JPWO2017110687A1 - Method for identifying cause of lung cancer by miRNA, marker, detection kit, and method for screening therapeutic agent - Google Patents

Abstract

  MiRNAs highly correlated with asbestos and smoking were extracted from 202 lung cancer cases, and miRNAs capable of characterizing each group were obtained. Thereby, a marker correlated with the cause of lung cancer can be provided.

Description

  The present invention relates to miRNA that identifies the cause of lung cancer. In particular, a method for identifying whether the cause of lung cancer is asbestos exposure, smoking, or both are related by detecting miRNA that increases or decreases in lung cancer caused by asbestos exposure or smoking, And a marker for identifying the cause. The present invention also relates to a method for detecting lung cancer due to asbestos exposure and smoking by detecting these miRNAs, and a kit for detection. Furthermore, the present invention relates to a therapeutic agent for lung cancer using these miRNA sequences and a screening method for a therapeutic agent for lung cancer.

  Lung cancer is the most deadly malignant tumor in Japan. According to materials from the National Cancer Center Cancer Control Information Center, 72,764 people died in 2013, 52,054 men and 20,680 women, and the number of deaths in 2014 was It is estimated to reach 76,500 people. Lung cancer has been the top malignant tumor since 1998. Lung cancer is a major cause of death from malignant tumors not only in Japan but also in Europe and the United States. Epidemiological studies have shown that lung cancer is closely related to smoking, but asbestos exposure has also become a major problem in recent years.

  Asbestos is a fibrous mineral that is highly insulating both in terms of electrical conduction and heat conduction, and has been widely used as a fireproof material since before the Second World War. It has been known since the beginning of the 20th century that asbestos is carcinogenic. However, it has only recently been recognized socially about the health hazard. Pathologically, malignant mesothelioma and lung cancer are well known as asbestos-related malignant tumors. The incidence is slightly higher in lung cancer, and an epidemiological study of occupationally exposed persons indicates that lung cancer: malignant mesothelioma = 2: 1 to 3: 2. Thus, it is estimated that two types of malignant tumors are generated by asbestos exposure, and the number is slightly higher in lung cancer. However, although asbestos is known to cause malignant mesothelioma, little attention has been paid to lung cancer caused by asbestos.

  The reason for this is that there are many lung cancers, and smoking, dust, radiation, air pollution, constitution, aging, etc. cause many cases, and asbestos-related lung cancer is only a part. Because. Therefore, lung cancer is a tumor that is unlikely to become a so-called “index cancer” in which the occurrence of cancer suggests a specific carcinogenic factor because of its large number and variety of causes.

  Smoking is the largest known cause of lung cancer. Lung cancer caused by smoking is mainly squamous cell carcinoma, small cell carcinoma, and large cell carcinoma, and adenocarcinoma is said to be relatively unrelated. On the other hand, cancers caused by asbestos exposure are said to have no histological characteristics, and the determination of histology is not an indicator of asbestos exposure.

  Asbestos, like tobacco, is an important inhalable carcinogen in lung cancer. In Japan, the asbestos relief system is required to accurately extract lung cancer caused by asbestos. At present, the laborious work of quantitatively ascertaining the amount of asbestos bodies and asbestos fibers in the lungs by light or electron microscopy is performed.

  In addition, asbestos includes serpentinite chrysotile and amphibole crocidolite and amosite, and it is known that there is a difference in carcinogenicity. Chrysotile is said to be digested in the lungs and may disappear, and it is thought that it is difficult to make asbestos bodies. That is, just because there is no asbestos body in the lung, it cannot be said that it is not necessarily asbestos lung cancer. For this reason, quantification with a microscope is time-consuming and has a problem that accurate determination cannot be made.

  Not only smoking and asbestos but also the changes in genes that correlate with various causes of lung cancer can be clarified, it can be used not only for early diagnosis of lung cancer but also for prevention and treatment.

  MicroRNA (hereinafter referred to as miRNA) is a single-stranded RNA molecule of about 20 to 25 bases, and is involved in the regulation of expression of genes after transcription in eukaryotes. The human genome is thought to encode over 1000 miRNAs. The miRNA binds partially to the target mRNA in a complementary manner, destabilizes the target mRNA and suppresses protein production by suppressing translation. Since miRNA generally binds to a plurality of target RNAs in a partially complementary manner and controls transcription, it controls by controlling a plurality of different mRNA amounts simultaneously.

  It has been shown that miRNA-mediated transcriptional repression is responsible for controlling a wide range of processes such as development, cell proliferation, cell differentiation, apoptosis, and metabolism. Furthermore, it has been reported that miRNA is involved in various diseases such as cancer, infectious diseases and lifestyle-related diseases. Among these, it is known that it is deeply involved in cell carcinogenesis (Non-patent Document 1). In addition, it has already been reported that miRNA having a high correlation with canceration exists in lung cancer (Patent Documents 1 to 6, Non-Patent Document 2).

  Patent Documents 1 to 4 all disclose miRNA capable of detecting lung cancer, but are intended for lung cancer in general. Patent Document 5 discloses a method for classifying lung cancer into squamous cell carcinoma, non-squamous cell non-small cell carcinoma, carcinoid lung cancer, and small cell carcinoma. Patent Document 6 discloses a method relating to prognosis prediction and the like of small cell cancer. Non-Patent Document 2 reports related miRNAs in lung cancer involving asbestos.

Lin, S. & Gregory, R.I., 2015, Nat. Rev. Cancer, Vol. 15 (6), p.321-333. Nymark, P. et al., 2011, Genes Chrom. Cancer, Vol. 50 (8), p.585-597. Roden, C. et al., 2015, Adv, Exp. Med. Biol., Vol. 888, p.409-421.

Special table 2009-531018 gazette Special table 2011-505143 gazette Special table 2011-516046 gazette Special table 2013-502931 gazette Special table 2014-509522 gazette International Publication No. 2011/125245

  However, Patent Documents 1 to 6 all disclose miRNA related to lung cancer, but do not analyze miRNA correlated with the cause of carcinogenesis. In Non-Patent Document 2, the number of cases of lung cancer used to extract asbestos-related miRNA is 7 and the number of cases of lung cancer without asbestos exposure is very small. Moreover, although miRNA which has fluctuated in lung cancer patients exposed to high asbestos has been reported, lung cancer caused only by asbestos has not been extracted and analyzed. As described above, since lung cancer develops due to many causes other than asbestos, just because of high exposure to asbestos does not necessarily mean lung cancer caused only by asbestos. In particular, the effects of smoking, which is said to be the biggest cause of lung cancer, cannot be ignored.

  An object of the present invention is to identify a cause of lung cancer, in particular, miRNA that changes due to smoking and asbestos, and to provide a method for identifying the cause of lung cancer using the miRNA, and a kit for identifying the cause. It is another object of the present invention to not only identify the cause of lung cancer, but also to provide a new therapeutic drug screening method and therapeutic drug using the miRNA as an index.

  The present invention relates to a method for identifying the cause of lung cancer, a marker, a kit, and a method for screening a drug for treating lung cancer as described below.

(1) The amount of miRNA in a sample obtained from a subject was measured, and miR-431, miR-378b, miR-92a, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648 , MiR-513b, miR-1471, miR-3137, miR-3651, miR-99b, miR-320c, miR-1972, miR-513a-5p, miR-99a, A method characterized by identifying the cause of lung cancer.
(2) The method for identifying the cause of lung cancer according to (1), wherein the cause of lung cancer is asbestos exposure and / or smoking.
(3) miR-431, miR-378b, miR-92a, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b, miR-1471, miR-3137, miR A marker for identifying the cause of lung cancer, characterized by being at least one of 3651, miR-99b, miR-320c, miR-1972, mmiR-513a-5p, miR-99a.
(4) A marker for identifying a cause of lung cancer according to (3), wherein the cause of lung cancer is asbestos exposure and / or smoking.
(5) The marker according to (3) or (4), which is a marker for detecting whether the cause of the lung cancer correlates with the amount of asbestos deposited, and the miRNA comprises miR-431, miR-378b, a cause of lung cancer characterized by being at least one of miR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b A marker to identify.
(6) The marker according to (3) or (4), which is a marker for detecting whether the cause of the lung cancer is due to asbestos exposure in a non-smoker, and the miRNA is miR-1471, miR-3137. , MiR-3651, miR-4257, miR-92a, miR-99b, a marker for identifying the cause of lung cancer,
(7) The marker according to (3) or (4), which is a marker for detecting whether the cause of the lung cancer is due to asbestos exposure in a heavy smoker, and the miRNA is miR-320c, miR-378b. A marker for identifying the cause of lung cancer, wherein the marker is at least one of miR574-5p.
(8) The marker according to (3) or (4), wherein the cause of the lung cancer is a marker for detecting the involvement of smoking in an asbestos exposure group, and the miRNA is miR-1972, miR-3137, A marker for identifying the cause of lung cancer, which is at least one of miR-3648, miR-513a-5p, miR-513b, miR-99a, miR-99b.
(9) miR-431, miR-378b, miR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b, miR-1471, miR A probe for measuring at least one of -3137, miR-3651, miR-99b, miR-1972, miR-513a-5p, miR-99a, and a reagent necessary for detection A kit for identifying the cause of lung cancer.
(10) The kit according to (9), wherein the cause of lung cancer is asbestos exposure and / or smoking, wherein the cause of lung cancer is identified.
(11) The kit according to (9) or (10), which is a kit for detecting whether the cause of the lung cancer correlates with an asbestos deposition amount, and the miRNA is miR-431, miR-378b. , MiR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, probe for measuring miR-513b, and detection A kit for identifying the cause of lung cancer, comprising a necessary reagent.
(12) The kit according to (9) or (10), which is a kit for detecting whether the cause of the lung cancer is due to asbestos exposure in a non-smoker, wherein the miRNA is miR-1471, miR- In order to identify the cause of lung cancer, comprising a probe for measuring at least one of 3137, miR-3651, miR-4257, miR-92a, miR-99b, and a reagent necessary for detection Kit.
(13) The kit according to (9) or (10), wherein the kit is for detecting whether the cause of the lung cancer is due to asbestos exposure in a heavy smoker, and the miRNA is miR-320c, miR- A kit for identifying the cause of lung cancer, comprising a probe for measuring at least one of 378b and miR-574-5p, and a reagent necessary for detection.
(14) The kit according to (9) or (10), wherein the cause of the lung cancer is a kit for detecting involvement of smoking in an asbestos exposure group, and the miRNA is miR-1972, miR-3137. A probe for measuring at least one of miR-3648, miR-513a-5p, miR-513b, miR-99a, miR-99b, and a reagent necessary for detection, comprising: A kit for identifying the cause.
(15) A method for screening a drug for treating lung cancer, comprising miR-431, miR-378b, miR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR- 765, miR-3648, miR-513b, miR-1471, miR-3137, miR-3651, miR-99b, miR-1972, miR-513a-5p, miR-99a A screening method characterized by using as an index.

  By using miRNA described in detail below, it is possible to identify whether the cause of lung cancer is asbestos exposure, smoking exposure, or both, and the cause of carcinogenesis. In addition, by finding a novel marker highly correlated with carcinogenesis, it becomes possible to provide a new test method, test kit, and drug screening method. Furthermore, it is possible to prepare a medicine using the miRNA sequence.

Case distribution and regression curve of miRNA that was significantly correlated with exposure in mixed exposure to asbestos and smoking.

  In the following examples, an array is used to identify miRNAs that correlate with asbestos, smoking, or both. However, when the miRNA specified in the present invention is used as an individual patient test, it can be analyzed by quantitative RT-PCR. The analysis method can be performed by a generally used method such as a method disclosed in Non-Patent Document 3. Since quantitative RT-PCR has high sensitivity, the cause of lung cancer can be identified more accurately.

  Here, a biological sample derived from a cancer patient uses miRNA extracted from a section material prepared by embedding formalin-fixed paraffin (FFPE), but does not fix a cancer tissue collected by surgery or an endoscope. Alternatively, fresh frozen tissue may be used. As a result of examining the coincidence degree of miRNA expression between fresh frozen tissue and FFPE material, the present inventors have good R = 0.76 to 0.90 for 200 to 300 miRNAs that are expressed above a certain level. It is confirmed that a correlation is obtained. Therefore, the cause of carcinogenesis can be identified using either FFPE material or fresh frozen tissue.

  Specifically, hsa-miR-1471, hsa-miR-3137, hsa-miR-3651, hsa-miR-4257, hsa which are miRNAs correlated with asbestos, smoking, or both specified in the present invention. -MiR-92a, hsa-miR-99b (miRNA group characterizing the presence or absence of asbestos in non-smokers), hsa-miR-320c, hsa-miR-378b, hsa-miR-574-5p (asbestos in heavy smokers) MiRNA group characterizing presence or absence), hsa-miR-1972, hsa-miR-3137, hsa-miR-3648, hsa-miR-513a-5p, hsa-miR-513b, hsa-miR-99a, hsa-miR-99b (Presence or absence of smoking in asbestos deposition group By analyzing the changes in miRNA or precursor thereof s) characterizing, asbestos responsible for the development of lung cancer, and / or smoking can identify whether involved. Hereinafter, “hsa” that specifies human miRNA may be omitted, but all are human miRNA.

  As the miRNA used for the test, one type of miRNA may be used alone in each group, or two or more types may be used in combination. However, the use of two or more types in combination improves the accuracy of the test. Can do.

  In addition, among miRNAs that show an increase or decrease due to the exposure to asbestos and smoking described above, a nucleic acid molecule that suppresses the function of miRNA that shows an increase due to the onset of cancer can be designed and used as a therapeutic agent. In addition, a therapeutic effect can be expected by supplementing miRNAs whose expression is reduced, such as miRNA supplements for let-7, which is being developed for lung cancer. Therefore, by developing an antagonist of miRNA that increases with the onset of cancer, or by using miRNA that decreases as a supplement, a drug that directly targets miRNA can be developed.

  The present invention also relates to a method for screening a candidate compound for a therapeutic agent for lung cancer caused by asbestos or smoking. Specifically, a candidate compound for a therapeutic agent can be added to a culture solution of cultured cells, and screening can be performed using the above-described miRNA that increases or decreases as a result of asbestos exposure and smoking. As the cell line, any cell can be used as long as it can detect the miRNA that is changed in the present invention. However, a cell line derived from lung tissue, for example, a cell line derived from a smoker's lung adenocarcinoma A549 can be used. Moreover, it is preferable to use primary cultured cells derived from the lung.

  Hereinafter, the present invention will be described in detail with reference to examples.

1. Materials and methods 1-1. About the cases The miRNAs whose expression changes were divided into 4 groups according to the presence or absence of smoking and asbestos deposition. The cases of 202 patients used for the analysis are shown in Table 1 (Table 1-1 to Table 1-4).

  In Table 1, Asbestos Burden represents the amount of asbestos deposited (AB) in terms of the number of asbestos bodies per dry lung weight. Smoking Index (SI, smoking index) indicates a value calculated by (number of cigarettes smoked per day) × (year of smoking). AB_012 represents the number of asbestos bodies: 0: no asbestos bodies, 1: 0 <AB <5000, 2: AB ≧ 5000. SI_012 represents a smoking index, 0: non-smoking, 1: 0 <SI <400, 2: SI ≧ 400. AB = 0 was 53 cases, AB = 1 was 38 cases, and AB = 2 was 111 cases. Further, SI = 0 was 49 cases, SI = 1 was 24 cases, and SI = 2 was 129 cases.

  The classification of patients based on the amount of asbestos deposited and the smoking index was performed by examining the threshold values of 110 patient samples in advance. When divided into a group of AB ≧ 5000 and a group of AB <5000, it was suggested that miRNAs characterized by a group with a lot of asbestos deposition and a group with a little asbestos could be extracted, and that both groups could be classified. In smoking-related cases, a result of comparing the SI ≧ 400 group with a threshold of SI = 400 and SI <400 group showed no significant results in the hierarchical cluster analysis, but in the volcano plot, the advanced smoking group tends to be a little It was observed. Therefore, the asbestos deposition amount is classified into three groups with a threshold value of 5000, AB = 0, 0 <AB <5000, and AB ≧ 5000. The smoking index has a threshold value of 400, SI = 0, 0 <SI < It was classified into three groups of 400 and SI ≧ 400.

  In the column of Histology, ADC is adenocarcinoma, SCLC is small cell lung cancer, NSCLC is non-small cell lung cancer, SCC is squamous cell carcinoma (us carcinoma).

1-2. Method (1) Measurement of the amount of asbestos In all 202 cases, the normal lung tissue in the paraffin block was deparaffinized by the low-temperature ashing method (Kamiyama modified method) used in labor accident hospitals nationwide, and the dried tissue The number of asbestos in the lung was measured with an optical microscope using the extract from the above, and the amount of asbestos per dry lung weight was determined.

(2) Extraction of RNA RNA was extracted from FFPE material of tumor tissue. For the tumor FFPE material, a pathologist manually collected a tumor tissue under a stereomicroscope from a section sliced to 8 μm and stained with hematoxylin, and extracted total RNA using miRNeasy FFPE Kit (QIAGEN). . The extracted RNA had a high peak at 23-26 nt by electrophoresis, and it was confirmed that a sufficient amount of miRNA was contained. The RNA integrity number (RIN value) was generally good at 2.1 to 2.6.

(3) miRNA analysis Comprehensive miRNA expression analysis was performed using total RNA extracted by the above method from FFPE tissues of all 202 cases. miRNA was analyzed using Agilent miRNA Microarray V16.0. This microarray carries 1368 miRNA probes. Of these, there were 1223 human miRNA (hsa miR) probes.

  In the present invention, analysis is performed based on cases obtained from multiple facilities (Cancer Research Institute Cancer Research Institute, Okayama Industrial Hospital, Tokyo Women's Medical University, Chiba Industrial Hospital, Hiroshima University). In general, gene expression analysis is said to have very different expression characteristics depending on the facility or country. In other words, the results obtained at one facility are often not reproduced at other facilities. Therefore, in general, when using specimens obtained at multiple facilities, all cases are divided into a training set and a validation set, and a result obtained by the training set is verified by the validation set. In the present invention, as shown in Table 2, 202 cases were designated as Training Set (n = 135) for two-thirds of the whole and Validation Set (n = 67) for the remaining one-third. In creating the set, each case was randomly assigned by a random number generated by Excel.

  As shown in Table 2, AB = 0 is 53 cases, 0 <AB <5000 is 38 cases, and AB ≧ 5000 is 111 cases. Regarding smoking, SI = 0 is 49 cases, 0 <SI <400 is 24 cases, and SI ≧ 400 is 129 cases. The exposure to carcinogenic factors in the target cases is a group of cases with a very wide distribution from zero asbestos deposition to occupational exposure and smoking from non-smokers to heavy smokers. When analyzing the correlation with asbestos exposure, smoking, and each carcinogenesis, the analysis was performed using the classification of patient cases shown in Table 2.

  Furthermore, in order to detect miRNAs that characterize asbestos alone, smoking alone, and the joint effects of asbestos and smoking, cases were divided into 4 groups according to two factors, asbestos and smoking. That is, as shown in Table 3, threshold values of AB = 0, AB = 5000, SI = 0, SI = 400 are set, and at least one of asbestos exposure / smoking amount is an intermediate group (0 <AB <5000, 0 <SI 149 cases not included in <400) were extracted and analyzed. By classifying the cases in this way, the analysis target of the mixed exposure is narrowed down to cases satisfying occupational exposure level asbestos deposition (AB ≧ 5000) and heavy smoking (SI ≧ 400). In the group of 149 cases, the training set was n = 98 and the validation set was n = 51.

  Table 3 below shows the number of cases when occupational exposure to asbestos is divided according to the presence or absence of heavy smoking (composition of occupational exposure and heavy smoking group), and when each group is combined. The breakdown of the number of people (combinations) is shown. When analyzing mixed exposure of asbestos and smoking, the analysis was performed using the classification of patient cases shown in Table 3.

2. Analysis result [Example 1]
[MiRNA whose expression varies with asbestos exposure (analysis in Training Set)]
In the training set, three types of comparisons were made between AB = 0 group (n = 34), 0 <AB <5000 group (n = 28), and AB ≧ 5000 group (n = 73), and the Oneway ANOVA test (Benjamini) Table 4 shows the number of miRNAs that showed a fluctuation range that was significant depending on the p value in (-Hochberg correction). Under the condition of p <0.05, 38 hsa-miRNAs (hsa-miR) were extracted.

  Under the condition of P <0.05, 38 human (hsa-) miRNAs remained. These 38 are shown in Table 5. For example, for hsa-miR-431, the p value is 1.39E-02 (p = 0.139), and a comparison between the AB ≧ 5000 group and the AB = 0 group ([2] vs [0] ) Was 3.82 times, and the expression level decreased with increasing asbestos (down-regulation, indicated as down in the table). This table shows a comparison between the AB = 0 group and the AB ≧ 5000 group ([2] vs. [0]), that is, a miRNA having a large FC in a group having a high asbestos deposition amount due to asbestos exposure and a group having no asbestos deposition. Are shown in order from the top. In the comparison between the AB = 0 group and the AB ≧ 5000 group ([2] vs. [0]), 14 types of miRNAs of FC1.50 or more are indicated by shading in the table.

  In the table, FC indicates the Fold Change which is the ratio of signal values, and [[1] vs. [0]) in the Regulation column is [1] (0 <AB <5000) and [0] (asbestos body). No), ([2] vs [0]) is a comparison between [2] (AB ≧ 5000) and [0] (no asbestos body), ([2] vs [1]) is [ 2] represents a comparison between (AB ≧ 5000) and [1] (0 <AB <5000). Further, up is up-regulation and the expression level is increased, and down is down-regulation and indicates a decrease in the expression level. Furthermore, chr indicates the position of the miRNA on the chromosome.

  In addition, principal component analysis and hierarchical cluster analysis were performed to determine how the expression level is related to the asbestos deposition level and how the cases are clustered. Although the results are not shown here, both cluster analysis results seem to be relatively coherent in cases with a large amount of asbestos deposition, but it can be said that AB ≧ 5000 and AB = 0 are clearly separated. There wasn't.

[Example 2]
[MiRNA whose expression is changed by smoking (analysis in Training Set)]
A similar analysis was performed on the Training Set for smoking. That is, 3 among SI = 0 group (n = 49, [0]), 0 <SI <400 group (n = 24, [1]), SI ≧ 400 group (n = 129, [2]). Table 6 shows the number of miRNAs that showed a variation range that was significant depending on the p-value in the Oneway ANOVA test (Benjamini-Hochberg correction). 50 human (hsa-) miRNA remained under the condition of P <0.05.

  The 50 miRNAs with P <0.05 are shown in Table 7. As in the case of asbestos, in this table, miRNAs with large FCs are shown in order from the top in the comparison between the SI = 0 group and the SI ≧ 400 group ([2] vs. [0]).

  Here, although the results are not shown in the figure, how the expression level is related to the amount of smoking is shown by principal component analysis and hierarchical cluster analysis. As a result of the principal component analysis, cases with a large amount of smoking seemed to be relatively coherent, but in the hierarchical cluster analysis, it was difficult to say that non-smokers were grouped.

[Example 3]
[Verification of miRNA obtained in Training Set related to asbestos exposure in Validation Set]
As shown in Example 1, 14 cases (Table 5, FC [2] vs [0]) satisfying the condition of FC> 1.5 related to asbestos exposure (AB ≧ 5000) were found. However, regarding 21 miRNAs extracted as FC> 1.4 with slightly relaxed conditions, it was examined whether the tendency in Training Set (up regulation or down regulation) was reproduced in Validation Set. Table 8 shows the target 21 miRNAs and their statistical values in the Validation Set.

  It became miR-431, miR-378b, miR-92a, miR-1973, miR-4257, miR-574-5p, miR-765, which became significant (p <0.05) by the test by correlation regression analysis. Nine miR-3648 and miR-513b. In addition, miR-320c has a p value of 0.0864, is a miRNA in the boundary region, and may be effective. These nine miRNAs were extracted as having a significant correlation with the amount of asbestos deposited in the training set, and further, a significant correlation was also found in the validation set. Therefore, the amount of asbestos deposition can be estimated by using these miRNAs.

[Example 4]
[In relation to smoking, validation of miRNA obtained by Training Set in Validation Set]
As in Example 3, the correlation in Validation Set was tested by single regression analysis using 8 miRNAs extracted by analysis in Training Set and significantly changed in expression in the smoking group. As a result, as shown in Table 9, none of the validation sets became significant at the level of p <0.05.

[Example 5]
[Analysis of mixed exposure to asbestos and smoking]
In order to analyze the mixed exposure of asbestos and tobacco, four groups using 149 cases shown in Table 3 were constructed. There are 98 training sets and 51 validation sets. Table 10 shows the number of cases composing the Training Set.

  Ten miRNAs that characterize asbestos exposure in non-smokers were extracted under conditions of p <0.05 and FC> 2.0 by comparing group a and group c. In comparison between group b and group d, 8 miRNAs characterizing asbestos exposure in heavy smokers were extracted under the conditions of p <0.05 and FC> 1.5. On the other hand, by comparing the d group and the c group, three miRNAs characterizing smoking in the group without asbestos exposure were extracted under the conditions of p <0.05 and FC> 1.2. In comparison between group b and group a, 20 miRNAs characterizing smoking in the asbestos deposition group could be extracted under the conditions of p <0.05 and FC> 1.5. The extracted miRNA in each group is shown in Table 11 (Table 11-1 to Table 11-2).

  Next, it was examined whether these miRNAs extracted with Training Set were significant in Validation Set. The results are summarized in Table 12.

  Of the 10 miRNAs that characterize asbestos exposure in nonsmokers, 6 of miR-1471, miR-3137, miR-3651, miR-4257, miR-92a, miR-99b were significantly correlated or borderline . Of the 8 miRNAs that characterize asbestos exposure in heavy smokers, 3 of miR-320c, miR-378b, and miR574-5p were significant or borderline. On the other hand, for smoking, none of the three miRNAs that characterize smoking in the group without asbestos deposition were significant in the Validation Set. Of the 20 miRNAs that were correlated with smoking in the asbestos-exposed group, 7 of miR-1972, miR-3137, miR-3648, miR-513a-5p, miR-513b, miR-99a, miR-99b were significant. Or the boundary value.

  From the above results, miR-1471, miR-3137, miR-3651, and miR-4257 are statistically significant as miRNAs that characterize the presence or absence of asbestos in non-smokers because the p-value is less than 0.05. It is. Moreover, miR-92a and miR-99b are miRNAs in the boundary region having a p value of 0.05 or more and less than 0.1, and may be effective as miRNAs that characterize the presence or absence of asbestos in non-smokers. Moreover, since miR-574-5p has a p value of less than 0.05, it is statistically significant as a miRNA that characterizes the presence or absence of asbestos in heavy smokers. miR-320c and miR-378b are miRNAs in the border region having a p value of 0.05 or more and less than 0.1, and may be effective as miRNAs that characterize the presence or absence of asbestos in heavy smokers. Furthermore, miR-3137, miR-3648, miR-513a-5p, and miR-513b are statistically significant as miRNAs that characterize the presence or absence of smoking in the asbestos deposition group because the p-value is less than 0.05. . miR-1972, miR-99a, and miR-99b are miRNAs having a p-value of 0.05 or more and less than 0.1, and may be effective as miRNAs that characterize the presence or absence of smoking in the asbestos deposition group. .

  Of these, 11 miRNAs that are significant at p <0.05 or are likely to be borderline and effective (miRNAs that characterize asbestos exposure in non-smoking: miR-1471, miR-3137, miR -3651, miR-4257, miR-92a, miRNA characterizing asbestos exposure in heavy smoking: miR-320c, miR-574-5p, miRNA characterizing smoking in the asbestos exposed group: miR-31972, miR-3648, miR- For 513a-5p, miR-513b), the case distribution and regression curve in Validation Set are shown in FIG. As shown in FIG. 1, the slope of the regression curve is relatively large, and it is clear that the change in the expression of these miRNAs can be used as a marker instead of asbestos body measurement. Therefore, by using these miRNAs as markers, patients to whom the asbestos rescue system can be applied can be accurately identified.

In addition, since these miRNAs are also new lung cancer markers, there is a possibility that drug screening using these miRNAs as an index, or miRNA itself can be used as a therapeutic agent for lung cancer.

Claims (15)

Measure the amount of miRNA in the sample obtained from the subject,
miR-431, miR-378b, miR-92a, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b, miR-1471, miR-3137, miR-3651, A method of identifying the cause of lung cancer by analyzing at least one of miR-99b, miR-320c, miR-1972, miR-513a-5p, miR-99a. A method for identifying the cause of lung cancer according to claim 1, comprising:
A method characterized in that the cause of lung cancer is asbestos exposure and / or smoking.   miR-431, miR-378b, miR-92a, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b, miR-1471, miR-3137, miR-3651, A marker for identifying the cause of lung cancer, which is at least one of miR-99b, miR-320c, miR-1972, miR-513a-5p, miR-99a. The marker according to claim 3, wherein
A marker for identifying a cause of lung cancer, characterized in that the cause of lung cancer is asbestos exposure and / or smoking. The marker according to claim 3 or 4,
A marker for detecting whether the cause of the lung cancer correlates with the amount of asbestos deposited;
The miRNA is at least one of miR-431, miR-378b, miR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b. A marker for identifying the cause of lung cancer, characterized by The marker according to claim 3 or 4,
A marker for detecting whether the cause of the lung cancer is due to asbestos exposure in non-smokers,
A marker for identifying the cause of lung cancer, wherein the miRNA is at least one of miR-1471, miR-3137, miR-3651, miR-4257, miR-92a, miR-99b. The marker according to claim 3 or 4,
A marker for detecting whether the cause of the lung cancer is due to asbestos exposure in heavy smokers,
A marker for identifying the cause of lung cancer, wherein the miRNA is at least one of miR-320c, miR-378b, and miR-574-5p. The marker according to claim 3 or 4,
The cause of the lung cancer is a marker for detecting the involvement of smoking in the asbestos exposure group,
The cause of lung cancer, wherein the miRNA is at least one of miR-1972, miR-3137, miR-3648, miR-513a-5p, miR-513b, miR-99a, miR-99b A marker to identify.   miR-431, miR-378b, miR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b, miR-1471, miR-3137, A cause of lung cancer comprising a probe for measuring at least one of miR-3651, miR-99b, miR-1972, miR-513a-5p, miR-99a, and a reagent necessary for detection Kit for identifying. The kit according to claim 9, wherein
A kit for identifying a cause of lung cancer, wherein the cause of lung cancer is exposure to asbestos and / or smoking. The kit according to claim 9 or 10, wherein
A kit for detecting whether the cause of the lung cancer correlates with the amount of asbestos deposited;
The miRNA is at least one of miR-431, miR-378b, miR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b. A kit for identifying the cause of lung cancer, comprising a probe for measuring one and a reagent necessary for detection. The kit according to claim 9 or 10, wherein
A kit for detecting whether the cause of lung cancer is due to asbestos exposure in non-smokers;
The miRNA includes a probe for measuring at least one of miR-1471, miR-3137, miR-3651, miR-4257, miR-92a, miR-99b, and a reagent necessary for detection. A kit for identifying the cause of lung cancer. The kit according to claim 9 or 10, wherein
A kit for detecting whether the cause of the lung cancer is due to asbestos exposure in heavy smokers;
In order to identify the cause of lung cancer, wherein the miRNA comprises a probe for measuring at least one of miR-320c, miR-378b, miR-574-5p, and a reagent necessary for detection Kit. The kit according to claim 9 or 10, wherein
The cause of lung cancer is a kit for detecting the involvement of smoking in an asbestos-exposed group,
The miRNA is a probe for measuring at least one of miR-1972, miR-3137, miR-3648, miR-513a-5p, miR-513b, miR-99a, miR-99b, and necessary for detection A kit for identifying the cause of lung cancer, comprising a reagent. A method of screening for a drug for treating lung cancer comprising:
miR-431, miR-378b, miR-92a, miR-320c, miR-1973, miR-4257, miR-574-5p, miR-765, miR-3648, miR-513b, miR-1471, miR-3137, A screening method characterized by using an increase or decrease in the expression level of at least one of miR-3651, miR-99b, miR-1972, miR-513a-5p, miR-99a as an index.