CN108461147A - Utilize the method for three cloudy breast cancer spindle assembly checkpoint exception of lncRNA-mRNA coexpressions neural network forecast - Google Patents
Utilize the method for three cloudy breast cancer spindle assembly checkpoint exception of lncRNA-mRNA coexpressions neural network forecast Download PDFInfo
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Abstract
The present invention relates to a kind of methods using three cloudy breast cancer spindle assembly checkpoint exception of lncRNA mRNA coexpressions neural network forecast, include the following steps:Experiment packet, RNA extractions, lncRNA chip datas differential expression analysis and the analysis of lncRNA target genes and regulated and control network structure.The method using three cloudy breast cancer spindle assembly checkpoint exception of lncRNA mRNA coexpressions neural network forecast of the present invention, the abnormal molecular marker new for the cloudy breast cancer of discovery three of neural network forecast spindle assembly checkpoint is co-expressed by lnRNA mRNA and provides new way, and reference frame is provided for the cloudy Prognosis in Breast Cancer of prediction three.
Description
Technical field
The present invention relates to lncRNA-mRNA to co-express network, more particularly to a kind of to co-express network using lncRNA-mRNA
The method of the cloudy breast cancer spindle assembly checkpoint exception of prediction three.
Background technology
Three cloudy breast cancer (Triple-negative breast cancer, TNBC) are that invasion is strongest in breast cancer
A kind of hypotype accounts for about the 20% of whole breast cancer.TNBC, that is, estrogen receptor (Estrogen receptor, ER), progestational hormone by
Body (Progesterone receptor, PR) and human epidermal growth factor receptor 2 (Human epidermal growth
Factor receptor2, Her-2) all expression deletions breast cancer.Clinically it is directed to the systemic therapy mode of TNBC still now
It is mainly so anthracene nucleus medicament adriamycin (Doxorubicin, DOX), epirubicin (Epirubicin, EPI) and pirarubicin
Based on cell toxicity medicaments such as (Pirarubicin, THP).It is now recognized that the reason of its poor prognosis of TNBC and its biological characteristics and
It is related to lack effective monitoring treatment means.
LC-PolScope image system (spindle assembly Checkpoint, SAC) is to ensure to contaminate in the cell mitogen phase
The important monitoring mechanism that colour solid properly separates, maintains Genome stability.There are LC-PolScope image system work(for many tumour cells
Energy defect, imbalance, gene mononucleotide polymorphism and promoter methylation of LC-PolScope image system expression quantity etc. may be with its work(
It can be abnormal related.Antitumor drug starts killing tumor effect by influencing the expression of LC-PolScope image system.It understands in depth swollen
Tumor LC-PolScope image system regulatory mechanism contributes to the target chemotherapy medicine for finding new killing tumour cell.
Invention content
The invention solves in the prior art the technical issues of, provide it is a kind of using lncRNA-mRNA coexpression network it is pre-
The method for surveying three cloudy breast cancer spindle assembly checkpoint exceptions.
In order to solve the above-mentioned technical problem, technical scheme of the present invention is specific as follows:
A kind of side using three cloudy breast cancer spindle assembly checkpoint exception of lncRNA-mRNA coexpressions neural network forecast
Method includes the following steps:
Step 1:Experiment packet
Human breast cancer cell is handled with pirarubicin, establishes test groups;
Not carry out the human breast cancer cell of pirarubicin processing, con groups are established;
Step 2:RNA is extracted
Extract the lncRNA and mRNA of human breast cancer cell in test groups and con groups;
Step 3:LncRNA chip data differential expression analysis
(1) preset value of each lncRNA probes is imported;
(2) the lncRNA probes that the Flag in the repetition probe of all chips is " -50 " are filtered out;
(3) fluorescence intensity level that probe is repeated contained by every chip is taken into median;
(4) value for coefficient of variation that probe is repeated in every chip is calculated;
(5) chip chamber carries out data homogenization using Invariant set Standardization Acts, repeats to take in probe in same sample
Average value;
(6) lncRNA probes are compared according to experimental design and grouping situation, are calculated | FC |>2 fold differences;
(7) differential gene list:Choose and meet | FC |>2, P<0.05 gene is differential gene;
(8) principle component and cluster analysis:Chip signal value sieves on demand after data log conversions and mean center
It selects an appropriate number of differential gene and clustering is carried out with average join algorithm;
Step 4:LncRNA target genes are analyzed and regulated and control network structure
Tentative prediction is carried out to lncRNA target genes using Targetscan databases;Using DAVID databases by mRNA
Target gene carries out the enrichment analysis of KEGG accesses, filters out the gene pathway of P < 0.05.
In the above-mentioned technical solutions, the grouping in step 1 is specially:
Test groups:The human breast cancer cell group of 5 μM of pirarubicin processing, every group of 3 biology repeat;
Con groups:Human breast cancer cell group, every group of 3 biology repeat.
In the above-mentioned technical solutions, step 2 specifically includes following steps:
(1) cell being grown in 6cm plates to logarithmic phase is removed into culture solution, PBS is washed 2 times and discarded;
(2) 1~2mL Trizol reagents are added into plate, blow and beat cellular layer repeatedly with pipettor and collect to centrifuge tube
In;
(3) 200~500 μ L chloroforms are added into centrifuge tube, upper and lower fully shaking to liquid emulsifies completely;Room temperature is quiet
Set 5min;12000rpm, 4 DEG C of centrifugation 15min;
(4) it collects in upper layer colourless liquid to another centrifuge tube, isometric isopropanol is added, mixes well up and down;Room temperature
Stand 10min;12000rpm, 4 DEG C of centrifugation 10min;
(5) liquid is discarded supernatant, bottom precipitation is avoided contact to, 1mL75% ethanol solutions are added along tube wall;Rotate centrifuge tube
Tube wall is fully soaked to liquid;12000rpm, 4 DEG C of centrifugation 5min;
(6) liquid is discarded supernatant, bottom precipitation is avoided contact to;Centrifuge tube is put into super-clean bench, is air-dried to bottom precipitation
It is transparent;The nuclease free water dissolution precipitation of 10~20 μ L is added;- 80 DEG C of preservations.
In the above-mentioned technical solutions, step 4 is further comprising the steps of:
Further filter out with tumour related pathways, and by can by lncRNA regulate and control quantity be ranked up;It will be further
The access correlation target gene of screening is integrated, and the target gene and lncRNA-mRNA of the lncRNA under pirarubicin intervention is obtained
Co-expression gene;
The lncRNA and mRNA of differential expression are screened, screening conditions are | FC |>2, P<0.05.
The present invention has advantageous effect below:
The present invention's co-expresses the cloudy breast cancer spindle assembly checkpoint exception of neural network forecast three using lncRNA-mRNA
Method, it is the new molecule of the cloudy breast cancer of discovery three to co-express neural network forecast spindle assembly checkpoint extremely by lnRNA-mRNA
Marker provides new way, and reference frame is provided for the cloudy Prognosis in Breast Cancer of prediction three.
Description of the drawings
Invention is further described in detail with reference to the accompanying drawings and detailed description.
Fig. 1 is that test groups count block diagram with con group differential genes, and left side is the gene dosage of up-regulation in figure, and right side is
The gene dosage of downward.
Specific implementation mode
The present invention's co-expresses the cloudy breast cancer spindle assembly checkpoint exception of neural network forecast three using lncRNA-mRNA
Method includes the following steps:
Step 1:Experiment packet
Human breast carcinoma MDA-MB-231 cells are handled with pirarubicin (THP), establish test groups (test group) and con groups
(contrast groups).
Test groups:MDA-MB-231 groups are handled in 5 μM of THP, every group of 3 biology repeats;
Con groups:MDA-MB-231 groups, every group of 3 biology repeat.
Step 2:RNA is extracted
(1) cell being grown in 6cm plates to logarithmic phase is removed into culture solution, PBS is washed 2 times and discarded.
(2) 1~2mL Trizol reagents are added into plate, blow and beat cellular layer repeatedly with pipettor and collect to centrifuge tube
In.
(3) 200~500 μ L chloroforms are added into centrifuge tube, upper and lower fully shaking to liquid emulsifies completely.Room temperature is quiet
Set 5min.12000rpm (~13400 × g), 4 DEG C of centrifugation 15min.
(4) it collects in upper layer colourless liquid to another centrifuge tube, isometric isopropanol is added, mixes well up and down.Room temperature
Stand 10min.12000rpm (~13400 × g), 4 DEG C of centrifugation 10min.
(5) liquid is carefully discarded supernatant, bottom precipitation is avoided contact to, 1mL75% ethanol solutions are slowly added into along tube wall.Gently
Light rotation centrifuge tube to liquid fully soaks tube wall.12000rpm (~13400 × g), 4 DEG C of centrifugation 5min.
(6) liquid is carefully discarded supernatant, bottom precipitation is avoided contact to.Centrifuge tube is put into super-clean bench, is air-dried to bottom
It precipitates transparent.Nuclease-free water (Nuclease-Free Water) the dissolving precipitation of 10~20 μ L is added.- 80 DEG C of preservations.
Step 3:LncRNA chip data differential expression analysis
(1) GPR documents are loaded into R 2.12.1 version softwares and carry out data operation, imported in GPR documents and each visit
" foreground value " (preset value) of needle.
(2) probe that the Flag in the repetition probe of all chips is " -50 " is filtered out.
(3) fluorescence intensity level that probe is repeated contained by every chip is taken into median.
(4) the CV values (coefficient variance, the coefficient of variation) that probe is repeated in every chip are calculated.
(5) chip chamber uses Invariant set Standardization Acts (invariant set normalization
Method data homogenization) is carried out, repeats to be averaged in probe in same sample.
(6) lncRNA probes are compared according to experimental design and grouping situation, are calculated | FC |>2 fold differences.
(7) differential gene list:Default setting, which is chosen, to be met | FC |>2, P<0.05 gene is differential gene, or according to
Corresponding adjustment is made in specific requirement.
(8) principle component and cluster analysis:Chip signal value sieves on demand after data log conversions and mean center
It selects an appropriate number of differential gene and clustering is carried out with average join algorithm (average linkage algorithm).
Step 4:LncRNA target genes are analyzed and regulated and control network structure
Tentative prediction is carried out to lncRNA target genes using Targetscan databases;Using DAVID databases by mRNA
Target gene carries out the enrichment analysis of KEGG accesses, filters out the gene pathway of P < 0.05.
Further filter out with tumour related pathways, and by can by lncRNA regulate and control quantity be ranked up.It will be further
The access correlation target gene of screening is integrated, and the target gene and lncRNA-mRNA coexpressions of the lncRNA under THP intervenes is obtained
Gene.
The lncRNA and mRNA of differential expression are screened, screening conditions are | FC |>2, P<0.05.
Differential gene statistical result
As shown in Figure 1, test groups are compared with con groups, and 2270 gene upregulations, 8139 gene deregulations.Wherein mRNA is poor
5777 genes of different expression raise 164 genes, lower 5613 genes;1547 genes of lncRNA differential expressions, up-regulation
487 genes lower 1060 genes.Clustering is carried out to whole chip samples, test groups and con group differentiations can be sought
LncRNA is looked for express trend.
The list of genes of partial down-regulation is shown in particular in following table.
1 Cell cycle-related genes of table (THP intervention group vs breast cancer group)
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
It changes.There is no necessity and possibility to exhaust all the enbodiments.And it is extended from this it is obvious variation or
It changes still within the protection scope of the invention.
Claims (4)
1. a kind of method using three cloudy breast cancer spindle assembly checkpoint exception of lncRNA-mRNA coexpressions neural network forecast,
It is characterized by comprising the following steps:
Step 1:Experiment packet
Human breast cancer cell is handled with pirarubicin, establishes test groups;
Not carry out the human breast cancer cell of pirarubicin processing, con groups are established;
Step 2:RNA is extracted
Extract the lncRNA and mRNA of human breast cancer cell in test groups and con groups;
Step 3:LncRNA chip data differential expression analysis
(1) preset value of each lncRNA probes is imported;
(2) the lncRNA probes that the Flag in the repetition probe of all chips is " -50 " are filtered out;
(3) fluorescence intensity level that probe is repeated contained by every chip is taken into median;
(4) value for coefficient of variation that probe is repeated in every chip is calculated;
(5) chip chamber carries out data homogenization using Invariant set Standardization Acts, repeats to be averaged in probe in same sample
Value;
(6) lncRNA probes are compared according to experimental design and grouping situation, are calculated | FC |>2 fold differences;
(7) differential gene list:Choose and meet | FC |>2, P<0.05 gene is differential gene;
(8) principle component and cluster analysis:Chip signal value filters out on demand after data log conversions and mean center
An appropriate number of differential gene carries out clustering with average join algorithm;
Step 4:LncRNA target genes are analyzed and regulated and control network structure
Tentative prediction is carried out to lncRNA target genes using Targetscan databases;Using DAVID databases by mRNA target bases
Because carrying out the enrichment analysis of KEGG accesses, the gene pathway of P < 0.05 is filtered out.
2. according to claim 1 examined using three cloudy breast cancer mitotic spindle assembly of lncRNA-mRNA coexpressions neural network forecast
Make an inventory of abnormal method, which is characterized in that the grouping in step 1 is specially:
Test groups:The human breast cancer cell group of 5 μM of pirarubicin processing, every group of 3 biology repeat;
Con groups:Human breast cancer cell group, every group of 3 biology repeat.
3. according to claim 1 examined using three cloudy breast cancer mitotic spindle assembly of lncRNA-mRNA coexpressions neural network forecast
Make an inventory of abnormal method, which is characterized in that step 2 specifically includes following steps:
(1) cell being grown in 6cm plates to logarithmic phase is removed into culture solution, PBS is washed 2 times and discarded;
(2) 1~2mL Trizol reagents are added into plate, blow and beat cellular layer repeatedly with pipettor and collect into centrifuge tube;
(3) 200~500 μ L chloroforms are added into centrifuge tube, upper and lower fully shaking to liquid emulsifies completely;It is stored at room temperature
5min;12000rpm, 4 DEG C of centrifugation 15min;
(4) it collects in upper layer colourless liquid to another centrifuge tube, isometric isopropanol is added, mixes well up and down;It is stored at room temperature
10min;12000rpm, 4 DEG C of centrifugation 10min;
(5) liquid is discarded supernatant, bottom precipitation is avoided contact to, 1mL75% ethanol solutions are added along tube wall;Centrifuge tube is rotated to liquid
Body fully soaks tube wall;12000rpm, 4 DEG C of centrifugation 5min;
(6) liquid is discarded supernatant, bottom precipitation is avoided contact to;Centrifuge tube is put into super-clean bench, is air-dried transparent to bottom precipitation;
The nuclease free water dissolution precipitation of 10~20 μ L is added;- 80 DEG C of preservations.
4. according to claim 1 examined using three cloudy breast cancer mitotic spindle assembly of lncRNA-mRNA coexpressions neural network forecast
Make an inventory of abnormal method, which is characterized in that step 4 is further comprising the steps of:
Further filter out with tumour related pathways, and by can by lncRNA regulate and control quantity be ranked up;It will further screen
Access correlation target gene integrated, the target gene and lncRNA-mRNA for obtaining the lncRNA under pirarubicin intervention are total to table
Up to gene;
The lncRNA and mRNA of differential expression are screened, screening conditions are | FC |>2, P<0.05.
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112651950A (en) * | 2020-12-30 | 2021-04-13 | 珠海碳云智能科技有限公司 | Data processing method, sample classification method, model training method and device |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102799796A (en) * | 2011-05-24 | 2012-11-28 | 上海聚类生物科技有限公司 | Method for association analysis of long noncoding ribonucleic acid (LncRNA) and messenger ribonucleic acid (mRNA) |
US20130178428A1 (en) * | 2011-11-30 | 2013-07-11 | Dave S.B. HOON | Long noncoding rna (lncrna) as a biomarker and therapeutic marker in cancer |
US20140073525A1 (en) * | 2011-05-13 | 2014-03-13 | The Board Of Trustees Of The Leland Stanford Junior University | Diagnostic, prognostic and therapeutic uses of long non-coding rnas for cancer and regenerative medicine |
CN105316341A (en) * | 2015-12-08 | 2016-02-10 | 浙江理工大学 | LncRNA and application thereof as prostatic cancer detection marker or prostatic cancer prognosis recurrence marker |
-
2018
- 2018-02-26 CN CN201810160944.3A patent/CN108461147B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140073525A1 (en) * | 2011-05-13 | 2014-03-13 | The Board Of Trustees Of The Leland Stanford Junior University | Diagnostic, prognostic and therapeutic uses of long non-coding rnas for cancer and regenerative medicine |
CN102799796A (en) * | 2011-05-24 | 2012-11-28 | 上海聚类生物科技有限公司 | Method for association analysis of long noncoding ribonucleic acid (LncRNA) and messenger ribonucleic acid (mRNA) |
US20130178428A1 (en) * | 2011-11-30 | 2013-07-11 | Dave S.B. HOON | Long noncoding rna (lncrna) as a biomarker and therapeutic marker in cancer |
CN105316341A (en) * | 2015-12-08 | 2016-02-10 | 浙江理工大学 | LncRNA and application thereof as prostatic cancer detection marker or prostatic cancer prognosis recurrence marker |
Non-Patent Citations (2)
Title |
---|
SHUNHENG ZHOU ET AL.: "Systematical analysis of lncRNA–mRNA competing endogenous RNA network in breast cancer subtypes", 《BREAST CANCER RESEARCH AND TREATMENT》 * |
曾慧娟 等.: "长链非编码RNA与乳腺癌治疗耐药相关的研究进展", 《医学研究生学报》 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112651950A (en) * | 2020-12-30 | 2021-04-13 | 珠海碳云智能科技有限公司 | Data processing method, sample classification method, model training method and device |
CN112651950B (en) * | 2020-12-30 | 2023-09-29 | 珠海碳云诊断科技有限公司 | Data processing method, sample classification method, model training method and device |
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