell Biology (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Organic Chemistry (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- General Physics & Mathematics (AREA)
- Hospice & Palliative Care (AREA)
- Oncology (AREA)
- Genetics & Genomics (AREA)
- Zoology (AREA)
- Wood Science & Technology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Biophysics (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
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, 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
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: AC, ACAN, ACTA, ACTG, ACTN, ACTR3, ADIRF, AKR7A, AL, ALKBH, ALPL, ANKH, ANP32, ANXA, AOC, APRT, ARF, ARGLU, ARL4, ARL6IP, ASPM, ASS, ATF, ATP2B, ATP5F1, ATP5IF, ATP5MC, ATP5, ATPAF, ATATRKB, AURKB, B2, B9D, BAMBI, BASP, BEX, BGN, BIBMP, BMP8, BNIP, BSG, BST, BTF, C12orf, C19orf, C1P, C1 QTC, CALNC 4 QTC, CALNC 5, CCKN, CCKNN, CCKN 5, CCKN, CCLNC 5, CCLN, CCLNC 5, CCLNC, CCLN, CCLNC, CCLNB, CCLNC 5, CCLNC, CCLNB, CCLNC 5, CCLNC, CCLNB, CCLN, CCLNB, CCLNC, CCLNB, CCLN, CCLNB, CCLNC, CCLNB, CCLNC, CCLNB, CCLNC, CCLNB, CCLN, CCLNB, CCLNC, CCLNB, CCLN, COL11A, COL18A, COL1A, COL3A, COL5A, COL6A, COL8A, COMMD, COPS, COPZ, COX6, COX7A, COX7, CPE, CPQ, CRABP, CRELD, CRISPLD, CRYAB, CRYGS, CSAG, CSNK2, CSRP, CTGF, CTHRC, CTNNAL, CXCL, CYB5R, CYC, CYP1B, CYR, CYTOR, DAD, DAP, DBI, DCN, DCP1, DDPC, DDOST, DDX, DEK, DGCR6, UJC, DNAJC, ENDNAH, ENHH, EGFP, DSEGFP, DSTK, DSDYNFP, FEEBBP, FEEDFA, FEEDF, FEEDFA, FEEDF, FSTL1, FUNDC1, FXYD1, GABARAP, GABPB1-AS1, GADD45 1, GAL, GAPDH, GAS 1, GCSH, GGT 1, GJA1, GLRX 1, GLT8D 1, GNAS, GNL1, GNPAT, GOLT 11, GPAA1, GPC1, GPX 1, GRP, GSTA1, GSTM 1, GTSE1, GUK1, PTMHH 2AFV, H2AFZ, HACD 1, HADH, HAPLN1, HAS 1, HCFC1R1, HES 1, HIDA 1, HIST1H1, HIST1H 72, HIST1H 21, HIST1H2 NG1, HIST1, HALDIDS 1, HNLIFTLIPG 1, HNLIFTIR 1, HNLAIDP 1, HNLIFTLIFTLIFO 1, HNLAIDP 1, HNLIGALIGALIFO 1, HNLAIDP 1, HN1, HNLAIDP 1, HN1, HNLAIDP 1, HNIDP 1, HN1, HNLAIDP 1, HN1, HNLAIDP 1, HN1, HNIDP 1, HN1, HNIDP 1, HN1, HNIDP 1, HN1, HNIDP 1, HNLAIDP 1, HNIDP 1, HN1, HNLAIDP 1, HN1, HNIDP 1, HN1, HNLAIDP 1, HN1, HNIDP 1, HNLAIDP 1, HN1, HNLAIDP 1, HN1, HNLAIDP 1, HNIDP 1, HNLAIDP 1, HNLAIDP 1, 1, LINC00662, LMN 1, LMNA, LOXL1, LRRC1, LRRFIP 1, LSM 1, LTBP1, LUC7L 1, LUM, LY 61, LYPLA1, MAD2L1, MAGEA 1, MAGED1, MAGEL 1, MAP 11, MAP3K1, MARCKSL1, MCRIP1, MDH 1, MDK 1, MED1, MEGF1, MELTTF, METTL 1, MFAP 1, MFGE 1, MFSD1, MGST 1, MIUFF, MIR4458 MY 1, MLF 1, MLLT1, MMP 72, MMP1, MNPSNL 1, MNS1, MNNA 1, MNND 1, MNNC N1, NMN 1, MND 1, MAG 1, MYN 1, MND 1, MYN 1, MND 1, MN, PCOLE, PDCD, PDGFD, PDGFRL, PDLIM, PDRG, PFDN, PHB, PHF, PHGDH, PHLDA, PHPT, PIGL, PIGT, PITX, PLAC, PLAT, PLEKHA, PLIN, PLOD, PLP, PLSA, PMEPA, PONL, POLR1, POLR2, POLR3, POP, POR, PPA, PPXB, PPIB, PPIC, PPP1, PRAF, PRDX, PRELP, PRKDC, PRPSAP, PRPTH, PRSS, PSIP, PSMA, PSMB, PSMG, RARB, PSRB, PSRC, PRRB, RARB, SELENOM, SELENOW, SEM, SEPTIN, SERBP, SERF, SERPINA, SERPINE, SERPINF, SERPING, SERPINH, SEZ6L, SF3B, SFTA1, SGCB, SHMT, SIVA, SLC25A, SLC29A, SLIRP, SMC1, SMC, SMDT, SMIM, SMOC, SNAI, SNHG, SNRNP, SNRPA, SNRPB, SNRPD, SNRPE, SNRPG, SNX, SOD, TPM, SPARC, SPATS2, SPHK, SPP, SPRY, SRSM, SRPX, SRSF, SSB, SSR, ST, STAT, STMN, STMP, STXBP, SUUMB, SULF, SUMF, SUPT16, SWI, SYNRF, TATF 1, TATF 1, TATSBB, TATSTM, TMTBEM 1, TMTBBE, TMTBFS, TMT 2, TMTBFS, TMT TET FEM, TMTBEM, TMTBCM, TMTBFS, TMT, TMTBFS, TMTBEM 1, TMTBCM, TMTBF, TMTBEM, TMTBBE, TMTBM, TMT, TMTBFS, TMT 2, TMT, TMTBF, TMT, TMTBM, TMTBFS, TMTBM, TMTBF, TMT 1, TMT 2, TMT 1, TMT TPBB, TMT 1, TMT TPBB, TMT 1, TMT TPBB, TMT 1, TMT TPBB, TMT 1, TMT TPM, TMT TPMS, TMT TPM, TMT TPMS, TMT TPM, TMT 1, TMT TPM, TMT TPMS, TMT TPM, TMT 1, TMT TPM, TMT TPMS, TMT TPM, TMT 1, TMT TPM, TMT TPMS, TMT TPM, TMT 1, TMT TPM, TMT TP, TXN, TXNL, TYMS, UACA, UBB, UBE2L, UBE2V, UCHL, UFD, UNC5, UQCRB, UQCRC, UQCRFS, UQCRH, URI, USP, UTP, VAMP, VBP, VCAN, VDR, VKORC, VWA, WDR, WFDC, WIPI, XRCC, 1, YPEL, YWHAE, YWHAQ, ZFHX, ZNHIT, ZRANB, ZSCAN-AS, and WIZSM.
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, 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 assessing gene expression of a prognostic gene set as described above.
In some of these embodiments, the test kit comprises: total RNA extraction reagents, reverse transcription reagents, and/or sequencing reagents.
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.
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.
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 more than 2 of any of ABCF1, ADAMTS1, ALDH3a2, akbh 5, 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 any 2 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 the osteosarcoma is still in the future; 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, akbh 5, 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 type 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 patients with the osteosarcoma can be found later; and/or
When the genes 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.
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 expression level and 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 to be understood that the experimental procedures in the following examples, where specific conditions are not noted, are generally in accordance with conventional conditions, or with conditions recommended by the manufacturer. The various reagents used in the examples are 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 (unified managed adaptation and project) is a new dimension-reducing Manifold learning technology, which 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 different and treatment-related pathway enrichment was shown using heat maps, as shown in fig. 2.
By calculating the difference genes of the tumor cell populations of the 3 different subtypes, we obtained a total of 840 gene sets relevant to prognosis and treatment of osteosarcoma, as shown in table 1 (wherein, the classification in the table is 3 different differentiation directions that may correspond to the high expression of the gene).
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 respectively, remove the repeated genes, obtain 850 differential genes in total, draw the violin diagram of the expression conditions of the 850 genes in the whole 28 cell populations, and screen out 44 genes which are characterized and highly expressed in the tumor populations 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_Ensembl | Typing | ||
| 1 | | ENSG00000204574 | A | 21 | PRR3 | ENSG00000204576 | A | ||
| 2 | ADAMTS1 | ENSG00000154734 | A | 22 | | ENSG00000175662 | A | ||
| 3 | ALDH3A2 | ENSG00000072210 | A | 23 | | ENSG00000011295 | A | ||
| 4 | | ENSG00000091542 | A | 24 | | ENSG00000204394 | A | ||
| 5 | | ENSG00000171953 | A | 25 | | ENSG00000069849 | B | ||
| 6 | C19orf12 | ENSG00000131943 | A | 26 | CDK4 | ENSG00000135446 | B | ||
| 7 | CENPV | ENSG00000166582 | A | 27 | | ENSG00000102580 | B | ||
| 8 | CPNE3 | ENSG00000085719 | A | 28 | | ENSG00000137563 | B | ||
| 9 | DRG2 | ENSG00000108591 | A | 29 | | ENSG00000081870 | B | ||
| 10 | EPN2 | ENSG00000072134 | A | 30 | LMO7 | ENSG00000136153 | B | ||
| 11 | FLII | ENSG00000177731 | A | 31 | | ENSG00000070087 | B | ||
| 12 | FSCN1 | ENSG00000075618 | A | 32 | SGO2 | ENSG00000163535 | B | ||
| 13 | | ENSG00000141034 | A | 33 | UPF3A | ENSG00000169062 | B | ||
| 14 | HIST1H2BD | ENSG00000158373 | A | 34 | | ENSG00000106624 | C | ||
| 15 | LSM2 | ENSG00000204392 | A | 35 | ASPN | ENSG00000106819 | C | ||
| 16 | MPRIP | ENSG00000133030 | A | 36 | | ENSG00000159403 | C | ||
| 17 | | ENSG00000091536 | A | 37 | | 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 | | ENSG00000162745 | C | 43 | | 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 osteosarcoma cases are classified with clinical information, and the clinical cases in the Target-OS dataset can be divided into corresponding Cluster1, Cluster2 and Cluster 3. 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 Cluster 3. We show the clinical information and differential gene expression of the classification datasets by heatmap as shown in fig. 3.
Based on retrospective analysis data of cases, survival curves of 3 patients with different types were prepared to demonstrate that there were differences 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 the Cluster3 (type B) patient was the worst, the prognosis for the Cluster2 (type C) patient was slightly better than Cluster3, and the prognosis for the Cluster1 (type a) patient was the best, as shown in figure 4.
Example 3 analytical method
The single cell sample preparation, sequencing and analysis methods described in example 1 were as follows:
first, 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, 10XGenomics, with a recovery number of target cells of 10,000.
Two, 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 from 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 the cells with nFeature _ RNA less than 200 or over 5000, the remaining cells and gene expression matrix will be used for subsequent analysis.
Third, unsupervised dimension reduction and clustering of downloaded sc-RNA sequencing data
The filtered expression matrix obtained in the previous step was integrated using Seurat V3.2.2, data integration was performed using FindIntegrationAnchors and IntegraData functions, and UMAP visualization and cell clustering were performed using RunUMAP and FindClusters functions. The cell types were annotated for each cell cluster using known marker genes.
Calculation and display of four, 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 the 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.
Fifthly, way 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.
Sixthly, 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 saurta object, and using the new _ cell _ data _ set function to create cds objects, and using default parameters for dimensionality reduction, clustering, and differentiation trajectory inference.
Next, as described in example 2, survival verification was performed by the Target-OS dataset inside the TCGA as follows:
seven, 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.
Survival curve of eight, Kaplan-Meier (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 model 1: the patient prognosis is best. Cluster model 2: the prognosis of the patient is good. Cluster model 3: the patient had 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 expression level of the gene set of 44 genes described in example 2 in an osteosarcoma sample is detected by an immunohistochemical method, and the typing of osteosarcoma patients is analyzed and suggested for treatment.
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 4 h-85% alcohol 2 h-90% alcohol 2 h-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 1 h.
(3) Embedding: embedding the wax-soaked tissue 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 is as follows: xylene I5 min-xylene II 5 min-absolute ethanol I30 sec-absolute 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 washing-0.3% H2O2 methanol treatment section 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) 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-H2O2Sections 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 process is strictly carried out according to the instruction flow of the Trizol RNA extraction kit.
(1) Trizol (3 ml/100mg tissue, little to no Ningduo) was added and homogenized in a glass homogenizer and ice-cooled for 10-15 min.
(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 10-15 min.
(4) 0.2ml of chloroform/1 ml of Trizol was added thereto, and the mixture was shaken for 15 seconds and left at room temperature for 5 min.
(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 10 min.
(8) After centrifugation at 12000g for 10min at 4 ℃ a white precipitate was visible 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 260/280nm absorbance 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 20ul, 42 ℃ 60min
(III) PCR reaction
The system comprises the following components: DEPC water 17.5ul, 10 XTaq buffer 2.5ul, MgCl22.0 ul, 10M dNTP Mix 0.5ul, upstream primer 0.5ul, downstream primer 0.5ul, Tap enzyme (5u/ul)0.5ul, CDNA 1.0 ul. A total of 25 ul.
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 model 1: the patient prognosis is best. Cluster model 2: the prognosis of the patient is good. Cluster model 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 9 type B genes, with 48 of the 85 patients expressing higher levels of the type B gene set and 37 patients expressing lower levels of the gene set, and 24 of the patients having lower levels of expression having a survival rate of 50% after 50 months, whereas only 17 of the patients having lower levels of expression 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. And only 8 of the population with lower expression levels survived 50 months, the survival rate was 38.09%, and only 1 patient survived 100 months, 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 osteosarcoma typing and evaluation of 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 visible gene set and the survival time thereof.
As can be seen, 23 genes out of 24 genes in the A-type gene set include 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 high in the prognosis survival rate of the corresponding patients, and low in the expression level, the prognosis survival rate is low.
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 the survival rate of patients with low expression levels is poorer.
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, better survival prognosis and lower survival rate of patients with low expression levels.
Example 7COX 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, β 1, β 2, … β m is a partial regression coefficient of an argument, which is a parameter 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 more specific and detailed, but not construed 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
<110> Shanghai Changcheng Hospital
<120> gene for osteosarcoma typing and osteosarcoma prognosis evaluation and use thereof
<130> all
<160> 88
<170> SIPOSequenceListing 1.0
<210> 1
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 1
tcagtgccaa ccagtgatga g 21
<210> 2
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 2
cggccttatt gatccgattc tt 22
<210> 3
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 3
actggaagca taagaaagaa gcg 23
<210> 4
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 4
aattctgcca tcgactggtc t 21
<210> 5
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 5
aaaccagtta agaagaacgt gct 23
<210> 6
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 6
cgaaggggta attccaagct c 21
<210> 7
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 7
cggcgaaggc tacacttacg 20
<210> 8
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 8
ccaccagctt ttggatcacc a 21
<210> 9
<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 9
ggacgccgtc tcctgaatc 19
<210> 10
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 10
gtccaggttt atctcaaagc ca 22
<210> 11
<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 11
ccaagagcag gcctggaga 19
<210> 12
<211> 17
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 12
<210> 13
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 13
cctgctagac acctttgaat acc 23
<210> 14
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 14
tcaggagctt gaagcgagaa g 21
<210> 15
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 15
gtcagaccct ttatgtgtgt tgt 23
<210> 16
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 16
tggaaaattg gggattcaag caa 23
<210> 17
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 17
aagctaagct cgccaagtat cg 22
<210> 18
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 18
gactcaagaa tgtggactta ccc 23
<210> 19
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 19
tctctcccac agcagactac g 21
<210> 20
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 20
gggacagatt gtacggtggc 20
<210> 21
<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 21
ctgcaatgac ctgacacgg 19
<210> 22
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 22
ggtgagctga tttcgggaca g 21
<210> 23
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 23
ccagggtatg gacctgtctg 20
<210> 24
<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 24
gtgtgggtac ggaaggcac 19
<210> 25
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 25
tcctaaggta gacagtgtag cg 22
<210> 26
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 26
atctggtacc ctgtaaactg c 21
<210> 27
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 27
gatgcctgaa cctaccaagt 20
<210> 28
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 28
gcttcttccc gtccttcttc 20
<210> 29
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 29
catctgtgga accctccatt c 21
<210> 30
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 30
gcacgtatcg gaccactgag 20
<210> 31
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 31
gagaacccgt gcaggaaatt c 21
<210> 32
<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 32
gttgagcaga tgcgactcg 19
<210> 33
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 33
ggacctgggc gagtattatg a 21
<210> 34
<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 34
ctcgtagcgg tggagtgac 19
<210> 35
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 35
ctggaatgtg gttgcacgat g 21
<210> 36
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 36
gcttagagag tagcaggcca 20
<210> 37
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 37
caagtaccgt cgtcctactc a 21
<210> 38
<211> 22
<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
<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 (16)
1. A gene set for use in typing of osteosarcoma and assessing prognosis of osteosarcoma, wherein said gene group comprises 44 genes as shown in the following table:
2. a gene set for use in typing osteosarcoma and assessing prognosis of osteosarcoma, wherein said gene group is a group consisting of 840 genes, said group being as shown in table 1.
3. Use of the gene set according to claim 1or 2 for the preparation of a test kit for osteosarcoma typing and prognosis evaluation, said test kit comprising: amplifying the primers of claim 1or 2 for assessing genes used for osteosarcoma typing and assessing a prognostic gene set; and/or
A probe specifically binding to a gene and/or its complementary sequence for assessing a gene set for osteosarcoma typing and assessing prognosis as claimed in claim 1or 2; and/or
An antibody that specifically binds to a protein of claim 1or 2 for assessing gene expression for osteosarcoma typing and for assessing a prognostic gene set.
4. The use of claim 3, wherein the primers comprise: the nucleotide sequence of the primer is shown as SEQ ID NO.1-SEQ ID NO. 88.
5. Use of the gene set of claim 1or 2 for the preparation of a protein detection chip for osteosarcoma typing and prognosis evaluation, wherein the gene chip comprises a solid phase carrier on which a capture antibody specifically binding to the genes of the gene set of claim 1or 2 is immobilized and a detection antibody linked to an antibody label.
6. The use according to claim 5, wherein the antibody marker is: enzyme labeling, fluorescein labeling, isotope labeling or biotin labeling.
7. A kit for typing osteosarcoma and evaluating prognosis of osteosarcoma, which comprises
1) Amplifying the primers of claim 1or 2 for assessing genes used for osteosarcoma typing and assessing a prognostic gene set; and/or
2) A probe specifically binding to a gene and/or its complementary sequence for assessing a gene set for osteosarcoma typing and assessing prognosis as claimed in claim 1or 2; and/or
3) An antibody that specifically binds to a protein of claim 1or 2 for assessing gene expression for osteosarcoma typing and for assessing a prognostic gene set.
8. The kit of claim 7, wherein the test kit comprises: total RNA extraction reagents, reverse transcription reagents, and/or sequencing reagents.
9. The kit of claim 7, wherein the test kit comprises: dNTP solution and/or RNA reverse transcriptase.
10. The kit of claim 7, wherein the sequencing reagents comprise secondary sequencing reagents or single cell sequencing reagents.
11. The kit according to any one of claims 7 to 10, wherein the primers comprise: the nucleotide sequence of the primer is shown as SEQ ID NO.1-SEQ ID NO. 88.
12. A gene chip for typing osteosarcoma and evaluating prognosis of osteosarcoma, comprising a solid phase carrier on which a capture antibody specifically binding to genes of the gene set of claim 1or 2 is immobilized, and a detection antibody linked to an antibody marker.
13. A method for typing osteosarcoma for non-diagnostic and non-therapeutic purposes, comprising detecting the amount of the gene or the protein expressed by the gene according to claim 1or 2 in a sample to be evaluated for osteosarcoma typing and prognosis evaluation.
14. The osteosarcoma typing method according to claim 13, wherein the kit according to claims 8 to 11 or the gene detecting chip according to claim 12 is used to detect the gene expression level in the sample, and the osteosarcoma typing result is obtained by data analysis.
15. The method of typing osteosarcoma according to claim 13, wherein 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 and VARS are highly expressed, the osteosarcoma typing is type 1, and the patient with osteosarcoma is well-expected; and/or
When more than 2 genes of any 2 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 the osteosarcoma is still in the future; 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.
16. The method of typing osteosarcoma according to claim 15, wherein when the genes 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 are highly expressed, the typing of osteosarcoma is type 1, which is well advanced in osteosarcoma patients; 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 patients with the osteosarcoma can be found later; and/or
When the genes 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110663031.5A CN113881768B (en) | 2021-06-15 | 2021-06-15 | Gene for osteosarcoma typing and assessing osteosarcoma prognosis and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110663031.5A CN113881768B (en) | 2021-06-15 | 2021-06-15 | Gene for osteosarcoma typing and assessing osteosarcoma prognosis and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113881768A true CN113881768A (en) | 2022-01-04 |
| CN113881768B CN113881768B (en) | 2023-10-03 |
Family
ID=79010185
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110663031.5A Active CN113881768B (en) | 2021-06-15 | 2021-06-15 | Gene for osteosarcoma typing and assessing osteosarcoma prognosis and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113881768B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1703522A (en) * | 2002-09-30 | 2005-11-30 | 肿瘤疗法科学股份有限公司 | Method for diagnosing testicular seminomas |
| US7256256B1 (en) * | 1999-06-18 | 2007-08-14 | United States Of America As Represented By The Secretary, Department Of Health And Human Services | CDK4 binding peptide |
| CN101283106A (en) * | 2005-07-27 | 2008-10-08 | 肿瘤疗法科学股份有限公司 | Method of diagnosing small cell lung cancer |
| US20090227464A1 (en) * | 2003-07-01 | 2009-09-10 | Smadar Avigad | Prognosis determination in ewing sarcoma patients by means of genetic profiling |
| US20160024588A1 (en) * | 2013-03-14 | 2016-01-28 | The Broad Institute, Inc. | Osteosarcoma-associated risk markers and uses thereof |
| CN107475356A (en) * | 2016-06-08 | 2017-12-15 | 益善生物技术股份有限公司 | Osteosarcoma circulating tumor cell identification kit |
| CN108893535A (en) * | 2018-07-13 | 2018-11-27 | 上海交通大学医学院附属瑞金医院 | Detection of osteosarcoma lung metastasis related gene mutation based on blood circulation exosome RNA and application thereof |
| CN112813156A (en) * | 2021-02-03 | 2021-05-18 | 山东第一医科大学附属省立医院(山东省立医院) | DNA library for detecting and diagnosing bone development disorder pathogenic gene and application thereof |
-
2021
- 2021-06-15 CN CN202110663031.5A patent/CN113881768B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7256256B1 (en) * | 1999-06-18 | 2007-08-14 | United States Of America As Represented By The Secretary, Department Of Health And Human Services | CDK4 binding peptide |
| CN1703522A (en) * | 2002-09-30 | 2005-11-30 | 肿瘤疗法科学股份有限公司 | Method for diagnosing testicular seminomas |
| US20090227464A1 (en) * | 2003-07-01 | 2009-09-10 | Smadar Avigad | Prognosis determination in ewing sarcoma patients by means of genetic profiling |
| CN101283106A (en) * | 2005-07-27 | 2008-10-08 | 肿瘤疗法科学股份有限公司 | Method of diagnosing small cell lung cancer |
| US20160024588A1 (en) * | 2013-03-14 | 2016-01-28 | The Broad Institute, Inc. | Osteosarcoma-associated risk markers and uses thereof |
| CN107475356A (en) * | 2016-06-08 | 2017-12-15 | 益善生物技术股份有限公司 | Osteosarcoma circulating tumor cell identification kit |
| CN108893535A (en) * | 2018-07-13 | 2018-11-27 | 上海交通大学医学院附属瑞金医院 | Detection of osteosarcoma lung metastasis related gene mutation based on blood circulation exosome RNA and application thereof |
| CN112813156A (en) * | 2021-02-03 | 2021-05-18 | 山东第一医科大学附属省立医院(山东省立医院) | DNA library for detecting and diagnosing bone development disorder pathogenic gene and application thereof |
Non-Patent Citations (4)
| Title |
|---|
| RADOSLAV ZAMBORSKY等: "Identification of Prognostic and Predictive Osteosarcoma Biomarkers", 《MED SCI (BASEL)》 * |
| 冯晓飞等: "骨肉瘤基因表达谱芯片的生物信息学分析", 《华中科技大学学报(医学版)》 * |
| 张浩: "骨肉瘤分子分型及各亚型肿瘤学性质表征", 《中国博士学位论文全文数据库 医药卫生科技辑》 * |
| 蔡郑东: "《骨盆外科学》", 30 April 2019, 上海科学技术出版社 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113881768B (en) | 2023-10-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6908571B2 (en) | Gene expression profile algorithms and tests to quantify the prognosis of prostate cancer | |
| CN113355418B (en) | Gene for typing osteosarcoma and evaluating prognosis of osteosarcoma and application thereof | |
| US11365450B2 (en) | Group classification and prognosis prediction system based on biological characteristics of gastric cancer | |
| Mehra et al. | A novel RNA in situ hybridization assay for the long noncoding RNA SChLAP1 predicts poor clinical outcome after radical prostatectomy in clinically localized prostate cancer | |
| Wang et al. | Automated quantitative RNA in situ hybridization for resolution of equivocal and heterogeneous ERBB2 (HER2) status in invasive breast carcinoma | |
| ES2735993T3 (en) | Methods to predict the clinical outcome of cancer | |
| Van Laere et al. | Identification of cell-of-origin breast tumor subtypes in inflammatory breast cancer by gene expression profiling | |
| CN104024436B (en) | Marker gene for carcinoma of prostate classification | |
| CN111394456B (en) | Early lung adenocarcinoma patient prognosis evaluation system and application thereof | |
| CN108884494A (en) | The unicellular Genome Atlas of circulating tumor cell is analyzed to characterize disease heterogeneity in metastatic disease | |
| Johansson et al. | Molecular profiling of male breast cancer–lost in translation? | |
| Bruno et al. | Molecular markers and new diagnostic methods to differentiate malignant from benign mesothelial pleural proliferations: a literature review | |
| EP1934615A2 (en) | Methods and materials for identifying the origin of a carcinoma of unknown primary origin | |
| CN113549691B (en) | Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof | |
| CN113512588A (en) | Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof | |
| JP2013511999A (en) | Multidrug response markers for breast cancer cells | |
| CN105779618A (en) | Novel target gene for diagnosing and treating tongue squamous carcinoma and application thereof | |
| CN114787374A (en) | Therapeutic methods based on molecular response to therapy | |
| CN108603233A (en) | The unicellular Genome Atlas of circulating tumor cell (CTC) is analyzed to characterize disease heterogeneity in metastatic disease | |
| Chen et al. | cDNA microarray analysis and immunohistochemistry reveal a distinct molecular phenotype in serous endometrial cancer compared to endometrioid endometrial cancer | |
| CN115482935B (en) | Lung adenocarcinoma patient prognosis model for predicting small cell transformation and establishment method thereof | |
| Chan et al. | Serial transplantation of NMU‐induced rat mammary tumors: a model of human breast cancer progression | |
| CN113881768A (en) | Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof | |
| CN111808966B (en) | Application of miRNA in diagnosis of breast cancer disease risk | |
| CN102851282B (en) | MicroRNA markers for discriminating constitutional lung cancer tissue and paracancerous tissue |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |