CN108461147B - Method for predicting three-negative breast cancer spindle assembly checkpoint abnormality by using lncRNA-mRNA coexpression network - Google Patents
Method for predicting three-negative breast cancer spindle assembly checkpoint abnormality by using lncRNA-mRNA coexpression network Download PDFInfo
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Abstract
The invention relates to a method for predicting three-negative breast cancer spindle assembly checkpoint abnormality by using an lncRNA-mRNA coexpression network, which comprises the following steps of: the method comprises the steps of experimental grouping, RNA extraction, lncRNA chip data differential expression analysis, lncRNA target gene analysis and regulation and control network construction. The method for predicting the three-negative breast cancer spindle assembly checkpoint abnormality by using the lnRNA-mRNA coexpression network, disclosed by the invention, provides a new way for discovering a new molecular marker of the three-negative breast cancer by predicting the spindle assembly checkpoint abnormality by using the lnRNA-mRNA coexpression network, and provides a reference basis for predicting the prognosis of the three-negative breast cancer.
Description
Technical Field
The invention relates to a lncRNA-mRNA coexpression network, in particular to a method for predicting three-negative breast cancer spindle assembly checkpoint abnormality by using the lncRNA-mRNA coexpression network.
Background
Triple-negative breast cancer (TNBC) is a most aggressive subtype of breast cancer, accounting for about 20% of all breast cancers. TNBC, an Estrogen Receptor (ER), a Progestin Receptor (PR), and a Human epidermal growth factor receptor 2(Human epidermal growth factor receptor2, Her-2) all express deficient breast cancers. Currently, the systemic treatment mode aiming at TNBC is mainly cytotoxic drugs such as anthracycline Doxorubicin (Doxorubicin, DOX), Epirubicin (EPI) and Pirarubicin (THP). It is currently believed that the cause of poor prognosis of TNBC is related to its biological properties and the lack of effective means of monitoring therapy.
Spindle Assembly Checkpoint (SAC) is an important monitoring mechanism to ensure correct chromosome segregation during cell mitosis, and maintains genome stability. Many tumor cells have functional defects of spindle checkpoint, and the dysfunction of the expression level of the spindle checkpoint, gene single nucleotide polymorphism, promoter methylation and the like may be related to the functional abnormality. Antineoplastic drugs initiate the tumor killing effect by affecting the expression of spindle checkpoints. The control mechanism of the check point of the tumor spindle is deeply understood, which is helpful for discovering a new targeted chemotherapy medicament for killing tumor cells.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art and provides a method for predicting three-negative breast cancer spindle assembly checkpoint abnormality by using an lncRNA-mRNA coexpression network.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for predicting triple negative breast cancer spindle assembly checkpoint abnormalities using lncRNA-mRNA co-expression network, comprising the steps of:
step 1: experiment grouping
Treating human breast cancer cells with pirarubicin to establish a test group;
establishing a con group by using human breast cancer cells which are not treated by pirarubicin;
step 2: RNA extraction
Extracting lncRNA and mRNA of human breast cancer cells in the test group and the con group;
and step 3: differential expression analysis of lncRNA chip data
(1) Introducing a preset value of each lncRNA probe;
(2) filtering lncRNA probes with Flag of "-50" in the repeated probes of all chips;
(3) taking the median of the fluorescence intensity values of the repeated probes contained in each chip;
(4) calculating the coefficient of variation value of the repeated probes in each chip;
(5) uniformizing data among chips by adopting an Invariant set standardization method, and taking an average value in the same repeated probe;
(6) comparing lncRNA probes according to experimental design and grouping conditions, and calculating the difference multiple of | FC | > 2;
(7) list of differential genes: selecting a gene meeting the conditions that FC < 2 and P <0.05 as a differential gene;
(8) principal component and clustering analysis: after the chip signal values are subjected to data log conversion and mean number centralization, proper numbers of different genes are screened out according to requirements, and clustering analysis is carried out by using an average connection algorithm;
and 4, step 4: lncRNA target gene analysis and regulation network construction
Performing preliminary prediction on lncRNA target genes by using a Targetscan database; and (3) carrying out KEGG channel enrichment analysis on the mRNA target gene by using a DAVID database, and screening out a gene channel with P less than 0.05.
In the above technical solution, the grouping in step 1 specifically includes:
test group: 5 μ M pirarubicin treated human breast cancer cell groups, 3 biological replicates per group;
group con: human breast cancer cell groups, 3 biological replicates per group.
In the above technical solution, step 2 specifically includes the following steps:
(1) removing the culture solution from the cells grown in the 6cm plate to the logarithmic phase, washing 2 times with PBS and discarding;
(2) adding 1-2 mL of Trizol reagent into the plate, repeatedly blowing the cell layer by using a liquid transfer device, and collecting the cell layer into a centrifuge tube;
(3) adding 200-500 mu L of trichloromethane into a centrifugal tube, and fully shaking up and down until the liquid is completely emulsified; standing at room temperature for 5 min; centrifuging at 12000rpm and 4 deg.C for 15 min;
(4) collecting the upper colorless liquid to another centrifugal tube, adding isopropanol with the same volume, and mixing up and down; standing at room temperature for 10 min; centrifuging at 12000rpm and 4 deg.C for 10 min;
(5) discarding the supernatant to avoid contacting the bottom precipitate, and adding 1mL of 75% ethanol solution along the tube wall; rotating the centrifugal tube until the liquid fully wets the tube wall; centrifuging at 12000rpm and 4 deg.C for 5 min;
(6) discarding the supernatant to avoid contact with the bottom precipitate; putting the centrifuge tube into a super clean bench, and air-drying until the bottom precipitate is transparent; adding 10-20 mu L of nuclease-free water to dissolve the precipitate; storing at-80 deg.C.
In the above technical solution, step 4 further includes the following steps:
further screening out tumor-related pathways, and sequencing according to the quantity which can be regulated and controlled by lncRNA; integrating the further screened pathway-related target genes to obtain a lncRNA target gene and an lncRNA-mRNA co-expression gene under the intervention of pirarubicin;
screening the lncRNA and mRNA which are differentially expressed under the conditions of | FC | >2 and P < 0.05.
The invention has the following beneficial effects:
the method for predicting the three-negative breast cancer spindle assembly checkpoint abnormality by using the lnRNA-mRNA coexpression network, disclosed by the invention, provides a new way for discovering a new molecular marker of the three-negative breast cancer by predicting the spindle assembly checkpoint abnormality by using the lnRNA-mRNA coexpression network, and provides a reference basis for predicting the prognosis of the three-negative breast cancer.
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The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a histogram of the statistics of the difference genes between the test and con groups, with the numbers of genes up-regulated on the left and down-regulated on the right.
Detailed Description
The method for predicting three-negative breast cancer spindle assembly checkpoint abnormality by using the lncRNA-mRNA coexpression network comprises the following steps of:
step 1: experiment grouping
Human breast cancer MDA-MB-231 cells were treated with pirarubicin (THP) and a test group (test group) and a con group (control group) were established.
test group: treatment of MDA-MB-231 groups in 5. mu.M THP, with 3 biological replicates per group;
group con: MDA-MB-231 groups of 3 biological replicates each.
Step 2: RNA extraction
(1) Cells grown in 6cm dishes to log phase were removed from the culture medium, washed 2 times with PBS and discarded.
(2) Adding 1-2 mL Trizol reagent into the plate, repeatedly blowing the cell layer by using a pipette, and collecting the cell layer into a centrifuge tube.
(3) Adding 200-500 mu L of trichloromethane into the centrifuge tube, and fully shaking up and down until the liquid is completely emulsified. Standing at room temperature for 5 min. Centrifuge at 12000rpm (-13400 Xg) for 15min at 4 ℃.
(4) Collecting the upper layer colorless liquid to another centrifuge tube, adding isopyknic isopropanol, and mixing up and down. Standing at room temperature for 10 min. Centrifugation was carried out at 12000rpm (. about.13400 Xg) at 4 ℃ for 10 min.
(5) The supernatant was carefully discarded to avoid contact with the bottom pellet, and 1mL of 75% ethanol solution was added slowly along the tube wall. The tube was gently rotated until the liquid fully wetted the tube wall. Centrifugation was carried out at 12000rpm (. about.13400 Xg) at 4 ℃ for 5 min.
(6) The supernatant was carefully discarded to avoid contact with the bottom pellet. And (4) putting the centrifuge tube into a super clean bench, and air-drying until the bottom precipitate is transparent. Adding 10-20 μ L of Nuclease-Free Water (Nuclease-Free Water) to dissolve the precipitate. Storing at-80 deg.C.
And step 3: differential expression analysis of lncRNA chip data
(1) The GPR document is loaded into R2.12.1 version software for data operation, and the foreground value (preset value) of each probe in the GPR document is imported.
(2) Among all the duplicate probes of the chip, probes with Flag of "-50" were filtered.
(3) The fluorescence intensity values of the duplicate probes contained in each chip were taken as the median.
(4) The CV value (coefficient of variation) of the replicate probes in each chip was calculated.
(5) Data were normalized between chips using Invariant set normalization method (innovative set normalization method), and averaged in duplicate probes of the same run.
(6) And comparing lncRNA probes according to experimental design and grouping conditions, and calculating the difference multiple of | FC | > 2.
(7) List of differential genes: and selecting a gene meeting the conditions that the | FC | is >2 and the P is less than 0.05 as a difference gene by default, or making corresponding adjustment according to specific requirements.
(8) Principal component and clustering analysis: after the chip signal values are subjected to data log conversion and mean number centering, proper numbers of different genes are screened out according to requirements, and clustering analysis is carried out by using an average linking algorithm (average algorithm).
And 4, step 4: lncRNA target gene analysis and regulation network construction
Performing preliminary prediction on lncRNA target genes by using a Targetscan database; and (3) carrying out KEGG channel enrichment analysis on the mRNA target gene by using a DAVID database, and screening out a gene channel with P less than 0.05.
Tumor-associated pathways were further screened and ranked by the number that can be regulated by lncRNA. And integrating the further screened pathway-related target genes to obtain the lncRNA target gene and lncRNA-mRNA co-expression gene under the intervention of THP.
Screening the lncRNA and mRNA which are differentially expressed under the conditions of | FC | >2 and P < 0.05.
Statistical results of differential genes
As shown in FIG. 1, the test group was up-regulated in 2270 genes and down-regulated in 8139 genes, as compared to the con group. Wherein the mRNA differentially expressed 5777 genes, up regulated 164 genes, down regulated 5613 genes; the incrna differentially expressed 1547 genes, up-regulated 487 genes, and down-regulated 1060 genes. And (3) performing cluster analysis on all chip samples, distinguishing a test group from a con group, and searching for the expression trend of lncRNA.
The table below gives a list of partially downregulated genes.
TABLE 1 cell cycle-related genes (THP intervention group vs breast cancer group)
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (4)
1. A method for predicting three-negative breast cancer spindle assembly checkpoint abnormalities by using a lncRNA-mRNA coexpression network, which is characterized by comprising the following steps:
step 1: experiment grouping
Treating human breast cancer cells with pirarubicin to establish a test group;
establishing a con group by using human breast cancer cells which are not treated by pirarubicin;
step 2: RNA extraction
Extracting lncRNA and mRNA of human breast cancer cells in the test group and the con group;
and step 3: differential expression analysis of lncRNA chip data
(1) Introducing a preset value of each lncRNA probe;
(2) filtering lncRNA probes with Flag of "-50" in the repeated probes of all chips;
(3) taking the median of the fluorescence intensity values of the repeated probes contained in each chip;
(4) calculating the coefficient of variation value of the repeated probes in each chip;
(5) uniformizing data among chips by adopting an Invariant set standardization method, and taking an average value in the same repeated probe;
(6) comparing lncRNA probes according to experimental design and grouping conditions, and calculating the difference multiple of | FC | > 2;
(7) list of differential genes: selecting a gene meeting the conditions that FC < 2 and P <0.05 as a differential gene;
(8) principal component and clustering analysis: after the chip signal values are subjected to data log conversion and mean number centralization, proper numbers of different genes are screened out according to requirements, and clustering analysis is carried out by using an average connection algorithm;
and 4, step 4: lncRNA target gene analysis and regulation network construction
Performing preliminary prediction on lncRNA target genes by using a Targetscan database; and (3) carrying out KEGG channel enrichment analysis on the mRNA target gene by using a DAVID database, and screening out a gene channel with P less than 0.05.
2. The method for predicting three negative breast cancer spindle assembly checkpoint abnormalities using lncRNA-mRNA co-expression network according to claim 1, wherein the grouping in step 1 is specifically:
test group: 5 μ M pirarubicin treated human breast cancer cell groups, 3 biological replicates per group;
group con: human breast cancer cell groups, 3 biological replicates per group.
3. The method for predicting three-negative breast cancer spindle assembly checkpoint abnormalities using lncRNA-mRNA co-expression network according to claim 1, wherein step 2 specifically comprises the following steps:
(1) removing the culture solution from the cells grown in the 6cm plate to the logarithmic phase, washing 2 times with PBS and discarding;
(2) adding 1-2 mL of Trizol reagent into the plate, repeatedly blowing the cell layer by using a liquid transfer device, and collecting the cell layer into a centrifuge tube;
(3) adding 200-500 mu L of trichloromethane into a centrifugal tube, and fully shaking up and down until the liquid is completely emulsified; standing at room temperature for 5 min; centrifuging at 12000rpm and 4 deg.C for 15 min;
(4) collecting the upper colorless liquid to another centrifugal tube, adding isopropanol with the same volume, and mixing up and down; standing at room temperature for 10 min; centrifuging at 12000rpm and 4 deg.C for 10 min;
(5) discarding the supernatant to avoid contacting the bottom precipitate, and adding 1mL of 75% ethanol solution along the tube wall; rotating the centrifugal tube until the liquid fully wets the tube wall; centrifuging at 12000rpm and 4 deg.C for 5 min;
(6) discarding the supernatant to avoid contact with the bottom precipitate; putting the centrifuge tube into a super clean bench, and air-drying until the bottom precipitate is transparent; adding 10-20 mu L of nuclease-free water to dissolve the precipitate; storing at-80 deg.C.
4. The method for predicting three negative breast cancer spindle assembly checkpoint abnormalities using lncRNA-mRNA co-expression network of claim 1, wherein step 4 further comprises the steps of:
further screening out tumor-related pathways, and sequencing according to the quantity which can be regulated and controlled by lncRNA; integrating the further screened pathway-related target genes to obtain a lncRNA target gene and an lncRNA-mRNA co-expression gene under the intervention of pirarubicin;
screening the lncRNA and mRNA which are differentially expressed under the conditions of | FC | >2 and P < 0.05.
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| Systematical analysis of lncRNA–mRNA competing endogenous RNA network in breast cancer subtypes;Shunheng Zhou et al.;《Breast Cancer Research and Treatment》;20180201;全文 * |
| 长链非编码RNA与乳腺癌治疗耐药相关的研究进展;曾慧娟 等.;《医学研究生学报》;20171231;第30卷(第12期);全文 * |
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