CN107064372A - The method that thyroid cancer is predicted using lipid biomarkers - Google Patents
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Abstract
The invention discloses a kind of method that utilization lipid biomarkers predict thyroid cancer, pass through OPLS DA models, filter out the R9 of otherness compound R 1 between normal person's group and thyroid cancer group, lipid biomarkers i.e. related to thyroid cancer, and pass through construction logic regression model, the method for obtaining predicting thyroid cancer according to these lipid marks, efficient and convenient, the degree of accuracy reaches 87.2%.
Description
Technical field
The side of thyroid cancer is predicted the present invention relates to biological technical field, more particularly to a kind of utilization lipid biomarkers
Method, being capable of Accurate Prediction thyroid cancer.
Background technology
Thyroid cancer is common endocrine malignant tumour, is traditionally divided into thyroid gland differentiation property cancer, thyroid gland undifferentiated
Cancer and medullary carcinoma of thyroid gland, differentiation property cancer are divided into papillary carcinoma and follicular gland cancer again, anaplastic thyroid carcinoma it is more rare and
Poor prognosis, medullary carcinoma of thyroid gland derives from parafollicular cell (C cells), one belonged in neuroendocrine tumor
Kind, about 25% patient is with medullary carcinoma of thyroid gland in multiple 2 type endocrine knurl.In recent years, China's thyroid cancer is occurred frequently,
Growth rate most fast malignant tumour is turned into, the incidence of disease increases nearly 5 times for 10 years.Thyroid cancer is especially apt to occur in young and middle-aged female
Property, women and masculinity proportion are 3：1, it is most fast swollen into the female malignant rate of climb in Jin20Nian Lai China cancer spectrum
Knurl.Nowadays in the female group of one, tier 2 cities, the sick incidence of disease ranks front three substantially, and what is had has even leapt to the umber one.
At present, the Main Means for making a definite diagnosis thyroid cancer are mainly pathology frost and postoperative stone in thyroid cell puncture, art
Wax pathological section, although thyroid cell, which is punctured, can reach the meaning made a definite diagnosis, because positive rate is low and limits it in clinic
On popularization, so clinical practice makes a definite diagnosis the method for thyroid cancer mainly or by frost in art or postoperative paraffin section.Remove
Outside this, in terms of thyroid cancer serology, although to the progress of thyroid cancer tumor markers quickly, but so far also
Do not have it is a kind of clinically obtain consistent accreditation, illustrate the research to thyroid cancer tumor markers be still within just step
Section, is worth further development.
Research shows that lipid-metabolism has with lesion tissue directly to be contacted very much, also disorderly including thyroid function.Mesh
Before, by nuclear magnetic resonance, MALDI/MS or GC/MS, it has been found that the lipid related to thyroid cancer a little.Wherein, to fat
Fat acid research show, the patient C14 of benign thyroid knurl:0,C16:1n7,C18:1n9,C20:1n9,C18:3n3 significantly drops
It is low, C16:0,C20:3n6,C20:4n6, C22:6n3 is significantly raised；Malignant tumor patient shows C14:0,C16:0, C18:
3n3 is raised, and C20:The trend of 3n6 reductions.In addition, also there is research and utilization mathematical modeling combination iipidomic, begin look for swelling
Tumor markers.They utilize PLS-DA models, it was found that dolichol, cholesterol, choline and 27 kinds of aliphatic acid, good in thyroid cancer
Property and pernicious crowd in, embody obvious difference.
But, so far, the forecast model being now able to using these mark Accurate Prediction thyroid cancers is not gone out also,
In particular for the thyroid cancer forecast model of Chinese population.
The content of the invention
It is an object of the invention to solve at least the above, and provide the advantage that at least will be described later.
, can it is a still further object of the present invention to provide a kind of method that utilization lipid biomarkers predict thyroid cancer
Fast, thyroid cancer is accurately and efficiently predicted.
First shape is predicted it is a still further object of the present invention to provide a kind of utilization lipid biomarkers for Chinese population
The method of gland cancer.
It is a still further object of the present invention to provide the compound closely related with thyroid cancer that one group is directed to Chinese population.
In order to realize that there is provided following technical scheme according to object of the present invention and further advantage：
A kind of method that utilization lipid biomarkers predict thyroid cancer, wherein, mainly include the following steps that：
Step 1, otherness compound R 1-R9 between normal person's group and thyroid cancer group is filtered out, be respectively：
R1:PG(17:0/14:1)；
R2:PS(O-20:0/18:1)；
R3:PC(16:0/18:2)；
R4:bacteriohopane-,32,33,34-triol-35-cyclitolguanine；
R5:PC(16:0/20:4)；
R6:TG(16:0/16:1/20:2)；
R7:PC(16:0/18:1)；
R8:PS(O-18:0/17:0)；
R9:PS(P-18:0/22:2)；
Step 2, calculated using Logic Regression Models 3, obtain TC values, the calculation formula of the Logic Regression Models 3
For：
TC=1.8002-2.2815*R1-2.3474*R4+2.8573*R9；
Step 3, according to gained TC values judged that TC=0 is no；TC=1 is yes.
Preferably, otherness compound is screened using OPLS-DA models in step 1, then by VIP>1 row
The data of preceding 9 bit variable of name, which are extracted, produces R1-R9.
Preferably, the specific method screened in step 1 is：
Sample is carried out ultra performance liquid chromatography and mass spectral analysis by step 1.1, iipidomic data is obtained, by normal person's group
Group and thyroid cancer group are calculated as CK and JC respectively；
Step 1.2 is standardized operation to iipidomic data, and S-plot is carried out to CK and JC using OPLS-DA models
Distribution obtains sigmoid curve, and carries out pressure packet, calculates the variable importance of influence CK and JC packets, produces VIP values；
The standard that step 1.3 is more than 5 according to VIP values obtains 15 compounds, and using 15 compounds as with first shape
Gland cancer degree of correlation highest otherness compound；
Step 1.4 arranges 15 compounds of gained according to VIP values size from high to low, takes first 9, produces in step 1
The otherness compound R I-R9.
Preferably, TC values or calculated in step 2 with Logic Regression Models 2, the meter of the Logic Regression Models 2
Calculating formula is：
TC=1.6361-12.5962*R+0.4081*R2-0.962*R3-1.7675*R4+0.7317* R5-7.3848*R6
+15.9658*R7+0.494*R8+2.5964*R9。
Preferably, TC values or calculated in step 2 with Logic Regression Models 1, the meter of the Logic Regression Models 1
Calculating formula is：
TC=1.6054-13.4331*R1-2.4503*R4+0.9397*R5+9.5919*R7+3.310 8*R9.
The present invention at least includes following beneficial effect：
The present invention has found the one group compound related to thyroid cancer first by screening, i.e., related to thyroid cancer
Lipid biomarkers, and by construction logic regression model, obtain predicting the side of thyroid cancer according to these lipid marks
Method, efficient and convenient, the degree of accuracy is high.Tentatively judged by AIC values, and carry out ROC curve drafting, AUC reaches 0.872.
Further advantage, target and the feature of the present invention embodies part by following explanation, and part will also be by this
The research and practice of invention and be understood by the person skilled in the art.
Brief description of the drawings
Fig. 1 is the S-plot distribution maps of heretofore described OPLS-DA models；
Fig. 2 is carries out the result of pressure packet using OPLS-DA models to CK and JC in the present invention；
Fig. 3 is is used for investigating the volcano figure of the compound filtered out in the present invention；
Fig. 4 is ROC curve figure in the present invention.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings, to make those skilled in the art with reference to specification text
Word can be implemented according to this.
It should be appreciated that such as " having ", "comprising" and " comprising " term used herein do not allot one or many
The presence or addition of individual other elements or its combination.
A kind of method that utilization lipid biomarkers predict thyroid cancer, wherein, mainly include the following steps that：
Step 1, otherness compound R 1-R9 between normal person's group and thyroid cancer group is filtered out, be respectively：
R1:PG(17:0/14:1)；
R2:PS(O-20:0/18:1)；
R3:PC(16:0/18:2)；
R4:bacteriohopane-,32,33,34-triol-35-cyclitolguanine；
R5:PC(16:0/20:4)；
R6:TG(16:0/16:1/20:2)；
R7:PC(16:0/18:1)；
R8:PS(O-18:0/17:0)；
R9:PS(P-18:0/22:2)。
Step 2, calculated using Logic Regression Models 1, obtain TC values, the calculation formula of the Logic Regression Models 3
For：
TC=1.8002-2.2815*R1-2.3474*R4+2.8573*R9；
Drawn by ROC curve, the AUC of model 3 is 0.872, and the degree of accuracy is up to 87.2%.
Step 3, according to gained TC values judged that TC=0 is no；TC=1 is yes.R1-R9 is to detect in sample to be somebody's turn to do
The content value of lipid.
Otherness compound is screened using OPLS-DA models in step 1, then by VIP>9 changes before 1 ranking
The data of amount, which are extracted, produces R1-R9.
The specific method screened in step 1 is：
Sample is carried out ultra performance liquid chromatography and mass spectral analysis by step 1.1, iipidomic data is obtained, by normal person's group
Group and thyroid cancer group are calculated as CK and JC respectively.
Step 1.2 is standardized operation to iipidomic data, and S-plot is carried out to CK and JC using OPLS-DA models
Distribution obtains sigmoid curve, and carries out pressure packet, calculates the variable importance of influence CK and JC packets, produces VIP values.
The standard that step 1.3 is more than 5 according to VIP values obtains 15 compounds, and using 15 compounds as with first shape
Gland cancer degree of correlation highest otherness compound.
Step 1.4 arranges 15 compounds of gained according to VIP values size from high to low, takes first 9, produces in step 1
The otherness compound R 1-R9.
TC values or calculated in step 2 with Logic Regression Models 2, the calculation formula of the Logic Regression Models 2 is：
TC=1.6361-12.5962*R+0.4081*R2-0.962*R3-1.7675*R4+0.7317* R5-7.3848*R6+
15.9658*R7+0.494*R8+2.5964*R9。
Drawn by ROC curve, the AUC of model 2 is 0.864, and the degree of accuracy is 86.4%.
TC values or calculated in step 2 with Logic Regression Models 1, the calculation formula of the Logic Regression Models 1 is：
TC=1.6054-13.4331*R1-2.4503*R4+0.9397*R5+9.5919*R7+3.310 8*R9.
Drawn by ROC curve, the AUC of model 1 is 0.866, and the degree of accuracy is 86.6%.
Embodiment 1
Material and method
1. experimental subjects (is selected from Chinese)：16 normal persons, 64 thyroid cancer patients, venous blood samples 5ml.It is accurate
100 μ L blood is really measured, 0.9mL extract solution (100% isopropanol) is added, it (must be import centrifugation to be transferred to 2mL centrifuge tubes
Pipe, plastics are not readily dissolved in organic solvent；Axygen brands) in, more than vortex oscillation 10s, ultrasonic 10min, then in -20 degree
Freezed 1 hour in refrigerator, vortex oscillation at room temperature after taking-up centrifuges 10min with refrigerated centrifuge 10000rpm, then gone
The organic phase filter membrane of 0.22 μm of clear liquid 1mL and mistake is stored in be measured in refrigerator to glass sample introduction kind.
2. key instrument
2.1.1 refrigerated centrifuge：Model D3024R, Scilogex companies, the U.S.
2.1.2 vortex oscillator：Model MX-S, Scilogex companies, the U.S.
2.1.3 high-resolution mass spectrometer：ESI-QTOF/MS；Model：Xevo G2-S Q-TOF；Producer：Waters
2.1.4 ultra performance liquid chromatography：UPLC；Model：ACQUITY UPLC I-Class systems；Producer：Waters
2.1.4 data acquisition software：MassLynx4.1；Producer：Waters
2.1.5 software is analyzed and identified：Progenesis QI；Producer：Waters
2.1.6 mapping software：EZinfo；HemI；Simca-P
3. main agents
Methanol, acetonitrile, formic acid, ammonium formate, leucine enkephalin, sodium formate.Producer is Fisher.
4. Setup Experiments
Using extract solution (100% isopropanol) as blank control (Blank) sample；Taken out from the sample introduction kind of each sample
100 μ L are mixed into new sample introduction kind as Quality Control (QC) sample；Official sample according to every group of sample room every sample introduction, for example before this
Blank1, followed by QC1, followed by W1, followed by W2 this order, next round sample introduction is then carried out again.
Liquid phase process
Chromatographic column：ACQUITY UPLC CSH C18 Column,1.7μm,1mm X 50mm,1/pkg
[186005292]；
Column temperature：55 degree
Flow velocity：0.4mL/min
Mobile phase：A：ACN/H2O (60%/40%), contains 10mM ammonium formates and 0.1% formic acid
B：IPA/ACN (90%/10%), contains 10mM ammonium formates and 0.1% formic acid
(note：ACN is acetonitrile, and IPA is isopropanol)
Sampling volume：0.2μL
Elution program：
Mass spectrometry method
Data acquisition modes：MSe；Molecular weight scanning range：50-1500m/z；Resolution model (profile diagram).
Negative ions pattern is respectively gathered once.
Ion gun：Electron spray ionisation source (ESI)
Capillary voltage：3KV
Taper hole voltage is：25V
Impact energy：15-60V
Source temperature：120 degree
Desolventizing temperature：500 degree
Taper hole gas velocity：50L/h
Desolvation gas speed：500L/h
Sweep time：0.2s
Use leucine enkephalin (m/z 556.2771, cation；554.2615, anion) and carry out real time correction.Make
It is corrected with sodium formate.
Iipidomic data analysis
Progenesis QI softwares (Waters, Massachusetts, USA) are used for interpretation of result, extract non-targeted fat
The characteristic peak of matter molecule, is compared and screens.Meanwhile, sieved with QC (quantifying control) and blank (blank)
Select background data.Final data, import EZinfo 3.0, and carry out Principal Component Analysis (PCA)
Analysis, Orthogonal signal correction Partial Least Square DiscriminationAnalysis
(OPLS-DA) model, Variable Importance in Projection (VIP) calculating, while obtaining volcano figure
(coefficients vs.VIP spots), as shown in Figure 3.Wherein, Logic Regression Models and ROC curve (such as Fig. 4) pass through
R language is built and drawn.
As a result describe
The identification of normal population and thyroid cancer crowd's lipid difference material.
We initially set up OPLS-DA models, and normal person (CK) and thyroid cancer (JC) group are classified, and studies
Cause the reason for they difference occur.We have seen that in OPLS-DA models, utilizing correlation (correlation) and association side
The S-plot that the p value of poor (covarience) is made forms extraordinary sigmoid curve, as shown in Figure 1.Will using OPLS-DA
Two groups of data carry out pressure packet, as a result as shown in Figure 2.Calculate the variable importance of influence CK and JC packets, i.e. VIP
(Variable Importance in Projection) value.308 compounds are screened altogether, and their VIP values are more than 1；
We select VIP>5,15 big compounds of contribution rate, as shown in table 1.
The variable importance of table 1. is projected
We pass through VIP>1 compound screened is marked on S-plot with red boxes, it is found that they are uniform
It is distributed in both sides.Meanwhile, we utilize volcano figure, to investigate the distribution of the compound filtered out, as shown in fig. 3, it was found that screening
Compound out is all distributed in the periphery of volcano figure.These results all illustrate that, by OPLS-DA models, we successfully screen
Go out to cause the compound of CK and JC differences.
Set up Logic Regression Models and ROC curve.We are standardized operation at the data to iipidomic
(Rproject：scale).Then by VIP>The data of 9 bit variables are extracted before 1 ranking, set up Logic Regression Models and
ROC curve.
In formula, TC：Whether tumour is suffered from, 0 is no, and 1 is yes
R1:PG(17:0/14:1)
R2:PS(O-20:0/18:1)
R3:PC(16:0/18:2)
R4:bacteriohopane-,32,33,34-triol-35-cyclitolguanine
R5:PC(16:0/20:4)
R6:TG(16:0/16:1/20:2)
R7:PC(16:0/18:1)
R8:PS(O-18:0/17:0)
R9:PS(P-18:0/22:2)
Model 3：TC=1.8002-2.2815*R1-2.3474*R4+2.8573*R9
AIC:57.484
Signif.codes:‘***’0.001；‘**’0.01；‘*’0.05；‘.’0.1；
Model 2：
TC=1.6361-12.5962*R+0.4081*R2-0.962*R3-1.7675*R4+0.7317* R5-7.3848*R6
+15.9658*R7+0.494*R8+2.5964*R9
AIC:65.973
Signif.codes:‘***’0.001；‘**’0.01；‘*’0.05；‘.’0.1；
Model 1：
TC=1.6054-13.4331*R1-2.4503*R4+0.9397*R5+9.5919*R7+3.310 8*R9
AIC:58.782
Signif.codes:‘***’0.001；‘**’0.01；‘*’0.05；‘.’0.1；
For three above Logic Regression Models, ROC curve drafting is carried out, as shown in figure 4, model 1 is M1, AUC reaches
To 0.866；Model 2 is M2, and AUC is 0.864；Model 3 is M3, and AUC reaches 0.872.We have found that model 3 is near a left side
Upper angle fixed point, while AUC highest, finally, determines model 3 to predict that diabetes hyperlipemia is preferable based on lipid index
Forecast model.Can also be according to institute's test sample product data cases, either model 2 carries out calculating prediction or preferential choosing to preference pattern 1
Select model 3 to be calculated, while carrying out auxiliary checking, Cooperative Analysis prediction using 1 and 2 models.
Although embodiment of the present invention is disclosed as above, it is not restricted in specification and embodiment listed
With it can be applied to various suitable the field of the invention completely, can be easily for those skilled in the art
Other modification is realized, therefore under the universal limited without departing substantially from claim and equivalency range, the present invention is not limited
In specific details and shown here as the legend with description.
Claims (5)
1. a kind of method that utilization lipid biomarkers predict thyroid cancer, it is characterised in that mainly include the following steps that：
Step 1, otherness compound R 1-R9 between normal person's group and thyroid cancer group is filtered out, be respectively：
R1:PG(17:0/14:1)；
R2:PS(O-20:0/18:1)；
R3:PC(16:0/18:2)；
R4:bacteriohopane-,32,33,34-triol-35-cyclitolguanine；
R5:PC(16:0/20:4)；
R6:TG(16:0/16:1/20:2)；
R7:PC(16:0/18:1)；
R8:PS(O-18:0/17:0)；
R9:PS(P-18:0/22:2)；
Step 2, calculated using Logic Regression Models 3, obtain TC values, the calculation formula of the Logic Regression Models 3 is：
TC=1.8002-2.2815*R1-2.3474*R4+2.8573*R9；
Step 3, according to gained TC values judged that TC=0 is no；TC=1 is yes.
2. the method for thyroid cancer is predicted using lipid biomarkers as claimed in claim 1, it is characterised in that step 1
It is middle that otherness compound is screened using OPLS-DA models, then by VIP>The data of 9 bit variables are extracted before 1 ranking
Out produce R1-R9.
3. the method for thyroid cancer is predicted using lipid biomarkers as claimed in claim 2, it is characterised in that step 1
The specific method of middle screening is：
Sample is carried out ultra performance liquid chromatography and mass spectral analysis by step 1.1, obtains iipidomic data, by normal person's group and
Thyroid cancer group is calculated as CK and JC respectively；
Step 1.2 is standardized operation to iipidomic data, and S-plot distributions are carried out to CK and JC using OPLS-DA models
Sigmoid curve is obtained, and carries out pressure packet, the variable importance of influence CK and JC packets is calculated, produces VIP values；
The standard that step 1.3 is more than 5 according to VIP values obtains 15 compounds, and using 15 compounds as with thyroid cancer
Degree of correlation highest otherness compound；
Step 1.4 arranges 15 compounds of gained according to VIP values size from high to low, takes first 9, produces described in step 1
Otherness compound R 1-R9.
4. the method for thyroid cancer is predicted using lipid biomarkers as claimed in claim 1, it is characterised in that step 2
Middle TC values are calculated with Logic Regression Models 2, and the calculation formula of the Logic Regression Models 2 is：
TC=1.6361-12.5962*R+0.4081*R2-0.962*R3-1.7675*R4+0.7317* R5-7.3848*R6+
15.9658*R7+0.494*R8+2.5964*R9。
5. the method for thyroid cancer is predicted using lipid biomarkers as claimed in claim 1, it is characterised in that step 2
Middle TC values are calculated with Logic Regression Models 1, and the calculation formula of the Logic Regression Models 1 is：
TC=1.6054-13.4331*R1-2.4503*R4+0.9397*R5+9.5919*R7+3.310 8*R9.
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| CN111999404A (en) * | 2020-08-14 | 2020-11-27 | 中元伯瑞生物科技(珠海横琴)有限公司 | Application of nervonic acid in preparation of detection reagent for thyroid malignant tumor |
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| CN111999404A (en) * | 2020-08-14 | 2020-11-27 | 中元伯瑞生物科技(珠海横琴)有限公司 | Application of nervonic acid in preparation of detection reagent for thyroid malignant tumor |
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