WO2021149719A1

WO2021149719A1 - Method of using mirna expression level as indicator of cancer

Info

Publication number: WO2021149719A1
Application number: PCT/JP2021/001836
Authority: WO
Inventors: 田口　好弘; 黒川　洋; 早苗中山; 謙也小林; 雅梅田; 貫治堀; 創梅村
Original assignee: アルプスアルパイン株式会社; 国立大学法人広島大学; 学校法人国際医療福祉大学
Priority date: 2020-01-24
Filing date: 2021-01-20
Publication date: 2021-07-29
Also published as: JP7511823B2; JPWO2021149719A1

Abstract

A method of using miRNA expression level as an indicator of caner, said method comprising a step for extracting miRNAs from a body fluid of a target subject and a step for detecting the miRNA expression level in the target subject's body fluid using the extracted miRNAs. In this method of using miRNAs as an indicator of caner, a decrease in the expression level of miR-122-5p in the target subject's body fluid, compared to the expression level of miR-122-5p in the body fluid of a normal subject, is used as an indicator of cancer with HOTAIR expression. The body fluid is blood plasma or serum.

Description

Method of using miRNA expression level as an index of cancer

The present invention relates to a method of using the expression level of miRNA as an index of cancer.

Since 1981, cancer has been the leading cause of death in Japan, and measures to reduce the risk of cancer and extend healthy life expectancy are important issues. Cancer has a poorer prognosis as the stage goes up. If cancer can be detected at the stage 1 stage, it can be almost completely cured, so a technology that can detect cancer at the earliest possible stage is important. In particular, since body fluids such as blood and urine are easy to diagnose, there is a great need for early screening and diagnosis of cancer using body fluids. There is also a need for surveillance diagnosis of cancer recurrence or metastasis at an early stage after surgery or other cancer treatments.

In addition, fecal occult blood is used in the primary screening for colorectal cancer.

However, due to the high false-positive rate, fecal occult blood-positive individuals must undergo a definitive diagnosis by endoscopy, which is highly invasive and resistant to consultation. For this reason, the ratio of those who are positive for fecal occult blood to undergo endoscopy is as low as about 50%, which is a problem.

PSA is also used in the primary screening for prostate cancer.

However, PSA levels are high even with benign prostatic hyperplasia, so people with high PSA levels must undergo a definitive diagnosis with a highly invasive biopsy.

If a subject can be predicted to have cancer with high sensitivity using body fluids such as urine and blood before such a highly invasive diagnosis, he / she is suffering from cancer. For non-subjects, there is no need to perform unnecessary, highly invasive tests. In addition, it is expected that more subjects will receive a definitive diagnosis after being diagnosed using body fluids such as urine and blood.

From the above viewpoint, various cancer markers have been searched for and efforts have been made to detect cancer at an early stage.

However, the above markers are overwhelmingly inadequate in sensitivity, so early cancer cannot be diagnosed, and they are only used for cancer monitoring. In addition, the specificity for cancer is not sufficient, and the concentration of markers increases in diseases other than cancer.

On the other hand, recently, cancer screening diagnosis by analysis of microRNA (miRNA) and surveillance diagnosis of cancer recurrence and / or metastasis have been attempted.

For example, Patent Document 1 describes at least one miRNA in a non-cellular biofluid sample obtained from a subject as a method for detecting or diagnosing gastric cancer in a subject or as a method for determining the likelihood that a subject will develop gastric cancer. Disclosed is a method in which the differential expression of miRNA expression when compared with a subject without gastric cancer is an index for a subject with gastric cancer, including a step of measuring the expression level of the miRNA. Here, the differential expression of miRNA is hsa-miR-142-5p, hsa-miR-29c-3p, hsa-miR-93-5p, hsa-miR-140-5p, hsa-miR-148a-3p. , Hsa-miR-183-5p, hsa-miR-29b-3p, hsa-miR-424-5p, hsa-miR-101-3p, hsa-miR-106b-3p, hsa-miR-128, hsa-miR -1280, hsa-miR-140-3p, hsa-miR-15b-3p, hsa-miR-186-5p, hsa-miR-18b-5p, hsa-miR-197-3p, hsa-miR-19a-3p , Hsa-miR-19b-3p, hsa-miR-20b-5p, hsa-miR-21-3p, hsa-miR-23a-5p, hsa-miR-25-3p, hsa-miR-27a-5p, hsa -MiR-29a-3p, hsa-miR-29b-2-5p, hsa-miR-29c-5p, hsa-miR-338-5p, hsa-miR-425-3p, hsa-miR-4306, hsa-miR -450a-5p, hsa-miR-486-5p, hsa-miR-500a-3p, hsa-miR-501-5p, hsa-miR-532-3p, hsa-miR-550a-5p, hsa-miR-579 , Hsa-miR-589-5p, hsa-miR-590-5p, hsa-miR-598, hsa-miR-616-5p, hsa-miR-627, hsa-miR-629-3p, hsa-miR-629 -5p, hsa-miR-93-3p, hsa-miR-195-5p, hsa-miR-18a-3p, hsa-miR-363-3p, hsa-miR-181a-2-3p, hsa-miR-16 -5p, hsa-miR-501-3p, hsa-miR-23a-3p, hsa-miR-339-3p, hsa-miR-15a-5p, hsa-miR-320b, hsa-miR-374b-5p, hsa -MiR-650, hsa-miR-1290, hsa-miR-22-3p, hsa-miR-320c, hsa-miR-130a-3p, hsa-miR-320e, hsa-miR-378a-3p, hsa-miR -9-5p, hsa-miR-200b-3p, hsa-miR-141-3p, hsa-miR-191-5p Is it an expression of an "upregulated" miRNA selected from the group consisting of, hsa-miR-628-5p, hsa-miR-484, hsa-miR-425-5p; or hsa-miR-103a. -3p, hsa-miR-30a-5p, hsa-miR-181a-5p, hsa-miR-107, hsa-miR-26a-5p, hsa-miR-126-3p, hsa-miR-99b-5p, hsa -MiR-339-5p, hsa-miR-122-5p, hsa-miR-136-5p, hsa-miR-139-5p, hsa-miR-146a-5p, hsa-miR-154-5p, hsa-miR -193b-3p, hsa-miR-23c, hsa-miR-30b-5p, hsa-miR-337-5p, hsa-miR-382-5p, hsa-miR-409-3p, hsa-miR-411-5p , Hsa-miR-485-3p, hsa-miR-487b, hsa-miR-495, hsa-miR-885-5p, hsa-miR-99a-5p, hsa-miR-362-5p, hsa-miR-671 -3p, hsa-miR-454-3p, hsa-miR-328, hsa-miR-320a, hsa-miR-126-5p, hsa-miR-27a-3p, hsa-miR-30d-5p, hsa-miR A "down-regulated" miRNA selected from the group consisting of -10a-5p, hsa-miR-10b-5p, hsa-miR-497-5p, hsa-miR-134, and hsa-miR-150-5p. Is one of the manifestations of.

Special Table 2017-525350

However, there is a problem that the expression level of a large number of miRNAs is used as an index of gastric cancer.

An object of the present invention is to provide a method in which a small number of miRNA expression levels can be used as an index for a large number of cancers, and a miRNA expression level can be used as an index for cancer.

One aspect of the present invention is a method of using the expression level of miRNA as an index of cancer, in which a step of extracting miRNA from the body fluid of a subject and miRNA in the body fluid of the subject using the extracted miRNA are used. The expression level of miR-122-5p, which is a miRNA in the body fluid of the subject, is lower than the expression level of miR-122-5p in the body fluid of a healthy subject, including the step of detecting the expression level of miR-122-5p. Is used as an index of cancer expressing HOTAIR (Hox transcript anticipation intergenic RNA), and the body fluid is hemorrhage or serum.

According to the present invention, it is possible to provide a method in which the expression level of a small number of miRNAs can be used as an index of a large number of cancers, and the expression level of miRNAs can be used as an index of cancer.

Relative expression level of miR-122-5p in plasma fractions of 30 plasma samples of healthy subjects and 1 to 3 pool samples in which 10 plasma samples of colorectal cancer (stages 2 to 4) patients are mixed in equal amounts. It is a scatter diagram of the value (2-ΔΔCT). Relative value of miR-122-5p expression level of the lectin column elution fraction of 30 plasma samples of healthy subjects and a pool sample in which 10 plasma samples of colorectal cancer (stages 2 to 4) patients are mixed in equal amounts ( It is a scatter diagram of 2-ΔΔCT). It is a figure which shows the box whisker diagram of the ΔΔCT value of miR-122-5p of the plasma fraction of 30 plasma samples of a colorectal cancer (stage 1) patient and 30 plasma samples of a healthy person, and the ROC curve. It is a box plot of the ΔΔCT value of miR-122-5p of the lectin column elution fraction of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects, and a diagram showing a ROC curve. .. It is a scatter diagram of the relative value (2-ΔΔCT) of the expression level of miR-122-5p of the lectin column elution fraction of plasma of eight kinds of cancer patients. It is a scatter diagram of the relative value (2-ΔΔCT) of the expression level of miR-451a in the plasma fraction of 40 plasma samples of colorectal cancer (stages 1 to 4) patients and 30 plasma samples of healthy subjects. It is a scatter plot of the relative value (2-ΔΔCT) of the expression level of miR-451a of the lectin column elution fraction of 40 plasma samples of colorectal cancer (stages 1 to 4) patients and 30 plasma samples of healthy subjects. .. In the multiple regression equations and logistic regression equations obtained in Tables 4A, B, and C, the values of the lectin column elution fractions of 30 plasma samples of healthy subjects and 30 plasma samples of colorectal cancer (stage 1) patients. It is a scatter diagram of the y-hat value when is substituted. The lectin column elution plots of plasma of 7 types of cancer patients are shown in the logistic regression equations of the ΔCT (miR-192-5p) values and the ΔΔCT (cel-miR-39-3p) values obtained in Tables 4B and C. It is a scatter diagram of the y-hat value obtained by substituting each value of the minute. Decision tree learning of ΔCT (miR-93-5p) values of 12 types of miRNA in the lectin column elution fraction of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects (C5. It is a figure which shows the decision tree obtained by 0), and the ROC curve. This is a determination tree (No. 1) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 2) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 3) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 4) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 5) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 6) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 7) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 8) was obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 9) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 10) was obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 11) was obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 12) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 13) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 14) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 15) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 16) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 17) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 18) obtained in the same manner as in FIG. 7A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. Decision tree learning of ΔCT (miR-192-5p) values of 12 types of miRNA in the lectin column elution fraction of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects (C5. It is a figure which shows the decision tree obtained by 0), and the ROC curve. This is a determination tree (No. 1) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 2) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 3) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 4) was obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 5) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 6) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 7) was obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 8) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 9) was obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 10) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 11) was obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 12) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 13) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 14) obtained in the same manner as in FIG. 8A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. Decision tree learning of ΔCT (miR-502-5p) values of 12 types of miRNA in the lectin column elution fraction of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects (C5. It is a figure which shows the decision tree obtained by 0), and the ROC curve. This is a determination tree (No. 1) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 2) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 3) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 4) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 5) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 6) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 7) was obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 8) was obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 9) was obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 10) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 11) was obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 12) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 13) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 14) obtained in the same manner as in FIG. 9A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. ΔCT (miR-93-5p, miR-192-5p, miR-) of 12 types of miRNA in the lectin column elution fraction of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects It is a figure which shows the decision tree obtained by the decision tree learning (C5.0) of 502-5p) value, and the ROC curve. This is a determination tree (No. 1) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 2) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 3) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 4) was obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 5) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. This is a determination tree (No. 6) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 7) was obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 8) was obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 9) was obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 10) was obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. The determination tree (No. 11) was obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 12) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 13) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. .. It is a determination tree (No. 14) obtained in the same manner as in FIG. 10A, except that 20 samples were extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. ..

Next, a mode for carrying out the present invention will be described.

[Method of using miRNA as an index of cancer]
In the method of using the miRNA of the present embodiment as an index of cancer, a step of extracting miRNA from the body fluid of the subject and a step of detecting the expression level of miRNA in the body fluid of the subject using the extracted miRNA are performed. include.

In the present embodiment, the expression level of miR-122-5p (UGGAGUGUGACAAUGGUGUGUUG; SEQ ID NO: 1) in the body fluid of the subject is lower than the expression level of miR-122-5p in the body fluid of a healthy subject. , HOTAIR (Hox transcript antisense intergenic RNA) is used as an index of cancer.

Here, as body fluids, blood including whole blood, plasma, serum, etc., spinal fluid, lymph fluid, tears, urine, sweat, breast milk, semen, saliva, nasal mucosa, sputum, sheep water, joint fluid, ascites, stool, cells Examples include culture supernatants, tissue mass-derived products, and body fluids derived from animals and the like. Cells include multicellular organisms and unicellular microorganisms such as microalgae. These may be used as a sample by themselves, or may be used as a liquid prepared by being added to, for example, a MEM medium or a physiological saline solution. Preferred body fluids are plasma or serum.

The subjects are animals of any species, including domestic animals, livestock animals, primates, and humans, mice, rats, cats, dogs, sheep, rabbits, horses, cows, goats, pigs, guinea pigs, hamsters, chickens, and turkeys. , Or non-human primates (eg, marmosets, macaques), etc., but not limited to these. A healthy person is one of these subjects who does not have cancer.

MicroRNA (miRNA) is a non-coding RNA that is a single-stranded RNA of about 21 to 22 bases, and the expression of the target gene at the post-transcriptional level by degrading the target mRNA or inhibiting its translation. To control. MicroRNA according to an embodiment of the present invention also includes equivalents thereof.

"Expression level" refers to the level of miRNA expression, activity and their amount. "Reduced expression level" means that the expression level of miRNA present in the body fluid of a subject is found at a level lower than that of the body fluid of a control subject. Control subjects include healthy subjects.

The expression level of miR-122-5p in the body fluid of a healthy subject is, for example, the average value of the expression level of miR-122-5p in the body fluid of one or more healthy subjects as a control subject, the median threshold value, and the like. Examples include the standard range of values. Those skilled in the art will evaluate the activity, expression or amount of a particular miRNA in the body fluids of a particular population of subjects with (or not) cancer, and based on this assessment, appropriate mean, median, threshold, etc. The "standard range" of values can be determined.

HOTAIR is a type of long non-coding RNA (lncRNA).

Cancers expressing HOTAIR include, for example, small intestinal cancer, colon cancer, gastrointestinal stromal tumor (GIST), gastrointestinal cultinoid, gastric cancer, esophageal cancer, liver cancer, biliary tract cancer, pancreas. Cancer, pancreatic / gastrointestinal neuroendocrine tumor, Langerhans cell histiocytosis, renal cell cancer, renal pelvis / urinary tract cancer, adrenal tumor, osteosarcoma, soft sarcoma, malignant lymphoma, bladder cancer, urinary tract cancer, prostate Cancer, testicular tumor, penis cancer, uterine body cancer, cervical cancer, uterine tumor, ovarian tumor, female organ cancer, lung cancer, thoracic adenocarcinoma, mesenteric tumor, breast cancer, hematopoietic tumor, leukemia, bone marrow proliferation Cancers such as sexually transmitted diseases and multiple myeloma.

According to the method of using the expression level of miRNA of the present embodiment as an index of cancer, for example, the expression level of miR-122-5p can be used as an index of cancer expressing HOTAIR, and thus the cancer. It can assist in screening diagnosis and surveillance diagnosis of cancer recurrence and / or metastasis. That is, when the expression level of miR-122-5p in the body fluid of the subject is lower than the expression level of miR-122-5p in the body fluid of a healthy subject, the subject suffers from cancer expressing HOTAIR. (The cancer that has had cancer in the past and expresses HOTAIR in the recovered subject has recurred and / or has metastasized).

Even if the method using the expression level of miRNA of the present embodiment as an index of cancer is used, it may not be possible to specify the type of cancer. Therefore, the method of using the expression level of miRNA of the present embodiment as an index of cancer is used for the primary screening of cancer, the secondary screening of fecal occult blood positive person in colorectal cancer screening, and the PSA high value person in prostate cancer screening. It is preferable to apply it to the secondary screening and the like.

As described above, the proportion of people who are positive for fecal occult blood in colorectal cancer screening to undergo endoscopic diagnosis, which is a definitive diagnosis, may be about 50%. The rate of fecal occult blood-positive persons diagnosed as having a high possibility of suffering from HOTAIR-expressing cancer can be significantly improved by the method used as an index of the above. In addition, a person with a high PSA level in a prostate examination is also diagnosed as having a high possibility of having a cancer expressing HOTAIR by a method using the expression level of miRNA of the present embodiment as an index of cancer, and then the prostate. By performing this biopsy, it becomes possible for a person with a high PSA who is unlikely to have a cancer expressing HOTAIR to avoid the biopsy of the prostate.

The type of cancer is identified by performing physical diagnosis such as NMR-CT, PET, ultrasonic diagnosis, and diagnosis using a next-generation sequencer targeting blood circulating tumor DNA (ctDNA), miRNA, etc. can do.

In addition, when assisting the surveillance diagnosis of cancer recurrence and / or metastasis, the location of cancer recurrence and / or metastasis can be easily estimated, so that the expression level of miRNA of the present embodiment can be used for cancer. The method used as an index of HOTAIR can be applied to all cancers expressing HOTAIR.

The fact that the expression level of miR-122-5p in the body fluid of the subject is lower than the expression level of miR-122-5p in the body fluid of a healthy subject can be used as an index of cancer expressing HOTAIR. The reason why it can be done is thought to be as follows.

After HOTAIR is loaded into extracellular vesicles (EVs) released into the blood from cancer expressing HOTAIR, it is transported to the liver through the bloodstream. HOTAIR transported to the liver suppresses the expression of miR-122-5p specific to hepatocytes, so that EVs released from hepatocytes into the blood, very low density lipoprotein (VLDL), and low density lipoprotein MiR-122-5p mounted on (LDL) is reduced. As a result, the expression level of miR-122-5p in the body fluid of a subject suffering from cancer expressing HOTAIR is lower than the expression level of miR-122-5p in the body fluid of a healthy subject.

Here, since hepatocyte-derived EVs have a transferrin receptor (TfR) having a high mannose-type sugar chain on the surface, they specifically bind to a high-mannose-type sugar chain-specific lectin. Further, since VLDL and LDL contain apolipoprotein B-100 having a high mannose type sugar chain, they are specifically bound by a high mannose type sugar chain specific lectin. Therefore, when the body fluid of the subject is brought into contact with a substrate on which a high mannose-type sugar chain-specific lectin is immobilized, components containing EVs, VLDL, and LDL derived from hepatocytes are selectively captured. As a result, extraction of miRNA from selectively captured components improves the ability to discriminate against cancer positivity.

As a method for specifically capturing miRNA derived from hepatocytes such as miR-122-5p, there is also a method for selectively capturing VLDL and LDL using dextran sulfate immobilized on a substrate and an anti-Apo-B antibody. It is possible to use. At this time, a device in which a lectin having a high mannose-type sugar chain-specific and monosaccharide-binding property such as BPL-17 is immobilized captures EVs, VLDL, LDL in a sugar chain-specific manner, and then mannose or the like. It may be eluted with a monosaccharide and purified with dextran sulfate in which the eluate is immobilized on a substrate. As a result, VLDL and LDL with higher purity can be obtained, and the accuracy of miRNA secreted from the liver is improved.

In addition, EVs derived from cancer cells of cancer expressing HOTAIR, EVs derived from some immune system cells (for example, Treg, etc.), and EVs derived from reticulocytes are also surfaced with a transferrin receptor having a high mannose-type sugar chain. Because it is present in, it specifically binds to high mannose-type sugar chain-specific lectins. Therefore, the miRNAs loaded in these EVs are also used as auxiliary miRNAs other than miR-122-5p in the method of using the expression level of miRNAs of the present embodiment as an index of cancer.

Examples of the high mannose-type sugar chain-specific lectin include red alga Solieria robusta-derived lectins (SolninA, SolninB, SolninC), red alga Euchema serra-derived lectins (ESA-1, ESA-2), and red alga E.I. Amakusaensis-derived lectins (EAA-1, EAA-2, EAA-3), red algae E. Denticularum-derived lectins (EDA-1, EDA-2, EDA-3), red alga Karakalpakus alvarezii-derived lectins (ECA-1 (KAA-1), ECA-2 (KAA-2)), KAA-3), red algae K. Striatum-derived lectins (KSA-1, KSA-2), red algae Meristopa papulosa-derived lectins (MPA-1, MPA-2), red algae Gracialriabursa-pastoris-derived lectins (Granin-BP), red algae Gardhiella sub -1, ASL-2), Oscillatoria agardhii-derived lectin (OAA), green alga Body coacta-derived lectin (BCA), green alga Bryopsis plumosa-derived lectin (BPL17), green alga B. Maxima-derived lectin (BML17), green algae B. Examples thereof include lectin derived from corticulans (BCL17) and lectin derived from the red alga Meristopapaplusa (MPL-1), and two or more of them may be used in combination. Among these, OAA is preferable in terms of the ability to discriminate against positive cancer.

Examples of the base material on which the high mannose type sugar chain-specific lectin is immobilized include a spin column, a pipette tip, and the like, which contains silica monolith as a carrier for immobilizing the high mannose type sugar chain specific lectin. Examples thereof include a microchannel plate (see, for example, Japanese Patent Application Laid-Open No. 2018-191636).

Examples of commercially available silica monoliths include the Mono Bis series (manufactured by Kyoto Monolith).

As a method for immobilizing a high mannose-type sugar chain-specific lectin on a substrate, a known method can be used (see, for example, Japanese Patent Application Laid-Open No. 2018-191636).

As a base material on which a high mannose type sugar chain-specific lectin other than the above is immobilized, for example, as a carrier for immobilizing a high mannose type sugar chain-specific lectin, a column or the like filled with a filler is used. May be good.

A known method should be used as a method for extracting miRNA from the body fluid of the subject (a component selectively captured by contacting the substance with a substrate on which a high mannose-type sugar chain-specific lectin is immobilized). Can be done.

Examples of commercially available products of reagents used for extracting miRNA include QIAzol Lysis Reagent (manufactured by QIAGEN) and the like.

MiRNAs are extracted from the subject's body fluids (components selectively captured by contacting the subject with a substrate on which a high mannose-type sugar chain-specific lectin is immobilized) and then purified using a known method. You may.

Examples of commercially available reagents used for purifying miRNA include NucleoSpin (registered trademark) miRNA Plasma (manufactured by MACHEREY-NAGEL) and the like.

As a method for detecting the expression level of miRNA in body fluid, for example, as a usual analysis format using ribonucleic acid hybridization, nuclear run-on assay, real-time PCR, LAMP (Loop-Mediated Isothermal Amplification) method, RNase protection assay (RNase protection assay) Melton et al., Nuc. Assays Res. 12: 7035), Northern blot method and InSitu hybridization, microarray analysis and the like.

In the present embodiment, it is preferable to detect the expression level of miR-451a (AAACCGUUACCAUUACUGAGUU; SEQ ID NO: 13) in the body fluid of the subject in addition to the expression level of miR-122-5p in the body fluid of the subject. At this time, the fact that the expression level of miR-451a in the body fluid of the subject is lower than the expression level of miR-451a in the body fluid of a healthy subject is used as an index of advanced cancer. That is, when the expression level of miR-451a in the body fluid of the subject is lower than the expression level of miR-451a in the body fluid of a healthy subject, the subject has advanced cancer (affected by cancer). There is a high probability that the advanced cancer has recurred and / or metastasized to the subject who had been treated.

The reason why the expression level of miR-451a in the body fluid of the subject is lower than the expression level of miR-451a in the body fluid of a healthy subject can be used as an index of advanced cancer is as follows. Is considered to be.

Advanced cancer expresses CRNDE (Colorectal Neoplasmia Differentially Expressed), which is a type of long non-coding RNA (lncRNA). Therefore, after CRNDE is loaded on EVs released from cancer cells into the blood, it is transported to the bone marrow. CRNDE transported to the bone marrow acts on erythrocyte progenitor cells and suppresses the expression of miR-451a, so that miR-451a in the blood is reduced. As a result, the expression level of miR-451a in the body fluid of a subject suffering from advanced cancer is lower than the expression level of miR-451a in the body fluid of a healthy subject.

In the present embodiment, the expression level of miRNA in body fluid is determined by measuring the CT (threshold cycle) value, which is the number of PCR cycles in which the amount of PCR product is constant, of miRNA in body fluid, and the CT value is a known method. Therefore, it is preferable to obtain it by correcting it.

As the expression level of miRNA in the body fluid, for example, CT of cel-miR-39-3p in the body fluid to which a specified amount of the known miRNA cel-miR-39-3p (UCACCGGGUGUAAAUCAGCUUG; SEQ ID NO: 14) was added. The relative value of the ΔCT value of miRNA in body fluid, which is the difference from the value, the ΔΔCT value of miRNA in body fluid, which is the difference from the ΔCT value of miRNA in the pooled sample of body fluid of a healthy person, and the relative value of the expression level of miRNA in body fluid. (2-ΔΔCT) and the like can be mentioned.

However, adding a prescribed amount of cel-miR-39-3p to the body fluid each time the expression level of miRNA in the body fluid is detected is troublesome and cannot be said to be an easy operation.

Therefore, when detecting the expression level of miRNA in the body fluid, miR-192-5p (CUGACCUAUGAAUUGACAGCC; SEQ ID NO: 9), miR-93-5p (CAAAGUGCUGUUCGUGCAGGUAG; SEQ ID NO: 5), miR-502-5p (SEQ ID NO: 5) It is preferable to correct the CT value of miRNA in the body fluid by the CT value of one or more miRNAs selected from the group consisting of AUCCUUGCUAUCUGGGUGCUA; SEQ ID NO: 12). As a result, in addition to improving the ability to discriminate against cancer positive, it is not necessary to add cel-miR-39-3p to the body fluid, and the operation becomes simple.

The CT value of miRNA in body fluid can be detected by real-time PCR.

In this embodiment, in addition to the expression level of miR-122-5p in the body fluid of the subject, miR-126-3p (UCGUACCGUGAGUAAAUAAUGCG; SEQ ID NO: 6), miR-192-5p, miR-93 in the body fluid of the subject. -5p, miR-423-3p (AGCUCGGUCUGAGGCCCUCAGU; SEQ ID NO: 11), miR-21-5p (UAGCUUAUCAGAUGAUGUGA; SEQ ID NO: 3), miR-130a-3p (CAGUGCAAUGUUAAAAAAGGUCAUG) SEQ ID NO: 4), miR-146a-5p (UGAGAACUGAAUCCAUCGGUU; SEQ ID NO: 8), miR-199a-3p (ACAGUAGUCUGCACUUGGUA; SEQ ID NO: 10), miR-15b-5p (selected from UAGCAGGACAUCAUGGUU; It is preferable to detect the expression level of one or more miRNAs. At this time, it is used as an index of cancer that the expression level of the above-mentioned one or more types of miRNA in the body fluid of the subject is higher than the expression level of the above-mentioned one or more types of miRNA in the body fluid of a healthy subject. .. That is, when the expression level of the above-mentioned one or more miRNAs in the body fluid of the subject is higher than the expression level of the above-mentioned one or more miRNAs in the body fluid of a healthy person, the subject suffers from cancer. There is a high probability that the cancer has recurred and / or metastasized to the subject after recovery.

In this case, it is preferable to select one or more of the above miRNAs by decision tree learning using the C5.0 algorithm. This improves the ability to discriminate against cancer positives.

Specifically, when detecting the ΔCT (miR-192-5p) value of one or more of the above miRNAs in the body fluid of the subject, miR-122-5p, miR-130a-3p, and miR-146a-5p are selected. Will be done.

At this time, the threshold value of the ΔCT (miR-192-5p) value of miR-122-5p is -2.2 to 0.1, and the threshold value of the ΔCT (miR-192-5p) value of miR-130a-3p is It is -2.5 to -1.5, and the threshold value of the ΔCT (miR-192-5p) value of miR-146a-5p is -6.7 to -3.0.

In addition, when detecting the ΔCT (miR-93-5p) value of one or more of the above miRNAs in the body fluid of the subject, miR-15b-5p, miR-122-5p, miR-146a-5p, miR-199a- 3p is selected.

At this time, the threshold value of the ΔCT (miR-93-5p) value of miR-15b-5p is 1.0 to 4.0, and the threshold value of the ΔCT (miR-93-5p) value of miR-122-5p is 0. It is .1 to 5.7, and the threshold value of the ΔCT (miR-93-5p) value of miR-146a-5p is -0.5 to 1.8.

Further, when detecting the ΔCT (miR-502-5p) value of one or more of the above miRNAs in the body fluid of the subject, miR-15b-5p, miR-122-5p, and miR-146a-5p are selected.

At this time, the threshold value of the ΔCT (miR-502-5p) value of miR-15b-5p is -7.1 to -2.6, and the threshold value of the ΔCT (miR-502-5p) value of miR-122-5p. Is −2.5 to −0.4, and the threshold value of the ΔCT (miR-502-5p) value of miR-146a-5p is −6.4 to −3.8.

In addition, when detecting the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) values of one or more of the above miRNAs in the body fluid of the subject, miR-122-5p, miR-126- 3p and miR-146a-5p are selected.

At this time, the threshold value of the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is −0.8 to 1.8, which is that of miR-126-3p. The threshold value of the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value is -4.6 to -3.4, and the ΔCT (miR-93-5p, miR-93-5p, The threshold value of miR-192-5p, miR-502-5p) is -4.9 to -2.0.

The ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) values of each miRNA are, from the CT values of each miRNA, miR-93-5p, miR-192-5p, and miR-502. It is a value obtained by subtracting the average CT value of -5p.

Hereinafter, examples of the present invention will be described, but the present invention is not limited to the examples.

[Plasma or serum]
In this example, the following plasma or serum was used.

Plasma of colon cancer (stage 1) patients 30 samples Plasma of colon cancer (stages 2-4) patients 10 samples Plasma of liver cancer patients 3 samples Plasma of breast cancer patients 3 samples Blood of prostate cancer patients Plasma 2 samples Plasma of lung cancer patients 2 samples Plasma of gastric cancer patients 2 samples Plasma of esophageal cancer patients 2 samples Plasma of pancreatic cancer patients 2 samples Plasma of healthy subjects (non-cancer patients) 30 samples Cancer patients Plasma was purchased from KAC Co., Ltd. or Kokusai Bio Co., Ltd.

Serum of pancreatic cancer patients was obtained from Fukushima Medical University.

Plasma of healthy people was obtained from volunteers.

For plasma or serum, the supernatant obtained by centrifugation (10,000 x G, 5 minutes, 4 ° C.) was filtered with a 0.45 μm filter and used as a sample.

[Collection of lectin column-eluted fraction]
The lectin column was set in a waste liquid tube, centrifuged (1,500 x G, 2 minutes, 4 ° C.), and the storage liquid was discarded. 300 μL of PBS solution was added, and the mixture was centrifuged (1,500 x G, 2 minutes, 4 ° C.) to wash the lectin column. 150 μL of each sample was added to the column, allowed to stand at room temperature for 5 minutes, centrifuged (1,500 x G, 2 minutes, 4 ° C.), and the flow-through liquid was discarded. A 10-fold concentration PBS solution of 300 μL was added, and the lectin column was washed by centrifugation (1,500 x G, 2 minutes, 4 ° C.). The operation of washing the same lectin column as described above was repeated three times. After discarding the third washing liquid, the lectin column liquid was completely removed by centrifugation (3,000 x G, 1 minute, 4 ° C.). The lectin column is set in a tube for collecting the eluate, 100 μL of QIAzol Lysis Regent (manufactured by QIAGEN) is added, and the mixture is allowed to stand at room temperature for 5 minutes and then centrifuged (400 x G, 2 minutes, 4 ° C.) to remove the eluate. It was collected and used as a lectin column-eluted fraction.

Here, as the lectin column, an AIST-OAA fixed spin column (diameter 4 mm, thickness) obtained by immobilizing OAA as a high mannose-type sugar chain-specific lectin on a silica monolith packed in a spin column. A diameter of 2 mm, a capacity of 27 μL, and an OAA immobilization amount of 42 μg) (manufactured by Kyoto Monotech) were used.

[Extraction and purification of miRNA from lectin column-eluted fraction]
400 μL of QIAzol Lysis Regent (manufactured by QIAGEN) was added to the lectin column elution fraction, and 250 μL of isopropyl alcohol (containing cel-miR-39-3p) was added after obtaining an extract of miRNA according to the manufacturer's prescription. Here, cel-miR-39-3p was used by diluting microRNA (cel-miR-39) Spike-In Kit (manufactured by NORGEN BIOTEK) 1000 times with isopropyl alcohol. Purification of miRNA was performed using NucleoSpin® miRNA Plasma (manufactured by MACHEREY-NAGEL) according to the manufacturer's prescription.

[Extraction and purification of miRNA from plasma fraction]
To 100 μL of each sample, 500 μL of QIAzol Lysis Regent (manufactured by QIAGEN) was added, and 250 μL of isopropyl alcohol (containing cel-miR-39-3p) was added after obtaining an extract of miRNA according to the manufacturer's prescription. Purification of miRNA was performed using NucleoSpin® miRNA Plasma (manufactured by MACHEREY-NAGEL) according to the manufacturer's prescription.

[MiRNA assay]
The miRNA assay was performed using TaqMan® MicroRNA Assays (manufactured by Thermo Fisher Scientific) as prescribed by the manufacturer. Real-time PCR was performed using a 7500 Fast real-time PCR system (manufactured by Thermo Fisher Scientific) according to the manufacturer's prescription.

Table 1 shows the 14 types of miRNA used in the miRNA assay.

Here, the ΔCT (miR-93-5p) value, the ΔCT (miR-192-5p) value, the ΔCT (miR-502-5p) value, and the ΔCT (miR-93-5p, miR-192-5p, miR) value of miRNA. -502-5p) values and ΔΔCT (cel-miR-39-3p) values were used for each statistical calculation. All data were taken 3 times each and the average value was used.

Table 2A shows the ΔCT (miR-93-5p) value of miRNA of the lectin column-eluted fraction of 30 plasma samples of healthy subjects.

The ΔCT (miR-93-5p) value of each miRNA is the value obtained by subtracting the CT value of miR-93-5p from the CT value of each miRNA.

Table 2B shows the ΔCT (miR-93-5p) values of miRNA of the lectin column-eluted fraction of 30 plasma samples of colorectal cancer (stage 1) patients.

Table 2C shows the ΔCT (miR-192-5p) value of the lectin column elution fraction of 30 plasma samples of healthy subjects.

The ΔCT (miR-192-5p) value of each miRNA is the CT value of each miRNA minus the CT value of miR-192-5p.

Table 2D shows the ΔCT (miR-192-5p) value of miRNA of the lectin column-eluted fraction of the sample of colorectal cancer (stage 1) patients.

Table 2E shows the ΔΔCT (cel-miR-39-3p) value of miRNA of the lectin column-eluted fraction of 30 plasma samples of healthy subjects.

The ΔCT (cel-miR-39-3p) value of each miRNA is the value obtained by subtracting the CT value of cel-miR-39-3p from the CT value of each miRNA.

Further, the ΔΔCT (cel-miR-39) value of each miRNA is obtained from the ΔCT (cel-miR-39-3p) value of each miRNA of each miRNA of the pool sample obtained by mixing an equal amount of 30 plasma samples of healthy subjects. It is a value obtained by subtracting the ΔCT (cel-miR-39-3p) value.

Table 2F shows the ΔΔCT (cel-miR-39-3p) value of miRNA of the lectin column-eluted fraction of 30 plasma samples of colorectal cancer (stage 1) patients.

Table 2G shows the ΔCT (miR-502-5p) value of the lectin column elution fraction of 30 plasma samples of healthy subjects.

The ΔCT (miR-502-5p) value of each miRNA is the CT value of each miRNA minus the CT value of miR-502-5p.

Table 2H shows the ΔCT (miR-502-5p) values of miRNA of the lectin column-eluted fraction of the sample of colorectal cancer (stage 1) patients.

Table 2I shows the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) values of the lectin column elution fraction of 30 plasma samples of healthy subjects.

Table 2J shows the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) values of miRNA of the lectin column-eluted fraction of the sample of colorectal cancer (stage 1) patients.

Table 2K shows the ΔΔCT (cel-miR-39-3p) values of miR-451a and miR-122-5p of the plasma fractions of 30 plasma samples of healthy subjects and colorectal cancer (stage 1) patients.

Table 2L shows the ΔΔCT (cel-miR-39-3p) values of miR-122-5p of pool samples 1 to 3 in which plasmas of 10 colorectal cancer (stages 2 to 4) patients were mixed in equal amounts. show.

Table 2M shows the ΔΔCT (cel-miR-39-3p) value of miR-451a of 9 plasma samples of colorectal cancer (stages 2 to 4) patients.

Table 2N shows the ΔCT (miR-192-5p) values of miRNA of the lectin column-eluted fractions of 16 plasma samples of 7 types of cancer patients.

Table 2O shows the ΔΔCT (cel-miR-39-3p) values of miRNA of the lectin column-eluted fractions of 16 plasma samples and 2 serum samples of 8 types of cancer patients.

Table 3 shows the t-test results of the ΔΔCT (cel-miR-39-3p) value of miRNA of the lectin column-eluted fraction of 30 plasma samples of colorectal cancer (stage 1) patients and healthy subjects.

[Statistical processing]
The multiple regression analysis, logistic regression analysis, and decision tree learning (C5.0) used in this example were carried out using statistical processing software R. The selection of miRNAs by multiple regression analysis and logistic regression analysis was performed by the stepwise method using AIC.

[Example 1: Determining colorectal cancer (stages 2 to 4) based on the relative value (2-ΔΔCT) of the expression level of miR-122-5p]
(statistics)
1A and 1B show plasma samples 1 to 3 in which 10 plasma samples of colorectal cancer (stages 2 to 4) patients are mixed in equal amounts, and (A) plasma fractions of 30 plasma samples of healthy subjects. (B) The dispersion diagram of the relative value (2-ΔΔCT) of the expression level of miR-122-5p of the lectin column elution fraction is shown. In FIGS. 1A and 1B, the bars are average values.

The ΔΔCT value of miR-122-5p is the miR-122- of a pool sample obtained by mixing an equal amount of 30 plasma samples of healthy subjects from the ΔCT (cel-miR-39-3p) value of miR-122-5p. It is a value obtained by subtracting the ΔCT (cel-miR-39-3p) value of 5p.

(result)
The (A) plasma fraction of the plasma pool samples 1 to 3 of colorectal cancer (stages 2 to 4) patients and the (B) lectin column elution fraction are the average values of 30 plasma samples of healthy subjects. In comparison, the relative values (2-ΔΔCT) of the expression level of miR-122-5p were 0.4 times and 0.08 times, respectively, which were clearly decreased. In addition, in the pooled samples 1 to 3 of colorectal cancer (stages 2 to 4) patients, the expression level of miR-122-5p was higher in the (B) lectin column-eluted fraction than in the (A) plasma fraction. The decrease in the relative value of (2-ΔΔCT) is more pronounced.

From the above, it is found that the expression level of miR-122-5p in the plasma of the subjects is lower than the expression level of miR-122-5p in the plasma of healthy subjects for colorectal cancer (stage). It can be seen that it serves as an index for 2 to 4).

[Example 2: Determining colorectal cancer (stage 1) based on the ΔΔCT value of miR-122-5p]
(statistics)
2A and 2B show miR-122 of 30 plasma samples of colorectal cancer (stage 1) patients, (A) plasma fractions of 30 healthy subjects, and (B) lectin column-eluting fractions. The plasma box of -5p ΔΔCT value and the ROC curve are shown.

Here, the ΔΔCT value of miR-122-5p is the miR-122 of a pool sample obtained by mixing an equal amount of 30 plasma samples of healthy subjects from the ΔCT (cel-miR-39-3p) value of miR-122-5p. It is a value obtained by subtracting the ΔCT (cel-miR-39-3p) value of −5p.

(result)
The ΔΔCT value of miR-122-5p of 30 plasma samples of colorectal cancer (stage 1) patients, (A) plasma fractions of 30 healthy subjects, and (B) lectin column-eluting fractions. The P values in the t-test were 0.002664 and 0.000144, respectively. Therefore, the ΔΔCT value of miR-122-5p of plasma of colorectal cancer (stage 1) patients is that of healthy subjects in both (A) plasma fraction and (B) lectin column elution fraction. It can be seen that there is a significant difference from the ΔΔCT value of plasma miR-122-5p.

Further, when the AUC was obtained from the ROC curves of FIGS. 2A and 2B, it was 0.269 and 0.783, respectively. Therefore, the ΔΔCT value of miR-122-5p of the plasma (B) lectin column-eluted fraction was It can be seen that it has a moderate ability to discriminate against plasma.

From the above, it is found that the expression level of miR-122-5p in the plasma of the subjects is lower than the expression level of miR-122-5p in the plasma of healthy subjects for colorectal cancer (stage). It can be seen that it serves as an index for 1).

[Example 3: Judgment of 8 types of cancer by relative value (2-ΔΔCT) of expression level of miR-122-5p]
(statistics)
FIG. 3 shows a scatter plot of the relative value (2-ΔΔCT) of the expression level of miR-122-5p in the lectin column-eluted fraction of plasma of eight types of cancer patients. Here, as a comparative control, the median value of 30 plasma samples of healthy subjects was shown.

(result)
The relative value (2-ΔΔCT) of the expression level of miR-122-5p in the lectin column-eluted fraction of hemorrhoids decreased in the liver with respect to the median value of 30 gypsum in healthy subjects. Cancer (stage 3A), breast cancer (

stage

1A, 3A, 4), prostate cancer (stage 3), lung cancer (

stage

1A, 2A), stomach cancer (1A, 3A), esophageal cancer (stage 3B), kidney cancer (Stage 3), a patient with pancreatic cancer (Stage 3, IPMC: Intraductal papillary-mucinous carcinoma (intrapancreatic papillary mucinous adenocarcinoma)).

From the above, it can be seen that the expression level of miR-122-5p in the plasma or serum of the subject is lower than the expression level of miR-122-5p in the plasma or serum of a healthy subject. However, liver cancer (stage 3A), breast cancer (

stage

1A, 3A, 4), prostate cancer (stage 3), lung cancer (

stage

1A, 2A), stomach cancer (1A, 3A), esophageal cancer (stage 3B) , Kidney cancer (stage 3), pancreatic cancer (stage 3, IPMC).

[Example 4: Determining colorectal cancer (stages 2 to 4) based on the relative value (2-ΔΔCT) of the expression level of miR-451a]
(statistics)
In FIGS. 4A and 4B, 40 plasma samples of colorectal cancer (stages 1 to 4) patients, 30 plasma samples of healthy subjects (A) plasma fractions, and (B) lectin column-eluting fraction miR. The dispersion diagram of the relative value (2-ΔΔCT) of the expression level of -451a is shown. Here, the reference is a pool sample in which 30 samples of plasma of a healthy person are mixed in equal amounts.

The ΔΔCT value of miR-451a is the ΔCT value of miR-122-5p of a pool sample obtained by mixing an equal amount of 30 plasma samples of healthy subjects from the ΔCT (cel-miR-39-3p) value of miR-451a. It is a value obtained by subtracting the value of cel-miR-39-3p).

(result)
Patients with colorectal cancer (stage 1) have a relative value (2-ΔΔCT) of plasma miR-451a expression in both the (A) plasma fraction and the (B) lectin column-eluted fraction. , Almost the same as a healthy person.

In contrast, in colorectal cancer (stages 2-4) patients, the expression level of plasma miR-451a was relative to both the (A) plasma fraction and the (B) lectin column-eluted fraction. The value (2-ΔΔCT) is significantly reduced for healthy subjects.

From the above, it is an index of stage 2 to 4 cancer that the expression level of miR-451a in the plasma of the subject is lower than the expression level of miR-451a in the plasma of a healthy subject. It turns out that

[Example 5: Selection of miRNA for determining colorectal cancer (stage 1) patients by multiple regression analysis and logistic regression analysis]
(statistics)
Tables 4A to 4 show (A) ΔCT (miR-93-5p) of 12 types of miRNAs of 30 plasma samples of colorectal cancer (stage 1) patients and 30 lectin column-eluted fractions of healthy subjects. ) Value, (B) ΔCT (miR-192-5p) value, (C) ΔΔCT (cel-miR-39-3p) value, (D) ΔCT (miR-502-5p) value, (E) ΔCT (miR) The results of multiple regression analysis and logistic regression analysis based on -93-5p, miR-192-5p, miR-502-5p) values are shown.

(result)
Results of multiple regression analysis and logistic regression analysis in the miRNA assay of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects in the lectin column elution fraction, (A) ΔCT (miR-93) -5p) value, (B) ΔCT (miR-192-5p) value, (C) ΔΔCT (cel-miR-39-3p) value, (D) ΔCT (miR-502-5p) value, (E) ΔCT In all of the values (miR-93-5p, miR-192-5p, miR-502-5p), the AUC was 0.91 or more, the sensitivity was 0.83 or more, and the specificity was 0.8 or more.

The miRNAs shown below were selected by the stepwise method using AIC.

For the ΔCT (miR-93-5p) value, miR-15b-5p, miR-122-5p, and miR-146a-5p were selected in the multiple regression analysis and the logistic regression analysis.

For ΔCT (miR-93-5p) values, miR-15b-5p, miR-122-5p, miR-130a-3p, miR-451a were selected in multiple regression analysis, and miR-15b, in logistic regression analysis. MiR-23a-3p, miR-122-5p, miR-130a-3p, miR-199a-3p, and miR-451a were selected.

For the ΔΔCT (cel-miR-39-3p) value, miR-15b-5p, miR-122-5p, miR-130a-3p, and miR-423-3p were selected in the multiple regression analysis, and in the logistic regression analysis, they were selected. miR-15b-5p, miR-122-5p, miR-146a-5p, and miR-423-3p were selected.

For ΔCT (miR-502-5p) values and ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) values, miR-122-5p, in multiple regression analysis and logistic regression analysis, miR-126-3p, miR-130a-3p, and miR-146a-5p were selected.

From the above, the ΔCT (miR-93-5p) value, the ΔCT (miR-93-5p) value, the ΔΔCT (cel-miR-39-3p) value, and the ΔCT (miR-502-5p) value. , ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value multiple regression analysis and logistic regression analysis show that miR-122-5p is selected.

The following six regression equations were obtained from the coefficient values and intercept values in Tables 4A, B, and C.

Multiple regression equation of ΔCT (miR-93-5p) y = 0.98177 + (-0.17856) * (ΔCT (miR-93-5p) value of miR-15b-5p) + (-0.16908) * ( ΔCT (miR-93-5p) value of miR-122-5p) + 0.32557 * (ΔCT (miR-93-5p) value of miR-146a-5p)
Logistic regression equation of ΔCT (miR-93-5p) y = 3.9677 + (-0.9179) * (ΔCT (miR-93-5p) value of miR-122-5p) + (-0.619) * ( ΔCT (miR-93-5p) value of miR-122-5p) +1.4793 * (ΔCT (miR-93-5p) value of miR-146a-5p)
Multiple regression equation of ΔCT (miR-192-5p) y = -0.95539 + (-0.15734) * (ΔCT (miR-192-5p) value of miR-15b-5p) + (-0.18545) * (ΔCT (miR-192-5p) value of miR-122-5p) + 0.35812 * (ΔCT (miR-192-5p) value of miR-130a-3p) + (-0.1467) * (miR-451a) ΔCT (miR-192-5p) value)
Logistic regression equation of ΔCT (miR-192-5p) y = -8.8281 + (-2.557) * (ΔCT (miR-192-5p) value of miR-15b-5p) + (-1.1962) * (ΔCT (miR-192-5p) value of miR-23a-3p) + (-0.8609) * (ΔCT (miR-192-5p) value of miR-122-5p) +2.7892 * (miR-130a) -3p ΔCT (miR-192-5p) value) + 1.2375 * (miR-199a-3p ΔCT (miR-192-5p) value) + (-1.2569) * (miR-451a ΔCT (miR) -192-5p) Value)
Multiple regression equation of ΔΔCT (cel-miR-39-3p) y = 1.35371 + (-0.25861) * (ΔΔCT (cel-miR-39-3p) value of miR-15b-5p) + (-0. 20624) * (ΔΔCT (cel-miR-39-3p) value of miR-122-5p) + 0.14329 * (ΔΔCT (cel-miR-39-3p) value of miR-130a-3p) + 0.28725 * ( ΔΔCT (cel-miR-39-3p) value of miR-423-3p)
Logistic regression equation of ΔΔCT (cel-miR-39-3p) y = 4.033 + (-1.2905) * (ΔΔCT (cel-miR-39-3p) value of miR-15b-5p) + (-0. 6683) * (ΔΔCT (cel-miR-39-3p) value of miR-122-5p) + 0.85 * (ΔΔCT (cel-miR-39-3p) value of miR-146a-5p) + 0.9574 * ( ΔΔCT (cel-miR-39-3p) value of miR-423-3p)

(statistics)
In FIG. 5, the lectin column elution plots of 30 plasma samples of healthy subjects and 30 plasma samples of colorectal cancer (stage 1) patients are shown in the multiple regression equations and logistic regression equations obtained in Tables 4A, B, and C. A scatter diagram of the y-hat value when each value of the minute is substituted is shown. Here, the cutoff value is set to 0, and when y> 0, it is determined to be cancer-negative, and when y <0, it is determined to be cancer-positive.

(result)
Of the 30 plasma samples of healthy subjects, 2 plasma samples of healthy subjects # 16 and # 22 were false positives in any of the regression equations. Of the 30 plasma samples of colorectal cancer (stage 1) patients, 2 plasma samples of colorectal cancer (stage 1) patients # 21 and # 26 were false negatives in any of the regression equations. ..

In addition, healthy subjects # 16 and # 22 may have colorectal cancer (stage 1).

[Example 6: Judgment of 7 types of cancer by the logistic regression equation obtained in Example 5]
(statistics)
In FIG. 6, the logistic regression equations of the ΔCT (miR-192-5p) values obtained in Tables 4B and C and the ΔΔCT (cel-miR-39-3p) values show the plasma of seven types of cancer patients. The scatter diagram of the y-hat value obtained by substituting each value of the lectin column elution fraction is shown. Here, the cutoff value is set to 0, and when y> 0, it is determined to be cancer-negative, and when y <0, it is determined to be cancer-positive.

(result)
Patients who are positive for cancer in both the logistic regression equation for ΔCT (miR-192-5p) value and the logistic regression equation for ΔΔCT (cel-miR-39-3p) value are liver cancer (stage). 3A, 4A), breast cancer (

stage

1A, 3A, 4), prostate cancer (stage 3), lung cancer (

stage

1A, 2A), stomach cancer (stage 3A), esophageal cancer (stage 3B), kidney cancer (stage) It was a patient of 1 and 3). Only in the logistic regression equation of ΔCT (miR-192-5p) value, it was the patients with gastric cancer (stage 1A) who were determined to be cancer-positive.

[Example 7: Selection of miRNA for determining colorectal cancer (stage 1) patients by decision tree learning (C5.0)]
(statistics)
FIG. 7A shows a decision tree for the ΔCT (miR-93-5p) values of 12 types of miRNA in the lectin column-eluted fractions of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. The decision tree obtained by learning (C5.0) and the ROC curve are shown.

FIG. 8A shows a decision tree for the ΔCT (miR-192-5p) values of 12 types of miRNA in the lectin column-eluted fractions of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. The decision tree obtained by learning (C5.0) and the ROC curve are shown.

FIG. 9A shows a decision tree for the ΔCT (miR-502-5p) values of 12 types of miRNA in the lectin column-eluted fractions of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. The decision tree obtained by learning (C5.0) and the ROC curve are shown.

FIG. 10A shows ΔCT (miR-93-5p, miR-192-) of 12 types of miRNAs of 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects lectin column-eluted fractions. The decision tree obtained by the decision tree learning (C5.0) of 5p, miR-502-5p) value and the ROC curve are shown.

(result)
The following miRNAs were selected by decision tree learning (C5.0) of 12 types of miRNAs in 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. Was done.

When the ΔCT (miR-93-5p) value was used, miR-15b-5p, miR-122-5p, miR-146a-5p, and miR-199a-5p were selected.

When using the ΔCT (miR-192-5p) value, miR-122-5p, miR-130a-3p, and miR-146a-5p were selected.

When using the ΔCT (miR-502-5p) value, miR-146a-5p, miR-122-5p, and miR-15b-5p were selected.

When using ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) values, miR-146a-5p, miR-126-3p, and miR-122-5p were selected.

When the AUC (sensitivity, specificity) is obtained from the ROC curves of FIGS. 7A, 8A, 9A, and 10A, AUC: 0.9989 (sensitivity: 0.967, specificity: 0.967), AUC: 0, respectively. .9856 (Sensitivity: 1.0, Specificity 0.867), AUC: 0.953 (Sensitivity: 0.967, Specificity: 0.767), AUC: 0.974 (Sensitivity: 0.900, Specificity: : 0.900).

In FIG. 7A, the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.125, and the ΔCT (miR-93-5p) value of miR-122-5p is> 3.917. The ΔCT (miR-93-5p) value of miR-199a-5p is> 1.115, or the ΔCT (miR-93-5p) value of (miR-15b-5p) is> 1.125. ) The ΔCT (miR-93-5p) value of miR-122-5p is ≦ 3.917, the ΔCT (miR-93-5p) value of miR-146a-5p is ≦ -0.128, and miR- When the ΔCT (miR-93-5p) value of 15b-5p is> 1.356, it is determined to be cancer-positive.

In FIG. 8A, the ΔCT (miR-192-5p) value of miR-122-5p is> −0.39, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ −0. It is .39, the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ -1.548, and the ΔCT (miR-192-5p) value of miR-146a-5p is> -6.626. If, it is determined to be cancer-positive.

In FIG. 9A, the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, and the ΔCT (miR-502-5p) value of miR-122-5p is> −1.413. In some cases, or the ΔCT (miR-502-5p) value of miR-146a-5p is ≤-3.828 and the ΔCT (miR-502-5p) value of miR-122-5p is ≤-1.413. When the ΔCT (miR-502-5p) value of miR-15b-5p is> -6.234, it is determined to be cancer-positive.

In FIG. 10A, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, and the ΔCT (miR-) of miR-126-3p. 93-5p, miR-192-5p, miR-502-5p) values> -4.458, or miR-146a-5p ΔCT (miR-93-5p, miR-192-5p, miR) -502-5p) The value is ≤ -2.066, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-126-3p is ≤-4.458. , MiR-122-5p ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value is> 1.756, it is determined to be cancer positive.

(statistics)
FIG. 7BS shows a determination tree obtained in the same manner as in FIG. 7A, except that 20 samples are extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. (18 ways) are shown.

Table 5 shows the sensitivity, specificity, and threshold of the decision tree of FIGS. 7AS.

FIG. 8B-O shows a determination tree obtained in the same manner as in FIG. 8A, except that 20 samples are extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. (14 ways) are shown.

Table 6 shows the sensitivity, specificity, and threshold of the decision tree of FIG. 8A-O.

FIG. 9B-O shows a determination tree obtained in the same manner as in FIG. 9A, except that 20 samples are extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. (14 ways) are shown.

Table 7 shows the sensitivity, specificity, and threshold of the decision tree of FIG. 9A-O.

FIG. 10B-O shows a determination tree obtained in the same manner as in FIG. 10A, except that 20 samples are extracted from 30 plasma samples of colorectal cancer (stage 1) patients and 30 plasma samples of healthy subjects. (14 ways) are shown.

Table 8 shows the sensitivity, specificity, and threshold of the decision tree of FIG. 10A-O.

(result)
When the ΔCT (miR-93-5p) value is used, the miRNA selected in FIG. 7A is used. Of FIG. 7BS, the determination of FIGS. 7BD, FN, P, R, S. The sensitivity of the tree was 0.95 or higher.

In FIG. 7B, when the ΔCT (miR-93-5p) value of miR-122-5p is> 4.569 and the ΔCT (miR-93-5p) value of miR-199a-5p is> 1.179. Alternatively, the ΔCT (miR-93-5p) value of miR-122-5p is ≦ 4.569, the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 1.707, and miR. When the ΔCT (miR-93-5p) value of -15b-5p is> 1.534, it is determined to be cancer-positive.

In FIG. 7C, when the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 4.28 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.153. In addition, it is determined to be cancer-positive.

In FIG. 7D, the ΔCT (miR-93-5p) value of miR-122-5p is> 3.917, or the ΔCT (miR-93-5p) value of miR-122-5p is ≦ 3.917. When the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 1.897, it is determined to be cancer-positive.

In FIG. 7E, when the ΔCT (miR-93-5p) value of miR-199a-3p is> 4.647 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.288. In addition, it is determined to be cancer-positive.

In FIG. 7F, when the ΔCT (miR-93-5p) value of miR-122-5p is> 3.971 and the ΔCT (miR-93-5p) value of miR-199a-3p is> 1.179. Alternatively, the ΔCT (miR-93-5p) value of miR-122-5p is ≦ 3.917, and the ΔCT (miR-93-5p) value of miR-146a-5p is ≦ -0.21. When the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.288, it is determined to be cancer-positive.

In FIG. 7G, when the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 4.28 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.387. Alternatively, the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 4.28, the ΔCT (miR-93-5p) value of miR-15b-5p is ≦ 1.387, and miR. Cancer when the ΔCT (miR-93-5p) value of -122-5p is> 4.618 and the ΔCT (miR-93-5p) value of miR-146a-5p is ≤ -0.413. Determined to be positive.

In FIG. 7H, when the ΔCT (miR-93-5p) value of miR-122-5p is> 4.647 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.288. Alternatively, the ΔCT (miR-93-5p) value of miR-122-5p is> 4.647, the ΔCT (miR-93-5p) value of miR-15b-5p is ≦ 1.288, and miR. When the ΔCT (miR-93-5p) value of -146a-5p is ≦ -0.392, it is determined to be cancer-positive.

In FIG. 7I, the ΔCT (miR-93-5p) value of miR-122-5p is> 2.62, or the ΔCT (miR-93-5p) value of miR-122-5p is ≦ 2.62. When the ΔCT (miR-93-5p) value of miR-146a-5p is ≦ -0.154, it is determined to be cancer-positive.

In FIG. 7J, when the ΔCT (miR-93-5p) value of miR-122-5p is> 3.917, it is determined to be cancer-positive.

In FIG. 7K, the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.288, and the ΔCT (miR-93-5p) value of miR-146a-5p is ≤0.938. When the ΔCT (miR-93-5p) value of miR-122-5p is> 0.131, it is determined to be cancer-positive.

In FIG. 7L, the ΔCT (miR-93-5p) value of miR-122-5p is> 4.569, or the ΔCT (miR-93-5p) value of miR-122-5p is ≤4.569. When the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 1.707, it is determined to be cancer-positive.

In FIG. 7M, the ΔCT (miR-93-5p) value of miR-122-5p is> 5.618, or the ΔCT (miR-93-5p) value of miR-122-5p is ≤5.618. When the ΔCT (miR-93-5p) value of miR-146a-5p is ≦ -0.154 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.298. In addition, it is determined to be cancer-positive.

In FIG. 7N, when the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.125 and the ΔCT (miR-93-5p) value of miR-122-5p is> 3.031. Alternatively, the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.125, the ΔCT (miR-93-5p) value of miR-122-5p is ≤3.031, and miR. When the ΔCT (miR-93-5p) value of -146a-5p is ≦ -0.128, it is determined to be cancer-positive.

In FIG. 7O, when the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 4.28 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.288. In addition, it is determined to be cancer-positive.

In FIG. 7P, the ΔCT (miR-93-5p) value of miR-122-5p is> 3.917, or the ΔCT (miR-93-5p) value of miR-122-5p is ≦ 3.917. When the ΔCT (miR-93-5p) value of miR-146a-5p is ≦ -0.154, it is determined to be cancer-positive.

In FIG. 7Q, when the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 2.009 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.288. In addition, it is determined to be cancer-positive.

In FIG. 7R, when the ΔCT (miR-93-5p) value of miR-122-5p is> 4.569 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.092. Or, when the ΔCT (miR-93-5p) value of miR-122-5p is ≦ 4.569 and the ΔCT (miR-93-5p) value of miR-146a-5p is ≦ -0.154. In addition, it is determined to be cancer-positive.

In FIG. 7S, when the ΔCT (miR-93-5p) value of miR-199a-3p is ≦ 4.28 and the ΔCT (miR-93-5p) value of miR-15b-5p is> 1.534. Alternatively, the ΔCT (miR-93-5p) value of miR-199a-3p is ≤4.28, the ΔCT (miR-93-5p) value of miR-15b-5p is ≤1.534, and miR. When the ΔCT (miR-93-5p) value of −122-5p is> 5.618, it is determined to be cancer-positive.

When the ΔCT (miR-192-5p) value was used, the sensitivity of all decision trees in FIG. 8B-O, in which the miRNA selected in FIG. 8A was used, was 0.95 or higher.

In FIG. 8B, the ΔCT (miR-192-5p) value of miR-122-5p is> -0.508, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ −0. If it is .508 and the ΔCT (miR-192-5p) value of miR-146a-5p is ≤-4.191 and> -6.626, it is determined to be cancer-positive. ..

In FIG. 8C, the ΔCT (miR-192-5p) value of miR-122-5p is> −0.39, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ −0. When the ΔCT (miR-192-5p) value of miR-146a-5p is ≤-4.411 and> -6.6638, it is determined to be cancer-positive. ..

In FIG. 8D, when the ΔCT (miR-192-5p) value of miR-122-5p is> -0.508, it is determined to be cancer-positive.

In FIG. 8E, the ΔCT (miR-192-5p) value of miR-122-5p is> -2.198, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ -2. It is .198, the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ -1.548, and the ΔCT (miR-192-5p) value of miR-146a-5p is> -6.626. If, it is determined to be cancer-positive.

In FIG. 8F, the ΔCT (miR-192-5p) value of miR-122-5p is> -1.56, or the ΔCT (miR-192-5p) value of miR-122-5p is ≤-1. It is .56, the ΔCT (miR-192-5p) value of miR-130a-3p is ≤-2.333, and the ΔCT (miR-192-5p) value of miR-146a-5p is> -6.282. If, it is determined to be cancer-positive.

In FIG. 8G, the ΔCT (miR-192-5p) value of miR-122-5p is> −0.39, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ −0. When the ΔCT (miR-192-5p) value of miR-146a-5p is ≤-4.191 and> -6.626, it is determined to be cancer-positive. ..

In FIG. 8H, the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ −1.548, or the ΔCT (miR-192-5p) value of miR-130a-3p is> -1. If it is .548 and the ΔCT (miR-192-5p) value of miR-122-5p is> 0.001, it is determined to be cancer-positive.

In FIG. 8I, the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ -2.43, and the ΔCT (miR-192-5p) value of miR-122-5p is> -0.508. In some cases, or the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ -2.43 and the ΔCT (miR-192-5p) value of miR-122-5p is ≦ -0.508. When the ΔCT (miR-192-5p) value of miR-146a-5p is> -6.626, it is determined to be cancer-positive.

In FIG. 8J, when the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ -2.0048, it is determined to be cancer-positive.

In FIG. 8K, the ΔCT (miR-192-5p) value of miR-122-5p is> -0.508, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ −0. If it is .508 and the ΔCT (miR-192-5p) value of miR-146a-5p is ≧ -3.021 and> -6.626, it is determined to be cancer-positive. ..

In FIG. 8L, the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ −1.548, or the ΔCT (miR-192-5p) value of miR-130a-3p is> -1. If it is .548 and the ΔCT (miR-192-5p) value of miR-122-5p is> 0.001, it is determined to be cancer-positive.

In FIG. 8M, the ΔCT (miR-192-5p) value of miR-122-5p is> −0.39, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ −0. It is .39, the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ -2.368, and the ΔCT (miR-192-5p) value of miR-146a-5p is> -6.626. If, it is determined to be cancer-positive.

In FIG. 8N, the ΔCT (miR-192-5p) value of miR-130a-3p is ≦ -1.548, and the ΔCT (miR-192-5p) value of miR-122-5p is> −1.469. In some cases, or the ΔCT (miR-192-5p) value of miR-130a-3p is ≤-1.548 and the ΔCT (miR-192-5p) value of miR-122-5p is ≤-1.469. When the ΔCT (miR-192-5p) value of miR-146a-5p is> -6.626, it is determined to be cancer-positive.

In FIG. 8O, the ΔCT (miR-192-5p) value of miR-122-5p is> -0.508, or the ΔCT (miR-192-5p) value of miR-122-5p is ≦ −0. If it is .508 and the ΔCT (miR-192-5p) value of miR-146a-5p is ≦ -3.021 and> -5.625, it is determined to be cancer-positive. ..

When using the ΔCT (miR-502-5p) value, the decision tree of FIG. 9BF, H, I, KO of FIG. 9B-O in which the miRNA selected in FIG. The sensitivity was 0.95 or higher.

In FIG. 9B, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, it is determined to be cancer-positive.

In FIG. 9C, the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, and the ΔCT (miR-502-5p) value of miR-15b-5p is> -5.977. In some cases, it is determined to be cancer-positive.

In FIG. 9D, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, it is determined to be cancer-positive.

In FIG. 9E, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -6.301, it is determined to be cancer-positive.

In FIG. 9F, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -5.291, it is determined to be cancer-positive.

In FIG. 9G, the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, and the ΔCT (miR-502-5p) value of miR-15b-5p is> -5.5354. In some cases, it is determined to be cancer-positive.

In FIG. 9H, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.997, it is determined to be cancer-positive.

In FIG. 9I, the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, and the ΔCT (miR-502-5p) value of miR-15b-5p is> -4.778. In some cases, or the ΔCT (miR-502-5p) value of miR-146a-5p is ≤-3.828 and the ΔCT (miR-502-5p) value of miR-15b-5p is ≤-4.778. If it is> -7.025, it is determined to be cancer-positive.

In FIG. 9J, the ΔCT (miR-502-5p) value of miR-146a-5p is ≤-3.997, and the ΔCT (miR-502-5p) value of miR-15b-5p is> -5.977. In some cases, it is determined to be cancer-positive.

In FIG. 9K, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, it is determined to be cancer-positive.

In FIG. 9L, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, it is determined to be cancer-positive.

In FIG. 9M, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -6.29, it is determined to be cancer-positive.

In FIG. 9N, when the ΔCT (miR-502-5p) value of miR-15b-5p is ≦ -2.658, it is determined to be cancer-positive.

In FIG. 9O, when the ΔCT (miR-502-5p) value of miR-146a-5p is ≦ -3.828, it is determined to be cancer-positive.

When ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) values are used, the miRNA selected in FIG. 10A is used, of FIG. 10B-O, FIGS. 10B, D. , H, L The sensitivity of the decision tree was 0.95 or more.

In FIG. 10B, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, and the ΔCT (miR-) of miR-122-5p. 93-5p, miR-192-5p, miR-502-5p) value is> 1.756, or miR-146a-5p ΔCT (miR-93-5p, miR-192-5p, miR- 502-5p) The value is> ≦ -2.066, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is ≦ 1.756. When the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is> -4.829, it is determined to be cancer-positive.

In FIG. 10C, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, and the ΔCT (miR-) of miR-122-5p. When the value of 93-5p, miR-192-5p, miR-502-5p) is> 0.496, or when the ΔCT (miR-93-5p, miR-192-5p, miR-) of miR-146a-5p 502-5p) The value is ≤ -2.066, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is ≤0.496, and miR. When the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of -126-3p is> -4.376, it is determined to be cancer-positive.

In FIG. 10D, when the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, it is determined to be cancer-positive. NS.

In FIG. 10E, when the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is> 0.97, or the ΔCT (miR-122-5p) of miR-122-5p ( The miR-93-5p, miR-192-5p, miR-502-5p) values are ≤0.97, and the ΔCT (miR-93-5p, miR-192-5p, miR-502) of miR-126-3p. When the -5p) value is ≤-3.445 and> -4.458, it is determined to be cancer-positive.

In FIG. 10F, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -3.033, and the ΔCT (miR-) of miR-122-5p. If the value of 93-5p, miR-192-5p, miR-502-5p) is> 0.97, or if the ΔCT (miR-93-5p, miR-192-5p, miR-) of miR-146a-5p 502-5p) The value is ≦ -3.033, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is ≦ 0.97, and miR. When the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of -126-3p is> -4.458, it is determined to be cancer-positive.

In FIG. 10G, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, and the ΔCT (miR-) of miR-126-3p. 93-5p, miR-192-5p, miR-502-5p) values> -4.458, or miR-146a-5p ΔCT (miR-93-5p, miR-192-5p, miR) -502-5p) The value is ≤ -2.066, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-126-3p is ≤-4.458. , MiR-122-5p ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value is> 1.756, it is determined to be cancer positive.

In FIG. 10H, when the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, it is determined to be cancer-positive. NS.

In FIG. 10I, when the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is> 0.496, or the ΔCT (miR-122-5p) of miR-122-5p ( The miR-93-5p, miR-192-5p, miR-502-5p) values are ≤0.496, and the ΔCT (miR-93-5p, miR-192-5p, miR-502) of miR-126-3p. When the -5p) value is ≤-3.445 and> -4.376, it is determined to be cancer-positive.

In FIG. 10J, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.19, and the ΔCT (miR-) of miR-126-3p. 93-5p, miR-192-5p, miR-502-5p) values> -4.458, or miR-146a-5p ΔCT (miR-93-5p, miR-192-5p, miR) The -502-5p) value is ≤-2.19, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-126-3p is ≤-4.458. , MiR-122-5p has a ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of> 1.415, and is determined to be cancer-positive.

In FIG. 10K, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, and the ΔCT (miR-) of miR-122-5p. 93-5p, miR-192-5p, miR-502-5p) values> 1.756, or miR-146a-5p ΔCT (miR-93-5p, miR-192-5p, miR- 502-5p) The value is ≦ -2.066, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is ≦ 1.756, and miR. When the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of -126-3p is> -4.53, it is determined to be cancer-positive.

In FIG. 10L, when the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is> 0.496, it is determined to be cancer-positive. ..

In FIG. 10M, when the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is> -0.734, or the ΔCT of miR-122-5p. (MiR-93-5p, miR-192-5p, miR-502-5p) The value is ≦ -0.734, and the ΔCT (miR-93-5p, miR-192-5p, miR) of miR-146a-5p The -502-5p) value is ≤ -2.066, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-126-3p is> -4.046. In some cases, it is determined to be cancer-positive.

In FIG. 10N, when the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-122-5p is> 0.353, or the ΔCT (miR-122-5p) of miR-122-5p ( The miR-93-5p, miR-192-5p, miR-502-5p) values are ≤0.353, and the ΔCT (miR-93-5p, miR-192-5p, miR-502) of miR-146a-5p. -5p) When the value is ≤-2.569 and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-126-3p is> -4.046. , It is judged to be cancer positive.

In FIG. 10O, the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-146a-5p is ≦ -2.066, and the ΔCT (miR-) of miR-126-3p. 93-5p, miR-192-5p, miR-502-5p) values> -4.458, or miR-146a-5p ΔCT (miR-93-5p, miR-192-5p, miR) -502-5p) The value is ≤ -2.066, and the ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value of miR-126-3p is ≤-4.458. , MiR-122-5p ΔCT (miR-93-5p, miR-192-5p, miR-502-5p) value is> 1.756, it is determined to be cancer positive.

This application claims priority based on Japanese Patent Application No. 2020-09850 filed on January 24, 2020, the entire contents of which are incorporated herein by reference.

Claims

A method that uses the expression level of miRNA as an index of cancer.
The process of extracting miRNA from the subject's body fluid and
The extracted miRNA is used to include the step of detecting the expression level of miRNA in the body fluid of the subject.
HOTAIR (Hox transcript antisense intergenic RNA) is expressed that the expression level of miR-122-5p in the body fluid of the subject is lower than the expression level of miR-122-5p in the body fluid of a healthy subject. Used as an indicator of cancer
A method in which the expression level of miRNA, which is plasma or serum, is used as an index of cancer.
The method in which the subject is a subject suffering from cancer and uses the expression level of miRNA according to claim 1 as an index of cancer.
It further includes the step of contacting the subject's body fluid with a substrate on which a high mannose-type sugar chain-specific lectin is immobilized.
The method according to claim 1 or 2, wherein the miRNA is extracted from a component selectively captured by the high mannose-type sugar chain-specific lectin, and the expression level of miRNA according to claim 1 or 2 is used as an index of cancer.
The method using the expression level of miRNA according to claim 3, wherein the high mannose-type sugar chain-specific lectin is an Oscillatoria agardhii-derived lectin (OAA).
In addition to the expression level of miR-122-5p in the body fluid of the subject, the expression level of miR-451a in the body fluid of the subject was detected.
The expression level of miR-451a in the body fluid of the subject is lower than the expression level of miR-451a in the body fluid of the healthy subject, which is used as an index of advanced cancer, according to claims 1 to 4. A method in which the expression level of miRNA according to any one of the items is used as an index of cancer.
CT (CT) of one or more miRNAs selected from the group consisting of miR-192-5p, miR-93-5p, and miR-502-5p in the body fluid when detecting the expression level of miRNA in the body fluid ( A method in which the expression level of miRNA according to any one of claims 1 to 5 is used as an index of cancer, in which the CT value of miRNA in the body fluid is corrected by the threshold cycle) value.
In addition to the expression level of miR-122-5p in the subject's body fluid, miR-126-3p, miR-192-5p, miR-93-5p, miR-423-3p, miR- Detects the expression level of one or more miRNAs selected from the group consisting of 21-5p, miR-130a-3p, miR-23a-3p, miR-146a-5p, miR-199a-3p, miR-15b-5p. death,
The fact that the expression level of the one or more miRNAs in the body fluid of the subject is higher than the expression level of the one or more miRNAs in the body fluids of the healthy subject is used as an index of cancer. A method in which the expression level of miRNA according to any one of claims 1 to 6 is used as an index of cancer.