CN108841962B - Non-small cell lung cancer detection kit and application thereof - Google Patents
Non-small cell lung cancer detection kit and application thereof Download PDFInfo
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Abstract
The invention discloses a non-small cell lung cancer detection kit and application thereof. The invention discloses a non-small cell lung cancer detection kit which comprises a substance for detecting the content of 9 miRNAs (miRNA-hsa-miR-20a, hsa-miR-25, hsa-miR-223, hsa-miR-320, hsa-miR-222, hsa-miR-146a, hsa-miR-191, hsa-miR-24 and hsa-miR-130a), CEA and CYFRA 21-1. The kit can be used for well screening the non-small cell lung cancer, has high sensitivity and high specificity, and is suitable for screening the non-small cell lung cancer, especially the early non-small cell lung cancer. Therefore, the 9 miRNAs can be used for carrying out early warning on the lung cancer risk of the population by combining the CEA and the CYFRA21-1, and the early diagnosis ratio is improved.
Description
Technical Field
The invention relates to the field of biomedicine, and discloses a non-small cell lung cancer detection kit and application thereof.
Background
Lung cancer is one of the most common malignant tumors in the clinic. Lung cancer is mainly classified into small cell lung cancer and non-small cell lung cancer. The main lung cancer types in China are non-small cell lung cancer, which accounts for about 80% of lung cancer patients and mainly comprises adenocarcinoma and squamous carcinoma. The lung cancer patients are discovered in time and at an early stage, and the survival rate and the death rate of the lung cancer patients are possibly improved and reduced by early treatment.
At present, the diagnosis of lung cancer usually depends on the imaging to find the tumor in the lung, including imaging methods such as chest X-ray, CT, Magnetic Resonance Imaging (MRI), etc., and combines the tumor markers in the serum such as carcinoembryonic antigen, etc. However, the imaging examination has certain limitations in the early diagnosis of tumors, such as difficulty in finding small-sized tumors; radiographic inspection methods such as CT have radiation damage to patients; high-precision instruments such as MRI and the like have high examination cost, long reservation time and limited popularization. Particularly, the imaging examination has high technical requirements on operators, and the misdiagnosis rate of inexperienced personnel is high. The circulating tumor biomarker has the advantages of being non-invasive, capable of being dynamically monitored and the like, and has a great application prospect in the aspect of screening tumors of asymptomatic people. At present, most of circulating tumor markers clinically applied are proteins, polypeptides, hormones, saccharides and the like, carcinoembryonic antigen (CEA) and cytokeratin 19 fragment (CYFRA21-1) are common tumor markers for clinically assisted diagnosis of lung cancer, have a certain reference value for the assisted diagnosis of lung cancer, but lack sufficient sensitivity and specificity for the diagnosis of lung cancer. Therefore, the discovery of new tumor markers for lung cancer is of great significance.
MicroRNA (abbreviated as miRNA) is a eukaryotic endogenous small-molecule single-stranded RNA with the length of about 22 nucleotides and used for regulating gene expression at the post-transcriptional level. It has been reported that the abnormal expression of miRNA is closely related to the generation and development of tumor, and miRNA has the same function as cancer gene or cancer suppressor gene. Many studies have shown that aberrant expression of free mirnas is associated with a variety of tumors, inflammation, infectious diseases, suggesting that free mirnas may serve as diagnostic markers for potential diseases, including tumors. Research proves that the circulating miRNA generates obvious abnormal expression in early stage of tumor generation and can be used for early diagnosis of tumor, and the circulating miRNA is used as a tumor diagnosis marker, has the advantages of non-invasion and dynamic monitoring, and is a good supplement to early tumor disease diagnosis technology.
At present, research reports that circulating miRNA can be used as a marker of non-small cell lung cancer and has high diagnostic value. However, different research groups mostly adopt a single reference miRNA for data normalization, different non-small cell lung cancer research screening markers have larger differences, and the diagnostic value of some mirnas is greatly changed in different research reports. In addition, researches on complementarity of miRNA and tumor protein markers, specificity of miRNA on benign and malignant diseases of other tissues and organs, and the like are rarely reported.
Disclosure of Invention
The invention aims to solve the technical problem of how to diagnose the non-small cell lung cancer.
In order to solve the technical problems, the invention firstly provides the application of a system for detecting the expression quantity of 9 miRNAs in the preparation of products for diagnosing or assisting in diagnosing non-small cell lung cancer;
the 9 miRNAs are hsa-miR-20a, hsa-miR-25, hsa-miR-223, hsa-miR-320, hsa-miR-222, hsa-miR-146a, hsa-miR-191, hsa-miR-24 and hsa-miR-130 a.
In the above application, the expression amount may be an absolute expression amount or a relative expression amount. The relative expression amounts can be relative expression amounts with respect to the content of ath-miR159a, hsa-miR1228 and/or hsa-miR-16, and can also be relative expression amounts with respect to the average content of ath-miR159a, hsa-miR1228 and hsa-miR-16. The expression amount may be an expression amount of the 9 mirnas in plasma and/or serum.
In the above application, the system for detecting the expression levels of 9 mirnas may include a system for detecting the expression levels of 9 mirnas by using quantitative PCR.
In the above application, the system for detecting the expression level of the 9 mirnas by quantitative PCR may include a set of primers, a set of probes, a set of reverse transcription primers, and/or other reagents and/or instruments required for performing quantitative PCR;
the primer set is 18 single-stranded DNAs shown as a sequence 1-sequence 18 in a sequence table; the sequences of the probe set are respectively shown as a sequence 19-a sequence 27 in a sequence table; the complete set of reverse transcription primers are 9 single-stranded DNAs shown as sequences 28-36 in a sequence table.
The other reagents required for performing quantitative PCR may be gene expression Master Mix and/or a set of reverse transcription primers. The Master Mix may specifically be a product of ABI, Inc. under the designation 4440046. The complete set of reverse transcription primers can be specifically a sequence in a sequence tableSingle-stranded DNAs shown in columns 28 to 36. The other reagents required for performing quantitative PCR may also be reverse transcriptase, DNA polymerase, buffer, dntps and/or the set of reverse transcription primers. The apparatus required for quantitative PCR may be ABI 7900HT, ABI ViiATM 7、ABI QuantStudioTM6Flex and/or ABI QuantstudioTMDX real-time fluorescence quantitative PCR instrument, etc. The data processing means may be software and/or a module.
In the above application, the system for detecting expression levels of 9 mirnas may further include a data processing device, where the data processing device is configured to convert the expression levels of 9 mirnas from the object to be detected into a diagnosis result of the object to be detected.
In the above application, the data processing device can diagnose non-small cell lung cancer and non-small cell lung cancer by recording the characteristics of X1 and X2; the X1 is the expression quantity of the 9 miRNAs in a non-small cell lung cancer group consisting of at least 50 patients with non-small cell lung cancer, and the X2 is the expression quantity of the 9 miRNAs in a non-small cell lung cancer group consisting of at least 50 individuals with non-small cell lung cancer.
The features of the X1 and the X2 may be documented in the data processing apparatus by a method comprising the steps of: the X1 and the X2 were introduced into the data processing apparatus, and a model that can be used for diagnosing non-small cell lung cancer was constructed by machine learning the X1 and the X2 (this model was named a non-small cell lung cancer diagnosis model).
The constructing a model useful for diagnosing non-small cell lung cancer by machine learning the X1 and the X2 may include Y1 or Y2;
y1, performing machine learning on the expression levels of the 9 miRNAs of the part of non-small cell lung cancer patients randomly selected from the non-small cell lung cancer group (such as 50% or more of the non-small cell lung cancer patients) and the expression levels of the 9 miRNAs of the part of non-small cell lung cancer individuals randomly selected from the non-small cell lung cancer group to construct a model for diagnosing the non-small cell lung cancer;
y2, carrying out m times on the Y1 to obtain m models for diagnosing the non-small cell lung cancer; selecting k models with high sensitivity (such as more than or equal to 0.5) and high specificity (such as more than 0.9) for diagnosing in the non-small cell lung cancer group and the non-small cell lung cancer group from the m models which can be used for diagnosing the non-small cell lung cancer, and using the k models as models for diagnosing the non-small cell lung cancer (named as C-RFLmi models); m is more than or equal to 1000; m is more than or equal to k and more than or equal to 10.
The method for diagnosing the subject by using the C-RFLmi model comprises the following steps: the result of not less than 50% of the k models available for diagnosing non-small cell lung cancer in the C-RFLMi model is that the subject is a non-small cell lung cancer patient, the subject is or is candidate for a non-small cell lung cancer patient; the results of less than 50% of the k models available for diagnosing non-small cell lung cancer in the C-RFLMI model as said subject is a non-small cell lung cancer patient, said subject is or is candidate for a non-small cell lung cancer individual.
In the above application, the data processing apparatus may process data through a decision tree algorithm. The decision tree algorithm may specifically be a random forest decision tree algorithm.
In the above application, the system for detecting the expression levels of 9 mirnas may be only the system for detecting the expression levels of 9 mirnas by using quantitative PCR, or may be composed of the system for detecting the expression levels of 9 mirnas by using quantitative PCR and the data processing device.
In the above application, the system for detecting the expression level of 9 mirnas may also be only a reagent or kit consisting of the set of primers, the set of probes, and/or the other reagents required for performing quantitative PCR.
In order to solve the technical problems, the invention also provides application of the system for diagnosing or assisting in diagnosing the non-small cell lung cancer by taking the 9 miRNAs as the non-small cell lung cancer markers in preparing products for diagnosing or assisting in diagnosing the non-small cell lung cancer.
The system for diagnosing or assisting in diagnosing the non-small cell lung cancer can be the system for detecting the expression quantity of the 9 miRNAs.
In order to solve the technical problems, the invention also provides application of the system for detecting the expression quantity, the CEA content and the CYFRA21-1 content of the 9 miRNAs in preparing products for diagnosing or assisting in diagnosing non-small cell lung cancer.
In the above application, the system for detecting the expression level of the 9 mirnas, the CEA content, and the CYFRA21-1 content may be composed of the system for detecting the expression level of the 9 mirnas, the system for detecting the CEA content, and the system for detecting the CYFRA21-1 content.
In one embodiment of the invention, the system for detecting CEA content is a carcinoembryonic antigen assay kit (chemiluminescence method), and the system for detecting CYFRA21-1 content is a cytokeratin 19 fragment assay kit (chemiluminescence method).
In the above application, the CEA content may be a CEA content in serum and/or plasma. The content of CYFRA21-1 can be the content of CYFRA21-1 in serum and/or plasma.
In the above application, the system may further include a data processing device, and the data processing device is configured to convert the 9 miRNA expression amounts, the CEA content, and the CYFRA21-1 content from the subject into a diagnosis result of the subject.
In the above application, the data processing device can diagnose non-small cell lung cancer and non-small cell lung cancer by recording the characteristics of E1 and E2; the E1 is the 9 miRNA expression levels, CEA content and CYFRA21-1 content of a non-small cell lung cancer group consisting of at least 50 non-small cell lung cancer patients, and may also be the 9 miRNA expression levels, CEA content, CYFRA21-1 content and age characteristics of a non-small cell lung cancer group consisting of at least 50 non-small cell lung cancer patients; the E2 is the 9 miRNA expression levels, CEA content, and CYFRA21-1 content for a non-small cell lung cancer group of at least 50 non-small cell lung cancer individuals, and may also be the 9 miRNA expression levels, CEA content, CYFRA21-1 content, and age profile for a non-small cell lung cancer group of at least 50 non-small cell lung cancer individuals.
The features of the E1 and the E2 may be documented in the data processing apparatus by a method comprising the steps of: the E1 and the E2 were introduced into the data processing apparatus, and a model that can be used for diagnosing non-small cell lung cancer was constructed by machine learning the E1 and the E2 (this model was named a non-small cell lung cancer diagnosis model).
The constructing a model useful for diagnosing non-small cell lung cancer by machine learning the E1 and the E2 may include F1 or F2;
f1, performing machine learning on the characteristics of the E1 of part of the non-small cell lung cancer patients randomly selected in the non-small cell lung cancer group (such as 50% or more of the non-small cell lung cancer patients) and the characteristics of the E2 of part of the non-small cell lung cancer individuals randomly selected in the non-small cell lung cancer group to construct a model for diagnosing non-small cell lung cancer;
f2, carrying out n times on the F1 to obtain n models for diagnosing the non-small cell lung cancer; selecting l models with high sensitivity (such as more than or equal to 0.5) and high specificity (such as more than 0.9) for diagnosing in the non-small cell lung cancer group and the non-small cell lung cancer group from the n models for diagnosing non-small cell lung cancer, and taking the l models as models for diagnosing non-small cell lung cancer (named as C-RFLCb models); n is more than or equal to 1000; n is more than or equal to l and more than or equal to 10.
The method for diagnosing the subject by using the C-RFLCb model comprises the following steps: the outcome of not less than 50% of the l models available for diagnosing non-small cell lung cancer in the C-RFLCb model is that the subject is a non-small cell lung cancer patient, the subject is or is candidate for a non-small cell lung cancer patient; the result of less than 50% of the models that can be used to diagnose non-small cell lung cancer in the l models of C-RFLCb is that the subject is a non-small cell lung cancer patient, and the subject is or is candidate for a non-small cell lung cancer individual.
In the above application, the data processing apparatus may process data through a decision tree algorithm. The decision tree algorithm may specifically be a random forest decision tree algorithm.
In order to solve the technical problems, the invention also provides application of the system for diagnosing or assisting in diagnosing the non-small cell lung cancer by taking the 9 miRNAs, the CEA and the CYFRA21-1 as non-small cell lung cancer markers in preparing products for diagnosing or assisting in diagnosing the non-small cell lung cancer.
In the above application, the system for diagnosing or assisting in diagnosing non-small cell lung cancer using the 9 mirnas, CEA, and CYFRA21-1 as non-small cell lung cancer markers may be the system for detecting the expression level of the 9 mirnas, the CEA content, and the CYFRA21-1 content.
In order to solve the technical problem, the invention also provides the following applications of M1) or M2):
m1) the 9 miRNAs are used as non-small cell lung cancer markers for diagnosing or assisting in diagnosing non-small cell lung cancer;
m2) and the 9 miRNAs, CEA and CYFRA21-1 are used as non-small cell lung cancer markers for diagnosing or assisting in diagnosing non-small cell lung cancer.
In order to solve the above technical problem, the present invention further provides the following system of P1) or P2):
p1) the system for detecting the expression quantity of 9 miRNAs;
p2) the system for detecting the expression quantity, the CEA content and the CYFRA21-1 content of 9 miRNAs.
In the present invention, the system may be a kit.
In the present invention, the non-small cell lung cancer may be stage 0, IA, IB, II or III non-small cell lung cancer.
Experiments prove that a model established by using the expression quantities of 9 miRNA-hsa-miR-20a, hsa-miR-25, hsa-miR-223, hsa-miR-320, hsa-miR-222, hsa-miR-146a, hsa-miR-191, hsa-miR-24 and hsa-miR-130a in plasma can well screen the non-small cell lung cancer, has high sensitivity and high specificity, and is suitable for screening early non-small cell lung cancer; the combination of the 9 miRNAs with CEA and CYFRA21-1 can better screen non-small cell lung cancer, especially early non-small cell lung cancer. Therefore, the 9 miRNAs or the 9 miRNAs combined with CEA and CYFRA21-1 can be used for screening the non-small cell lung cancer, early warning can be carried out on the lung cancer risk of people, the early diagnosis ratio is improved, only the blood plasma/blood serum of a to-be-detected object is needed without any other tissue, the detection sensitivity is improved by detecting the blood plasma miRNA level through the quantitative PCR technology, the means for detecting the lung cancer is enriched, and the early discovery ratio of the lung cancer can be improved.
Detailed Description
The present invention is described in further detail below with reference to specific embodiments, which are given for the purpose of illustration only and are not intended to limit the scope of the invention. The experimental procedures in the following examples are conventional unless otherwise specified. Materials, reagents, instruments and the like used in the following examples are commercially available unless otherwise specified.
Sensitivity (true positive rate): if the patient is actually ill, the patient is correctly judged as the percentage of ill according to the test standard, and the sensitivity is better if the sensitivity is larger, and the ideal sensitivity is 100%.
Specificity (true negative rate): the percentage of the disease-free area is judged to be the disease-free area according to the test standard, the larger the specificity is, the better the ideal specificity is 100%.
Examples 1 and 9 MiRNAs useful for diagnosing non-small cell lung cancer
Firstly, collecting samples and arranging sample data
The inventor of the application collects plasma samples meeting the standard by a Standard Operation Procedure (SOP), systematically collects complete demographic data, clinical data and the like, and selects 1375 human plasma samples as experimental samples for miRNA chip detection and a subsequent series of qRT-PCR verification through sample data arrangement, wherein the 1375 human samples are divided into four groups:
non-small cell lung cancer group: 462 cases of non-small cell lung cancer patients were included, and the inclusion criteria in this group were: patients with primary and untreated lung adenocarcinoma or lung squamous carcinoma who are clearly diagnosed by pathology, and who have not undergone surgery and chemoradiotherapy and no preoperative chemoradiotherapy before blood collection.
Normal control group: included 390 normal controls, the inclusion criteria for this group were: normal control population with no history of neoplastic disease.
Benign disease group of lung: the group included 250 benign lung disease patients and included criteria: patients suffering from benign diseases of the lung, such as pneumonia, tuberculosis, bullae, bronchiectasis, pneumothorax and/or chronic obstructive pulmonary disease, or at least one benign tumor of pulmonary cyst, pulmonary hamartoma, sclerosing pulmonary hemangioma, and tuberculoma.
Other tissue and organ benign and malignant groups: 273 patients with benign and malignant other tissues and organs were included, and the group was selected as follows: the cancer treatment method comprises the following steps of treating patients suffering from at least one benign disease of other tissues and organs such as bronchogenic cysts, mediastinal tumors, mediastinal neurofibroma, thymocyst, pericardial cyst, esophagitis, esophageal diverticulum, gastritis, stomach polyp, hepatitis, hepatic cyst, colorectal inflammation, colorectal polyp, colorectal adenoma, appendicitis and the like, or patients who are diagnosed with pathologically well-defined and untreated colorectal cancer, esophageal cancer, gastric cancer or thymus cancer, and the cancer patients do not undergo surgery and chemoradiotherapy before blood collection and do not undergo chemoradiotherapy before surgery.
The study was conducted using 1375 samples meeting the above criteria.
Second, discovery phase
Selecting 27 cases of non-small cell lung cancer (18 cases of lung adenocarcinoma and 9 cases of lung squamous carcinoma) in the group of non-small cell lung cancer and 27 normal controls with matched gender and age in the normal control group, adopting a customized TQMN ADV MIRNA CARD FRMT96A chip (Thermo Fisher, 4449141, comprising 96 miRNAs, hsa-let-7b, hsa-let-7c, hsa-let-7g, hsa-miR-101, hsa-miR-106a, hsa-miR-106b, hsa-miR-10b, hsa-miR-125b, hsa-miR-126, hsa-miR-128a, hsa-miR-130a, hsa-miR-138, hsa-miR-139-3p, hsa-miR-140-3p, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-143, hsa-miR-144, hsa-miR-145, hsa-miR-146a, hsa-miR-146b, hsa-miR-150, hsa-miR-151-5P, hsa-miR-152, hsa-miR-155, hsa-miR-15a, hsa-miR-15b, hsa-miR-16, hsa-miR-17, hsa-miR-181a, hsa-miR-186, hsa-miR-18a, hsa-miR-191, hsa-miR-194, hsa-miR-195, hsa-miR-199a, hsa-miR-19b, hsa-miR-18a, hsa-miR-191, hsa-miR-194, hsa-miR-195, hsa-miR-199a, hsa-miR-20a, hsa-miR-20b, hsa-miR-21, hsa-miR-220, hsa-miR-221, hsa-miR-222, hsa-miR-223, hsa-miR-24, hsa-miR-25, hsa-miR-26a, hsa-miR-26b, hsa-miR-27a, hsa-miR-28, hsa-miR-296, hsa-miR-29a, hsa-miR-29c, hsa-miR-301, hsa-miR-31, hsa-miR-320, hsa-miR-324-5p, hsa-miR-340, hsa-miR-342-3p, hsa-miR-34a, hsa-miR-374, hsa-miR-26 a-miR-26b, hsa-miR-376a, hsa-miR-376c, hsa-miR-425-5p, hsa-miR-4298, hsa-miR-432, hsa-miR-4327, hsa-miR-4443, hsa-miR-451, hsa-miR-4634, hsa-miR-4763-3p, hsa-miR-484, hsa-miR-486-3p, hsa-miR-500, hsa-miR-502-3p, hsa-miR-505, hsa-miR-532-3p, hsa-miR-579, hsa-miR-590-5p, hsa-miR-625, hsa-miR-636, hsa-miR-638, hsa-miR-532-3p, hsa-miR-579, hsa-miR-102 p, hsa-miR-642, hsa-miR-652, hsa-miR-744, hsa-miR-886-5p, hsa-miR-92a, hsa-miR-93, hsa-miR-942, hsa-miR-99b and U6snRNA) are detected, and the specific steps are as follows:
(1) plasma total RNA was extracted using miRNeasy Mini kit (Qiagen, 217184);
(2) carrying out reverse transcription on the total RNA obtained in the step (1) by utilizing a MicroRNA reverse transcription kit (Thermo Fisher, 4366596) and a reverse transcription primer (Thermo Fisher, 4459661) to obtain cDNA;
(3) carrying out pre-amplification reaction on the cDNA sample: adding Master Mix (ABI,4391128) and a pre-amplification primer (Thermo Fisher, 4459661) into the cDNA obtained in the step (2) to pre-amplify the miRNA specific to the chip so as to increase the amount of the cDNA required by expression, and obtaining a pre-amplification product;
(4) detecting the pre-amplification product obtained in the step (3) by using a customized Taqman miRNA chip to obtain an expression profile of 96 miRNAs; quantitative PCR reactions were performed by adding Master Mix (Thermo Fisher, 4440049) to CUSTOM TAQMAN ARRAY MIRNA CARD (Thermo Fisher, 4449141). Using ABI 7900HT fluorescent quantitative PCR instrument, 384-well TaqMan Low sensitivity Array specific program was selected for the reaction.
(5) And (5) analyzing and processing data. According to the chip result, 96 miRNAs are subjected to quality control by detecting the detection rate of more than 80% in the non-small cell lung cancer group and the normal control group through the CT value of 35 or below, so that 63 miRNAs are obtained. Different expression levels of miRNAs at 2^ a(-ΔCt)Wherein Δ Ct ═ CtSample(s)-CTReference toThe relative expression level of each miRNA was calculated by normalization using hsa-miR-16 stably expressed in plasma as a reference. Selection of non-small cell Lung based on the above TLDA resultsThe expression of the cancer group is 2 times or more than that of the normal control group, the statistical difference is less than 0.05, the miRNAs meeting the conditions are shown in Table 1, and the obtained miRNAs comprise hsa-miR-223, hsa-miR-221, hsa-miR-191, hsa-miR-140, hsa-miR-24, hsa-miR-146a, hsa-miR-130a, hsa-miR-146b, hsa-miR-27a and hsa-miR-145.
Table 1, expression results of 10 candidate mirnas in chip
Note: by "fold change" is meant the ratio of the expression level of miRNA in the non-small cell lung cancer group to its expression level in the normal control group.
(6) And combining literature investigation, and taking lung cancer miRNA markers hsa-miR-152, hsa-miR-20a, hsa-miR-222, hsa-miR-25 and hsa-miR-320 as candidate markers for further verification.
Third, preliminary verification stage
In the preliminary verification stage, a real-time fluorescent quantitative PCR method is adopted to verify the expression difference of the above 15 candidate miRNAs in different groups in 104 cases of non-small cell lung cancer patients in a non-small cell lung cancer group and 102 cases of normal controls in a normal control group, and the specific steps are as follows:
(1) extracting total RNA of plasma: respectively extracting 104 non-small cell lung cancer patients and 102 normal human plasma total RNA, and adding synthetic ath-miR159a with a certain concentration into the plasma before extraction.
(2) To the total RNA obtained in step (1), a mixture of reverse transcription primers (Table 2) was added (the average molar number of each reverse transcription primer in the mixture was equal), and reverse transcription was performed using a reverse transcription kit (Thermo Fisher, 4366596) to obtain cDNA.
TABLE 2 miRNA sequences and reverse transcription primer sequences
(3) After the step (2) is finished, quantitative PCR reaction is carried out by using gene expression Master Mix (Thermo Fisher, 4440046) and amplification upstream and downstream primers and probes (table 3), and the expression condition of each miRNA in each group is detected. The apparatus used was an ABI 7900HT fluorescent quantitative PCR apparatus.
TABLE 3 qPCR primer and Probe sequences
In Table 3, the 5 '-end of the probe was labeled with FAM and the 3' -end was labeled with MGB.
(4) Data analysis and processing: according to the qRT-PCR result of the step (3), firstly, the quality control is carried out through the expression level of the miRNAs in the total sample, the CT value in more than 80% of the samples is required to be less than 35, the detection rate of 6 miRNAs (hsa-miR-27a, hsa-miR-140, hsa-miR-145, hsa-miR-146b, hsa-miR-152 and hsa-miR-221) in lung cancer and contrast, the CT value of which is less than 35, is found to be less than 80%, the quality control standard is not met, and the miRNAs do not participate in detection and analysis in the follow-up process.
In the quantitative PCR verification experiment of the invention, besides hsa-miR-16 which is commonly used at present, hsa-miR-1228 which is relatively stably expressed in blood plasma is also considered, and exogenous Arabidopsis ath-miR159a is added. Endogenous miRNA is used for reflecting the endogenous difference of the sample, and deviation in the operation processes of RNA extraction, reverse transcription, qRT-PCR and the like can be adjusted through the doped exogenous miRNA, so that more reliable expression level of miRNA is obtained. The performance of these three references was evaluated by correlation analysis and the NormFinder method. The results of the correlation analysis are shown in Table 4 by quantitative PCR data of 104 cases of non-small cell lung cancer and 102 cases of normal control, and the combination of ath-miR159a and hsa-miR-16 has the highest correlation according to the correlation analysis; according to the NormFinder method, the combination of hsa-miR-1228 and hsa-miR-16 has the minimum fluctuation and is most stable. Therefore, the data of the miRNA to be detected is normalized by taking ath-miR159a as an external reference and hsa-miR-1228 and hsa-miR-16 as internal references and taking the average value of the ath-miR-1228 and hsa-miR-16.
TABLE 4 correlation analysis and NormFinder method evaluation of reference genes and combinations thereof
At this stage, the ratio of the expression amounts of miRNA in the two groups of sample plasma can be obtained by equation 2-ΔCtWherein Δ Ct ═ CtSample(s)-CTReference toAnd calculating the relative expression quantity by taking the mean expression quantity of ath-miR159a, hsa-miR-1228 and hsa-miR-16 as a reference standard. The expression conditions of 9 miRNAs (hsa-miR-20a, hsa-miR-25, hsa-miR-223, hsa-miR-320, hsa-miR-222, hsa-miR-146a, hsa-miR-191, hsa-miR-24 and hsa-miR-130a) are shown in Table 5, and the expression difference between the non-small cell lung cancer group and the normal control group is obvious and significant (p-miR-130 a)<0.001), so 9 non-small cell lung cancer miRNA markers are selected in total, and clinical sample detection is continued to be expanded.
TABLE 5 expression of 9 miRNAs in qRT-PCR validation
Note: by "fold change" is meant the ratio of the expression level of miRNA in the non-small cell lung cancer group to its expression level in the normal control group.
Fourth, training phase
The training set samples used in the training phase included 183 normal control group samples, 129 benign lung disease group samples, and 186 early stage non-small cell lung cancer (stage 0, stage I) samples from the non-small cell lung cancer group. The method comprises the steps of standardizing 9 lung cancer miRNA markers (hsa-miR-20a, hsa-miR-25, hsa-miR-223, hsa-miR-320, hsa-miR-222, hsa-miR-146a, hsa-miR-191, hsa-miR-24 and hsa-miR-130a) of each sample of a training set by using externally-doped Arabidopsis ath-miR159a and endogenous hsa-miR-16 and hsa-miR-1228 as reference genes to obtain dCT values, and establishing a lung cancer joint diagnosis model through an optimized random forest algorithm. Meanwhile, the combination of the miRNA markers and the existing serum tumor markers CEA and CYFRA21-1 for clinical application are subjected to combined detection and analysis, so that the detection rate of early diagnosis of lung cancer is improved, and a better clinical joint detection effect is obtained. The CEA and CYFRA21-1 content is detected by carcinoembryonic antigen determination kit (chemiluminescence method) and cytokeratin 19 fragment determination kit (chemiluminescence method) (Chongqing Boo Xinjing medical science and technology Co., Ltd.). Therefore, dCT values standardized by 9 miRNAs of the lung cancer and values obtained after natural logarithm standardization of CEA and CYFRA21-1 detection values (because the data span of the CEA and CYFRA21-1 is too large, the natural logarithm of the detection values is taken to participate in modeling), and meanwhile, an age factor is introduced, and a lung cancer combined diagnosis model is established through an optimized random forest algorithm.
The random forest is composed of a plurality of decision trees, and the decision trees are constructed by adopting an attribute and sample dual-random method, so that the random forest is also called as a random decision tree. In random forest, there is no correlation between the decision trees. When test data enters a random forest, each decision tree is used for classification, and finally the class with the highest classification result in all decision trees is taken as a final result, namely a decision tree 'voting' result. The invention optimizes the method based on the traditional random forest algorithm, randomly samples the samples 10000 times, establishes 10000 models, and selects the optimal model from the 10000 models. When the test set samples are tested, the preferred models are used for predicting the test set samples respectively, and the ratio of the number of the models which vote positive to the total number of the models is used as a predicted value. In the training stage, sampling the sample 10000 times by adopting an optimized random forest algorithm through an optimized random forest method, establishing 10000 models, and selecting the model with the sensitivity larger than 0.5 and the specificity larger than 0.9 from the 10000 models. 64 optimal models are selected from 10000 models established by single miRNA, and a "strong leaner" model is formed by combining 64 models and is used as a classifier (C-RFLMI model) for risk assessment of non-small cell lung cancer. And for 10000 models jointly modeled by 9 miRNAs, protein markers and ages, 104 models are selected, and a "strong leaner" model is formed by combining the 104 models and serves as a classifier (C-RFLCb model) for risk assessment of the non-small cell lung cancer.
Specifically, when a human with no non-small cell lung cancer is taken as a test object, the C-RFLmi model is used for diagnosis, for example, the result of more than or equal to 50% of 64 models in the C-RFLmi model is that a subject is a patient with non-small cell lung cancer, and the subject is a patient with non-small cell lung cancer; the results of less than 50% of 64 models as C-RFLMI model were that the subject was a non-small cell lung cancer patient and the subject was not a non-small cell lung cancer patient; diagnosing with C-RFLCb model, wherein the results of more than or equal to 50% of 104 models in C-RFLCb model are that the subject is non-small cell lung cancer patient and the subject is non-small cell lung cancer patient; results for less than 50% of the 104 models as C-RFLCb model were non-small cell lung cancer patients and the subject was not. The diagnosis value of the 9 miRNA markers in the C-RFLmi model, the 9 miRNA and protein markers in the C-RFLCb model and the age factors for the early diagnosis of the non-small cell lung cancer patients in the test set is evaluated in the mode, and specifically, only the vote value of the model which is taken as the test set sample is counted for each sample. The specificity of the C-RFLmi model to a normal control group is 90.2 percent, the specificity of a lung benign disease group is 82.9 percent, and the sensitivity of a non-small cell lung cancer group is 52.2 percent (Table 6); according to the analysis of the TNM stage and the grouping thereof, the sensitivity of the C-RFLVmi model to the non-small cell lung cancer of the 0 or IA stage is 52.6 percent, and the sensitivity of the non-small cell lung cancer of the IB stage is 50.0 percent. The specificity of the C-RFLCb model to a normal control group is 87.4 percent, the specificity of a lung benign disease group is 81.4 percent, and the sensitivity of a non-small cell lung cancer group is 58.6 percent (Table 6); according to the grouping analysis of the TNM stage, the sensitivity of the C-RFLCb model to the non-small cell lung cancer of 0 or IA stage is 57.1 percent, and the sensitivity of the non-small cell lung cancer of IB stage is 65.6 percent. From the above data, it can be seen that both models are relatively high in specificity and sensitivity, with the miRNA alone being relatively high in specificity and the combined model being relatively high in sensitivity.
TABLE 6 analysis of Performance index of miRNA combination versus test set population
Fifth, a further verification stage
Further, the inventors further verified the combination of 9 mirnas in independent validation sets (249 samples of the non-small cell lung cancer group, 180 samples of the normal control group, 121 samples of the lung benign disease group, and 273 samples of the other tissue and organ benign and malignant group).
The sensitivity of the C-RFLmi model to the validated set of non-small cell lung cancer was 35.3%, the specificity of the normal control was 84.4%, the specificity of the benign lung disease was 86.0%, the specificity of the other tissue and organ diseases (with bronchogenic cysts, mediastinal tumors, mediastinal neurofibromas, thymocysts, pericardial cysts, esophagitis, esophageal cysts, esophageal diverticulum, gastritis, polyp of stomach, hepatitis, hepatic cysts, colorectal inflammation, colorectal polyps, colorectal adenomas, appendicitis, and other tissue and organ diseases) was 83.3%, and the specificity of the other tissue and organ cancers (patients with colorectal, esophageal, gastric, or thymus cancer who were diagnosed definitively by pathology and who were not treated by surgery, radiotherapy and chemotherapy, and without radiotherapy) was 82.8% (table 7). For non-small cell lung cancer, the sensitivity of the C-RFLmi model to stage 0 or IA was 27.6%, and the sensitivity to stage IB was 29.0%. The sensitivity of the middle and late stage non-small cell lung cancer was also further examined in the validation process, with the C-RFLmi model having 54.8% sensitivity to stage II, 43.9% sensitivity to stage III, and 31.3% sensitivity to stage IV lung cancer (Table 7). The sensitivity of the C-RFLCb model to the verified non-small cell lung cancer is 45.4%, the specificity of the normal control is 85.0%, the specificity of the benign lung disease is 80.2%, the specificity of the benign diseases of other tissues and organs is 81.7%, and the specificity of the cancer of other tissues and organs is 78.5% (Table 7). For non-small cell lung cancer, the sensitivity of the C-RFLCb model to stage 0 or IA was 35.7%, and the sensitivity to stage IB was 35.5%. The sensitivity of non-small cell lung cancer above the middle and late stages was also further examined in the validation process, and the sensitivity of the C-RFLCb model to stage II was 61.3%, stage III was 56.1%, and stage IV lung cancer was 50.0% (Table 7). From the stage data, it can be seen that the sensitivity of the two models to the non-small cell lung cancer sample above stage II is also higher and higher than that of the early non-small cell lung cancer sample.
In addition, the sensitivity of the lung cancer sample in the verification set is reduced by a certain proportion compared with that in the test set, the specificity of the lung cancer sample in the control and the lung benign disease can be kept or increased, and the sensitivity of the C-RFLmi model and the C-RFLCb model to the verification set is kept to be about 35 percent and 45 percent respectively and is stable in the gradual verification process of sample expansion. In addition, 273 cases of benign and malignant disease interferences of other tissues, tissues and organs are introduced in a large sample amount in the verification stage, and both models keep high specificity, so that the method has extremely important significance for differential diagnosis of non-small cell lung cancer and cancers or benign interferences of other tissues and organs.
TABLE 7 analysis of Performance indicators in the validation set population by miRNA combination
The performance of the combined modeling is better than that of the single miRNA in the performance comparison of different groups, so that the single protein marker, the joint detection of the protein marker (for a detected sample, if any protein marker is detected to be positive, the sample is predicted to be positive), the C-RFLmi, the joint detection of the protein (for the detected sample, if any marker is detected to be positive, the sample is predicted to be positive) and the C-RFLcb are compared. As shown in table 8, the specificity of diagnosing non-small cell lung cancer using CEA alone and CYFRA21-1 was high, but the sensitivity was low, particularly the detection rate for stage 0& IA samples was low; the specificity of the non-small cell lung cancer is low by utilizing the C-RFLmi, the CEA and the CYFRA21-1 for joint detection; therefore, the CEA & CYFRA21-1, C-RFLMI and C-RFLCb classifiers are compared with each other. Table 8 shows that the specificity of CEA & CYFRA21-1, C-RFLMI and C-RFLCb to the normal control is high and the performance is similar. The specificity of the CEA & CYFRA21-1, the C-RFLMI and the C-RFLCb to benign diseases of the lung and other tissues and organs is higher, wherein the specificity of miRNA to the benign diseases of the lung is higher, and the specificity of CEA & CYFRA21-1 to the benign diseases of other tissues is higher. For other tissue and organ cancers, the specificity of CEA & CYFRA21-1 is slightly low, which is probably because CEA and CYFRA21-1 have a certain detection rate in other cancers, so the specificity for lung cancer is relatively low; and the specificity of C-RFLmi and C-RFLCb to other tissue cancers is higher. The detection of the whole non-small cell lung cancer has the C-RFLCb sensitivity which is much higher than that of the C-RFLmi, and the whole or different stages of the non-small cell lung cancer are detected; the detection rate of CEA & CYFRA21-1 on non-small cell lung cancer is slightly higher than that of single 9 miRNAs, but from different stages, the detection rate of C-RFLVmi on early and middle stage non-small cell lung cancer is relatively higher than that of CEA & CYFRA21-1 joint detection, and the detection rate on late stage non-small cell lung cancer is relatively lower than that of CEA & CYFRA21-1 joint detection; compared with the CEA & CYFRA21-1, the C-RFLCb model is higher than the CEA & CYFRA21-1, particularly the detection rate in the 0& IA stage is improved by more than 2 times compared with the CEA & CYFRA21-1, and the detection rate in the IB stage, the II stage and the III stage is also improved compared with protein markers, but the detection rate of the IV stage lung cancer is lower than the CEA & CYFRA 21-1. Therefore, the performance of the joint inspection model before the middle and later stages of the non-small cell lung cancer is better than that of a protein marker, particularly, the sensitivity of the joint inspection model in the 0& IA stage is far higher than that of the joint inspection model in CEA and CYFRA21-1 or the joint inspection model in the 0& IA stage, high-risk groups of the non-small cell lung cancer can be screened more effectively, and the joint inspection model has important significance for early discovery and early diagnosis of the non-small cell lung cancer.
TABLE 8 verification of the analysis of the Combined detection Performance of the concentrated miRNA markers and proteins
Sequence listing
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Claims (2)
1. The system for detecting the expression quantity, CEA content and CYFRA21-1 content of 9 miRNAs is applied to the preparation of products for diagnosing or assisting in diagnosing non-small cell lung cancer;
the 9 miRNAs are hsa-miR-20a, hsa-miR-25, hsa-miR-223, hsa-miR-320, hsa-miR-222, hsa-miR-146a, hsa-miR-191, hsa-miR-24 and hsa-miR-130 a;
the system comprises a system for detecting 9 miRNA expression levels, a system for detecting CEA content, a system for detecting CYFRA21-1 content and a data processing device, wherein the data processing device is used for converting the 9 miRNA expression levels, the CEA content and the CYFRA21-1 content from a to-be-detected object into a diagnosis result of the to-be-detected object;
the system for detecting the expression quantity of the 9 miRNAs comprises a system for detecting the expression quantity of the 9 miRNAs by using quantitative PCR;
the system for detecting the CEA content is a carcinoembryonic antigen determination kit, and the system for detecting the CYFRA21-1 content is a cytokeratin 19 fragment determination kit;
the data processing device diagnoses the non-small cell lung cancer and the non-small cell lung cancer by recording the characteristics of E1 and E2; the E1 is the expression level, CEA content, CYFRA21-1 content and age profile of the 9 miRNAs of a group of non-small cell lung cancer consisting of at least 50 non-small cell lung cancer patients; the E2 is the expression level, CEA content, CYFRA21-1 content, and age characteristic of the 9 miRNAs of a group of non-small cell lung cancers consisting of at least 50 individuals of non-small cell lung cancer;
characterizing the E1 and the E2 in the data processing apparatus by a method comprising: importing the E1 and the E2 into the data processing device, and constructing a model for diagnosing the non-small cell lung cancer by performing machine learning on the E1 and the E2;
the model for diagnosing non-small cell lung cancer constructed by machine learning the E1 and the E2 comprises F1 or F2;
f1, performing machine learning on the characteristics of the E1 of 50 percent or more of non-small cell lung cancer patients randomly selected from the non-small cell lung cancer group and the characteristics of the E2 of part of non-small cell lung cancer individuals randomly selected from the non-small cell lung cancer group to construct a model for diagnosing non-small cell lung cancer;
f2, carrying out n times on the F1 to obtain n models for diagnosing the non-small cell lung cancer; selecting l models having a sensitivity of 0.5 or more and a specificity of 0.9 or more for diagnosis in the non-small cell lung cancer group and the non-small cell lung cancer group among the n models for diagnosis of non-small cell lung cancer, and using the l models as models for diagnosis of non-small cell lung cancer; n is more than or equal to 1000; n is more than or equal to l and more than or equal to 10;
the method for diagnosing a subject by using the model for diagnosing non-small cell lung cancer comprises the following steps: the result of not less than 50% of the i models for diagnosing non-small cell lung cancer in the model for diagnosing non-small cell lung cancer as described is that the subject is a non-small cell lung cancer patient, the subject is or is candidate for a non-small cell lung cancer patient; the subject is a non-small cell lung cancer patient, the subject is or is candidate for a non-small cell lung cancer individual as a result of less than 50% of the models used to diagnose non-small cell lung cancer in the model for diagnosing non-small cell lung cancer;
the data processing device processes data through a decision tree algorithm; the decision tree algorithm is specifically a random forest decision tree algorithm.
2. Use according to claim 1, characterized in that: the system for detecting the expression quantity of the 9 miRNAs by using the quantitative PCR comprises a set of primers, a set of probes, a set of reverse transcription primers and/or other reagents and/or instruments required for performing the quantitative PCR;
the primer set is 18 single-stranded DNAs shown as a sequence 1-sequence 18 in a sequence table; the sequences of the probe set are respectively shown as a sequence 19-a sequence 27 in a sequence table; the complete set of reverse transcription primers are 9 single-stranded DNAs shown as sequences 28-36 in a sequence table.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101400361A (en) * | 2006-01-05 | 2009-04-01 | 俄亥俄州立大学研究基金会 | Microrna-based methods and compositions for the diagnosis, prognosis and treatment of lung cancer |
| CN102308004A (en) * | 2008-10-30 | 2012-01-04 | 卡里斯生命科学卢森堡控股有限责任公司 | Methods for assessing RNA patterns |
| CN102725632A (en) * | 2009-08-28 | 2012-10-10 | 奥斯瑞根公司 | MiRNA biomarkers of lung disease |
| CN103874770A (en) * | 2011-08-08 | 2014-06-18 | 卡里斯生命科学卢森堡控股有限责任公司 | Biomarker compositions and methods |
| CN105603101A (en) * | 2016-03-03 | 2016-05-25 | 博奥颐和健康科学技术（北京）有限公司 | Application of system for detecting expression quantity of eight miRNAs in preparation of product for diagnosing or assisting in diagnosing hepatocellular carcinoma |
| CN107574243A (en) * | 2016-06-30 | 2018-01-12 | 博奥生物集团有限公司 | The construction method of molecular marker, reference gene and its application, detection kit and detection model |
-
2018
- 2018-08-01 CN CN201810865121.0A patent/CN108841962B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101400361A (en) * | 2006-01-05 | 2009-04-01 | 俄亥俄州立大学研究基金会 | Microrna-based methods and compositions for the diagnosis, prognosis and treatment of lung cancer |
| CN102308004A (en) * | 2008-10-30 | 2012-01-04 | 卡里斯生命科学卢森堡控股有限责任公司 | Methods for assessing RNA patterns |
| CN102725632A (en) * | 2009-08-28 | 2012-10-10 | 奥斯瑞根公司 | MiRNA biomarkers of lung disease |
| CN103874770A (en) * | 2011-08-08 | 2014-06-18 | 卡里斯生命科学卢森堡控股有限责任公司 | Biomarker compositions and methods |
| CN105603101A (en) * | 2016-03-03 | 2016-05-25 | 博奥颐和健康科学技术（北京）有限公司 | Application of system for detecting expression quantity of eight miRNAs in preparation of product for diagnosing or assisting in diagnosing hepatocellular carcinoma |
| CN107574243A (en) * | 2016-06-30 | 2018-01-12 | 博奥生物集团有限公司 | The construction method of molecular marker, reference gene and its application, detection kit and detection model |
Non-Patent Citations (1)
| Title |
|---|
| Applications of microRNAs in the diagnosis and prognosis of lung cancer;Jun Shen等;《Expert Opinion on Medicinal Diagnostics》;20120318;第6卷(第3期);第3页最后一段、第4页第1段、第5页第2段 * |
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