METHOD FOR AIDING DETECTION OF PANCREATIC CANCER

Information

  • Patent Application
  • 20230167504
  • Publication Number
    20230167504
  • Date Filed
    December 13, 2018
    6 years ago
  • Date Published
    June 01, 2023
    a year ago
Abstract
The present invention aims at providing a method of assisting the detection of pancreatic cancer with high accuracy. The present invention provides a method of assisting the detection of pancreatic cancer, which includes using as an index the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, transfer RNA fragments (tRFs), or non-coding RNA fragments (MiscRNAs) contained in a test sample isolated from a living body, whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19 to 21, 2, 3, 5, 11, 13, 14, 16, 18, and 22 to 38, wherein a higher abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 20 than that of healthy subjects or a lower abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or non-coding RNA fragments (MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 21 to 38 than that of healthy subjects indicates a higher likelihood of having pancreatic cancer.
Description
TECHNICAL FIELD

The present invention relates to a method of assisting the detection of pancreatic cancer.


BACKGROUND ART

Among various cancers, the incidence of pancreatic cancer has been increasing year by year. The westernization of eating habits is pointed out as a reason behind the increase. Pancreatic cancer has few early symptoms and are highly proliferative and invasive, which reflects that the annual number of deaths from pancreatic cancer is almost equal to the annual number of new pancreatic cancer cases, and currently causes a particularly low survival rate. Since pancreas is located deep in the abdomen, it is very difficult to detect pancreatic cancer by some examination methods, such as X-ray imaging.


Thus, methods in which the abundance of microRNA (hereinafter referred to as “miRNA”) in plasma is used as an index to detect pancreatic cancer are proposed (Patent Documents 1 to 4).


PRIOR ART DOCUMENTS
Patent Documents



  • Patent Document 1: WO 2014/003053 A1

  • Patent Document 2: JP 2009-521952 T

  • Patent Document 3: JP 2009-528070 T

  • Patent Document 4: JP 2010-527235 T



SUMMARY OF THE INVENTION
Problem to be Solved by the Invention

As described above, various miRNAs have been proposed as indexes for the detection of pancreatic cancer and, needless to say, it is advantageous if pancreatic cancer can be detected with higher accuracy.


Thus, an object of the present invention is to provide a method of assisting the detection of pancreatic cancer which assists in highly accurate detection of pancreatic cancer.


Means for Solving the Problem

As a result of intensive study, the inventors newly found miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, transfer RNA fragments (tRFs), and non-coding RNA fragments (MiscRNAs) which increase or decrease in abundance in pancreatic cancer, and discovered that use of those RNA molecules as indexes enables highly accurate detection of pancreatic cancer, and thereby completed the present invention.


That is, the present invention provides the followings.


(1) A method of assisting the detection of pancreatic cancer, using as an index the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, transfer RNA fragments (tRFs), or non-coding RNA fragments (MiscRNAs) contained in a test sample isolated from a living body, whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19 to 21, 2, 3, 5, 11, 13, 14, 16, 18, and 22 to 38, wherein a higher abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 20 than that of healthy subjects or a lower abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or non-coding RNA fragments (MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 21 to 38 than that of healthy subjects indicates a higher likelihood of having pancreatic cancer.


(2) The method according to (1), wherein the abundance of at least one of miRNAs, isomiRs, precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19 to 21, 2, 3, 5, 11, 13, 14, 16, and 18 is used as an index.


(3) The method according to (2), wherein the abundance of at least one of miRNAs or isomiRs whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19, 20, and 21 is used as an index.


(4) The method according to (2) or (3), wherein the abundance of at least one of miRNAs or isomiRs whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 4, 6, and 7 is used as an index.


Effect of the Invention

By the method of the present invention, pancreatic cancer can be highly accurately and yet conveniently detected. Thus, the method of the present invention will greatly contribute to the detection of pancreatic cancer.







MODE FOR CARRYING OUT THE INVENTION

As described above, the abundance of a specified miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments (tRFs), or non-coding RNA fragments (MiscRNAs) (hereinafter sometimes referred to as “miRNAs or the like” for convenience) contained in a test sample isolated from a living body is used as an index in the method of the present invention. The nucleotide sequences of these miRNAs or the like are as shown in Sequence Listing. The list of miRNAs or the like used in the method of the present invention is presented in Tables 1-1 and 1-2 below.

















SEQ ID



Length



NO:
Class
Archetype
Type
(nucleotides)
Sequence




















1
isomiR
mir-584
Mature 5′ sub
21
uuaugguuugccugggacuga





2
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
19
uucauugcugucggugggu





3
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
18
ucauugcugucggugggu





4
miRNA
mir-335
Mature 5′
23
ucaagagcaauaacgaaaaaugu





5
isomiR
mir-335
Mature 5′ sub
22
ucaagagcaauaacgaaaaaug





6
isomiR
mir-320a
Mature 3′ sub
21
aaagcuggguugagagggcga





7
isomiR
mir-122
Mature 5′ sub
21
uggagugugacaaugguguuu





8
isomiR
mir-423
Mature 5′ super
24
ugaggggcagagagcgagacuuuu





9
miRNA
mir-423
Mature 5′
23
ugaggggcagagagcgagacuuu





10
miRNA
mir-130a
Mature 3′
22
cagugcaauguuaaaagggcau





11
isomiR
mir-99a
Mature 5′ sub
21
aacccguagauccgaucuugu





12
miRNA
mir-126
Mature 5′
21
cauuauuacuuuugguacgcg





13
tRF

Homo_sapiens_tRNA-

Exact
25
gcauuggugguucagu




Gly-CCC-1-1//*1


gguagaauu





14
isomiR
mir-146a
Mature 5′ super
23
ugagaacugaauuccauggguug





15
isomiR
mir-146a
Mature 5′ sub
21
ugagaacugaauuccaugggu





16
isomiR
mir-145
Mature 5′ sub
19
guccaguuuucccaggaau





17
miRNA
mir-484
Mature 5′
22
ucaggcucaguccccucccgau





18
miRNA
mir-1307
Mature 5′
21
ucgaccggaccucgaccggcu





19
isomiR
mir-4286
Mature 5′ super
19
accccacuccugguaccau





20
isomiR
mir-126
Mature 3′ sub
20
cguaccgugaguaauaaugc





21
isomiR
mir-451a
Mature 5′ sub
21
aaaccguuaccauuacugagu





22
miRNA
mir-106b
Mature 5′
21
uaaagugcugacagugcagau





23
isomiR
mir-30d
Mature 5′ super
24
uguaaacauccccgacuggaagcu





24
isomiR
mir-30d
Mature 5′ sub
21
uguaaacauccccgacuggaa





25
miRNA
mir-93
Mature 5′
23
caaagugcuguucgugcagguag





26
MiscRNA
ENST00000516507.1
Exact
32
uacaaccecccacugcuaaauuu







gacuggcuu





27
isomiR
mir-18a
Mature 5′ sub
21
uaaggugcaucuagugcagau





28
MiscRNA
ENST000003637
Exact
25
cccccacugcuaaauuugac




45.1//*2


uggcu





29
miRNA
mir-144
Mature 3′
20
uacaguauagaugauguacu





30
MiscRNA
ENST00000516507.1
Exact
33
uacaaccccccacugcuaaauu







ugacuggcuuu





31
isomiR
mir-106b
Mature 5′ super
23
uaaagugcugacagugcagauag





32
isomiR
mir-101-1//mir-101-2
Mature 3′ super
22
guacaguacugugauaacugaa





33
MiscRNA
ENST0000057
Exact
34
ggcugguccgaugguagugggu




7883.2//*3


uaucagaacuua





34
isomiR
mir-17
Mature 5′ sub
21
caaagugcuuacagugcaggu





35
miRNA
mir-324
Mature 5′
23
cgcauccccuagggcauuggugu





36
isomiR
mir-18a
Mature 5′ sub
22
uaaggugcaucuagugcagaua





37
isomiR
mir-19b-1//mir-19b-2
Mature 3′ sub
18
ugugcaaauccaugcaaa





38
isomiR
mir-106a
Mature 5′ sub
22
aaaagugcuuacagugcaggua





*1: Homo_sapiens_tRNA-Gly-CCC-1-1//Homo_sapiens_tRNA-Gly-CCC-1-2//Homo_sapiens_tRNA-Gly-GCC-2-1 //Homo_sapiens_tRNA-Gly-GCC-2-2//Homo_sapiens_tRNA-Gly-GCC-2-3//Homo_sapiens_tRNA-Gly-GCC-2-4//Homo_sapiens_tRNA-Gly-GCC-2-5//Homo_sapiens_tRNA-Gly-GCC-2-6//Homo_sapiens_tRNA-Gly-GCC-3-1//Homo_sapiens_tRNA-Gly-GCC-5-1


*2: ENST00000363745.1//ENST00000364409.1//ENST00000516507.1//ENST00000391107.1//ENST00000459254.1


*3: ENST00000577883.2//ENST00000577984.2//ENST00000516507.1//ENST00000481041.3//ENST00000579625.2//ENST00000365571.2//ENST00000578877.2//ENST00000364908.1






Among the miRNAs or the like, miRNAs or the like whose nucleotide sequences are represented by SEQ ID NOs: 1 to 21 (for example, “a miRNA or the like whose nucleotide sequence is represented by SEQ ID NO: 1” is hereinafter sometimes referred to simply as “a miRNA or the like represented by SEQ ID NO: 1” or “one represented by SEQ ID NO: 1” for convenience) are present in plasma. Among the miRNAs or the like, miRNAs or the like whose nucleotide sequences are represented by SEQ ID NOs: 22 to 38 are present in serum.


In all of those miRNAs or the like, the logarithm of the ratio of the abundance in plasma from patients with pancreatic cancer to the abundance in plasma from healthy subjects (represented by “log FC” which means the logarithm of FC (fold change) to base 2) is more than 2.0 in absolute value (that is, a ratio of not less than about 4 or not more than about ¼), showing a statistical significance (t-test; p<0.05).


The abundance of miRNAs or the like represented by SEQ ID NOs: 1 to 20 are higher in patients with pancreatic cancer than in healthy subjects, while the abundance of miRNAs or the like represented by SEQ ID NOs: 21 to 38 is lower in patients with pancreatic cancer than in healthy subjects.


Among those, the miRNAs or the like represented by SEQ ID NOs: 1 to 4, 6, and 7 have a log FC value of not less than 4.0 in absolute value and thus function as indexes with especially high sensitivity, and are preferable.


The accuracy of each cancer marker is indicated using the area under the ROC curve (AUC: Area Under Curve) as an index, and cancer markers with an AUC value of 0.7 or higher are generally considered effective. AUC values of 0.90 or higher, 0.97 or higher, 0.99 or higher, and 1.00 correspond to cancer markers with high accuracy, very high accuracy, quite high accuracy, and complete accuracy (with no false-positive and false-negative events), respectively. Thus, the AUC value of each cancer marker is likewise preferably 0.90, more preferably not less than 0.97, still more preferably not less than 0.99, and most preferably 1.00 in the present invention. The ones represented by SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, and 19 to 21 have an AUC value of 1.00, as specifically described in Examples below, and are especially preferable.


The test sample is not specifically limited, provided that the test sample is a body fluid containing miRNAs; typically, it is preferable to use a blood sample (including plasma, serum, and whole blood). For the ones represented by SEQ ID NOs: 22 to 38, which are present in serum, it is simple and preferable to use serum or plasma as a test sample. The method of extracting total RNA in serum or plasma is well known and is specifically described in Examples below. The method of extracting total RNA from exosomes in serum or plasma is itself known and is specifically described in more detail in Examples below.


The abundance of each miRNA or the like is preferably measured (quantified) using a next-generation sequencer. Any instrument may be used and is not limited to a specific type of instrument, provided that the instrument determines sequences, similarly to next-generation sequencers. In the method of the present invention, as specifically described in Examples below, use of a next-generation sequencer is preferred over quantitative reverse-transcription PCR (qRT-PCR), which is widely used for quantification of miRNAs, to perform measurements from the viewpoint of accuracy because miRNAs or the like to be quantified include, for example, isomiRs, in which only one or more nucleotides are deleted from or added to the 5′ and/or 3′ ends of the original mature miRNAs thereof, and which should be distinguished from the original miRNAs when measured. Briefly, though details will be described specifically in Examples below, the quantification method can be performed as follows. When the RNA content in serum or plasma is constant, the number of reads for each isomiR or mature miRNA per million reads is considered as the measurement value, where the total counts of reads measured in a next-generation sequencing analysis of the RNA content are normalized to one million reads. When the RNA content in serum or plasma is variable in comparison with healthy subjects due to a disease, miRNAs showing little abundance variation in serum and plasma may be used. In cases where the abundance of miRNAs or the like in serum or plasma is measured, at least one miRNA selected from the group consisting of let-7g-5p, miR425-3p, and miR425-5p is preferably used as an internal control, which are miRNAs showing little abundance variation in serum and plasma.


The cut-off value for the abundance of each miRNA or the like for use in evaluation is preferably determined based on the presence or absence of a statistically significant difference (t-test; p<0.05, preferably p<0.01, more preferably p<0.001) from healthy subjects with regard to the abundance of the miRNA or the like. Specifically, the value of log2 read counts (the cut-off value) can be preferably determined for each miRNA or the like, for example, at which the false-positive rate is optimal (the lowest); for example, the cut-off values (the values of log2 read counts) for several miRNAs or the like are as indicated in Table 2. The cut-off values indicated in Table 2 are only examples, and other values may be employed as cut-off values as long as those values are appropriate to determine statistically significant difference. Additionally, the optimal cut-off values vary among different populations of patients and healthy subjects from which data is collected. However, the cut-off values indicated in Table 2 with an interval of usually ±20%, particularly ±10%, may be set as cut-off values.


Moreover, a method of detecting the abundance of miRNAs or the like in a test sample from human suspected of having or affected with pancreatic cancer is also provided.


That is, a method of detecting the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, transfer RNA fragments (tRFs), or non-coding RNA fragments (MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19 to 21, 2, 3, 5, 11, 13, 14, 16, 18, and 22 to 38 in a test sample from human suspected of having or affected with pancreatic cancer is also provided, wherein the method includes the steps of:


collecting a blood sample from human; and


measuring the abundance of the miRNA(s), isomiR(s), precursor miRNA(s), transfer RNA fragment(s), or non-coding RNA fragment(s) in the blood sample by means of a next-generation sequencer or qRT-PCR,


wherein the abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 20 is higher than that in healthy subjects, or the abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or non-coding RNA fragments (MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 21 to 38 is lower than that in healthy subjects.


Additionally, in cases where the detection of pancreatic cancer is successfully achieved by the above-described method of the present invention, an effective amount of an anti-pancreatic cancer drug can be administered to patients in whom pancreatic cancer is detected, to treat the pancreatic cancer. Examples of the anti-pancreatic cancer drug can include gemcitabine, Folfirinox (combinational use of fluorouracil, levofolinate, irinotecan, and oxaliplatin), gemeitabine/nab-paclitaxel (Gem/nabPTX), and TS-1 (S1).


The present invention will be specifically described below by way of examples and comparative examples. Naturally, the present invention is not limited by the examples below.


Examples 1 to 38
1. Materials and Methods
(1) Clinical Samples

Plasma samples from 5 patients with pancreatic cancer and from 5 healthy subjects were used.


Serum samples from 75 patients with pancreatic cancer and from 111 healthy subjects were used.


(2) Extraction of RNA in Plasma or Serum

Extraction of RNA in serum or plasma was performed using the miRNeasy Mini kit (QIAGEN).


1) Each frozen plasma or serum sample was thawed and centrifuged at 10000 rpm for 5 minutes at room temperature to precipitate aggregated proteins and blood cell components.


2) To a new 1.5-mL tube, 200 μL of the supernatant was transferred.


3) To the tube, 1000 μL of the QIAzol Lysis Reagent was added and mixed thoroughly to denature protein components.


4) To the tube, 10 μL of 0.05 nM cel-miR-39 was added as a control RNA for RNA extraction, mixed by pipetting, and then left to stand at room temperature for 5 minutes.


5) To promote separation of the aqueous and organic solvent layers, 200 μL of chloroform was added to the tube, mixed thoroughly, and left to stand at room temperature for 3 minutes.


6) The tube was centrifuged at 12000×g for 15 minutes at 4° C. and 650 μL of the upper aqueous layer was transferred to a new 2-mL tube.


7) For the separation of RNA, 975 μL of 100% ethanol was added to the tube and mixed by pipetting.


8) To a miRNeasy Mini spin column (hereinafter referred to as column), 650 μL of the mixture in the step 7 was transferred, left to stand at room temperature for 1 minute, and then centrifuged at 8000×g for 15 seconds at room temperature to allow RNA to be adsorbed on the filter of the column. The flow-through solution from the column was discarded.


9) The step 8 was repeated until the total volume of the solution of the step 7 was filtered through the column to allow all the RNA to be adsorbed on the filter.


10) To remove impurities attached on the filter, 650 μL of Buffer RWT was added to the column and centrifuged at 8000×g for 15 seconds at room temperature. The flow-through solution from the column was discarded.


11) To clean the RNA adsorbed on the filter, 500 μL of Buffer RPE was added to the column and centrifuged at 8000×g for 15 seconds at room temperature. The flow-through solution from the column was discarded.


12) To clean the RNA adsorbed on the filter, 500 of Buffer RPE was added to the column and centrifuged at 8000×g for 2 minutes at room temperature. The flow-through solution from the column was discarded.


13) To completely remove any solution attached on the filter, the column was placed in a new 2-mL collection tube and centrifuged at 10000×g for 1 minute at room temperature.


14) The column was placed into a 1.5-mL tube and 50 μL of RNase-free water was added thereto and left to stand at room temperature for 1 minute.


15) Centrifugation was performed at 8000×g for 1 minute at room temperature to elute the RNA adsorbed on the filter. The eluted RNA was used in the following experiment without further purification and the remaining portion of the eluted RNA was stored at −80° C.


(3) Quantification of miRNAs or the Like


The quantification of miRNAs or the like was performed as follows.


In cases where miRNAs or the like from, for example, two groups are quantified, extracellular vesicles (including exosomes) isolated by the same method are used to purify RNAs through the same method, from which cDNA libraries are prepared and then analyzed by next-generation sequencing. The next-generation sequencing analysis is not limited by a particular instrument, provided that the instrument determines sequences.


(4) Calculation of Cut-Off Value and AUC

Specifically, the cut-off value and the AUC were calculated from measurement results as follows.


The logistic regression analysis was carried out using the JMP Genomics 8 to draw the ROC curve and to calculate the AUC. Moreover, the value corresponding to a point on the ROC curve which was closest to the upper left corner of the ROC graph (sensitivity: 1.0, specificity: 1.0) was defined as the cut-off value.


2. Results

The results are presented in Tables 2-1 and 2-2.



















TABLE 2-1






SEQ




Average in
Average in






ID



Length
pancreatic
healthy
log2

Cut-off


Example
NO:
Class
Archetype
Type
(nucleotides)
cancer patients
subjects
FC
AUC
value

























Example 1
1
isomiR
mir-584
Mature 5′ sub
21
3989
162
4.6
1.000
11.21


Example 2
2
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
19
3274
143
4.5
0.960
8.34


Example 3
3
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
18
4807
267
4.2
0.840
12.65


Example 4
4
miRNA
mir-335
Mature 5′
23
5007
233
4.4
1.000
9.63


Example 5
5
isomiR
mir-335
Mature 5′ sub
22
3481
227
3.9
0.980
8.85


Example 6
6
isomiR
mir-320a
Mature 3′ sub
21
2380
117
4.3
1.000
9.63


Example 7
7
isomiR
mir-122
Mature 5′ sub
21
10981
630
4.1
1.000
11.05


Example 8
8
isomiR
mir-423
Mature 5′ super
24
1365
114
3.6
1.000
8.92


Example 9
9
miRNA
mir-423
Mature 5′
23
9623
1114
3.1
1.000
11.21


Example 10
10
miRNA
mir-130a
Mature 3′
22
6851
647
3.4
1.000
12.17


Example 11
11
isomiR
mir-99a
Mature 5′ sub
21
1694
207
3
0.920
10.01


Example 12
12
miRNA
mir-126
Mature 5′
21
17554
2520
2.8
1.000
12.90


Example 13
13
tRF

Homo

sapiens_tRNA-

Exact
25
1706
268
2.7
0.920
10.09





Gly-CCC-1-1// . . . *1


Example 14
14
isomiR
mir-146a
Mature 5′ super
23
1360
252
2.4
0.880
8.51


Example 15
15
isomiR
mir-146a
Mature 5′ sub
21
2467
411
2.6
1.000
10.63


Example 16
16
isomiR
mir-145
Mature 5′ sub
19
6590
1150
2.5
0.800
10.43


Example 17
17
miRNA
mir-484
Mature 5′
22
10667
1914
2.5
1.000
13.06


Example 18
18
miRNA
mir-1307
Mature 5′
21
2713
489
2.5
0.920
10.51


Example 19
19
isomiR
mir-4286
Mature 5′ super
19
977
180
2.4
1.000
9.42


Example 20
20
isomiR
mir-126
Mature 3′ sub
20
807
206
2
1.000
8.85


Example 21
21
isomiR
mir-451a
Mature 5′ sub
21
50334
296017
−2.6
1.000
17.88


Example 22
22
miRNA
mir-106b
Mature 5′
21
133
460
−2.12
0.812
7.36


Example 23
23
isomiR
mir-30d
Mature 5′ super
24
281
962
−2.17
0.870
9.02


























TABLE 2-2






SEQ




Average in
Average in






ID



Length
pancreatic
healthy
log2

Cut-off


Example
NO:
Class
Archetype
Type
(nucleotides)
cancer patients
subjects
FC
AUC
value

























Example 24
24
isomiR
mir-30d
Mature 5′ sub
21
209
534
−2.33
0.820
8.14


Example 25
25
miRNA
mir-93
Mature 5′
23
919
2689
−2.43
0.823
10.78


Example 26
26
MiscRNA
ENST00000516507.1
Exact
32
34
109
−2.47
0.815
6.03


Example 27
27
isomiR
mir-18a
Mature 5′ sub
21
22
83
−2.47
0.804
4.99


Example 28
28
MiscRNA
ENST00000363745.1// . . . *2
Exact
25
64
288
−2.64
0.844
7.09


Example 29
29
miRNA
mir-144
Mature 3′
20
146
689
−2.72
0.832
7.55


Example 30
30
MiscRNA
ENST00000516507.1
Exact
33
26
101
−2.79
0.817
5.31


Example 31
31
isomiR
mir-106b
Mature 5′ super
23
27
67
−2.82
0.803
4.43


Example 32
32
isomiR
mir-101-1//mir-101-2
Mature 3′ super
22
31
123
−2.87
0.831
5.21


Example 33
33
MiscRNA
ENST00000577883.2// . . . *3
Exact
34
42
159
−2.88
0.855
5.91


Example 34
34
isomiR
mir-17
Mature 5′ sub
21
353
1377
−2.91
0.805
9.07


Example 35
35
miRNA
mir-324
Mature 5′
23
9
39
−2.94
0.855
4.06


Example 36
36
isomiR
mir-18a
Mature 5′ sub
22
68
244
−3.06
0.831
6.9


Example 37
37
isomiR
mir-19b-1//mir-19b-2
Mature 3′ sub
18
11
54
−3.08
0.834
3.45


Example 38
38
isomiR
mir-106a
Mature 5′ sub
22
30
96
−3.08
0.801
4.28









As seen in these results, the abundance of the miRNAs or the like represented by SEQ ID NOs: 1 to 20 was significantly higher in the patients with pancreatic cancer than that in the healthy subjects, and the abundance of the miRNAs or the like represented by SEQ ID NOs: 21 to 38 was significantly lower in the patients with pancreatic cancer than in the healthy subjects. It was indicated that pancreatic cancer was able to be detected with high accuracy by the method of the present invention (Examples 1 to 38). Moreover, all the p-values determined by t-test in Examples 1 to 38 were less than 0.05, indicating the effectiveness in detection of pancreatic cancer.


Moreover, the ones represented by SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19, 20, and 21 were indicated to have an AUC value of 1.00, which are especially preferable.

Claims
  • 1. A method of assisting the detection of pancreatic cancer, using as an index the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, transfer RNA fragments (tRFs), or non-coding RNA fragments (MiscRNAs) contained in a test sample isolated from a living body, whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19 to 21, 2, 3, 5, 11, 13, 14, 16, 18, and 22 to 38, wherein a higher abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 20 than that of healthy subjects or a lower abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, or non-coding RNA fragments (MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 21 to 38 than that of healthy subjects indicates a higher likelihood of having pancreatic cancer.
  • 2. The method according to claim 1, wherein the abundance of at least one of miRNAs, isomiRs, precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19 to 21, 2, 3, 5, 11, 13, 14, 16, and 18 is used as an index.
  • 3. The method according to claim 2, wherein the abundance of at least one of miRNAs or isomiRs whose nucleotide sequence is represented by any one of SEQ ID NOs: 1, 4, 6 to 10, 12, 15, 17, 19, 20, and 21 is used as an index.
  • 4. The method according to claim 3, wherein the abundance of at least one of miRNAs or isomiRs whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 4, 6, and 7 is used as an index.
Priority Claims (1)
Number Date Country Kind
2017-238839 Dec 2017 JP national
PCT Information
Filing Document Filing Date Country Kind
PCT/JP2018/045993 12/13/2018 WO