CN111681712A - Application of composition in preparation of diagnostic reagent for determining brain metastasis risk state of non-small cell lung cancer patient - Google Patents
Application of composition in preparation of diagnostic reagent for determining brain metastasis risk state of non-small cell lung cancer patient Download PDFInfo
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Abstract
The invention provides application of a composition in preparing a diagnostic reagent for determining the brain metastasis risk state of a patient with non-small cell lung cancer, wherein the composition comprises a detection reagent of a biomarker WNT5 a-L. The invention provides application of a composition in preparing a kit for a method for determining brain metastasis risk state of a patient with non-small cell lung cancer, which is characterized by collecting data of individuals without brain metastasis and brain metastasis of the non-small cell lung cancer, dividing a training set by R language software through a bootstrapping method, voting the brain metastasis risk state by utilizing the training set through a random forest method, and determining the brain metastasis risk state of the individual to be detected according to a voting result; or by random forest methods to evaluate biomarkers and other clinical influencing factors that most affect brain metastases. The scheme of the invention can determine the brain metastasis risk state of the patient with the non-small cell lung cancer, evaluate the brain metastasis risk, and provide basis for the prevention, early screening, intervention and treatment and disease prognosis of the brain metastasis of the non-small cell lung cancer.
Description
Technical Field
The invention relates to the field of non-small cell lung cancer metastasis determination, in particular to application of a composition in preparing a diagnostic reagent for determining a brain metastasis risk state of a non-small cell lung cancer patient.
Background
Lung cancer is one of the most common malignant tumors in the world, and has become the first cause of death of malignant tumors in urban population in China. Common types of non-small cell lung cancer include squamous cell carcinoma (squamous carcinoma), adenocarcinoma, large cell carcinoma, which has slower growth and division of cancer cells and relatively late metastatic spread as compared to small cell carcinoma. Non-small cell lung cancer accounts for about 80% of all lung cancers, with about 75% of patients finding a very low 5-year survival rate at the middle and advanced stages.
Researchers have conducted extensive studies on the early diagnosis of small cell lung cancer, and found many biomarkers related to the early diagnosis, such as serum squamous cell carcinoma antigen (SCC-Ag), glycolytic enolase (NSE), carcinoembryonic antigen (CEA), and cytokeratin (CYFRA 21-1), etc., which provide some ideas for the early diagnosis of small cell lung cancer.
However, another problem in the field of non-small cell lung cancer treatment at present is that about 50% of non-small cell lung cancers can have brain metastasis during the course of disease, and brain metastasis seriously affects the survival time and life quality of non-small cell lung cancer patients, and after patients are diagnosed with non-small cell lung cancer for the first time, it is difficult to predict whether the non-small cell lung cancer patients have a brain metastasis risk, and it is difficult to establish a treatment scheme for early intervention of brain metastasis and a disease prognosis scheme. .
Therefore, how to provide a method for determining the cancer cell diffusion metastasis, especially the brain metastasis risk state, for a patient who is initially diagnosed with the non-small cell lung cancer, so as to evaluate the brain metastasis risk, provide basis for the prevention, early screening, intervention treatment and disease prognosis of the non-small cell lung cancer brain metastasis, and become a problem to be solved.
Disclosure of Invention
The invention provides an application of a composition in preparing a diagnostic reagent for determining a brain metastasis risk state of a patient with non-small cell lung cancer, and an application of the composition in preparing a kit for a method for determining the brain metastasis risk state of the patient with non-small cell lung cancer.
The invention also provides a system for evaluating the brain metastasis risk state of the patient with the non-small cell lung cancer, and the brain metastasis risk state of the individual to be detected can be accurately evaluated by using a bootstrap method and a random forest method.
The invention provides a composition for preparing a diagnostic reagent for determining the brain metastasis risk state of a patient with non-small cell lung cancer, wherein the composition comprises a detection reagent of biomarkers in blood, and the biomarkers comprise WNT5a-L (namely WNT5 a-long).
Further, the biomarker further includes MLPH (melanophilin), or the biomarker further includes a combination of MLPH and miR-330-3P (mircoRNA-330-3P).
WNT5a-L may be, for example, Bauer M, Bnard J, Gasterland T, Willert K, Cappellen D (2013) WNT5A Encodes Two Iso of with Distingt Functions in caps ONE 8(11) E80526.doi: 10.1371/journel. p. 0080526, human WNT 5A-L.
MLPH is also a known Prostate Cancer biomarker in the art, such as disclosed in Zhang et al, MLPH Accelerostest Epithelial-sensory Transition in State Cancer, oncotarget and transduction, 2020: 13701-708, or a protein complex associated with the transfer of melanin in melanocytes, such as in Jeong Ah Lee et al, Identification of MicroRNA Targeting Mlph and influencing Melanosome Transport, biomoles 2019,9, 265; doi: 10.3390/boom 9070265.
miR-330-3p is a human breast Cancer biomarker known in the art, such as disclosed in Aruz Mesci et al, Targeting of CCBE1 by miR-330-3p in human breast Cancer models, British Journal of Cancer (2017)116, 1350-; 22:1726 and 1730.
Still further, the detection reagent is a reagent capable of detecting the level of the biomarker based on mRNA levels.
Further, the mRNA is obtained from peripheral blood.
The invention also provides application of a composition in preparing a kit for a method for determining the brain metastasis risk state of a patient with non-small cell lung cancer, wherein the composition comprises a detection reagent of a biomarker in blood, and the method comprises the following steps
1) Collecting the biomarker levels and other clinical impact factor data for non-small cell lung cancer brain metastasis free individuals, and the biomarker levels and other clinical impact factor data for non-small cell lung cancer brain metastasis free individuals;
2) dividing the collected data into training sets aiming at the non-small cell lung cancer brain metastasis free individuals and the non-small cell lung cancer brain metastasis individuals by using R language software through a bootstrap method,
3) then, the biomarker level and other clinical influence factors of the individual to be detected are voted for the brain transfer risk state by using the training set through a random forest method, and the brain transfer risk state of the individual to be detected is determined according to the voting result; or the biomarkers and other clinical influencing factors which have the greatest influence on the brain metastasis are evaluated by a random forest method by utilizing the average descent precision and the average kini index,
the biomarkers include WNT5a-L, MLPH and miR-330-3 p;
the other clinical influencing factors include one or more of: individual sex, age, smoking history, type of pathology, presence or absence of lymph node metastasis, EGFR status.
Determining the brain metastasis risk status of a non-small cell lung cancer patient in a protocol of the invention includes determining the brain metastasis status of a non-small cell lung cancer patient.
In the scheme of the application, the bootstrap method and the random forest method are conventional processing methods in the field of data statistics, and those skilled in the art can process the acquired data based on the 3 biomarker levels and the clinically relevant influence factor data by adopting the conventional methods to finally obtain the voting result aiming at the brain metastasis risk state of the individual to be tested, which can be realized by those skilled in the art.
In the protocol of the present application, the individual to be tested may be a patient suffering from non-small cell lung cancer but not yet developing brain metastases; or patients with non-small cell lung cancer who have developed brain metastases but have not yet been diagnosed, as will be appreciated by those skilled in the art.
Further, the detection reagent is a reagent capable of detecting the level of the biomarker based on mRNA levels.
Further, the mRNA is obtained from peripheral blood.
The invention also provides a system for evaluating the brain metastasis risk state of the patient with the non-small cell lung cancer, which comprises a data acquisition module, a training module and a voting module;
the data acquisition module is used for acquiring the biomarker level and other clinical influence factor data of the non-small cell lung cancer brain metastasis-free individual and the biomarker level and other clinical influence factor data of the non-small cell lung cancer brain metastasis-free individual;
the training module is used for dividing the acquired data into a training set aiming at the non-small cell lung cancer brain-free transfer individual and the non-small cell lung cancer brain transfer individual by R language software through a bootstrap method;
the voting module is used for voting the biomarker level and other clinical influence factors of the individual to be detected by using the training set through a random forest method to determine the brain metastasis risk state of the individual to be detected according to the voting result; or evaluating biomarkers and other clinical influence factors which have the greatest influence on brain metastasis by a random forest method by utilizing the average reduction precision and the average kini index;
the biomarkers include WNT5a-L, MLPH and miR-330-3 p;
the other clinical influencing factors include one or more of: individual sex, age, smoking history, type of pathology, presence or absence of lymph node metastasis, EGFR status.
Further, the detection reagent is a reagent capable of detecting the level of the biomarker based on mRNA levels.
Further, the mRNA is obtained from peripheral blood.
The invention adopts R software to evaluate the level of the biomarker, wherein R is free and open-source free software, has strong statistical analysis function and mapping function, and is internally provided with rich mathematical calculation, statistics and calculation functions. After all clinical relevant influence factors and all values of biomarker levels are collected, all information is input into computer R software, data are processed by adopting a random forest method, the random forest is a statistical learning theory, in the invention, a plurality of samples are extracted from original samples by using a bootstrap resampling method, decision tree modeling is carried out on each bootstrap sample, then prediction of a plurality of decision trees is combined, and a final prediction result is obtained by voting.
A large number of theoretical and empirical researches in the prior art prove that the random forest method has high prediction accuracy. The method was first proposed by Breiman L in 2001 (Random forms. machine learning.2001.18.45 (1): 5-32). The advantages of random forests are: the prediction accuracy is improved on the premise that the operation amount is not obviously increased, the method is insensitive to multivariate collinearity, the result is stable to missing data and unbalanced data, and overfitting is not easy to occur.
In the scheme of the application, the bootstrap method and the random forest method are conventional processing methods in the field of data statistics, and a person skilled in the art can process the obtained biomarker level data by adopting the conventional methods to finally obtain a voting result for the metabolic state of the biomarker of the individual to be tested, which can be realized by the person skilled in the art.
The scheme of the invention has the following advantages:
1) the method and the system have lower misjudgment rate when determining the brain metastasis risk state of the individual to be detected.
2) By using the biomarker, a bootstrapping resampling method and a random forest method are utilized, the brain metastasis risk state of an individual to be detected can be accurately evaluated, data support is provided for the brain metastasis risk evaluation of the non-small cell lung cancer, and meanwhile, a basis can be provided for intervention treatment and disease prognosis aiming at the brain metastasis of the non-small cell lung cancer.
3) The system can obtain the target molecule which has the largest influence on the brain metastasis of the non-small cell lung cancer, thereby providing a basis for medicine or physical intervention aiming at the target molecule and finally realizing the effect of improving the brain metastasis prognosis of the non-small cell lung cancer.
Drawings
FIG. 1 is a graph of the mean reduction accuracy and mean Kiney index for 3 biomarkers and other clinical influencing factors calculated in the protocol of the invention.
FIG. 2 is a ROC graph reflecting the predictive effect of the system of the present invention.
Detailed Description
Example 1 determination of the brain metastasis risk status of a patient with non-small cell lung cancer Using the systematic voting results of the present invention
The system comprises a data acquisition module, a training module and a voting module; the data acquisition module is used for acquiring the biomarker level and other clinical influence factor data of the non-small cell lung cancer brain metastasis-free individual and the biomarker level and other clinical influence factor data of the non-small cell lung cancer brain metastasis-free individual; the training module is used for dividing the acquired data into a training set aiming at the non-small cell lung cancer brain-free transfer individual and the non-small cell lung cancer brain transfer individual by R language software through a bootstrap method; the voting module is used for voting the biomarker level and other clinical influence factors of the individual to be detected by using the training set through a random forest method to determine the brain metastasis risk state of the individual to be detected according to the voting result;
the biomarkers include WNT5a-L, MLPH and miR-330-3 p;
the other clinical influencing factors include one or more of: individual sex, age, smoking history, type of pathology, presence or absence of lymph node metastasis, EGFR status.
In this example, non-small cell lung cancer brain metastasis free individuals (53) and non-small cell lung cancer brain metastasis individuals (61) were collected and the biomarkers included the levels of WNT5a-L, MLPH and miR-330-3p and other clinical influencing factors. The levels of each biomarker in table 1 were obtained by collecting blood from a test subject and performing the test using a method conventional in the art.
The other clinical influencing factors of 53 individuals without brain metastasis of the non-small cell lung cancer are shown in table 1, and the other clinical influencing factors of 61 individuals without brain metastasis of the non-small cell lung cancer are shown in table 1.
Individuals from the affiliated hospital of the college of Hospital's medical college of science and technology, Huazhong, collected their peripheral blood and obtained the levels of each biomarker based on mRNA level measurements in Table 1.
The specific analysis of the data is as follows:
dividing the data collected in the table 1 into training sets aiming at the non-small cell lung cancer brain-free metastasis individuals and the non-small cell lung cancer brain metastasis individuals by using R language software through a bootstrap method; and then, voting the biomarker level and other clinical influence factors of the individual to be detected by using the training set through a random forest method, and determining the brain metastasis risk state of the individual to be detected according to the voting result.
In the scheme of the application, the bootstrap method and the random forest method are conventional processing methods in the field of data statistics, and those skilled in the art can process the data based on the existing data in table 1 to finally obtain the vote of the brain metastasis risk state, which can be realized by those skilled in the art.
In this example, the above-mentioned non-small cell lung cancer brain metastasis individuals (61) were used as individuals to be tested, and the system and method of the present invention were verified by voting for the brain metastasis risk status based on the biomarker level and other clinical influence factors, respectively.
The final voting results showed that 2 of the known non-small cell lung cancer brain metastases (61) were voted for no brain metastases, and the misjudgment rate was 2/61-0.033.
It can be seen that the misjudgment rate of the method and the system for the non-small cell lung cancer brain metastasis risk state is less than 0.1, which shows that the method and the system are effective and reliable.
Further, the prediction effect of the above system is reflected by the area under the ROC curve (generated by R software), and as a result, as shown in fig. 2, the area AUC under the ROC curve is 0.995, which indicates that the system has high accuracy and a good prediction effect.
Example 2 determination of biomarkers and their metabolite levels that most affect brain metastases from non-small cell lung cancer Using the methods and systems of the invention
The average descending precision and the average kini index of the 3 biomarkers and other clinical influence factors are calculated by using a random forest model fitted by a training set, and the result is shown in figure 1, wherein the abscissa is the average descending precision or the average kini index, and the average descending precision and the average kini index are indexes reflecting the importance degree of variables in the random forest method.
The circles in figure 1 represent the biomarkers and clinical parameters. As can be seen, the biomarker that was evaluated to have the greatest effect on brain metastasis of non-small cell lung cancer was WNT5a-L, followed by MLPH and miR-330-3p, and the other clinical influencing factors that had the greatest effect on brain metastasis of non-small cell lung cancer were age, followed by EGFR status, presence or absence of lymph node metastasis, smoking history, individual gender, and type of pathology.
The evaluation result obtained by the scheme of the application can be used as a basis for medicine or physical intervention aiming at the metabolite of the biomarker, and finally the effect of improving the metabolic state of the biomarker in vivo is realized.
Claims (10)
1. Use of a composition comprising a reagent for detecting a biomarker in blood, the biomarker comprising WNT5a-L, in the manufacture of a diagnostic reagent for determining the risk status of brain metastases in a patient with non-small cell lung cancer.
2. The use according to claim 1,
the biomarker further comprises MLPH, or the biomarker further comprises MLPH and miR-330-3 p.
3. Use according to claim 1 or 2,
the detection reagent is a reagent capable of detecting the level of the biomarker based on mRNA levels.
4. The use of claim 3, wherein the mRNA is obtained from peripheral blood.
5. Use of a composition comprising a reagent for detecting a biomarker in blood for the manufacture of a kit for use in a method of determining the risk status of brain metastases in a patient with non-small cell lung cancer, said method comprising
1) Collecting the biomarker levels and other clinical impact factor data for non-small cell lung cancer brain metastasis free individuals, and the biomarker levels and other clinical impact factor data for non-small cell lung cancer brain metastasis free individuals;
2) dividing the collected data into training sets aiming at the non-small cell lung cancer brain metastasis free individuals and the non-small cell lung cancer brain metastasis individuals by using R language software through a bootstrap method,
3) then, the biomarker level and other clinical influence factors of the individual to be detected are voted for the brain transfer risk state by using the training set through a random forest method, and the brain transfer risk state of the individual to be detected is determined according to the voting result; or the biomarkers and other clinical influencing factors which have the greatest influence on the brain metastasis are evaluated by a random forest method by utilizing the average descent precision and the average kini index,
the biomarkers include WNT5a-L, MLPH and miR-330-3 p;
the other clinical influencing factors include one or more of: individual sex, age, smoking history, type of pathology, presence or absence of lymph node metastasis, EGFR status.
6. The use of claim 5, wherein the detection reagent is a reagent capable of detecting the level of the biomarker based on mRNA levels.
7. The use of claim 6, wherein the mRNA is obtained from peripheral blood.
8. A system for evaluating the brain metastasis risk state of a patient with non-small cell lung cancer is characterized by comprising a data acquisition module, a training module and a voting module;
the data acquisition module is used for acquiring the biomarker level and other clinical influence factor data of the non-small cell lung cancer brain metastasis-free individual and the biomarker level and other clinical influence factor data of the non-small cell lung cancer brain metastasis-free individual;
the training module is used for dividing the acquired data into a training set aiming at the non-small cell lung cancer brain-free transfer individual and the non-small cell lung cancer brain transfer individual by R language software through a bootstrap method;
the voting module is used for voting the biomarker level and other clinical influence factors of the individual to be detected by using the training set through a random forest method to determine the brain metastasis risk state of the individual to be detected according to the voting result; or evaluating biomarkers and other clinical influence factors which have the greatest influence on brain metastasis by a random forest method by utilizing the average reduction precision and the average kini index;
the biomarkers include WNT5a-L, MLPH and miR-330-3 p;
the other clinical influencing factors include one or more of: individual sex, age, smoking history, type of pathology, presence or absence of lymph node metastasis, EGFR status.
9. The system of claim 7, wherein the detection reagent is a reagent capable of detecting the level of the biomarker based on mRNA levels.
10. The system of claim 9, wherein the mRNA is obtained from peripheral blood.
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