CN107574243A - The construction method of molecular marker, reference gene and its application, detection kit and detection model - Google Patents
The construction method of molecular marker, reference gene and its application, detection kit and detection model Download PDFInfo
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Abstract
The present invention relates to biological technical field, more particularly to and draw composition and its application, the construction method of detection kit and detection model.Using follow-up information method as a comparison, kit provided by the invention is 70% for the forecasting accuracy of ER and PR positive breast cancer first visit patients recurrence in postoperative 3 10 years or mortality risk, and the forecasting accuracy of low group of its risk and the high group of risk is respectively 81.1% and 54.4%.The forecasting accuracy of corresponding FFPE pathological examination results is respectively 71.9% and 56.8%.The supporting risk forecast model of the kit only need molecular marker Ct values, patient age, pT by stages, LN quantity, other clinical pathology information need not be relied on, evaluation performance to Prognosis in Breast Cancer is better than simple pathology prediction result, the malpractice caused by pathology prediction error can be reduced to a certain extent, the further perfect technical method of Prognosis in Breast Cancer evaluation.
Description
Technical field
The present invention relates to biological technical field, more particularly to molecular marker, reference gene and its application, detection kit
And the construction method of detection model.
Background technology
One of the main reason for breast cancer is threat women worldwide life and health, American Cancer Society's issue in 2013 are complete
U.S. cancer statistics shows that breast cancer incidence is occupied first of female cancer, and the death rate occupies second.Newest National Cancer
Centre data shows, the new breast cancer of American Women's in 2013 232,340, dead 39,620.It is average every 8 in the U.S.
Just there is one in women to suffer from breast cancer.Although China belongs to the country of the low hair of breast cancer, the incidence of disease and the death rate are obvious in recent years
Rise.In global annual 1300000 patient with breast cancers being newly diagnosed to be, about 15% from China.The system of Chinese Breast Cancer net
Counting display China, newly-increased breast cancer reaches 3%-4% every year, and more than world standard 1%-2%, the incidence of disease is that women is susceptible to suffer from swelling
First of knurl.Urgently develop mammary gland cancerous precaution, diagnosis, prognosis, individualized treatment technology.
Breast cancer is a kind of tumour with height heterogeneity, and its prognosis relative factors is numerous, have identical clinical stages,
The patient with breast cancer of histological grade and expression of hormonal receptors receives identical therapeutic scheme, and its prognosis may also be different.How
The prognosis of accurate judgement patient with breast cancer and the corresponding individualized treatment scheme of formulation, avoid over-treatment and malpractice to trouble
The problem of injury and burden that person brings are clinical in the urgent need to address.
With the rapid development of Protocols in Molecular Biology, polymerase chain reaction (PCR), probe hybridization and genetic chip are used
Equimolecular biological method finds and detects breast cancer prognosis-related gene to be possibly realized.Van ' t Veer in 2002 etc. pass through
117 breast cancer cases of DNA chip technology examination, find 70 genes related to Prognosis in Breast Cancer；American science in 2004
Family is verified using RT-PCR method to 675 breast cancer samples again, obtains 21 genes related to prognosis, Genomic
Health companies have researched and developed Prognosis in Breast Cancer Related product Oncotype according to thisOncotypeIt is current
It is uniquely a to be pushed away jointly by the clinical common recognition most authoritative clinical guidelines in 3 whole world of NCCN guides, ASCO clinical guidelines and St Gallen
The Prognosis in Breast Cancer detection product recommended.In addition, Yasuto Naoi etc. use DNA chip technology to the ER positives, lymph in Japanese population
Node negative breast cancer patient's cancerous tissue sample is studied, and finds 95 genes related to prognosis.TorstenO.Nielsen
Seminar finds that compared with clinical factor and immunohistochemical staining, 50 assortments of genes can provide more breast cancer
Prognosis prediction information, and replace fresh sample or quick-frozen sample to be detected with FFPE samples, expand detectable sample model
Enclose.Prognosis in Breast Cancer coherent detection product Oncotype between 2002-2013Mammaprint、ProsIgnaTM、
MapQuant DxTMFDA, CE certification are obtained in succession.But these current products are based on American-European crowd's research and development, these products enter
Not only expensive after China, whether gene and its detection model are applicable Chinese population and also wait to verify.Therefore, exploiting economy has
Chinese's Prognosis in Breast Cancer detection technique of effect is significant.
The content of the invention
In view of this, the invention provides molecular marker and its application, the structure of detection kit and detection model
Method.The kit is better than clinical pathology evaluation result in Prognosis in Breast Cancer evaluation detection performance, to a certain extent may be used
Precisely controlled with reducing the over-treatment and malpractice, the individuation for meeting breast cancer patients that occur by pathological diagnosis mistake
The demand for the treatment of, the technical method in terms of further perfect domestic Prognosis in Breast Cancer prediction.
In order to realize foregoing invention purpose, the present invention provides following technical scheme：
The invention provides genome compound, including molecular marker MAPT and/or MS4A1.
The invention provides genome compound, by molecular marker BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1,
MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT and MS4A1 are formed.
The present invention some specific embodiments in, the genome compound also include reference gene ACTB, GAPDH,
GUSB、NUP214、VCAN。
Present invention also offers the genome compound prepare Mammary cancer 3-10 recurrence and/or mortality risk it is pre-
Application in the detection means of survey.
In some specific embodiments of the present invention, Prognosis in Breast Cancer 3-10 recurrences and/or death in the application
Risk assessment detects：The total serum IgE of sample to be tested is obtained, cDNA is obtained through reverse transcription, is obtained using fluorescence quantifying PCR method
The Ct values of the molecular marker and the reference gene are obtained, the Ct values of the reference gene are averaged, obtain internal reference base
Because of the Average Ct values (Ct ') of combination, the Ct values of the molecular marker are then combined into Ct ' values with reference gene respectively subtracted each other and do
Normalization, obtains △ Ct, by the age of △ Ct values and person under inspection, pT values, LN values through the breast cancer constructed by random forests algorithm
Postoperative 3-10 recurrences or the analysis of mortality risk forecast model, obtain result.Wherein, pT values are pathological staging, LN value lymph nodes
Shift quantity.The numerical value that the analysis obtains obtains result compared with threshold value, and the threshold value is 5.The numerical value that the analysis obtains
>=5 be good prognosis, and the numerical value ＜ 5 that the analysis obtains is poor prognosis.
In some specific embodiments of the present invention, Prognosis in Breast Cancer 3-10 recurrences or mortality risk in the application
Assess detection model construction method be：By the △ Ct values of the molecular marker of sample to be tested and person under inspection's age, pT values, LN values
Math matrix is built, 1/2 is randomly selected and is used as training set, 1/2, as checking collection, is established by the algorithm of random forest and included
The forecast model of 10000 decision trees, random sampling >=1000 time, establish >=1000 forecast models altogether, from >=1000 predictions
The submodel with follow-up information concordance rate highest >=39 optimization model for final mask, and use >=39 are chosen in model
The median of submodel is as final prognostic risk predicted value.
Random forest is made up of many decision trees, and the structure of decision tree employs attribute and the double random methods of sample, because
This is also referred to as stochastic decision tree.In random forest, between each decision tree be do not have it is related.When test data enters at random
During forest, classified by each decision tree, it is final knot finally to take that class that classification results are most in all decision trees
The result of fruit, i.e. decision tree " ballot ", in other words, random forest are a graders for including multiple decision trees, and its is defeated
The classification gone out is by depending on indivedual modes for setting the classifications exported.In the present invention, our bases in traditional random forests algorithm
It is optimized on plinth, sample random sampling 1000 times is established 1000 models by us, and is chosen from 1000 models accurate
True rate and the submodel of the higher 39 optimization models final mask the most of specificity values, and using the middle position of 39 submodels
Number is as final prediction result.
The morning of untreated, the age of mid-term ER or PR positive breast cancer first visit patient, PT by stages, LN transfer quantity, and
14 molecular marker BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2, MMP11, CD68,
BAG1, MAPT, MS4A1 and 5 house-keeping genes ACTB, GAPDH, GUSB, NUP214, VCAN Ct values, input 39 prediction moulds
Analyzed in type, obtain predictive analysis results, obtain 3-10 recurrences or mortality risk value, and according to risk threshold value (threshold value
5) to be predicted as good prognosis or poor prognosis.
In some specific embodiments of the present invention, the sample to be tested in the present invention is the morning of untreated, mid-term ER
Or PR positive breast cancer first visit patient's FFPE samples.
Present invention also offers the primer sets for expanding the genome compound, sequence such as SEQ ID No.1~SEQ
Shown in ID No.28.
Present invention also offers the probe groups for expanding the genome compound, sequence such as SEQ ID No.29~SEQ
Shown in ID No.42.
Present invention also offers the primer sets of the reference gene for expanding the genome compound, such as SEQ ID No.43
Shown in~SEQ ID No.47.
Present invention also offers the probe groups of the reference gene for expanding the genome compound, such as SEQ ID No.48
Shown in~SEQ ID No.52.
Present invention also offers the detection kit of Mammary cancer 3-10 recurrences and/or mortality risk prediction, including
The reagent commonly used in the primer sets and/or described probe groups and kit.
, will present invention also offers the construction method that detection model is assessed in Prognosis in Breast Cancer 3-10 recurrences or mortality risk
The △ Ct values of sample to be tested molecular marker and person under inspection's age, pT values, LN values structure math matrix, randomly select 1/2 conduct
Training set, 1/2 collects as checking, and the forecast model for including 10000 decision trees is established by the algorithm of random forest, random altogether
Sampling >=1000 times, >=1000 forecast models are established, chosen and follow-up information concordance rate highest from >=1000 forecast models
>=39 optimization models be final mask submodel, and the median of use >=39 submodel is as final prognosis wind
Dangerous predicted value.
Present invention also offers Prognosis in Breast Cancer 3-10 recurrences or the assessment detection method of mortality risk, acquisition to treat test sample
This total serum IgE, cDNA is obtained through reverse transcription, the molecular marker and reference gene are obtained using fluorescence quantifying PCR method
Ct values, the Ct values of reference gene are averaged, the Average Ct values (Ct ') of reference gene combination are obtained, then by molecular marker
The Ct values of thing combine Ct ' values with reference gene respectively subtracts each other and normalizes, and obtains △ Ct, by the age of △ Ct values and person under inspection,
PT values, LN values are analyzed through the Mammary cancer 3-10 recurrences constructed by random forests algorithm or mortality risk forecast model, are obtained
Result, that is, obtain 3-10 recurrence or mortality risk value, and according to risk threshold value (risk threshold value 5) be predicted as good prognosis or
Poor prognosis.
Sample to be tested in the present invention is the morning of untreated, mid-term ER or PR positive breast cancer first visit patient's FFPE samples
This.
The technical scheme of the present invention solved the problems, such as includes：(1) investigated through document and data storehouse, choose 192 breast cancer phases
The candidate gene (Prognosis in Breast Cancer correlation being not limited to, containing reference gene) of pass, customizes TLDA gene expression detection chips
(Applied Biosystems companies)；(2) the complete demographic data of systematic collection, clinical data and Follow-up Data (recurrence
Transfer time, time-to-live), morning, mid-term ER or PR the positive breast cancer first visit patient's FFPE samples of untreated are selected, is used
The TLDA chips of customization carry out the detection of 192 genes, carry out the reference gene molecular marker related to Prognosis in Breast Cancer
Screening；(3) the candidate molecules mark and reference gene that screening obtains are verified in independent sample, using random forest
Algorithm structure patient postoperative 3-10 recurrences or the forecast model of mortality risk, and assessment prediction model and Follow-up results is consistent
Rate；(4) further verified using separate clinic sample：The ER or PR of 19 known Clinical Follow-up data positive early metaphase mammary gland
Cancer first visit patient's FFPE samples, assess testing result and Follow-up results concordance rate.
The invention provides the morning for untreated, the postoperative 3-10 of mid-term ER or PR positive breast cancer first visit patient to answer
Hair or the prognostic evaluation gene detection system of death.Fixed by extracting formalin, (the Formalin-Fixed of FFPE
And Parrffin-Embedded, FFPE) breast cancer tissue's sample in total serum IgE, after universal primer reverse transcription, use
The expression Ct values of 14 related molecular markers of PCR method detection Prognosis in Breast Cancer and 5 reference genes.By Ct values with being examined
The postoperative 3-10 of ER or PR positive early metaphase patient with breast cancers that person's age, pT values and LN numbers import random forests algorithm structure is answered
The forecast model of hair or mortality risk carries out good prognosis or poor prognosis judges.Compared with follow-up information, accuracy reaches the system
70%, except patient age, pT by stages, LN quantity, without relying on other clinical pathology information.
The prediction for the breast cancer first visit patient that kit provided by the invention recurs to 3-10 or mortality risk value is low is accurate
True rate is 81.1%, and pathology predicted detection accuracy is 71.9%, the breast cancer that it recurs to 3-10 or mortality risk value is high
The predictablity rate sensitiveness of first visit patient is 54.4%, close with corresponding pathology predicted detection accuracy 56.8%.The reagent
For box compared with Clinical Follow-up information, concordance rate reaches 70%, except patient age, pT by stages, LN quantity, it is clinical without relying on other
Pathological information.
This detecting system and kit are better than clinical pathology prediction result in Prognosis in Breast Cancer evaluation detection performance, one
Determine that the over-treatment occurred by pathological diagnosis mistake and malpractice can be reduced in degree, meet of breast cancer patients
The demand that bodyization is precisely treated, the technical method in terms of further perfect domestic Prognosis in Breast Cancer prediction.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described.
Fig. 1 shows reference gene and detection gene associations analysis result；
Fig. 2 shows Prognosis in Breast Cancer 3-10 recurrences or the foundation of mortality risk assessment models.
Embodiment
The invention discloses the structure of molecular marker, reference gene and its application, detection kit and detection model
Method, those skilled in the art can use for reference present disclosure, be suitably modified technological parameter realization.In particular, institute
Have similar replacement and change it is apparent to those skilled in the art, they are considered as being included in the present invention.
The method of the present invention and application are described by preferred embodiment, and related personnel can be not substantially being departed from the present invention
Hold, method described herein and application be modified or suitably changed with combining in spirit and scope, to realize and using this
Inventive technique.
The technical scheme of the present invention solved the problems, such as includes：(1) investigated through document and data storehouse, choose 192 breast cancer phases
The candidate gene (Prognosis in Breast Cancer correlation being not limited to, containing reference gene) of pass, customizes TLDA gene expression detection chips
(Applied Biosystems companies)；(2) the complete demographic data of systematic collection, clinical data and Follow-up Data (recurrence
Transfer time, time-to-live), morning, mid-term ER or PR the positive breast cancer first visit patient's FFPE samples of untreated are selected, is used
The TLDA chips of customization carry out the detection of 192 genes, carry out the reference gene molecular marker related to Prognosis in Breast Cancer
Screening；(3) the candidate molecules mark and reference gene that screening obtains are verified in independent sample, using random forest
Algorithm structure patient postoperative 3-10 recurrences or the forecast model of mortality risk, and assessment prediction model and Follow-up results is consistent
Rate；(4) further verified using separate clinic sample：The ER or PR of 19 known Clinical Follow-up data positive early metaphase mammary gland
Cancer first visit patient's FFPE samples, assess testing result and Follow-up results concordance rate.
1. study the selection of sample
(1) early, the middle primary breast cancer first visit patient of untreated；
(2) morning of untreated, mid-term ER or (and) PR breast cancer patients with positive；
(3) LN have or (and) without transfer, and LN transfer quantity；
(4) there is accurate, detailed follow-up information；
This research is studied using 339 standard compliant samples altogether.
2.FFPE sample Total RNAs extractions
FFPE sample total serum IgEs are extracted using High Pure FFPET RNA Isolation Kit (Roche), concentration exists
25ng-400ng/ μ L, OD260/280 are in the range of 1.8-2.0, and OD260/230 is between 1.5-2.0.
3.TLDA (Applied Biosystems companies) chip detects.
Following test is carried out using the 26 pairs of good prognosis and prognosis difference sample of known Clinical Follow-up information.
(1) total serum IgE obtains cDNA samples through reverse transcription reaction；
(2) cDNA products carry out TLDA chip detections；
(3) data analysis and processing, obtain candidate molecules mark and reference gene.
Real-time quantitative RT-PCR 4. (qRT-PCR) method
The checking of candidate molecules mark is carried out using 289 samples of known Clinical Follow-up information.
(1) total serum IgE obtains cDNA samples through reverse transcription reaction；
(2) cDNA products carry out RT-PCR detections；
(3) data analysis and processing.
5. diagnostic reagent box preparation method
By customizing TLDA chip detecting methods, the good sample of 26 Prognosis in Breast Cancer and 26 Prognosis in Breast Cancer difference samples are determined
This reference gene and gene expression difference, filters out reference gene and difference expression gene.Candidate molecules mark passes through anti-
Transcribe the checking that quantitative fluorescent PCR carries out large sample size.14 relevant with the Prognosis in Breast Cancer gene finally filtered out and 5
Reference gene composition diagnostic kit (BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2,
MMP11、CD68、BAG1、MAPT、MS4A1；ACTB、GAPDH、GUSB、NUP214、VCAN).Diagnostic kit includes these bases
The other conventional reagents of the primer of cause, probe, and qRT-PCR.Described kit also includes a forecast model, wherein,
BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT and MS4A1
Expression be to detect to obtain as reference gene using ACTB, GAPDH, GUSB, NUP214, VCAN average, it is simultaneously comprehensive
The clinical information comprehensive assessment such as patient with breast cancer's age, PT, LN its postoperative 3-10 prognosis recurrences or mortality risk, carry out prognosis
The judgement of good goods poor prognosis.
6. the foundation of risk assessment detection model
(1) Mammary cancer 3-10 recurrences or mortality risk forecast model are established.
Postoperative 3-10 recurrences or the mortality risk of detection sample are assessed using random forests algorithm in machine learning method
Value, establish Prognosis in Breast Cancer evaluation genetic test model.Random forest is made up of many decision trees, and the structure of decision tree employs
Attribute and the double random methods of sample, therefore also referred to as stochastic decision tree.It is not have between each decision tree in random forest
Association.When test data enters random forest, classified by each decision tree, finally take in all decision trees and classify
As a result that most classes is the result of final result, i.e. decision tree " ballot ", and in other words, random forest is one comprising multiple
The grader of decision tree, and the classification of its output is by depending on indivedual modes for setting the classifications exported.In the present invention, we
It is optimized on the basis of traditional random forests algorithm, sample random sampling 1000 times is established 1000 moulds by us
Type, and the submodel of accuracy rate highest 39 optimization models final mask the most is chosen from 1000 models, and use 39
The median of submodel is as final prediction result.
It is further instruction of the present invention below：
The research first stage detects the related candidate gene 192 of breast cancer altogether using TLDA detection techniques, detects 26
The gene expression difference of the good sample of Prognosis in Breast Cancer and 26 Prognosis in Breast Cancer difference samples, filters out difference expression gene.Gene
Different expressions with 2-ΔCtRepresent, wherein Δ Ct=CT samples-CT references, the reference gene to filter out enters as reference
Row is standardized to calculate relative expression quantity.The wherein screening process of reference gene：Using genorm, bestkeeper,
Normfinder, tetra- kinds of delta Ct based on stability algorithm and consider biological function and its and tumour of the fluctuation compared with mini gene
Relation screening candidate's reference gene；It is related to 192 gene C t averages to calculate all candidate's reference genes combination Ct averages
Property, the combination of correlation highest is that reference gene includes：ACTB、GAPDH、GUSB、NUP214、VCAN.Candidate gene screening
Standard：(1) fold difference of two groups of global analysis-good prognosis and poor prognosis is up to 2 times or less than 0.5, and Ct ＜ 35 case institute
Accounting example reaches 50%；(2) chromatographic analysis-fold difference without two groups of lymphatic metastasis group good prognosis and poor prognosis 2 times with
On, and significant difference<0.05；(3) two groups of fold differences of global analysis are not notable, but have in Prognosis in Breast Cancer pertinent literature
Report, and Ct ＜ 35 case proportion reaches 90%.Meeting the gene of above-mentioned screening criteria includes：BCL2、PGR、
SCUBE2、ESR1、MKi67、CCNB1、MYBL2、GRB7、ERBB2、MMP11、CD68、BAG1、MAPT、MS4A1
First, it is provided by the invention that there is China women characteristic Prognosis in Breast Cancer to assess gene：Same kind of products at abroad at present
American-European crowd's exploitation is all based on, different ethnic populations have different gene expression, 19 genes are filtered out in the present invention, its
What middle MAPT and MS4A1 were filtered out based on China Female breast cancer patients assesses dependency basis with postoperative 3-10 recurrences or death
Although cause, the gene have been reported that related to breast cancer, but not yet find the first-hand report related to Prognosis in Breast Cancer.Secondly, originally
Invention establishes the new reference gene combination different from other inventions and product, assortment of genes RNA mass in by FFPE samples
Influence small, make the testing result of molecular marker more reliable.3rd, integrated using the forecast model of random forests algorithm
Analysis, model carry out the risk profile of postoperative 3-10 recurrences or death to morning, mid-term ER+ or PR+ breast cancer first visit patient.
In summary, the invention provides the expressions of ER for untreated or PR positive I phases and II phase patient arts
The prognostic evaluation gene detection system of 3-10 recurrences or death afterwards.For the system compared with follow-up information, accuracy reaches 70%,
Except patient age, pT by stages, LN quantity, without relying on other clinical pathology information.
The structure of molecular marker, reference gene and its application provided by the invention, detection kit and detection model
Raw materials used and reagent can be reached by market in method.
With reference to embodiment, the present invention is expanded on further：
Collection, the arrangement of sample data of the sample of embodiment 1
Using the FFPE samples of untreated first visit patient with breast cancer, the complete Clinical Follow-up data of systematic collection, pass through
Arrangement to sample data, inventor therefrom have selected 341 samples for meeting following standard as TLDA (Taqman Low
Density Array, TLDA) chip detects and a series of laboratory sample of follow-up qRT-PCR checkings：
(1) early, the middle primary breast cancer first visit patient of untreated；
(2) morning of untreated, mid-term ER or (and) PR breast cancer patients with positive；
(3) LN have or (and) without transfer, and LN transfer quantity；
(4) there is accurate, detailed follow-up information.
Embodiment 2TLDA cDNA microarrays molecular marker and reference gene
TLDA cores are carried out to the good sample of 26 Prognosis in Breast Cancer and 26 Prognosis in Breast Cancer difference samples for meeting above-mentioned condition
Piece detects, and obtains correlated results.Concretely comprise the following steps：
(1) RNA is extracted in FFPE samples：The 20 μm of sections of every part of sample take 4 or 10 μm sections to take 8, according to High
Pure FFPET RNA Isolation Kit (Roche) specification carries out RNA extraction, and the RNA after extraction is through NanoDrop-
Downstream reverse transcription experiment is carried out after 2000 quantitative Quality Controls.
(2) total serum IgE obtains cDNA samples through reverse transcription reaction：Take 1 μ g total serum IgEs according toVILOTM
Master Mix kit (Invitrogen) specification carries out reverse transcription.
(3) cDNA samples carry out TLDA detections：Above cDNA products withUniversal PCR Master
After Mix is fully mixed, test experience is carried out according to TLDA standardization programs on the quantitative real time PCR Instruments of ABI 7900.
(4) data analysis and processing：
The research first stage is detected from the related candidate gene 192 of breast cancer, inspection altogether using TLDA detection techniques
The gene expression difference of the good sample of 26 Prognosis in Breast Cancer and 26 Prognosis in Breast Cancer difference samples is surveyed, filters out differential expression base
Cause.The different expressions of gene are with 2-ΔCtRepresent, wherein Δ Ct=CT samples-CT references, made with the reference gene filtered out
It is standardized for reference to calculate relative expression quantity.The wherein screening process of reference gene：Using genorm,
Bestkeeper, tetra- kinds of normfinder, delta Ct based on stability algorithm and consider biology work(of the fluctuation compared with mini gene
And its candidate's reference gene can be screened with the relation of tumour；Calculate all candidate's reference gene combination Ct averages and 192 genes
The correlation of Ct averages, the combination of correlation highest are that reference gene includes：ACTB、GAPDH、GUSB、NUP214、VCAN.
Candidate gene screening standard：(1) fold difference of two groups of global analysis-good prognosis and poor prognosis is up to 2 times or less than 0.5, and Ct
＜ 35 case proportion reaches 50%；(2) chromatographic analysis-multiple without lymphatic metastasis group good prognosis Yu two groups of poor prognosis
Difference is more than 2 times, and significant difference<0.05；(3) two groups of fold differences of global analysis are not notable, but pre- in breast cancer
Pertinent literature has been reported that afterwards, and Ct ＜ 35 case proportion reaches 90%.Meeting the gene of above-mentioned standard includes：
BCL2、PGR、SCUBE2、ESR1、MKi67、CCNB1、MYBL2、GRB7、ERBB2、MMP11、CD68、BAG1、MAPT、MS4A1
14 gene functions of the above see the table below 1.
The gene function analysis of table 1
| Sequence number | Gene Name | Functional dependency |
| 1. | BCL2 | Estrogen is related |
| 2. | PGR | Estrogen is related |
| 3. | SCUBE2 | Estrogen is related |
| 4. | ESR1 | Estrogen is related |
| 5. | MKi67 | Propagation is related |
| 6. | CCNB1 | Propagation is related |
| 7. | MYBL2 | Propagation is related |
| 8. | GRB7 | Her-2 is related |
| 9. | ERBB2 | Her-2 is related |
| 10. | MMP11 | Invasion and attack are related |
| 11. | CD68 | Break up race 68 |
| 12. | BAG1 | BCL2 combinations anti-apoptotic genes expression 1 |
| 13. | MAPT | Microtubule associated protein tau |
| 14. | MS4A1 | The domain subfamily A member 1 of cross-film 4 |
The large sample size qRT-PCR checkings of the molecular marker of embodiment 3
14 molecular markers and 5 reference genes that TLDA is filtered out：BCL2、PGR、SCUBE2、ESR1、MKi67、
CCNB1、MYBL2、GRB7、ERBB2、MMP11、CD68、BAG1、MAPT、MS4A1；ACTB、GAPDH、GUSB、NUP214、
VCAN.Requirement and 289 FFPE samples of Clinical Follow-up information completely are collected using sample above is met, carry out single tube qRT-PCR
Checking.
(1) 289 FFPE sample rnas extraction：The 20 μm of sections of every part of sample take 4 or 10 μm sections to take 8, according to High
Pure FFPET RNA Isolation Kit (Roche) specification carries out RNA extraction, and the RNA after extraction is through NanoDrop-
Downstream reverse transcription experiment is carried out after 2000 quantitative Quality Controls.
(2) 289 FFPE sample rnas reverse transcriptions are into cDNA：Take 1 μ g total serum IgEs according toVILOTM Master
Mix kit (Invitrogen) specification carries out reverse transcription.
(3) 289 FFPE sample cDNA products carry out qPCR detections：CDNA products, probe and the primer of every part of sample,After Universal Master Mix II are mixed, test experience is carried out on the quantitative real time PCR Instruments of ABI 7900.
QPCR primers and probe sequence are as shown in 2~table of table 5.
Table 2qRT-PCR primer sequences
Table 3qRT-PCR probe sequences
The house-keeping gene qRT-PCR primer sequences of table 4
The house-keeping gene qRT-PCR probe sequences of table 5
The Prognosis in Breast Cancer 3-10 of embodiment 4 is recurred or mortality risk forecast model is established
Postoperative 3-10 recurrences or the mortality risk of detection sample are assessed using random forests algorithm in machine learning method
Value, establish Prognosis in Breast Cancer evaluation genetic test model.Random forest is made up of many decision trees, and the structure of decision tree employs
Attribute and the double random methods of sample, therefore also referred to as stochastic decision tree.It is not have between each decision tree in random forest
Association.When test data enters random forest, classified by each decision tree, finally take in all decision trees and classify
As a result that most classes is the result of final result, i.e. decision tree " ballot ", and in other words, random forest is one comprising multiple
The grader of decision tree, and the classification of its output is by depending on indivedual modes for setting the classifications exported.In the present invention, we
It is optimized on the basis of traditional random forests algorithm, sample random sampling 1000 times is established 1000 moulds by us
Type, and the submodel of accuracy rate highest 39 optimization models final mask the most is chosen from 1000 models, and use 39
The median of submodel is as final prediction result.
The further checking of the separate clinic sample of embodiment 5
The ER or PR of 19 known Clinical Follow-up data positive early metaphase patient with breast cancer's FFPE samples：PT is 1 by stages
Phase and 2 phases, wherein corrective surgery time, follow-up observation was by 2011 to 2015, during follow-up between 2004 to 2008
Between in more than 3-10.
It is total that 19 parts of FFPE samples above are extracted using High Pure FFPET RNA Isolation Kit (Roche)
RNA, RNA obtains cDNA samples through reverse transcription reaction after Quality Control is qualified, and cDNA products carry out qRT-PCR reactions, detect internal reference base
Because of ACTB, GAPDH, GUSB, NUP214, VCAN, and BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2,
GRB7, ERBB2, MMP11, CD68, BAG1, MAPT, MS4A1 gene.The Ct values of above gene import random forest method structure
Mammary cancer 3-10 recurrences or mortality risk assessment models in obtain 3-10 recurrences or mortality risk value, and according to wind
Dangerous threshold value is predicted as good prognosis or poor prognosis.Predictive analysis results are 73.6% with known follow-up information concordance rate, concrete outcome
Refer to table 6：
6 19 patient with breast cancer's prognostic evaluation results of table
Table 7
Embodiment 6
At present, the decision of breast cancer clinical treatment and therapeutic scheme ultimately depends on the result of pathological examination, while disease
Inspection result of science is also the most important objective basis of judging prognosis.This detecting system using tumour hospital of Medical University Of Tianjin with
289 first visit patient's FFPE samples of breast cancer for the known Clinical Follow-up data that Henan Prov. Tumour Hospital collects, to 5 internal reference bases
Cause and 14 molecular markers are detected respectively.
Testing result is shown in Table 8.
Table 8
The accuracy rate for the breast cancer first visit patient that kit provided by the invention recurs to 3-10 or mortality risk value is low
For 81.1%, pathological examination accuracy is 71.8%, the breast cancer first visit patient that it recurs to 3-10 or mortality risk value is high
Accuracy rate sensitiveness be 54.4%, it is close with corresponding pathological examination accuracy 56.8%.The kit and Clinical Follow-up information
Compare, concordance rate reaches 70%, except patient age, pT by stages, LN quantity, without relying on other clinical pathology information.
This detecting system and kit are better than Clinicopathologic Diagnosis result in Prognosis in Breast Cancer evaluation detection performance,
The over-treatment occurred by pathological diagnosis mistake and malpractice can be reduced to a certain extent, meet breast cancer patients
The demand that individuation is precisely treated, the technical method in terms of further perfect domestic Prognosis in Breast Cancer prediction.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (15)
1. genome compound, it is characterised in that including molecular marker MAPT and/or MS4A1.
2. genome compound, it is characterised in that by molecular marker BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1,
MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT and MS4A1 are formed.
3. genome compound according to claim 2, it is characterised in that also including reference gene ACTB, GAPDH, GUSB,
NUP214、VCAN。
4. the genome compound according to claim 1 or 2 or 3 is preparing Mammary cancer 3-10 recurrences and/or death
Application in the detection means of risk profile.
5. application according to claim 4, it is characterised in that the Mammary cancer 3-10 recurrences and/or dead wind
Predict danger：Obtain the total serum IgE of sample to be tested, cDNA obtained through reverse transcription, obtained using fluorescence quantifying PCR method described in
The Ct values of molecular marker and the reference gene, the Ct values of the reference gene are averaged, obtain reference gene combination
Average Ct values (Ct '), the Ct values of the molecular marker then combined into Ct ' values with reference gene respectively subtracted each other and do normalizing
Change, △ Ct are obtained, by the age of △ Ct values and person under inspection, pT values, LN values through the Mammary cancer constructed by random forests algorithm
3-10 recurs or the analysis of mortality risk forecast model, obtains result.
6. application according to claim 5, it is characterised in that the Mammary cancer 3-10 recurrences or mortality risk are pre-
Survey model construction method be：By the △ Ct values of sample to be tested molecular marker and person under inspection's age, pT values, LN values structure mathematics
Matrix, randomly select 1/2 and be used as training set, 1/2, as checking collection, is established comprising 10000 certainly by the algorithm of random forest
The forecast model of plan tree, random sampling >=1000 time, establish >=1000 forecast models, are chosen from >=1000 forecast models altogether
Submodel with follow-up information concordance rate highest >=39 optimization model for final mask, and in the submodel of use >=39
Digit is as final prognostic risk predicted value.
7. the application according to claim 5 or 6, it is characterised in that the sample to be tested is the morning of untreated, mid-term ER
Or PR positive breast cancer first visit patient's FFPE samples.
8. the primer sets for expanding genome compound as claimed in claim 1 or 2, it is characterised in that sequence such as SEQ ID
Shown in No.1~SEQ ID No.28.
9. the probe groups for expanding genome compound as claimed in claim 1 or 2, it is characterised in that sequence such as SEQ ID
Shown in No.29~SEQ ID No.42.
10. the primer sets of the reference gene for expanding genome compound as claimed in claim 3, it is characterised in that such as SEQ
Shown in ID No.43~SEQ ID No.47.
11. the probe groups of the reference gene for expanding genome compound as claimed in claim 3, it is characterised in that such as SEQ
Shown in ID No.48~SEQ ID No.52.
12. Mammary cancer 3-10 recurs and/or the detection kit of mortality risk prediction, it is characterised in that including such as weighing
Profit requires to commonly use in the primer sets described in 8 and/or 10 and/or the probe groups as described in claim 9 and/or 11 and kit
Reagent.
13. Mammary cancer 3-10 recurs or the construction method of mortality risk forecast model, it is characterised in that by sample to be tested
The △ Ct values of molecular marker and person under inspection's age, pT values, LN values structure math matrix, randomly select 1/2 and be used as training set, 1/
2 as checking collection, and the forecast model for including 10000 decision trees is established by the algorithm of random forest, altogether random sampling >=
1000 times, >=1000 forecast models are established, are chosen and follow-up information concordance rate highest >=39 from >=1000 forecast models
Individual optimization model is the submodel of final mask, and the median of use >=39 submodel is predicted as final prognostic risk
Value.
14. Mammary cancer 3-10 recurs or the detection method of mortality risk, it is characterised in that obtains the total of sample to be tested
RNA, cDNA is obtained through reverse transcription, the Ct of the molecular marker and the reference gene is obtained using fluorescence quantifying PCR method
Value, the Ct values of the reference gene are averaged, and obtain the Average Ct values (Ct ') of reference gene combination, then will be described point
The Ct values of sub- mark combine Ct ' values with reference gene respectively subtracts each other and normalizes, and obtains △ Ct, by △ Ct values and person under inspection
Age, pT values, LN values are through the Mammary cancer 3-10 recurrences constructed by random forests algorithm or mortality risk forecast model point
Analysis, obtain result.
15. the detection method described in construction method according to claim 13 or claim 14, it is characterised in that described
Sample to be tested is the morning of untreated, mid-term ER or PR positive breast cancer first visit patient's FFPE samples.
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| PCT/CN2017/090740 WO2018001295A1 (en) | 2016-06-30 | 2017-06-29 | Molecular marker, reference gene, and application and test kit thereof, and method for constructing testing model |
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| WO2018001295A1 (en) | 2018-01-04 |
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