CN116949181B - Molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patient and application thereof - Google Patents
Molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patient and application thereof Download PDFInfo
- Publication number
- CN116949181B CN116949181B CN202311207483.8A CN202311207483A CN116949181B CN 116949181 B CN116949181 B CN 116949181B CN 202311207483 A CN202311207483 A CN 202311207483A CN 116949181 B CN116949181 B CN 116949181B
- Authority
- CN
- China
- Prior art keywords
- osteosarcoma
- mageb4
- patients
- prognosis
- patient
- 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.)
- Active
Links
- 201000008968 osteosarcoma Diseases 0.000 title claims abstract description 111
- 101001036691 Homo sapiens Melanoma-associated antigen B4 Proteins 0.000 title claims abstract description 78
- 102100039476 Melanoma-associated antigen B4 Human genes 0.000 title claims abstract description 78
- 230000004083 survival effect Effects 0.000 title claims abstract description 55
- 238000004393 prognosis Methods 0.000 title claims abstract description 42
- 239000003147 molecular marker Substances 0.000 title abstract description 12
- 210000001808 exosome Anatomy 0.000 claims abstract description 69
- 230000014509 gene expression Effects 0.000 claims abstract description 55
- 230000034994 death Effects 0.000 claims abstract description 18
- 231100000517 death Toxicity 0.000 claims abstract description 18
- 238000012216 screening Methods 0.000 claims abstract description 11
- 238000010837 poor prognosis Methods 0.000 claims description 10
- 230000003321 amplification Effects 0.000 claims description 6
- 239000003153 chemical reaction reagent Substances 0.000 claims description 6
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 6
- 108020004414 DNA Proteins 0.000 claims description 4
- 102000053602 DNA Human genes 0.000 claims description 4
- 108020004682 Single-Stranded DNA Proteins 0.000 claims description 4
- 239000002773 nucleotide Substances 0.000 claims description 2
- 125000003729 nucleotide group Chemical group 0.000 claims description 2
- 239000003795 chemical substances by application Substances 0.000 claims 1
- 238000011282 treatment Methods 0.000 abstract description 17
- 230000007774 longterm Effects 0.000 abstract description 10
- 230000000694 effects Effects 0.000 abstract description 7
- 239000000126 substance Substances 0.000 abstract description 7
- 230000009897 systematic effect Effects 0.000 abstract description 3
- 108020004999 messenger RNA Proteins 0.000 description 30
- 108090000623 proteins and genes Proteins 0.000 description 23
- 206010028980 Neoplasm Diseases 0.000 description 21
- 238000000034 method Methods 0.000 description 19
- 206010027476 Metastases Diseases 0.000 description 16
- 230000009401 metastasis Effects 0.000 description 16
- 238000001514 detection method Methods 0.000 description 15
- 239000000047 product Substances 0.000 description 14
- 238000004458 analytical method Methods 0.000 description 13
- 210000004369 blood Anatomy 0.000 description 10
- 239000008280 blood Substances 0.000 description 10
- 238000002512 chemotherapy Methods 0.000 description 10
- 238000012163 sequencing technique Methods 0.000 description 10
- 210000000988 bone and bone Anatomy 0.000 description 8
- 210000004027 cell Anatomy 0.000 description 8
- 239000006228 supernatant Substances 0.000 description 8
- 230000009885 systemic effect Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 102000004169 proteins and genes Human genes 0.000 description 7
- 108091032973 (ribonucleotides)n+m Proteins 0.000 description 6
- 238000011529 RT qPCR Methods 0.000 description 6
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 6
- 239000002245 particle Substances 0.000 description 6
- 239000000523 sample Substances 0.000 description 6
- 210000002966 serum Anatomy 0.000 description 6
- 210000004881 tumor cell Anatomy 0.000 description 6
- 201000010099 disease Diseases 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 5
- 239000012634 fragment Substances 0.000 description 5
- 230000008076 immune mechanism Effects 0.000 description 5
- 210000004072 lung Anatomy 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 239000013614 RNA sample Substances 0.000 description 4
- 239000002299 complementary DNA Substances 0.000 description 4
- 238000011002 quantification Methods 0.000 description 4
- 239000000725 suspension Substances 0.000 description 4
- 102000002260 Alkaline Phosphatase Human genes 0.000 description 3
- 108020004774 Alkaline Phosphatase Proteins 0.000 description 3
- 206010054094 Tumour necrosis Diseases 0.000 description 3
- 230000009102 absorption Effects 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 3
- 238000001574 biopsy Methods 0.000 description 3
- 230000008416 bone turnover Effects 0.000 description 3
- 210000000170 cell membrane Anatomy 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000013211 curve analysis Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 108091070501 miRNA Proteins 0.000 description 3
- 239000002679 microRNA Substances 0.000 description 3
- 210000000056 organ Anatomy 0.000 description 3
- 239000008188 pellet Substances 0.000 description 3
- 239000002244 precipitate Substances 0.000 description 3
- 230000019491 signal transduction Effects 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 101800001415 Bri23 peptide Proteins 0.000 description 2
- 101800000655 C-terminal peptide Proteins 0.000 description 2
- 102400000107 C-terminal peptide Human genes 0.000 description 2
- 108010056891 Calnexin Proteins 0.000 description 2
- 102000034342 Calnexin Human genes 0.000 description 2
- 102000012422 Collagen Type I Human genes 0.000 description 2
- 108010022452 Collagen Type I Proteins 0.000 description 2
- 102000003688 G-Protein-Coupled Receptors Human genes 0.000 description 2
- 108090000045 G-Protein-Coupled Receptors Proteins 0.000 description 2
- 101100075865 Homo sapiens MAGEB4 gene Proteins 0.000 description 2
- 208000022559 Inflammatory bowel disease Diseases 0.000 description 2
- 108010052285 Membrane Proteins Proteins 0.000 description 2
- 102000004067 Osteocalcin Human genes 0.000 description 2
- 108090000573 Osteocalcin Proteins 0.000 description 2
- 239000011324 bead Substances 0.000 description 2
- 230000004071 biological effect Effects 0.000 description 2
- 230000031018 biological processes and functions Effects 0.000 description 2
- 201000011510 cancer Diseases 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000004043 dyeing Methods 0.000 description 2
- 238000010201 enrichment analysis Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 150000002632 lipids Chemical class 0.000 description 2
- 238000011528 liquid biopsy Methods 0.000 description 2
- 239000003550 marker Substances 0.000 description 2
- 230000004060 metabolic process Effects 0.000 description 2
- 230000004879 molecular function Effects 0.000 description 2
- 230000000877 morphologic effect Effects 0.000 description 2
- 102000039446 nucleic acids Human genes 0.000 description 2
- 108020004707 nucleic acids Proteins 0.000 description 2
- 150000007523 nucleic acids Chemical class 0.000 description 2
- 230000007170 pathology Effects 0.000 description 2
- 230000037361 pathway Effects 0.000 description 2
- 230000002980 postoperative effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- XOAAWQZATWQOTB-UHFFFAOYSA-N taurine Chemical compound NCCS(O)(=O)=O XOAAWQZATWQOTB-UHFFFAOYSA-N 0.000 description 2
- 230000026683 transduction Effects 0.000 description 2
- 238000010361 transduction Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000001262 western blot Methods 0.000 description 2
- 102000040125 5-hydroxytryptamine receptor family Human genes 0.000 description 1
- 206010006187 Breast cancer Diseases 0.000 description 1
- 208000026310 Breast neoplasm Diseases 0.000 description 1
- 230000005653 Brownian motion process Effects 0.000 description 1
- 102100025222 CD63 antigen Human genes 0.000 description 1
- 208000005623 Carcinogenesis Diseases 0.000 description 1
- 208000005443 Circulating Neoplastic Cells Diseases 0.000 description 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 102000004594 DNA Polymerase I Human genes 0.000 description 1
- 108010017826 DNA Polymerase I Proteins 0.000 description 1
- 206010064571 Gene mutation Diseases 0.000 description 1
- 101000934368 Homo sapiens CD63 antigen Proteins 0.000 description 1
- 102100034343 Integrase Human genes 0.000 description 1
- 230000004163 JAK-STAT signaling pathway Effects 0.000 description 1
- OUYCCCASQSFEME-QMMMGPOBSA-N L-tyrosine Chemical compound OC(=O)[C@@H](N)CC1=CC=C(O)C=C1 OUYCCCASQSFEME-QMMMGPOBSA-N 0.000 description 1
- 108090000590 Neurotransmitter Receptors Proteins 0.000 description 1
- 102000004108 Neurotransmitter Receptors Human genes 0.000 description 1
- 102000012547 Olfactory receptors Human genes 0.000 description 1
- 108050002069 Olfactory receptors Proteins 0.000 description 1
- 108091034117 Oligonucleotide Proteins 0.000 description 1
- 238000012408 PCR amplification Methods 0.000 description 1
- 102000003946 Prolactin Human genes 0.000 description 1
- 108010057464 Prolactin Proteins 0.000 description 1
- 238000002123 RNA extraction Methods 0.000 description 1
- 108010092799 RNA-directed DNA polymerase Proteins 0.000 description 1
- 230000010799 Receptor Interactions Effects 0.000 description 1
- 102000000763 Survivin Human genes 0.000 description 1
- 108010002687 Survivin Proteins 0.000 description 1
- 102100033254 Tumor suppressor ARF Human genes 0.000 description 1
- COQLPRJCUIATTQ-UHFFFAOYSA-N Uranyl acetate Chemical compound O.O.O=[U]=O.CC(O)=O.CC(O)=O COQLPRJCUIATTQ-UHFFFAOYSA-N 0.000 description 1
- 230000004156 Wnt signaling pathway Effects 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000000149 argon plasma sintering Methods 0.000 description 1
- 208000010928 autoimmune thyroid disease Diseases 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 235000019658 bitter taste Nutrition 0.000 description 1
- 238000005537 brownian motion Methods 0.000 description 1
- 230000036952 cancer formation Effects 0.000 description 1
- 235000021258 carbohydrate absorption Nutrition 0.000 description 1
- 235000021257 carbohydrate digestion Nutrition 0.000 description 1
- 231100000504 carcinogenesis Toxicity 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000024245 cell differentiation Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000001447 compensatory effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000021095 detection of chemical stimulus involved in sensory perception of bitter taste Effects 0.000 description 1
- 230000030784 detection of chemical stimulus involved in sensory perception of smell Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000009274 differential gene expression Effects 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000001493 electron microscopy Methods 0.000 description 1
- 230000002121 endocytic effect Effects 0.000 description 1
- 230000012202 endocytosis Effects 0.000 description 1
- 210000001163 endosome Anatomy 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 210000001723 extracellular space Anatomy 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 239000000706 filtrate Substances 0.000 description 1
- 239000003205 fragrance Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 108091005708 gustatory receptors Proteins 0.000 description 1
- 210000002758 humerus Anatomy 0.000 description 1
- VVIUBCNYACGLLV-UHFFFAOYSA-N hypotaurine Chemical compound [NH3+]CCS([O-])=O VVIUBCNYACGLLV-UHFFFAOYSA-N 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 210000003963 intermediate filament Anatomy 0.000 description 1
- 230000003834 intracellular effect Effects 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 230000000366 juvenile effect Effects 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 230000033001 locomotion Effects 0.000 description 1
- 230000036210 malignancy Effects 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 210000002487 multivesicular body Anatomy 0.000 description 1
- 239000002105 nanoparticle Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000002188 osteogenic effect Effects 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 238000011248 postoperative chemotherapy Methods 0.000 description 1
- 229940097325 prolactin Drugs 0.000 description 1
- 230000035755 proliferation Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000013777 protein digestion Effects 0.000 description 1
- 230000002685 pulmonary effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000002271 resection Methods 0.000 description 1
- 230000004043 responsiveness Effects 0.000 description 1
- 206010039073 rheumatoid arthritis Diseases 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000008786 sensory perception of smell Effects 0.000 description 1
- 210000004872 soft tissue Anatomy 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 235000019640 taste Nutrition 0.000 description 1
- 229960003080 taurine Drugs 0.000 description 1
- 210000002303 tibia Anatomy 0.000 description 1
- 230000002103 transcriptional effect Effects 0.000 description 1
- 238000011269 treatment regimen Methods 0.000 description 1
- OUYCCCASQSFEME-UHFFFAOYSA-N tyrosine Natural products OC(=O)C(N)CC1=CC=C(O)C=C1 OUYCCCASQSFEME-UHFFFAOYSA-N 0.000 description 1
- 238000005199 ultracentrifugation Methods 0.000 description 1
- 210000000689 upper leg Anatomy 0.000 description 1
- 210000001835 viscera Anatomy 0.000 description 1
- 239000011782 vitamin Substances 0.000 description 1
- 229940088594 vitamin Drugs 0.000 description 1
- 229930003231 vitamin Natural products 0.000 description 1
- 235000013343 vitamin Nutrition 0.000 description 1
- 150000003722 vitamin derivatives Chemical class 0.000 description 1
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
-
- 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
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Organic Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Engineering & Computer Science (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Zoology (AREA)
- Genetics & Genomics (AREA)
- Wood Science & Technology (AREA)
- Physics & Mathematics (AREA)
- Biotechnology (AREA)
- Microbiology (AREA)
- Molecular Biology (AREA)
- Hospice & Palliative Care (AREA)
- Biophysics (AREA)
- Oncology (AREA)
- Biochemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patients and application thereof. The invention also discloses application of a substance for detecting MAGEB4 expression in exosomes in predicting or assisting in predicting prognosis survival, overall survival rate and death risk of osteosarcoma patients. According to the invention, the molecular marker MAGEB4 capable of being used for predicting the prognosis survival condition of the osteosarcoma patient is obtained through experimental screening, the long-term survival condition of the osteosarcoma patient can be effectively predicted before systematic treatment is carried out on the patient through detecting the expression quantity of the molecular marker in the exosome, and good reference is provided for evaluating the comprehensive treatment long-term curative effect and prognosis of the osteosarcoma patient in clinical work, and the molecular marker MAGEB4 is expected to be applied to clinical practical work in future.
Description
Technical Field
The invention relates to the technical field of biology, in particular to a molecular marker MAGEB4 for predicting the prognosis survival condition of osteosarcoma patients and application thereof.
Background
Osteosarcoma is the most common primary malignancy of bone, with most patients occurring between 10 and 30 years old, well at the distal femur, proximal tibia and proximal humerus. Patients with osteosarcoma have short disease course and rapid progression, and can have local jumping lesions and invade adjacent soft tissues. Osteosarcoma tends to migrate through the blood to the lungs and to other bones at the end of the disease. At present, the osteosarcoma adopts a comprehensive treatment strategy of preoperative chemotherapy, operation and postoperative chemotherapy, and the limb-protecting operation rate of patients with the osteosarcoma can be improved to 90-95% by normal operation excision and new auxiliary chemotherapy, and the survival rate of the patients with the osteosarcoma can reach 60-80% in 5 years. Although standardized treatment of osteosarcoma achieves favorable results, the incidence of postoperative pulmonary metastasis can be up to about 30%, especially in patients insensitive to preoperative chemotherapy, with higher recurrence and metastasis rates. For patients receiving standardized osteosarcoma treatment, how to predict the long-term survival condition of the patients in the treatment process is always plagued by a large number of doctors. If a series of tumor markers can be found, early warning can be conveniently carried out on the long-term survival condition of the osteosarcoma patient, and the determination of the postoperative comprehensive treatment scheme and the comprehensive monitoring of the disease progress can be better guided.
After patients with osteosarcoma receive standardized treatment, lung CT and whole-body bone scanning inspection are required to be carried out periodically to monitor the recurrence and metastasis of tumors. However, the above-mentioned examination is not frequently performed and can be found only when recurrence and metastasis occur, so that it is difficult to effectively evaluate the risk of long-term death at the time of patient's visit. Most of the current studies are mainly focused on the relevant study of prognosis prediction of patients after systemic treatment, and more results have been published. Ambroszkiewicz et al show that bone turnover index osteocalcin, type I collagen, C-terminal peptide and alkaline phosphatase levels have a certain predictive effect on prognosis after surgical treatment. Bacci et al consider that disease-free survival and overall survival are related to alkaline phosphatase levels 5 years after osteosarcoma normalization treatment, and that patient prognosis can be predicted by this index. The tumor necrosis rate after the new auxiliary chemotherapy is more than 90 percent, which often indicates that the prognosis of the patient is better, and the research shows that the p16 protein expression condition of the tumor tissue before the chemotherapy, the osteosarcoma histology type and other factors are related to the tumor necrosis rate after the chemotherapy. Although the indexes can reflect the posttreatment of the osteosarcoma patient in a standardized way to different degrees, certain problems still exist. First, most predictors are for prediction of efficacy responsiveness after systemic treatment or whether systemic progression of lung metastasis occurs after treatment, with less prediction of patient survival by tumor markers prior to systemic treatment. Second, although the osteogenic properties of osteosarcoma itself may be reflected in the bone turnover index (osteocalcin, type I collagen, C-terminal peptide, and alkaline phosphatase levels), the bone turnover index does not uniquely reflect the properties of osteosarcoma itself due to the increased post-osteoclastic compensatory bone synthesis by osteosarcoma. Third, tumor necrosis rate is an important clinical indicator for judging susceptibility of osteosarcoma to chemotherapy, and to some extent, it also reflects prognosis of patients, but it cannot be used to monitor recurrence and metastasis of patients after surgery in real time. The acquisition of protein or micro-RNA in tumor tissue is completed by puncture biopsy, biopsy or post-operation specimen, so that the effect of the novel auxiliary chemotherapy is inconvenient to rapidly and conveniently predict, and the detection specimen cannot be obtained again for disease prognosis prediction after tumor resection. Even though studies have shown that the prognosis of a patient can be judged by the level of micro-RNA expressed in serum, micro-RNA does not fully represent the biological properties of osteosarcoma itself. Therefore, it is imperative to find an early warning method which is rapid, sensitive, real-time and dynamic, and can reflect the long-term survival condition of osteosarcoma patients before systematic treatment.
Compared with traditional tumor tissue biopsy, the liquid biopsy for extracting blood does not need complex operation, so that the cost is greatly reduced; the invasiveness is low, the convenience in use is greatly improved, and the popularization and application of the technology in clinical practice are facilitated. Exosome detection techniques are the latest method of "liquid biopsy" for tumor gene mutation, by detecting RNA and protein in exosomes to discover ectopic, abnormal amplification and protein-specific expression of tumor genes. Exosomes (exosomes) are a membranous vesicle structure secreted by living cells, of endocytic origin, secreted by normal and diseased cells and act as mediators of intercellular biological information transmission to influence the cellular microenvironment. Exosomes are released outside the cell by fusion of the polypore endosomes (multivesicular bodies, MVBs) with the cell membrane, typically 30-100-nm in diameter, and typically are cup-shaped under electron microscopy and can be deposited under 100000g centrifugal force.
The exosomes secreted by the tumor cells can carry various biological information of the "parent" tumor cells and can be used as the reduction of the biological characteristics of the "parent" tumor cells. The surface membrane proteins of the tumor cells can form primary small bodies through endocytosis, then the small body envelopes further sink into a plurality of cavity vesicle small bodies, and the intracellular small bodies comprise a plurality of vesicle small bodies, namely tumor-derived exosomes, and the proteins, nucleic acids and lipids carried by the tumor cell surface membrane proteins represent the biological characteristics of the tumor parent cells to a certain extent. The tumor exosome can regulate and control the gene expression of adjacent normal cells and remote organ cells by carrying proteins, lipids and nucleic acids with biological activity, and the tumor exosome is fully prepared for the colonization of the remote organ by the circulating tumor cells like a pioneer troop tumor metastasis. The tumor cell source exosome realizes the purpose of promoting tumor proliferation, invasion and metastasis by changing the tumor microenvironment.
Therefore, the development of the circulating exosome blood marker which can be used for predicting the prognosis survival condition of the osteosarcoma patient when the patient is in a doctor has important clinical significance and practical value.
Disclosure of Invention
It is an object of the present invention to provide a novel use of a substance for detecting the expression level of MAGEB4.
The present invention provides an application of a substance for detecting the expression level of MAGEB4 in any one of the following (a 1) to (a 5):
(a1) Preparing a product for predicting or assisting in predicting the prognosis survival of a osteosarcoma patient;
(a2) Preparing a product for predicting or assisting in predicting the risk of prognosis death of a osteosarcoma patient;
(a3) Preparing a product for assessing or aiding in assessing the overall survival of a patient with osteosarcoma in prognosis;
(a4) Preparing a product for screening or assisting in screening patients with osteosarcoma with poor prognosis or patients with osteosarcoma with good prognosis;
(a5) Products are prepared for distinguishing or aiding in distinguishing between patients with osteosarcoma that have a poor prognosis and patients with osteosarcoma that have a good prognosis.
It is another object of the invention to provide a kit; the function of the kit is any one of the following (b 1) - (b 5):
(b1) Predicting or assisting in predicting the prognosis survival of a osteosarcoma patient;
(b2) Predicting or assisting in predicting the risk of prognosis death of a osteosarcoma patient;
(b3) Assessing or aiding in assessing the overall survival of a patient with osteosarcoma in a prognosis;
(b4) Screening or aiding in screening for patients with osteosarcoma that have a poor prognosis or patients with osteosarcoma that have a good prognosis;
(b5) Distinguishing or aiding in distinguishing between patients with osteosarcoma that have a poor prognosis and patients with osteosarcoma that have a good prognosis.
The kit provided by the invention comprises a substance for detecting the MAGEB4 expression level.
It is yet another object of the present invention to provide a product; the function of the product is any one of the following (b 1) - (b 5):
(b1) Predicting or assisting in predicting the prognosis survival of a osteosarcoma patient;
(b2) Predicting or assisting in predicting the risk of prognosis death of a osteosarcoma patient;
(b3) Assessing or aiding in assessing the overall survival of a patient with osteosarcoma in a prognosis;
(b4) Screening or aiding in screening for patients with osteosarcoma that have a poor prognosis or patients with osteosarcoma that have a good prognosis;
(b5) Distinguishing or aiding in distinguishing between patients with osteosarcoma that have a poor prognosis and patients with osteosarcoma that have a good prognosis.
The product provided by the invention comprises a data processing device and a substance for detecting the MAGEB4 expression quantity;
the data processing device consists of a data input module, a data recording module, a data comparison module and a conclusion output module;
the data input module is configured to input MAGEB4 expression quantity values of patients with osteosarcoma to be detected;
the data recording module is configured to store MAGEB4 expression quantity values and judgment thresholds of patients with osteosarcoma to be detected;
the data comparison module is configured to receive the MAGEB4 expression value of the patient with the osteosarcoma to be detected sent by the data input module, and call the judgment threshold value from the data recording module to compare with the MAGEB4 expression value of the patient with the osteosarcoma to be detected;
the conclusion output module is configured to receive the comparison result sent by the data comparison module and determine the comparison result according to a predetermined determination condition.
Further, the predetermined determination condition is as follows: poor prognosis in patients with osteosarcoma with high MAGEB4 expression, and mortality risk in patients with osteosarcoma with high MAGEB4 expression is higher than that in patients with osteosarcoma with low MAGEB4 expression, and overall survival rate in patients with osteosarcoma with high MAGEB4 expression is lower than that in patients with osteosarcoma with low MAGEB4 expression.
Furthermore, the osteosarcoma patient with high MAGEB4 expression level is the osteosarcoma patient with MAGEB4 expression level higher than the judging threshold value.
The osteosarcoma patient with low MAGEB4 expression level is osteosarcoma patient with MAGEB4 expression level equal to or lower than the judgment threshold value.
The judging threshold value can be obtained by referring to a method known in the technical field, for example, a coxph function of a "survivinMisc" of an R packet can be used for analyzing MAGEB4 expression quantity of each sample, and then a MAGEB4 expression quantity value corresponding to a minimum p value is selected from analysis results as the judging threshold value; the MAGEB4 expression level of each sample may be arranged in the order from low to high by the median method, and then the MAGEB4 expression level value in which the position is centered and the MAGEB4 expression level is divided into two halves may be used as the determination threshold.
In any of the above applications or kits or products, the substance for detecting the expression level of MAGEB4 may be a reagent and/or an instrument for detecting the relative expression level of MAGEB4 in the exosomes.
Further, the reagent for detecting the relative expression level of MAGEB4 in the exosome may be a reagent for detecting the relative expression level of MAGEB4 mRNA in the exosome.
The reagent for detecting the relative expression level of MAGEB4 mRNA in the exosomes comprises MAGEB4 amplification primers.
The instrument for detecting the relative expression level of MAGEB4 mRNA in the exosomes comprises a real-time quantitative fluorescent PCR instrument.
The exosomes may be extracted from the isolated serum of a blood sample.
Still further, the MAGEB4 amplification primer consists of a single-stranded DNA molecule shown in a sequence 2 in a sequence table and a single-stranded DNA molecule shown in a sequence 3 in the sequence table.
Furthermore, the method for calculating the relative expression level of MAGEB4 mRNA in the exosome comprises the following steps: after extracting mRNA in exosomes, carrying out qPCR quantitative detection by adopting the MAGEB4 amplification primer, defining a numerical value obtained by qPCR detection as an absolute quantitative value, and calculating the relative expression quantity of MAGEB4 mRNA according to the following formula: MAGEB4 mRNA relative expression = absolute quantitative value/average, where average is the average of absolute quantitative values for several surviving osteosarcoma patients. In a specific embodiment of the invention, the average is the average of absolute quantitative values of 10 surviving osteosarcoma patients.
In any of the above uses or kits or products, the prognostic survival includes prognostic mortality and prognostic survival.
The bad prognosis refers to bad survival conditions within 5 years, specifically to death prognosis.
The good prognosis means that the survival condition is good within 5 years, in particular the survival of prognosis.
In any of the above applications or kits or products, the osteosarcoma patient may be a juvenile osteosarcoma patient, and specifically may be an osteosarcoma patient within the age range of 7-34 years (or 10-18 years).
In any of the above applications, kits or products, the nucleotide sequence of MAGEB4 is shown as sequence 1 in the sequence table.
The invention has the following advantages:
1. because the exosomes are derived from 'parent' cells, mRNA carried by the exosomes can well reflect the biological process of the 'parent' cells to a certain extent, the circulating exosomes of the osteosarcoma patient are taken as carriers of detection markers, and pathological states of the osteosarcoma patient tumor and the tumor microenvironment where the osteosarcoma patient is located can be well displayed.
2. The invention mainly adopts MAGEB4 mRNA carried by circulating exosome in blood of osteosarcoma patients before systemic treatment to predict the risk of long-term death of osteosarcoma patients, and can be used for efficiently predicting the long-term survival condition of osteosarcoma patients before systemic treatment.
3. The invention has longer follow-up observation time for patients in the research process, and the detection object is a circulating exosome when the patients are not treated by any system, and the found molecular marker MAGEB4 can well predict the long-term survival state of the osteosarcoma patients.
4. The immune mechanism plays an important role in the development of osteosarcoma diseases, and MAGEB4 in the circulating exosomes of patients with osteosarcoma, which are provided by the invention, participates in the occurrence process of the osteosarcoma immune mechanism, and is a molecular marker reflecting the ideal tumor microenvironment.
Drawings
FIG. 1 is a characterization of serum exosomes in osteosarcoma patients. A is the morphological characteristics of the extracted exosomes. B is the grain size of the extracted exosomes. C is the Western blot detection result of positive protein markers (Alix, tsg101, CD 63) and negative markers (Calnexin) of the extracted exosomes.
FIG. 2 is a MAGEB4 survival analysis.
FIG. 3 shows that circulating exosome MAGEB4 mRNA relative quantification was significantly higher in the deaths compared to the surviving patients in the present invention verification experiments.
FIG. 4 is an evaluation of MAGEB4 predicted efficacy.
Detailed Description
The following detailed description of the invention is provided in connection with the accompanying drawings that are presented to illustrate the invention and not to limit the scope thereof. The examples provided below are intended as guidelines for further modifications by one of ordinary skill in the art and are not to be construed as limiting the invention in any way.
The experimental methods in the following examples, unless otherwise specified, are conventional methods, and are carried out according to techniques or conditions described in the literature in the field or according to the product specifications. Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
Example 1 discovery of the molecular marker MAGEB4 for prognosis of survival in osteosarcoma patients
10mL of blood samples of 20 cases of osteosarcoma patients which are not subjected to systematic treatment are extracted, and a circulating exosome Exosomes is obtained by adopting an ultracentrifugation mode; the characteristics of exosomes are characterized by adopting Nanosight, WB and a scanning electron microscope; and (3) extracting total RNA after the extracted exosomes are cracked, obtaining an RNA sample, performing quality control on the RNA sample, confirming that the RNA sample loading quantity meets the quality requirement of the on-line detection of NGS, and obtaining mRNA information carried by the exosomes by adopting Illumina sequencing. Average follow-up of 20 patients with osteosarcoma in the study is carried out for 3.9 years, survival information of the patients is obtained in the last follow-up, the 20 patients are divided into two groups of Group Alive and Group read, NGS data are analyzed, group Alive vs Group Dead differential mRNA is obtained, and GO and KEGG enrichment analysis is carried out; and carrying out survival analysis on the obtained DEmRNA, and screening out molecular markers related to an immune mechanism by combining the molecular functions of genes. The specific method comprises the following steps:
1. experimental materials and groupings
1. Experimental materials and groupings
Is prepared from 20 primary-diagnosis osteosarcoma patients in bone oncology department of Beijing university people's hospital, and 10mL of blood sample is left for extracting exosomes before preoperative chemotherapy after puncturing to define pathology. The average follow-up time of 20 osteosarcoma patients is 3.9+/-1.5 years (median 4.8 years, range 1.2-5.2 years), and the systemic metastasis (bone metastasis, lung metastasis and/or other viscera metastasis) of the patients is recorded at the last follow-up time. Of the 20 osteosarcoma patients, 12 men and 8 women had an average age of 16.8.+ -. 7.9 years (median 15 years, range 7-34 years).
2. Grouping situation
Patients were divided into two groups according to the follow-up period:
group Alive: patients with osteosarcoma who survived during the follow-up period, 12 patients with osteosarcoma survived the last follow-up.
Group read: patients with osteosarcoma who died during follow-up, 8 patients with mortality were last followed.
2. Experimental methods and results
1. Exosome extraction
Serum was isolated from 10mL blood samples for exosome extraction. The method comprises the following specific steps:
1) The serum samples were centrifuged at 300g at 4℃for 10min, the pellet was discarded and the supernatant was taken.
2) After completion of step 1), the supernatant was centrifuged at 800g at 4℃for 30min, the precipitate was discarded, and the supernatant was taken.
3) After completion of step 2), the supernatant was centrifuged at 10,000g at 4℃for 30min, the precipitate was discarded and the supernatant was taken.
4) After completion of step 3), the supernatant was diluted with 10mL of PBS and filtered through a 0.22 μm sieve; and the filtrate was centrifuged at 150,000g at 4℃for 2h, the supernatant was discarded and the precipitate was collected.
5) After completion of step 4), the pellet was resuspended in 5mL PBS, then centrifuged at 150,000 at 4℃for 2h, the supernatant discarded, and the pellet was collected as the isolated serogenic exosomes. The exosomes were stored at-80 ℃ for RNA extraction.
2. Exosome characterization
1) The scanning electron microscope is used for identifying the exosome shape, and comprises the following specific steps: and adding 10 mu L of suspension of the exosome sample onto a copper mesh of an electron microscope detection device, dyeing for 5min by 20 mu L of 2% uranyl acetate, placing under a lamp for 10-15 min after dyeing, and observing and photographing the exosome by a scanning electron microscope. The morphological characteristics of the extracted exosomes are shown in fig. 1A.
2) The nanoparticle tracking analysis characterizes the diameter distribution range of the extracted exosomes, and the principle is that the diameter size of the particles is determined according to the speed of the particles in Brownian motion, and the size of each particle is calculated according to the average value of the squares of the distances of the particles moving in a specific time. The method comprises the following specific steps: taking 0.25mL of a suspension of an exosome sample, injecting the suspension into an observation chamber, opening a laser beam, illuminating the exosome suspension outside the observation chamber, collecting light scattering signals, analyzing the movement process of particles by NTA software, and calculating the radius of the measured particles by means of Stokes-Einstein equation. The results are shown in FIG. 1B, which shows that: the grain size of the extracted exosomes is mainly distributed between 100-150 nm, and the grain size characteristics of the exosomes are met.
3) Western blot detection is carried out to separate the expression levels of specific proteins Alix, tsg101, CD63 and negative protein Calnexin in exosomes. The detection result is shown in FIG. 1C.
3. Library construction quality inspection and on-machine sequencing
1) RNA was extracted from exosomes and subsequently the RNA samples were tightly quality controlled, mainly by accurate detection of RNA integrity by the Agilent 2100 bioanalyzer.
2) Then NEBNExt cube Ultra ™ RNA Library Prep Kit for Illumina cube is adopted for NEB common library construction, and the specific steps are as follows: the first strand of cDNA is synthesized in M-MuLV reverse transcriptase system with fragmented mRNA as template and random oligonucleotide as primer, and then RNA strand is degraded with RNaseH, and the second strand of cDNA is synthesized with dNTPs as material in DNA polymerase I system. The purified double-stranded cDNA is subjected to end repair, A tail addition and sequencing joint connection, about 250-300 bp cDNA is screened by using AMPure XP beads, PCR amplification is carried out, and the PCR product is purified again by using the AMPure XP beads, so that a library is finally obtained.
3) After the library construction was completed, initial quantification was performed using a qubit2.0 Fluorometer, the library was diluted to 1.5 ng/. Mu.L, then the library was examined for insert size using an Agilent 2100 bioanalyzer, and after insert size was expected, qRT-PCR was performed to accurately quantify the effective concentration of the library (library effective concentration was higher than 2 nM) to ensure library quality.
4. Sequencing information analysis procedure
The mRNA sequencing core is the significance analysis of gene expression difference, the statistical method is used for comparing the gene expression difference under two or more conditions, the specific genes related to the conditions are found out from the gene expression difference, then the biological significance of the specific genes is further analyzed, and the analysis process comprises links such as quality control, comparison, quantification, difference significance analysis, functional enrichment and the like. The image data of the sequenced fragments detected by the high-throughput sequencer are converted into sequence data (reads) through CASAVA base recognition, and the file is in fastq format, wherein the file mainly comprises the sequence information of the sequenced fragments and the corresponding sequencing quality information.
The original data obtained by sequencing contains a small amount of reads with sequencing joints or lower sequencing quality, and in order to ensure the quality and reliability of data analysis, the original data needs to be filtered, and the filtering content comprises: (1) removing reads with adapters; (2) Removing reads containing N (N represents indeterminate base information); (3) Low quality reads (reads with number of bases Qphred < = 20 accounting for more than 50% of the total read length) are removed. And filtering raw data, checking a sequencing error rate and GC content distribution, and obtaining clean reads used for subsequent analysis. Sequenced fragments (fragments) are randomly disrupted from the mRNA and in order to determine which genes these fragments are transcribed from, it is necessary to align the quality controlled clean reads to the reference genome. And (3) rapidly and accurately comparing the clear Reads with a reference genome by using HISAT2 software to acquire positioning information of the Reads on the reference genome. The number of reads covered by each gene (including the new predicted gene) from start to stop was counted based on the positional information of the gene alignment on the reference genome. Reads with quality values below 10 were filtered out separately, reads on unpaired alignments, aligned to reads of multiple regions of the genome. The partial analysis uses the featuresource tool in the open software. After the quantitative completion of gene expression, the expression data of the gene are subjected to statistical analysis, the genes with obvious difference of the expression levels of the samples in different states are screened, and the obtained Group Alive vs Group Dead differential mRNA (DE mRNA) is subjected to GO and KEGG enrichment analysis.
The results show that: in 20 osteosarcoma patients, 904 different mrnas were present between the two groups of Group Alive (n=12) and Group read (n=8), 121 up-regulated mrnas and 783 down-regulated mrnas. The 20 th functions before GO function enrichment are protein binding, plasma membrane, integral component of membrane, extracellular region, G protein-coupled receptor signaling pathway, G protein-coupled receptor activity, extracellular space, integral component of plasma membrane, detection of chemical stimulus involved in sensory perception of smell, olfactory receptor activity, molecular function, biological process, keratanization, sensory perception of smell, intermediate filament, odorant binding, neurotransmitter receptor activity, detection of chemical stimulus involved in sensory perception of bitter taste, bitter taste receptor activity, G protein-coupled serotonin receptor signaling pathway. Relevant pathways for the top 20 bits of KEGG pathway enrichment include Olfactory transduction, neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, taste transduction, transcriptional misregulation in cancer, jak-STAT signaling pathway, wnt signaling pathway, breast cancer, th17 cell differentiation, protein digestion and absorption, inflammatory Bowel Disease (IBD), rheumatoid arthritis, chemical carcinogenesis, drug meta-cytochrome P450, prolactin signaling pathway, carbohydrate digestion and absorption, autoimmune thyroid disease, tyrosine metabolism, vitamin digestion and absorption, taurine and hypotaurine metabolism.
5. Differential gene survival analysis
And (3) carrying out survival curve analysis (over-all survivin) on the obtained DEmRNA, respectively carrying out survival analysis on Group Alive vs Group Dead comparative obtained differential gene sets, combining the functions of gene molecules, and screening out molecular markers related to immune mechanisms. The method comprises the following specific steps: and scoring each differential gene by using a curp function in a survivinMisc package, and obtaining the most significant p value and the expression quantity threshold value (the gene expression value corresponding to the minimum p value is selected from the results as the threshold value) associated with the OS of each differential gene in each comparison group. And then the high expression group and the low expression group are distinguished according to the threshold value. If the significant p value cannot be obtained, the median of the differential gene expression level in the comparison group is used as a threshold value, so that the high expression group and the low expression group are distinguished. Finally, the survival curves were plotted using the "surviviner" and "survivinal" packages.
The results show that: the 904 exosome DEmRNAs obtained by comparison of Group Alive and Group read were included in a survival curve analysis, wherein 492 genes related to patient survival were added, and a molecular marker MAGEB4 related to immune mechanism was selected.
As shown in fig. 2, the results of the analysis of the survival curves of the MAGEB4 show that the overall survival rate of the patients with the MAGEB4 high-expression group osteosarcoma (n=4, the osteosarcoma patients with the MAGEB4 gene expression level greater than 0.002961453) is significantly lower than that of the patients with the MAGEB4 low-expression group osteosarcoma (p=0.0021) (n=16, the osteosarcoma patients with the MAGEB4 gene expression level equal to or lower than 0.002961453), which indicates that the high-expression of the MAGEB4 is related to the poor prognosis of the osteosarcoma patients, and indicates that the survival condition is poor for 5 years, and the result is statistically significant.
Example 2 verification experiment of molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patient
1. Experimental materials and groupings
Another 20 cases were obtained from primary osteosarcoma patients in bone oncology in Beijing university people's hospitals, and after puncture to confirm the pathology, 10mL of blood samples were left before preoperative chemotherapy for exosome extraction. The average follow-up time of 20 osteosarcoma patients is 2.0 years (median 2.2 years, range 0.7-3.6 years), and the last follow-up time records the whole body metastasis (bone metastasis, lung metastasis and/or other organ metastasis) of the patients. Of the 20 osteosarcoma patients, 11 men and 9 women had an average age of 14 years (median 15 years, range 10 to 18 years).
2. Grouping situation
Patients were divided into two groups according to the follow-up period:
survival group: patients with osteosarcoma who survived during the follow-up period, 10 patients with osteosarcoma survived the last follow-up period.
Death group: patients with osteosarcoma who died during follow-up, 10 cases of patients with mortality were followed up last time.
2. Experimental methods and results
Comparing the difference in MAGEB4 mRNA expression level in circulating exosomes at the visit of 10 surviving and 10 dying groups of osteosarcoma patients, thereby verifying the predictive effect of circulating exosome MAGEB4 mRNA level on the prognosis of osteosarcoma patients. The specific method comprises the following steps:
exosomes in blood samples of 20 osteosarcoma patients were extracted according to the method of example 1, mRNA was extracted, qPCR quantitative detection was performed, and two groups of MAGEB4 mRNA level differences were compared. The method comprises the steps of defining a numerical value directly obtained by qPCR detection as an absolute quantitative value, and firstly calculating the average of absolute quantitative values of 10 patients in a survival group to obtain an average value a; the relative quantitative value c for each patient in the survival group was then calculated according to the following formula: relative quantitative value c = absolute quantitative value b/average a, the relative quantitative value cc for each patient of the death group was calculated according to the following formula: relative quantitative value cc=absolute quantitative value bb/average a; and finally, calculating the average number of relative quantitative values of 10 patients in the survival group to obtain the relative quantitative values of circulating exosome MAGEB4 mRNA of 10 patients in the survival group, and calculating the average number of relative quantitative values of 10 patients in the death group to obtain the relative quantitative values of circulating exosome MAGEB4 mRNA of 10 patients in the death group so as to compare the difference of the two groups of MAGEB4 mRNA levels. The primer sequences used for qPCR detection were as follows:
F：ACGAAGATGTTAGTGCAGTTCC；
R：GTGCGCTGAGAGACTTTCC。
the results are shown in tables 1, 2 and 3, and the results show that: the relative quantitative value of the circulating exosome MAGEB4 mRNA of 10 patients in the death group is 1.969, the relative quantitative value of the circulating exosome MAGEB4 mRNA of 10 patients in the survival group is 1.000, the relative quantitative value of the circulating exosome MAGEB4 mRNA of 10 patients in the death group is obviously higher than the relative quantitative value of the circulating exosome MAGEB4 mRNA of 10 patients in the survival group, and the statistical difference is present (P= 1.0211243209127285 ×10) -5 ). From this, MAGEB4 in serum circulating exosomes prior to systemic treatment can be used as a marker to predict long-term survival in osteosarcoma patients.
Table 1 relative quantitative values of circulating exosome MAGEB4 mRNA for each patient in the surviving group
Table 2 relative quantitative values of circulating exosome MAGEB4 mRNA for each patient in the death group
EXAMPLE 3 evaluation of MAGEB4 predictive Performance
ROC curve analysis was performed to evaluate the predictive efficacy of MAGEB4 using 10 surviving and 10 dying groups as subjects in example 2 (table 3).
Table 3 shows MAGEB4 relative quantification values and mortality for 10 surviving and 10 dying samples
Note that: 1 indicates death, and 0 indicates survival.
The results are shown in FIG. 4. The ROC curve shows that the sensitivity of the exosome-derived MAGEB4 to death event prediction is 100%, the specificity is 90%, and the prediction efficiency is higher.
Claims (3)
1. Use of an agent for detecting the expression level of MAGEB4 in any one of the following (a 1) to (a 5):
(a1) Preparing a kit for predicting or assisting in predicting the prognosis survival of a osteosarcoma patient;
(a2) Preparing a kit for predicting or assisting in predicting the prognosis death risk of a osteosarcoma patient;
(a3) Preparing a kit for evaluating or assisting in evaluating the overall survival rate of the prognosis of the osteosarcoma patient;
(a4) Preparing a kit for screening or assisting in screening patients with osteosarcoma with poor prognosis or patients with osteosarcoma with good prognosis;
(a5) Preparing a kit for distinguishing or assisting in distinguishing between patients with osteosarcoma with poor prognosis and patients with osteosarcoma with good prognosis;
the nucleotide sequence of MAGEB4 is shown as SEQ ID No. 1.
2. The use according to claim 1, characterized in that: the reagent for detecting the MAGEB4 expression level is a MAGEB4 amplification primer for detecting the relative expression level of MAGEB4 in exosomes.
3. The use according to claim 2, characterized in that: the MAGEB4 amplification primer consists of a single-stranded DNA molecule shown as SEQ ID No.2 and a single-stranded DNA molecule shown as SEQ ID No. 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311207483.8A CN116949181B (en) | 2023-09-19 | 2023-09-19 | Molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patient and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311207483.8A CN116949181B (en) | 2023-09-19 | 2023-09-19 | Molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patient and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116949181A CN116949181A (en) | 2023-10-27 |
| CN116949181B true CN116949181B (en) | 2024-01-12 |
Family
ID=88458690
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311207483.8A Active CN116949181B (en) | 2023-09-19 | 2023-09-19 | Molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patient and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116949181B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014074874A1 (en) * | 2012-11-09 | 2014-05-15 | Regents Of The University Of Minnesota | Osteosarcoma diagnostic and prognostic methods |
| CN112063720A (en) * | 2020-09-22 | 2020-12-11 | 上海市第一人民医院 | Osteosarcoma prognosis marker and prognosis evaluation model |
| CN113549691A (en) * | 2021-06-15 | 2021-10-26 | 上海长征医院 | Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof |
-
2023
- 2023-09-19 CN CN202311207483.8A patent/CN116949181B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014074874A1 (en) * | 2012-11-09 | 2014-05-15 | Regents Of The University Of Minnesota | Osteosarcoma diagnostic and prognostic methods |
| CN112063720A (en) * | 2020-09-22 | 2020-12-11 | 上海市第一人民医院 | Osteosarcoma prognosis marker and prognosis evaluation model |
| CN113549691A (en) * | 2021-06-15 | 2021-10-26 | 上海长征医院 | Gene for osteosarcoma typing and osteosarcoma prognosis evaluation and application thereof |
Non-Patent Citations (2)
| Title |
|---|
| MAGEA在骨肉瘤中的表达和预后分析;邹昌业等;实用医学杂志;第32卷(第16期);第2636-2641页 * |
| Urine exosome gene expression of cancer-testis antigens for prediction of bladder carcinoma;Fatemeh Yazarlou等;Cancer Manag Res;第5卷(第10期);第5373-5381页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116949181A (en) | 2023-10-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Pinzani et al. | Isolation by size of epithelial tumor cells in peripheral blood of patients with breast cancer: correlation with real-time reverse transcriptase–polymerase chain reaction results and feasibility of molecular analysis by laser microdissection | |
| KR20210045953A (en) | Cell-free DNA for the evaluation and/or treatment of cancer | |
| AU2010311535A1 (en) | Means and methods for non-invasive diagnosis of chromosomal aneuploidy | |
| WO2014160645A2 (en) | Neuroendocrine tumors | |
| JP2008528001A (en) | Cancer marker and detection method | |
| CN112941181A (en) | Method for detecting cfDNA mutation information in peripheral blood of subject | |
| CN106661623A (en) | Diagnosis of neuromyelitis optica vs. multiple sclerosis using mirna biomarkers | |
| CN111833963A (en) | cfDNA classification method, device and application | |
| CN109280702A (en) | Determine the method and system of individual chromosome textural anomaly | |
| WO2020015621A1 (en) | Method for constructing platelet nucleic acid library for gene detection and kit | |
| CN104428426B (en) | The diagnosis miRNA overview of multiple sclerosis | |
| US20220084632A1 (en) | Clinical classfiers and genomic classifiers and uses thereof | |
| WO2015079060A2 (en) | Mirnas as advanced diagnostic tool in patients with cardiovascular disease, in particular acute myocardial infarction (ami) | |
| CN110724743B (en) | Methylated biomarker related to colorectal cancer diagnosis in human blood and application thereof | |
| WO2020194057A1 (en) | Biomarkers for disease detection | |
| CN116949181B (en) | Molecular marker MAGEB4 for predicting prognosis survival of osteosarcoma patient and application thereof | |
| KR101704828B1 (en) | Method for diagnosing inflammatory diseases through analysis of protein or gene of extracellular vesicle in a body fluid | |
| JP2010502177A (en) | Diagnosis method | |
| BR112020012280A2 (en) | compositions and methods for diagnosing lung cancers using gene expression profiles | |
| WO2019018764A1 (en) | Methods for determining response to parp inhibitors | |
| CN114599801A (en) | Kits and methods for testing risk of lung cancer | |
| JP2010502940A (en) | Diagnosis method | |
| SG185254A1 (en) | 3.4 kb mitochondrial dna deletion for use in the detection of cancer | |
| CN110607370A (en) | Gene combination for human tumor molecular typing and application thereof | |
| WO2022121894A1 (en) | Methods using characteristics of urinary and other dna |
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 |