METHOD FOR ASSISTING DETECTION OF HEAD AND NECK CANCER

Information

  • Patent Application
  • 20210071259
  • Publication Number
    20210071259
  • Date Filed
    December 13, 2018
    6 years ago
  • Date Published
    March 11, 2021
    3 years ago
Abstract
The present invention aims at providing a method of assisting the detection of head and neck cancer with high accuracy. The present invention provides a method of assisting the detection of head and neck 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 (LincRNAs or MiscRNAs) contained in a test sample isolated from a living body. whose nucleotide sequence is represented by any one of SEQ ID NOs: 162, 160, 145, 143, 146, 140, 141, 1 to 139, 142, 144, 147 to 159, and 161, wherein a higher abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 71 and 118 to 136 than that of healthy subjects or a lower abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 72 to 117 and 137 to 162 than that of healthy subjects indicates a higher likelihood of having head and neck cancer.
Description
TECHNICAL FIELD

The present invention relates to a method of assisting the detection of head and neck cancer.


BACKGROUND ART

Head and neck cancer refers to cancer that occurs in a body region below the brain and above the clavicles. Vital functions such as breathing and eating, and socially important daily life functions such as speaking, tasting, and hearing are predominantly related to the head and neck region. Thus, a therapy to treat cancer while keeping the balance between curability and QOL is needed because any lesion in the head and neck region may directly affect QOL. Additionally, aesthetic considerations are also necessary because the head and neck region is involved in maintenance of facial morphology and/or in expression of feelings.


As methods to detect such cancer including head and neck cancer, methods in which the abundance of microRNA (hereinafter referred to as “miRNA”) in blood is used as an index are proposed (Patent Documents 1 to 5).


PRIOR ART DOCUMENTS
Patent Documents



  • Patent Document 1 WO 2009/133915

  • Patent Document 2 WO 2012/161124

  • Patent Document 3 JP 2013-539018 T

  • Patent Document 4 JP 2015-502176 T

  • Patent Document 5 JP 2015-51011 A



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 cancer including head and neck cancer and, needless to say, it is advantageous if head and neck cancer can be detected with higher accuracy.


Thus, an object of the present invention is to provide a method of assisting the detection of head and neck cancer which assists in highly accurate detection of head and neck 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 (LincRNAs, MiscRNAs) which increase or decrease in abundance in head and neck cancer. and discovered that use of those RNA molecules as indexes enables highly accurate detection of head and neck cancer, and thereby completed the present invention.


That is, the present invention provides the followings.

  • (1) A method of assisting the detection of head and neck 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 (LincRNAs or MiscRNAs) contained in a test sample isolated from a living body, whose nucleotide sequence is represented by any one of SEQ ID NOs: 162, 160, 145, 143, 146, 140, 141, 1 to 139, 142, 144, 147 to 159, and 161, wherein a higher abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 71 and 118 to 136 than that of healthy subjects or a lower abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 72 to 117 and 137 to 162 than that of healthy subjects indicates a higher likelihood of having head and neck cancer.
  • (2) The method according to (1), wherein the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, transfer RNA fragments (tRFs), or non-coding RNA fragments (LincRNAs or MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 162, 160, 145, 143, 146, 140, and 141 is used as an index.
  • (3) The method according to (1), wherein the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 92, 2, 74, 73, 75, 84, 32, 77, 18, 1, 3 to 31, 33 to 72, 76, 78 to 83, 85 to 91, and 93 to 116 is used as an index.
  • (4) The method according to (3), wherein the abundance of an isomiR whose nucleotide sequence is represented by SEQ ID NO: 115 or 116 is used as an index.
  • (5) The method according to (3) or (4), wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 11 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 30 is used as an index.
  • (6) The method according to (3) or (4), wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 11 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 26 is used as an index.
  • (7) The method according to (3) or (4), wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 11 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 117 is used as an index.
  • (8) The method according to (3) or (4), wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 30 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 118 is used as an index.
  • (9) The method according to (1), wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 157 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 162 is used as an index.
  • (10) The method according to any one of (3) to (8), wherein the head and neck cancer is tongue cancer.


Effect of the Invention

By the method of the present invention, head and neck cancer can be highly accurately and yet conveniently detected. Thus, the method of the present invention will greatly contribute to the detection of head and neck 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 (LincRNAs or MiscRNA) (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 sequence of these miRNAs or the like themselves 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 to 1-7 below.














TABLE 1-1





SEQ



Length



ID



(nucleo-



NO:
Class
Archetype
Type
tides)
Sequence




















1
tRF
tRNA-Gly-CCC-1-1// . . . *1
Exact
30
gcauuggugguucagugguagaauucucgc





2
tRE
tRNA-Lys-TTT-3-1// . . . *2
Exact
28
cggauagcucagucgguagagcaucaga





3
tRF
tRNA-Glu-CTC-1-1// . . . *3
Exact
32
ucccugguggucuagugguuaggauucggcgc





4
tRF
tRNA-Pro-TGG-2-1
Exact
31
ggcucguuggucuagggguaugauucucggu





5
tRF
tRNA-Lys-TTT-3-1// . . . *4
Exact
31
gcccggauagcucagucgguagagcaucaga





6
tRF
tRNA-iMet-CAT-1-1// . . . *5
Exact
33
agcagaguggcgcagcggaagcgugcugggccc





7
tRF
tRNA-Lys-CTT-1-1// . . . *6
Exact
31
gcccggcuagcucagucgguagagcauggga





8
tRF
tRNA-iMet-CAT-1-1// . . . *7
Exact
31
agcagaguggcgcagcggaagcgugcugggc





9
isomiR
mir-183
Mature 5′ sub
21
auggcacugguagaauucacu





10
isomiR
mir-223
Mature 3′ sub
17
ugucaguuugucaaaua





11
miRNA
mir-150
Mature 5′
22
ucucccaacccuuguaccagug





12
isomiR
mir-223
Mature 3′ super
24
ugucaguuugucaaauaccccaag





13
tRF
tRNA-Lys-CTT-1-1// . . . *8
Exact
28
cggcuagcucagucgguagagcauggga





14
isomiR
mir-150
Mature 5′ super
23
ucucccaacccuuguaccagugc





15
isomiR
mir-150
Mature 5 sub
19
ucucccaacccuuguacca





16
tRF
tRNA-Pro-AGG-1-1// . . . *9
Exact
30
ggcucguuggucuagggguaugauucucgc





17
isomiR
mir-146b
Mature 5′ super
23
ugagaacugaauuccauaggcug





18
tRF
tRNA-iMet-CAT-1-1// . . . *10
Exact
30
agcagaguggcgcagcggaagcgugcuggg





19
isomiR
mir-361
Mature 3′ super
24
ucccccaggugugauucugauuug





20
isomiR
mir-223
Mature 3′ sub/
21
ucaguuugucaaauaccccaa





super







21
precursor
mir-223
precursor miRNA
15
ugucaguuugucaaa





22
precursor
mir-223
precursor miRNA
16
ugucaguuugucaaau





23
isomiR
mir-146a
Mature 5′ sub
20
ugagaacugaauuccauggg





24
isomiR
mir-150
Mature 5′ sub
20
ucucccaacccuuguaccag





25
isomiR
mir-223
Mature 3′ sub
18
ugucaguuugucaaauac





26
miRNA
mir-29a
Mature 3′
22
uagcaccaucugaaaucgguua





27
isomiR
mir-223
Mature 3′ sub
20
ucaguuugucaaauacccca





28
miRNA
mir-339
Mature 5′
23
ucccuguccuccaggagcucacg





















TABLE 1-2





SEQ



Length



ID



(nucleo-



NO:
Class
Archetype
Type
tides)
Sequence







29
isomiR
mir-223
Mature 3′ super
23
ugucaguuugucaaauaccccaa





30
miRNA
mir-146b
Mature 5′ 
22
ugagaacugaauuccauaggcu





31
isomiR
mir-365a//mir-365b
Mature 3′ sub
21
uaaugccccuaaaaauccuua





32
miRNA
mir-140
Mature 5′
22
cagugguuuuacccuaugguag





33
miRNA
mir-223
Mature 3′
22
ugucaguuugucaaauacccca





34
isomiR
mir-223
Mature 3′ sub/
22
gucaguuugucaaauaccccaa





super







35
tRF
tRNA-Leu-AAG-1-1// . . . *11
Exact
16
gguagcguggccgagc





36
isomiR
mir-150
Mature 5′ sub
21
ucucccaacccuuguaccagu





37
isomiR
mir-146b
Mature 5′ super
24
ugagaacugaauuccauaggcugu





38
tRF
tRNA-Glu-CTC-1-1// . . . *12
Exact
30
ucccugguggucuagugguuaggauucggc





39
isomiR
mir-223
Mature 3′ sub
20
ugucaguuugucaaauaccc





40
isomiR
mir-145
Mature 5′ super
24
guccaguuuucccaggaaucccuu





41
isomiR
mir-186
Mature 5′ sub
21
caaagaauucuccuuuugggc





42
miRNA
mir-365a//mir-365b
Mature 3′
22
uaaugccccuaaaaauccuuau





43
isomiR
mir-223
Mature 3′ super
23
gugucaguuugucaaauacccca





44
isomiR
mir-192
Mature 5′ sub
20
ugaccuaugaauugacagcc





45
tRF
tRNA-Gly-GCC-2-1// . . . *13
Exact
33
gcauuggugguucagugguagaauucucgccug





46
miRNA
mir-17
Mature 5′
23
caaagugcuuacagugcagguag





47
isomiR
mir-339
Mature 5′ sub
19
ucccuguccuccaggagcu





48
isomiR
mir-223
Mature 3′ sub
21
ugucaguuugucaaauacccc





49
isomiR
mir-223
Mature 3′ sub
21
gucaguuugucaaauacccca





50
isomiR
mir-30c-2//mir-30c-1
Mature 5′ sub
22
uguaaacauccuacacucucag





51
isomiR
mir-1307
Mature 3′ super
23
acucggcguggcgucggucgugg





52
miRNA
mir-29c
Mature 3′
22
uagcaccauuugaaaucgguua





53
isomiR
mir-223
Mature 3′ sub
20
gucaguuugucaaauacccc





54
isomiR
mir-223
Mature 3′ super
24
gugucaguuugucaaauaccccaa





55
isomiR
mir-30b
Mature 5′ sub
21
uguaaacauccuacacucagc





56
isomiR
mir-766
Mature 3′ sub
21
acuccagccccacagccucag





57
isomiR
mir-26b
Mature 3′ sub
21
ccuguucuccauuacuuggcu





















TABLE 1-3





SEQ



Length



ID



(nucleo-



NO:
Class
Archetype
Type
tides)
Sequence







58
tRF
tRNA-Gly-CCC-1-1// . . . *14
Exact
22
gcauuggugguucagugguaga





59
miRNA
let-7d
Mature 3′
22
cuauacgaccugcugccuuucu





60
tRF
tRNA-Gly-CCC-1-1// . . . *15
Exact
25
gcauuggugguucagugguagaauu





61
isomiR
mir-30d
Mature 5′ sub
19
uguaaacauccccgacugg





62
miRNA
mir-505
Mature 3′
22
cgucaacacuugcugguuuccu





63
isomiR
mir-93
Mature 5′ sub
22
aaagugcuguucgugcagguag





64
isomiR
mir-30e
Mature 5′ super
23
uguaaacauccuugacuggaagc





65
precursor
mir-16-1//mir-16-2
precursor miRNA
16
uagcagcacguaaaua





66
miRNA
mir-193a
Mature 5′
22
ugggucuuugcgggcgagauga





67
isomiR
mir-320a
Mature 3′ super
25
aaaagcuggguugagagggcgaaaa





68
isomiR
mir-29b-1//mir-29b-2
Mature 3′ sub
21
uagcaccauuugaaaucagug





69
isomiR
mir-142
Mature 5′ sub/super
22
cccauaaaguagaaagcacuac





70
isomiR
mir-142
Mature 5′ sub/super
21
cccauaaaguagaaagcacua





71
miRNA
mir-744
Mature 5′
22
ugcggggcuagggcuaacagca





72
isomiR
mir-200b
Mature 3′ sub
21
aauacugccugguaaugauga





73
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
19
uucauugcugucggugggu





74
isomiR
mir-200a
Mature 3′ sub
18
acugucugguaacgaugu





75
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
18
ucauugcugucggugggu





76
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
20
auucauugcugucggugggu





77
miRNA
mir-340
Mature 3′
22
uccgucucaguuacuuuauagc





78
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
21
cauucauugcugucggugggu





79
miRNA
mir-378c
Mature 3′
19
acuggacuuggagucagga





80
precursor
mir-181b-1//mir-181b-2
precursor miRNA
17
cauugcugucggugggu





81
isomiR
mir-145
Mature 5′ sub
19
aguuuucccaggaaucccu





82
precursor
mir-181b-1//mir-181b-2
precursor miRNA
16
auugcugucggugggu





83
isomiR
mir-181b-1//mir-181b-2
Mature 5′ sub
22
acauucauugcugucggugggu





84
isomiR
mir-451a
Mature 5′ sub
18
cguuaccauuacugaguu





85
isomiR
mir-29b-1//mir-29b-2
Mature 3′ sub
22
agcaccauuugaaaucaguguu





















TABLE 1-4





SEQ



Length



ID



(nucleo-



NO:
Class
Archetype
Type
tides)
Sequence




















86
isomiR
mir-451a
Mature 5′ sub
17
guuaccauuacugaguu





87
precursor
mir-181b-1//mir-181b-2
precursor miRNA
15
uugcugucggugggu





88
isomiR
mir-144
Mature 3′ sub
17
uacaguauagaugaugu





89
isomiR
mir-451a
Mature 5′ sub/super
18
guuaccauuacugaguuu





90
isomiR
mir-451a
Mature 5′ sub
19
accguuaccauuacugagu





91
miRNA
let-7e
Mature 5′
22
ugagguaggagguuguauaguu





92
isomiR
mir-16-2
Mature 3′ sub/super
20
accaauauuacugugcugcu





93
isomiR
mir-451a
Mature 5′ super
25
aaaccguuaccauuacugaguuuag





94
isomiR
mir-486-1
Mature 5′ super
23
uccuguacugagcugccccgagg





95
isomiR
mir-126
Mature 3′ sub
20
ucguaccgugaguaauaaug





96
isomiR
mir-363
Mature 3′ sub
19
aauugcacgguauccaucu





97
isomiR
mir-574
Mature 5′ sub
21
ugagugugugugugugagugu





98
miRNA
let-7b
Mature 5′
22
ugagguaguagguugugugguu





99
miRNA
mir-144
Mature 3′
20
uacaguauagaugauguacu





100
isomiR
mir-574
Mature 3′ sub
21
cacgcucaugcacacacccac





101
isomiR
let-7b
Mature 5′ sub
21
ugagguaguagguuguguggu





102
isomiR
mir-103a-2//mir-
Mature 3′ sub
19
agcagcauuguacagggcu




103a-1//mir-107








103
isomiR
mir-126
Mature 3′ sub
21
cguaccgugaguaauaaugcg





104
isomiR
mir-451a
Mature 5′ super
24
gaaaccguuaccauuacugaguuu





105
miRNA
mir-106b
Mature 5′
21
uaaagugcugacagugcagau





106
miRNA
let-71
Mature 5′
22
ugagguaguaguuugugcuguu





107
precursor
mir-451a
precursor miRNA
15
uuaccauuacugagu





108
isomiR
mir-425
Mature 5′ sub
19
aaugacacgaucacucccg





109
isomiR
mir-16-2
Mature 3′ sub
20
ccaauauuacugugcugcuu





110
miRNA
mir-139
Mature 5′
23
ucuacagugcacgugucuccagu





111
isomiR
mir-451a
Mature 5′ super
23
gaaaccguuaccauuacugaguu





112
isomiR
mir-18a
Mature 5′ sub
21
uaaggugcaucuagugcagau





113
miRNA
mir-126
Mature 3′
22
ucguaccgugaguaauaaugcg





















TABLE 1-5





SEQ



Length



ID



(nucleo-



NO:
Class
Archetype
Type
tides)
Sequence







114
isomiR
mir-550a-1//mir-550a-2//mir-550a-3
Mature 3′ sub
21
ugucuuacucccucaggcaca





115
isomiR
mir-142
Mature 3′ sub
22
guaguguuuccuacuuuaugga





116
isomiR
mir-142
Mature 3′ sub
21
guaguguuuccuacuuuaugg





117
miRNA
mir-339
Mature 3′
23
ugagcgccucgacgacagagccg





118
miRNA
mir-17
Mature 3′
22
acugcagugaaggcacuuguag





119
MiscRNA
ENST00000363745.1// . . . *16
Exact
28
cccccacugcuaaauuugacug







gcuuuu





120
MiscRNA
ENST00000364600.1// . . . *17
Exact
31
gcugguccgaugguaguggguua







ucagaacu





121
miRNA
mir-221
Mature 3′
23
agcuacauugucugcuggguuuc





122
miRNA
mir-374b
Mature 5′
22
auauaauacaaccugcuaagug





123
isomiR
mir-130a
Mature 3′ super
23
cagugcaauguuaaaagggcauu





124
miRNA
mir-340
Mature 5′
22
uuauaaagcaaugagacugauu





125
miRNA
mir-199a-1//mir-199a-2//mir-199b
Mature 3′
22
acaguagucugcacauugguua





126
isomiR
mir-23a
Mature 3′ super
23
aucacauugccagggauuuccaa





127
miRNA
mir-335
Mature 5′
23
ucaagagcaauaacgaaaaaugu





128
miRNA
mir-130a
Mature 3′'
22
cagugcaauguuaaaagggcau





129
isomiR
mir-584
Mature 5′ sub
21
uuaugguuugccugggacuga





130
MiscRNA
ENST00000363745.1// . . . *18
Exact
26
cccccacugcuaaauuugacu







ggcuu





131
miRNA
mir-26a-1//mir-26a-2
Mature 5′
22
uucaaguaauccaggauaggcu





132
MiscRNA
ENST00000364600.11/ . . . *17
Exact
32
ggcugguccgaugguaguggguu







aucagaacu





133
isomiR
mir-23a
Mature 3′ super
22
aucacauugccagggauuucca





134
miRNA
mir-146a
Mature 5′
22
ugagaacugaauuccauggguu





135
miRNA
mir-191
Mature 5′
23
caacggaaucccaaaagcagcug





136
MiscRNA
ENST00000364600.1// . . . *17
Exact
31
ggcugguccgaugguaguggguu







aucagaac





137
miRNA
mir-92a-1//mir-92a-2
Mature 3′
22
uauugcacuugucccggccugu





138
isomiR
let-7b
Mature 5′ sub
20
ugagguaguagguugugugg





139
isomiR
mir-451a
Mature 5′ sub
21
aaaccguuaccauuacugagu





140
isomiR
mir-30e
Mature 5′ sub/
23
guaaacauccuugacuggaagcu





super







141
isomiR
let-7g
Mature 5′ sub
21
ugagguaguaguuuguacagu





142
miRNA
mir-486-1//mir-486-2
Mature 5′
22
uccuguacugagcugccccgag





















TABLE 1-6





SEQ



Length



ID



(nucleo-



NO:
Class
Archetype
Type
tides)
Sequence







143
isomiR
mir-16-1//mir-16-2
Mature 5′ sub
20
uagcagcacguaaauauugg





144
isomiR
mir-451a
Mature 5′ sub
20
aaaccguuaccauuacugag





145
isomiR
mir-185
Mature 5′ sub
21
uggagagaaaggcaguuccug





146
isomiR
let-7a-1//let-7a-2//let-7a-3
Mature 5′ sub
20
ugagguaguagguuguauag





147
isomiR
mir-92a-1//mir-92a-2
Mature 3′ sub
21
uauugcacuugucccggccug





148
isomiR
mir-25
Mature 3′ sub
21
cauugcacutigucucggucug





149
isomiR
mir-16-2
Mature 3′ sub/super
21
accaauauuacugugcugcuu





150
isomiR
let-7f-1//let-7f-2
Mature 5′ sub
20
ugagguaguagauuguauag





151
isomiR
mir-25
Mature 3′ sub
20
cauugcacuugueucggucu





152
isomiR
mir-425
Mature 5′ sub
21
aaugacacgaucacucccguu





153
isomiR
mir-423
Mature 5′ sub
21
ugaggggcagagagcgagacu





154
isomiR
mir-484
Mature 5′ sub
21
ucaggcucaguccccucccga





155
isomiR
mir-486-1//mir-486-2
Mature 5′ sub
21
uccuguacugagcugccccga





156
isomiR
mir-486-1//mir-486-2
Mature 5′ sub
20
uccuguacugagcugccccg





157
isomiR
let-7i
Mature 5′ sub
21
ugagguaguaguuugugcugu





158
isomiR
let-7d
Mature 5′ sub
20
agagguaguagguugcauag





159
isomiR
mir-486-1//mir-486-2
Mature 5′ sub
17
uccuguacugagcugcc





160
isomiR
let-7i
Mature 5′ sub
20
ugagguaguaguuugugcug





161
isomiR
mir-484
Mature 5′ sub
20
ucaggcucaguccccucccg





162
LincRNA
ENST00000627566.1
Exact
15
ucauguaugaugcug
















*1: tRNA-Gly-CCC-1-1//tRNA-Gly-CCC-1-2//tRNA-Gly-





GCC-2-1//tRNA-Gly-GCC-2-2//tRNA-Gly-GCC-2-3//tRNA-





Gly-GCC-2-4//tRNA-Gly-GCC-2-5//tRNA-Gly-GCC-2-





6//tRNA-Gly-GCC-3-1//tRNA-Gly-GCC-5-1





*2: tRNA-Lys-TTT-3-1//tRNA-Lys-TTT-3-2//tRNA-Lys-





TTT-3-3//tRNA-Lys-TTT-3-4//tRNA-Lys-TTT-3-5//tRNA-





Lys-TTT-5-1





*3: tRNA-Glu-CTC-1-1//tRNA-Glu-CTC-1-2//tRNA-Glu-





CTC-1-3//tRNA-Glu-CTC-1-4//tRNA-Glu-CTC-1-5//tRNA-





Glu-CTC-1-6//tRNA-Glu-CTC-1-7//tRNA-Glu-CTC-2-1





*4: tRNA-Lys-TTT-3-1//tRNA-Lys-TTT-3-2//tRNA-Lys-





TTT-3-3//tRNA-Lys-TTT-3-4//tRNA-Lys-TTT-3-5//tRNA-





Lys-TTT-5-1





*5: tRNA-iMet-CAT-1-1//tRNA-iMet-CAT-1-2//tRNA-





iMet-CAT-1-3//tRNA-iMet-CAT-1-4//tRNA-iMet-CAT-1-





5//tRNA-iMet-CAT-1-6//tRNA-iMet-CAT-1-7//tRNA-





iMet-CAT-1-8//tRNA-iMet-CAT-2-1





*6: tRNA-Lys-CTT-1-1//tRNA-Lys-CTT-1-2//tRNA-Lys-





CTT-4-1





*7: tRNA-iMet-CAT-1-1//tRNA-iMet-CAT-1-2//tRNA-





iMet-CAT-1-3//tRNA-iMet-CAT-1-4//tRNA-iMet-CAT-1-





5//tRNA-iMet-CAT-1-6//tRNA-iMet-CAT-1-7//tRNA-





iMet-CAT-1-8//tRNA-iMet-CAT-2-1





*8: tRNA-Lys-CTT-1-1//tRNA-Lys-CTT-1-2//tRNA-Lys-





CTT-4-1





*9: tRNA-Pro-AGG-1-1//tRNA-Pro-AGG-2-1//tRNA-Pro-





AGG-2-2//tRNA-Pro-AGG-2-3//tRNA-Pro-AGG-2-





4//tRNA-Pro-AGG-2-5//tRNA-Pro-AGG-2-6//tRNA-Pro-





AGG-2-7//tRNA-Pro-AGG-2-8//tRNA-Pro-CGG-1-1//tRNA-





Pro-CGG-1-2//tRNA-Pro-CGG-1-3//tRNA-Pro-CGG-2-





1//tRNA-Pro-TGG-3-1//tRNA-Pro-TGG-3-2//tRNA-Pro-





TGG-3-3//tRNA-Pro-TGG-3-4//tRNA-Pro-TGG-3-5





*10: tRNA-iMet-CAT-1-1//tRNA-iMet-CAT-1-2//tRNA-





iMet-CAT-1-3//tRNA-iMet-CAT-1-4//tRNA-iMet-CAT-1-





5//tRNA-iMet-CAT-1-6//tRNA-iMet-CAT-1-7//tRNA-





iMet-CAT-1-8//tRNA-iMet-CAT-2-1





*11: tRNA-Leu-AAG-1-1//tRNA-Leu-AAG-1-2//tRNA-Leu-





AAG-1-3//tRNA-Leu-AAG-2-1//tRNA-Leu-AAG-2-2//tRNA-





Leu-AAG-2-3//tRNA-Leu-AAG-2-4//tRNA-Leu-AAG-3-





1//tRNA-Leu-TAG-1-1





*12: tRNA-Glu-CTC-1-1//tRNA-Glu-CTC-1-2//tRNA-Glu-





CTC-1-3//tRNA-Glu-CTC-1-4//tRNA-Glu-CTC-1-5//tRNA-





Glu-CTC-1-6//tRNA-Glu-CTC-1-7//tRNA-Glu-CTC-2-1





*13: tRNA-Gly-GCC-2-1//tRNA-Gly-GCC-2-2//tRNA-Gly-





GCC-2-3//tRNA-Gly-GCC-2-4//tRNA-Gly-GCC-2-5//tRNA-





Gly-GCC-2-6//tRNA-Gly-GCC-3-1//tRNA-Gly-GCC-5-1





*14: tRNA-Gly-CCC-1-1//tRNA-Gly-CCC-1-2//tRNA-Gly-





GCC-2-1//tRNA-Gly-GCC-2-2//tRNA-Gly-GCC-2-3//tRNA-





Gly-GCC-2-4//tRNA-Gly-GCC-2-5//tRNA-Gly-GCC-2-





6//tRNA-Gly-GCC-3-1//tRNA-Gly-GCC-5-1





*15: tRNA-Gly-CCC-1-1//tRNA-Gly-CCC-1-2//tRNA-Gly-





GCC-2-1//tRNA-Gly-GCC-2-2//tRNA-Gly-GCC-2-3//tRNA-





Gly-GCC-2-4//tRNA-Gly-GCC-2-5//tRNA-Gly-GCC-2-





6//tRNA-Gly-GCC-3-1//tRNA-Gly-GCC-5-1





*16: ENST00000363745.1//ENST00000516507.1





*17: ENST00000364600.1//ENST00000577883.2//





ENST00000577984.2//ENST00000516507.1//





ENST00000481041.3//ENST00000579625.2//





ENST00000365571.2//ENST00000578877.2//





ENST00000364908.1





*18: ENST00000363745.1//ENST00000364409.1//





ENST00000516507.1//ENST00000391107.1//





ENST00000459254.1






Among those miRNAs or the like, miRNAs or the like whose nucleotide sequences are represented by SEQ ID NOs: 1 to 162 (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 serum or exosomes.


In many of those miRNAs or the like, the logarithm of the ratio of the abundance in serum or exosomes from patients with head and neck cancer to the abundance in serum or exosomes from healthy subjects (represented by “log FC” which means the logarithm of FC (fold change) to base 2) is not less than 1.00 in absolute value, showing a statistical significance (t-test; p<0.05).


The abundance of miRNAs or the like represented by SEQ ID NOs: 1 to 71 and 118 to 136 is higher in patients with head and neck cancer than in healthy subjects, while the abundance of miRNAs or the like represented by SEQ ID NOs: 72 to 117 and 137 to 162 is lower in patients with head and neck cancer than in healthy subjects.


By a method in which among those, any of the combinations of the miRNAs represented by SEQ ID NOs: 11 and 30, SEQ ID NOs: 11 and 26, SEQ ID NOs: 11 and 117, SEQ ID NOs: 30 and 118, and SEQ ID NOs: 157 and 162 is used as an index, even early tongue cancer can be detected, as specifically described in Examples below.


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 whose nucleotide sequences are represented by SEQ ID NOs: 162, 160, 145, 143, 146, 140, and 141 are preferable due to an AUC value of 0.97 or higher; among those, ones represented by SEQ ID NOs: 162 and 160 are more preferable due to an AUC value of 0.98 or higher.


Furthermore, because the FC (fold change) in the abundance of an isomiR represented by either SEQ ID NO: 115 or SEQ ID NO: 116 is changed before and after surgery for tongue cancer, the isomiRs can be used to assess the success or failure of the surgery.


The test sample is not specifically limited, provided that the test sample is a body fluid containing miRNAs or the like; typically, it is preferable to use a blood sample (including plasma, serum, and whole blood). For the ones or the like present in serum, it is simple and preferable to use serum or plasma as a test sample. For the miRNAs or the like present in exosomes, it is preferable to use serum or plasma as a test sample, from which exosomes are isolated to extract total RNA and to measure the abundance of each miRNA or the like. 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, among reads measured in a next-generation sequencing analysis of the RNA content, 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 with human-derived sequences 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, miR-425-3p, and miR-425-5p 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 or 3 with an interval of usually ±20%, particularly ±10%, may be set as cut-off values.


Each of the above miRNAs or the like is statistically significantly different in abundance between patients with head and neck cancer and healthy subjects, and may thus be used alone as an index. However, a combination of multiple miRNAs or the like may also be used as an index, which can assist in more accurate detection of head and neck cancer.


Moreover, a method of detecting the abundance of miRNAs or the like in a test sample from human suspected of having or affected with head and neck 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 (LincRNAs or MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 162, 160, 145, 143, 146, 140, 141, 1 to 139, 142, 144, 147 to 159, and 161 in a test sample from human suspected of having or affected with head and neck 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), isoform miRNA(s) (isomiR(s)), precursor miRNA(s), transfer RNA fragment(s) (tRF(s)), or non-coding RNA fragment(s) (LincRNA(s) or MiscRNA(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, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 71 and 118 to 136 is higher than that in healthy subjects, or the abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 72 to 117 and 137 to 162 is lower than that in healthy subjects.


In the present invention, the term head and neck cancer includes, for example, tongue cancer (oral cavity cancer), maxillary sinus cancer, nasopharyngeal cancer, oropharyngeal cancer, hypopharyngeal cancer, laryngeal cancer, thyroid cancer, salivary gland cancer, and metastatic cervical carcinoma from unknown primary.


Additionally, in cases where the detection of head and neck cancer is successfully achieved by the above-described method of the present invention, an effective amount of an anti-head and neck cancer drug can be administered to patients in whom head and neck cancer is detected, to treat the head and neck cancer. Examples of the anti-head and neck cancer drug can include cisplatin (CDDP), 5-FU (5-fluorouracil), and docetaxel.


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 165
1. Materials and Methods
(1) Clinical Samples

Plasma samples from 24 patients with head and neck cancer and from 10 healthy subjects were used.


(2) Extraction of RNA in Serum

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

  • 1) Each frozen plasma 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 μL 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) Extraction of RNA from Exosomes


Exosomes in serum were collected as follows.


Exosome isolation was performed with the Total Exosome Isolation (from serum) from Thermo Fisher Scientific, Inc. Extraction of RNA from the collected exosomes was performed using the miRNeasy Mini kit (QIAGEN).


(4) 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.


(5) 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 to 2-10.




















TABLE 2-1











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 1
 1
tRF
tRNA-Gly-CCC-1-1/ . . . *1
Exact
30
1758
65
3.81
0.900
6.08
0.000


Example 2
 2
tRF
tRNA-Lys-TTT-3-1// . . . *2
Exact
28
98
5
4.57
0.958
5.18
0.000


Example 3
 3
tRF
tRNA-Glu-CTC-1-1// . . . *3
Exact
32
735
52
3.67
0.879
6.59
0.001


Example 4
 4
tRF
tRNA-Pro-TGG-2-1
Exact
31
106
8
4.12
0.883
4.60
0.000


Example 5
 5
tRF
tRNA-Lys-TTT-3-1// . . . *4
Exact
31
243
20
3.68
0.921
6.26
0.000


Example 6
 6
tRF
tRNA-iMet-CAT-1-1// . . . *5
Exact
33
83
8
3.48
0.896
5.11
0.000


Example 7
 7
tRF
tRNA-Lys-CTT-1-1// . . . *6
Exact
31
136
15
3.14
0.888
5.00
0.001


Example 8
 8
tRF
tRNA-iMet-CAT-1-1// . . . *7
Exact
31
51
7
3.48
0.904
4.15
0.000


Example 9
 9
isomiR
mir-183
Mature 5′ sub
21
91
12
2.32
0.777
5.16
0.007


Example 10
10
isomiR
mir-223
Mature 3′ sub
17
526
78
2.96
0.879
5.95
0.000


Example 11
11
miRNA
mir-150
Mature 5′
22
17236
2591
2.39
0.896
12.74 
0.000


Example 12
12
isomiR
mir-223
Mature 3′ super
24
289
44
2.59
0.865
6.70
0.003


Example 13
13
tRF
tRNA-Lys-CTT-1-l// . . . *8
Exact
28
94
15
3.10
0.850
4.72
0.001


Example 14
14
isomiR
mir-150
Mature 5′ super
23
80
13
3.10
0.875
5.51
0.000


Example 15
15
isomiR
mir-150
Mature 5′ sub
19
337
60
3.33
0.846
7.32
0.008


Example 16
16
tRF
tRNA-Pro-AGG-1-1// . . . *9
Exact
30
523
94
4.22
0.850
5.68
0.003


Example 17
17
isomiR
mir-146b
Mature 5′ super
23
191
35
2.16
0.873
5.77
0.005


Example 18
18
tRF
tRNA-iMet-CAT- 1-1// . . . *10
Exact
30
125
22
3.03
0.931
5.97
0.000



























TABLE 2-2











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 19
19
isomiR
mir-361
Mature 3′ super
24
35
7
2.58
0.850
4.59
0.001


Example 20
20
isomiR
mir-223
Mature 3′
21
270
59
2.56
0.842
7.16
0.001






sub/super









Example 21
21
precursor
mir-223
precursor
15
293
67
2.14
0.821
5.68
0.005






miRNA









Example 22
22
precursor
mir-223
precursor
16
317
73
2.71
0.833
6.67
0.005






miRNA









Example 23
23
isomiR
mir-146a
Mature 5′ sub
20
31
8
2.37
0.796
3.61
0.002


Example 24
24
isomiR
mir-150
Mature 5′ sub
20
1205
298
2.01
0.800
9.70
0.002


Example 25
25
isomiR
mir-223
Mature 3′ sub
18
356
92
2.11
0.838
6.44
0.009


Example 26
26
miRNA
mir-29a
Mature 3′
22
1384
355
2.23
0.858
9.40
0.000


Example 27
27
isomiR
mir-223
Mature 3′ sub
20
117
30
2.31
0.821
5.23
0.004


Example 28
28
miRNA
mir-339
Mature 5′
23
39
10
2.51
0.796
3.71
0.002


Example 29
29
isomiR
mir-223
Mature 3′ super
23
110411
30866
1.80
0.846
14.64
0.001


Example 30
30
miRNA
mir-146b
Mature 5′
72
303
83
1.35
0.829
6.73
0.001


Example 31
31
isomiR
mir-365a//mir-365b
Mature 3′ sub
21
55
16
1.98
0.833
4.11
0.003


Example 32
32
miRNA
mir-140
Mature 5′
22
172
49
2.15
0.938
6.41
0.006


Example 33
33
miRNA
mir-223
Mature 3′
22
78031
24601
1.57
0.825
15.54
0.002


Example 34
34
isomiR
mir-223
Mature 3′
27
24932
7946
1.73
0.821
12.89
0.001






sub/super









Example 35
35
tRF
tRNA-Leu-AAG-1-1// . . . *11
Exact
16
134
42
1.68
0.546
7.34
0.041


Example 36
36
isomiR
mir-150
Mature 5′ sub
21
7252
2372
1.61
0.738
11.13
0.023


Example 37
37
isomiR
mir-146b
Mature 5′ super
24
255
85
1.53
0.850
6.54
0.001


Example 38
38
tRF
tRNA-Glu-CTC-1-l// . . . *12
Exact
30
86
28
1.63
0.771
5.99
0.001


Example 39
39
isomiR
mir-223
Mature 3′ sub
20
2960
1043
1.86
0.792
8.85
0.002


Example 40
40
isomiR
mir-145
Mature 5′ super
24
116
41
1.50
0.790
5.48
0.005


Example 41
41
isomiR
mir-186
Mature 5′ sub
21
322
112
1.53
0.921
7.74
0.000



























TABLE 2-3











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 42
42
miRNA
mir-365a//mir-365b
Mature 3′
22
169
61
1.29
0.808
6.55
0.005


Example 43
43
isomiR
mir-223
Mature 3′ super
23
167
62
1.43
0.700
6.90
0.012


Example 44
44
isomiR
mir-192
Mature 5′ sub
20
344
130
1.40
0.608
7.93
0.033


Example 45
45
tRF
tRNA-Gly-GCC-
Exact
33
131
50
1.38
0.733
4.10
0.047





2-1// . . . *13










Example 46
46
miRNA
mir-17
Mature 5′
23
1458
590
1.39
0.888
9.88
0.000


Example 47
47
isomiR
mir-339
Mature 5′ sub
19
156
64
1.29
0.748
5.61
0.011


Example 48
48
isomiR
mir-223
Mature 3′ sub
21
6065
2585
1.23
0.763
11.58
0.007


Example 49
49
isomiR
mir-223
Mature 3′ sub
21
10177
4407
1.21
0.754
11.30
0.010


Example 50
50
isomiR
mir-30c-2//mir-30c-1
Mature 5′ sub
22
86
36
1.26
0.754
5.77
0.007


Example 51
51
isomiR
mir-1307
Mature 3′ super
23
46
20
1.18
0.767
5.33
0.003


Example 52
52
miRNA
mir-29c
Mature 3′
22
704
310
1.50
0.796
8.76
0.002


Example 53
53
isomiR
mir-223
Mature 3′ sub
20
517
232
1.16
0.738
6.16
0.016


Example 54
54
isomiR
mir-223
Mature 3′ super
24
94
42
1.17
0.617
6.32
0.047


Example 55
55
isomiR
mir-30b
Mature 5′ sub
21
93
41
1.19
0.742
6.27
0.008


Example 56
56
isomiR
mir-766
Mature 3 sub
21
78
36
1.11
0.733
5.34
0.012


Example 57
57
isomiR
mir-26b
Mature 3′ sub
21
37
17
1.11
0.744
4.02
0.017


Example 58
58
tRF
tRNA-Gly-CCC-
Exact
22
310
140
1.14
0.631
9.06
0.037





1-1// . . . *14










Example 59
59
miRNA
let-7d
Mature 3′
22
103
48
1.12
0.802
6.86
0.003


Example 60
60
tRF
tRNA-Gly-CCC-
Exact
25
415
191
1.12
0.617
9.15
0.053





1-1// . . . *15










Example 61
61
isomiR
mir-30d
Mature 5′ sub
19
144
69
1.07
0.721
6.82
0.016


Example 62
62
miRNA
mir-505
Mature 3′
22
55
26
1.08
0.767
5.34
0.007


Example 63
63
isomiR
mir-93
Mature 5′ sub
22
61
28
1.13
0.767
4.66
0.032


Example 64
64
isomiR
mir-30e
Mature 5′ super
23
817
384
1.09
0.867
9.44
0.000



























TABLE 2-4











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 65
65
precursor
mir-16-1//
precursor miRNA
16
114
54
1.09
0.740
6.33
0.012





mir-16-2










Example 66
66
miRNA
mir-193a
Mature 5
22
245
121
1.19
0.771
7.30
0.006


Example 67
67
isomiR
mir-320a
Mature 3′ super
25
46
22
1.07
0.717
4.37
0.019


Example 68
68
isomiR
mir-29b-1//
Mature 3′ sub
21
187
93
1.01
0.650
7.06
0.023





mir-29b-2










Example 69
69
isomiR
mir-142
Mature 5′ sub/super
22
458
242
0.92
0.717
8.13
0.043


Example 70
70
isomiR
mir-142
Mature 5′ sub/super
21
117
60
0.97
0.731
5.33
0.045


Example 71
71
miRNA
mir-744
Mature 5′
22
131
69
0.92
0.758
6.31
0.012


Example 72
72
isomiR
mir-200b
Mature 3′ sub
21
2
27
−3.48
0.900
2.69
0.000


Example 73
73
isomiR
mir-181b-1//
Mature 5′ sub
19
20
203
−5.29
0.946
5.09
0.000





mir-181b-2










Example 74
74
isomiR
mir-200a
Mature 3′ sub
18
5
47
−4.05
0.950
4.13
0.000


Example 75
75
isomiR
mir-181b-1//
Mature 5′ sub
18
37
296
−5.43
0.942
5.40
0.000





mir-181b-2










Example 76
76
isomiR
mir-181b-1//
Mature 5′ sub
20
79
583
−5.95
0.917
5.40
0.000





mir-181b-2










Example 77
77
miRNA
mir-340
Mature 3′
22
312
2209
−7.02
0.938
8.82
0.000


Example 78
78
isomiR
mir-181b-1//
Mature 5′ sub
21
33
223
−4.97
0.921
5.40
0.000





mir-181b-2










Example 79
79
miRNA
mir-378e
Mature 3′
19
5
33
−3.37
0.865
2.69
0.000


Example 80
80
precursor
mir-181b-1//
precursor miRNA
17
17
100
−4.43
0.925
5.80
0.000





mir-181b-2










Example 81
81
isomiR
mir-145
Mature 5′ sub
19
6
32
−3.42
0.867
3.21
0.000


Example 82
82
precursor
mir-181b-1//
precursor miRNA
16
12
71
−3.96
0.873
4.61
0.000





mir-181b-2



























TABLE 2-5











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 83
 83
isomiR
mir-181b-1//mir-181
Mature 5′ sub
22
64
343
−4.91
0.925
6.37
0.000





b-2










Example 84
 84
isomiR
mir-451a
Mature 5′ sub
18
7
33
−3.31
0.942
3.81
0.000


Example 85
 85
isomiR
mir-29b-1//mir-29b-2
Mature 3′ sub
22
15
69
−3.75
0.863
2.69
0.000


Example 86
 86
isomiR
mir-451a
Mature 5′ sub
17
13
55
−2.90
0.913
4.67
0.000


Example 87
 87
precursor
mir-181b-1//mir-181
precursor
15
9
38
−3.16
0.844
4.63
0.000





b-2
miRNA









Example 88
 88
isomiR
mir-144
Mature 3′ sub
17
20
75
−2.55
0.854
5.64
0.002


Example 89
 89
isomiR
mir-451a
Mature 5′
18
16
55
−2.15
0.850
5.48
0.009





sub/super










Example 90
 90
isomiR
mir-451a
Mature 5′ sub
19
14
46
−2.46
0.850
4.58
0.000


Example 91
 91
miRNA
let-7c
Mature 5′
22
11
35
−2.24
0.821
3.18
0.002


Example 92
 92
isomiR
mir-16-2
Mature 3′
20
119
362
−1.87
0.967
7.97
0.000






sub/super









Example 93
 93
isomiR
mir-451a
Mature 5′ super
25
11282
31795
−1.49
0.671
14.65
0.043


Example 94
 94
isomiR
mir-486-1
Mature 5′ super
23
15
42
−1.48
0.796
4.18
0.020


Example 95
 95
isomiR
mir-126
Mature 3′ sub
20
29
80
−1.87
0.842
5.55
0.006


Example 96
 96
isomiR
mir-363
Mature 3′ sub
19
15
38
−1.39
0.802
3.98
0.022


Example 97
 97
isomiR
mir-574
Mature 5′ sub
21
22
56
−2.16
0.829
5.18
0.001


Example 98
 98
miRNA
let-7b
Mature 5′
22
1771
4518
−1.28
0.817
10.67
0.001


Example 99
 99
miRNA
mir-144
Mature 3′
20
660
1687
−1.35
0.771
9.97
0.028


Example 100
100
isomiR
mir-574
Mature 3′ sub
21
17
43
−2.04
0.846
4.22
0.000


Example 101
101
isomiR
let-7b
Mature 5′ sub
21
1614
3915
−1.50
0.900
10.98
0.000


Example 102
102
isomiR
mir-103a-2//mir-
Mature 3′ sub
19
648
1544
−1.06
0.717
10.94
0.008





103a-1//mir-107










Example 103
103
isomiR
mir-126
Mature 3′ sub
21
301
713
−1.56
0.854
8.66
0.002


Example 104
104
isomiR
mir-451a
Mature 5′ super
24
19
43
−1.18
0.738
4.01
0.072


Example 105
105
miRNA
mir-106b
Mature 5′
21
670
1524
−1.13
0.888
10.36
0.001



























TABLE 2-6











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 106
106
miRNA
let-7i
Mature 5′
22
107
247
−1.20
0.804
7.46
0.014


Example 107
107
precursor
mir-451a
precursor
15
49
106
−1.11
0.783
6.13
0.036






miRNA









Example 108
108
isomiR
mir-425
Mature 5′ sub
19
14
31
−1.13
0.819
4.10
0.031


Example 109
109
isomiR
mir-16-2
Mature 3′ sub
20
15
33
−1.82
0.754
4.51
0.003


Example 110
110
miRNA
mir-139
Mature 5′
23
69
155
−1.18
0.771
7.08
0.024


Example 111
111
isomiR
mir-451a
Mature 5′ super
23
38
80
−1.10
0.715
6.35
0.047


Example 112
112
isomiR
mir-18a
Mature 5′ sub
21
138
296
−1.10
0.767
7.79
0.030


Example 113
113
miRNA
mir-126
Mature 3′
22
335
706
−1.23
0.833
8.69
0.004


Example 114
114
isomiR
mir-550a-1//mir-550a-
Mature 3′ sub
21
63
133
−1.50
0.775
6.23
0.005





2//mir-550a-3










Example 115
115
isomiR
mir-142
Mature 3′ sub
22
181
222
−0.30
0.504
8.05
0.548


Example 116
116
isomiR
mir-142
Mature 3′ sub
21
156
135
0.21
0.517
5.74
0.577


Example 122
119
MiscRNA
ENST00000363745.
Exact
28
484
40
6.44
0.936
5.79
0.000





1// . . . *16










Example 123
120
MiscRNA
ENST00000364600.
Exact
31
1504
95
6.35
0.951
8.41
0.000





1// . . . *17










Example 124
121
miRNA
mir-221
Mature 3′
23
457
32
5.92
0.923
7.09
0.000


Example 125
122
miRNA
mir-374b
Mature 5′
22
465
44
5.44
0.931
7.50
0.000


Example 126
123
isomiR
mir-130a
Mature 3′ super
23
293
32
5.43
0.904
6.27
0.000


Example 127
124
miRNA
mir-340
Mature 5′
22
495
47
5.40
0.932
7.23
0.000


Example 128
125
miRNA
mir-199a-1//mir-199a-
Mature 3′
22
2387
161
5.21
0.958
9.23
0.000





2//mir-199b










Example 129
126
isomiR
mir-23a
Mature 3′ super
23
927
92
4.98
0.914
8.22
0.000


Example 130
127
miRNA
mir-335
Mature 5′
23
632
89
4.84
0.949
7.50
0.000


Example 131
128
miRNA
mir-130a
Mature 3′
22
3873
417
3.70
0.962
10.40
0.000


Example 132
129
isomiR
mir-584
Mature 5′ sub
21
619
121
3.38
0.897
8.04
0.000


Example 133
130
MiscRNA
ENST00000363745.
Exact
26
13226
2207
2.72
0.908
12.82
0.000





1// . . . *18



























TABLE 2-7











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 134
131
miRNA
mir−26a-1//
Mature 5′
22
5509
853
2.66
0.931
11.03
0.000





mir−26a-2










Example 135
132
MiscRNA
ENST00000364600.
Exact
32
151813
17667
2.56
0.932
15.67
0.000





1// . . . *17










Example 136
133
isomiR
mir-23a
Mature 3′ super
22
12447
2197
2.19
0.947
12.60
0.000


Example 137
134
miRNA
mir-146a
Mature 5′
22
2236
549
2.05
0.915
10.03
0.000


Example 138
135
miRNA
mir-191
Mature 5′
23
3434
726
2.04
0.926
10.19
0.000


Example 139
136
MiscRNA
ENST00000364600.
Exact
31
106642
25718
2.02
0.939
15.70
0.000





1// . . . *17










Example 140
137
miRNA
mir-92a-1//
Mature 3
22
2418
8103
−2.07
0.941
11.90
0.000





mir-92a-2










Example 141
138
isomiR
let-7b
Mature 5′ sub
20
416
1273
−2.15
0.901
9.56
0.000


Example 142
139
isomiR
mir-451a
Mature 5′ sub
21
13722
36210
−2.15
0.905
14.34
0.000


Example 143
140
isomiR
mir-30e
Mature 5′
23
414
1361
−2.21
0.972
9.67
0.000






sub/super









Example 144
141
isomiR
let-7g
Mature 5′ sub
21
875
3513
−2.28
0.972
10.48
0.000


Example 145
142
miRNA
mir-486-1//
Mature 5′
22
2037
7408
−2.44
0.935
11.36
0.000





mir-486-2










Example 146
143
isomiR
mir-16-1//mir-16-2
Mature 5′ sub
20
2087
8031
−2.47
0.977
12.12
0.000


Example 147
144
isomiR
mir-451a
Mature 5′ sub
20
7902
30578
−2.61
0.957
14.22
0.000


Example 148
145
isomiR
mir-185
Mature 5′ sub
21
595
2886
−2.67
0.978
10.52
0.000


Example 149
146
isomiR
let-7a-1//let-7a-2//
Mature 5′ sub
20
633
3159
−2.67
0.975
10.97
0.000





let-7a-3










Example 150
147
isomiR
mir-92a-1//
Mature 3′ sub
21
247
882
−2.73
0.904
8.30
0.000





mir-92a-2










Example 151
148
isomiR
mir−25
Mature 3′ sub
21
214
916
−2.86
0.961
8.79
0.000


Example 152
149
isomiR
mir-16-2
Mature 3′
21
159
708
−2.87
0.921
8.60
0.000






sub/super



























TABLE 2-8











Average in
Average







SEQ



Length
head and
in


Cut-off




ID



(nucleo-
neck cancer
healthy
Log2

value



Example
NO:
Class
Archetype
Type
tides)
patients
subjects
FC
AUC
(Log2)
p-value


























Example 153
150
isomiR
let-7f-1//let-7f-2
Mature 5′ sub
20
253
1372
−2.98
0.956
9.04
0.000


Example 154
151
isomiR
mir-25
Mature 3′ sub
20
117
538
−3.01
0.931
7.93
0.000


Example 155
152
isomiR
mir-425
Mature 5′ sub
21
147
634
−3.15
0.945
8.53
0.000


Example 156
153
isomiR
mir-423
Mature 5′ sub
21
588
2940
−3.15
0.962
10.52
0.000


Example 157
154
isomiR
mir-484
Mature 5′ sub
21
635
3996
−3.27
0.966
10.23
0.000


Example 158
155
isomiR
mir-486-1//mir-486-2
Mature 5 sub
21
2876
17383
−3.32
0.956
12.95
0.000


Example 159
156
isomiR
mir-486-1//mir-486-2
Mature 5′ sub
20
280
1771
−3.48
0.952
9.47
0.000


Example 160
157
isomiR
let-7i
Mature 5′ sub
21
460
3333
−3.61
0.969
10.35
0.000


Example 161
158
isomiR
let-7d
Mature 5′ sub
20
116
685
−3.75
0.943
8.46
0.000


Example 162
159
isomiR
mir-486-1//mir-486-2
Mature 5′ sub
17
20
207
−4.08
0.917
6.00
0.000


Example 163
160
isomiR
let-7i
Mature 5′ sub
20
89
857
−4.36
0.981
8.54
0.000


Example 164
161
isomiR
mir-484
Mature 5′ sub
20
43
497
−4.85
0.964
7.76
0.000


Example 165
162
LincRNA
ENST00000627566.1
Exact
15
8
349
−7.39
0.986
3.97
0.000


Example 167
117
miRNA
mir-339
Mature 3′
23
4
8
 0.55
0.625
11.4
0.413


Example 168
118
miRNA
mir-17
Mature 3′
22
17
8
−0.96
0.621
17.17
0.250




















TABLE 2-9






SEQ ID

Archetype



Example
NOs:
Class
and Type
Fold Change







Example
115, 116
isomiRNA
mir-142 Mature
Before surgery: −2.1


117


3’ sub
After surgery: −2.4





















TABLE 2-10






SEQ ID

Archetype
Cut-off
AUC


Examples
NOs:
Class
and Type
value
value







Example
11 and
miRNA
mir-150-5p and
4.83
0.97628


118
30

mir-146b-5p




Example
11 and
miRNA
mir-150-5p and
5.05
0.96443


119
26

mir-29a-3p




Example
11 and
miRNA
mir-150-5p and
4.82
0.94071


120
117

mir-339-3p




Example
30 and
miRNA
mir-146b-5p and
5.05
0.91406


121
118

mir-17-3p




Example
157 and
isomiR,
let-7i Mature 5’ sub and
3.03
0.967 


166
162
LincRNA
ENST00000627566.1









As seen in these results, the abundance of the miRNAs or the like represented by SEQ ID NOs: 1 to 71 and 118 to 136 was significantly higher in the patients with head and neck cancer than that in the healthy subjects, and the miRNAs or the like represented by SEQ ID NOs: 72 to 117 and 137 to 162 was significantly lower in the patients with head and neck cancer than in the healthy subjects. It was indicated that head and neck cancer was able to be detected with high accuracy by the method of the present invention (Examples Ito 116, 122 to 165, and 167 to 168).


Moreover, the result presented in Table 2-9 showed that the FC (fold change) in the abundance of the isomiR represented by either SEQ ID NO: 115 or SEQ ID NO: 116 was changed before and after surgery for tongue cancer, indicating that the isomiRs can be used to assess the success or failure of the surgery. Furthermore, the result presented in Table 2-10 showed that the combinations of the miRNAs represented by SEQ ID NOs: 11 and 30, SEQ ID NOs: 11 and 26, SEQ ID NOs: 11 and 117, SEQ ID NOs: 30 and 118, and SEQ ID NOs: 157 and 162 had an AUC value ranging from 0.91406 to 0.97628, indicating that even early tongue cancer can be detected by using any of the combinations.

Claims
  • 1. A method of assisting the detection of head and neck 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 (LincRNAs or MiscRNAs) contained in a test sample isolated from a living body, whose nucleotide sequence is represented by any one of SEQ ID NOs: 162, 160, 145, 143, 146, 140, 141, 1 to 139, 142, 144, 147 to 159, and 161, wherein a higher abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 1 to 71 and 118 to 136 than that of healthy subjects or a lower abundance of at least one of the miRNAs, isomiRs, precursor miRNAs, transfer RNA fragments, or non-coding RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 72 to 117 and 137 to 162 than that of healthy subjects indicates a higher likelihood of having head and neck cancer.
  • 2. The method according to claim 1, wherein the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, transfer RNA fragments (tRFs), or non-coding RNA fragments (LincRNAs or MiscRNAs) whose nucleotide sequence is represented by any one of SEQ ID NOs: 162, 160, 145, 143, 146, 140, and 141 is used as an index.
  • 3. The method according to claim 1, wherein the abundance of at least one of miRNAs, isoform miRNAs (isomiRs), precursor miRNAs, or transfer RNA fragments whose nucleotide sequence is represented by any one of SEQ ID NOs: 92, 2, 74, 73, 75, 84, 32, 77, 18, 1, 3 to 31, 33 to 72, 76, 78 to 83, 85 to 91, and 93 to 116 is used as an index.
  • 4. The method according to claim 3, wherein the abundance of an isomiR whose nucleotide sequence is represented by SEQ ID NO: 115 or 116 is used as an index.
  • 5. The method according to claim 3, wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 11 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 30 is used as an index.
  • 6. The method according to claim 3, wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 11 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 26 is used as an index.
  • 7. The method according to claim 3, wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 11 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 117 is used as an index.
  • 8. The method according to claim 3, wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 30 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 118 is used as an index.
  • 9. The method according to claim 1, wherein the abundance of a miRNA whose nucleotide sequence is represented by SEQ ID NO: 157 and a miRNA whose nucleotide sequence is represented by SEQ ID NO: 162 is used as an index.
  • 10. The method according to claim 1, wherein the head and neck cancer is tongue cancer.
Priority Claims (1)
Number Date Country Kind
2017-238856 Dec 2017 JP national
PCT Information
Filing Document Filing Date Country Kind
PCT/JP2018/045994 12/13/2018 WO 00