(19)
(11)EP 3 725 897 A1

(12)EUROPEAN PATENT APPLICATION
published in accordance with Art. 153(4) EPC

(43)Date of publication:
21.10.2020 Bulletin 2020/43

(21)Application number: 18889432.3

(22)Date of filing:  13.12.2018
(51)International Patent Classification (IPC): 
C12Q 1/6886(2018.01)
G01N 33/50(2006.01)
C12N 15/113(2010.01)
(86)International application number:
PCT/JP2018/045994
(87)International publication number:
WO 2019/117270 (20.06.2019 Gazette  2019/25)
(84)Designated Contracting States:
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated Extension States:
BA ME
Designated Validation States:
KH MA MD TN

(30)Priority: 13.12.2017 JP 2017238856

(71)Applicant: Hiroshima University
Higashihiroshima-shi Hiroshima 739-8511 (JP)

(72)Inventors:
  • TAHARA, Hidetoshi
    Hiroshima-shi, Hiroshima 734-8553 (JP)
  • TAHARA, Makoto
    Tokyo 104-0045 (JP)

(74)Representative: Mewburn Ellis LLP 
Aurora Building Counterslip
Bristol BS1 6BX
Bristol BS1 6BX (GB)

  


(54)METHOD FOR ASSISTING DETECTION OF HEAD AND NECK CANCER


(57) 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



[0001] The present invention relates to a method of assisting the detection of head and neck cancer.

BACKGROUND ART



[0002] 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.

[0003] 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



[0004] 

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



[0005] 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.

[0006] 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



[0007] 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.

[0008] That is, the present invention provides the followings.
  1. (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. (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. (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. (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. (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. (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. (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. (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. (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. (10) The method according to any one of (3) to (8), wherein the head and neck cancer is tongue cancer.

EFFECT OF THE INVENTION



[0009] 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



[0010] 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 ID NO:ClassArchetypeTypeLength (nucleotides)Sequence
1 tRF tRNA-Gly-CCC-1-1//··· *1 Exact 30 gcauuggugguucagugguagaauucucgc
2 tRF 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 tR-NA-Ls-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/super 21 ucaguuugucaaauaccccaa
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 ID NO:ClassArchetypeTypeLength (nucleotides)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/super 22 gucaguuugucaaauaccccaa
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 ID NO:ClassArchetypeTypeLength (nucleotides)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-81b-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-378e 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-451 a Mature 5' sub 18 cguuaccauuacugaguu
85 isomiR mir-29h-1//mir-29b-2 Mature 3' sub 22 agcaccauuugaaaucaguguu
Table 1-4
SEQ ID NO:ClassArchetypeTypeLength (nucleotides)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-451 a 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-451 a 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 ugaguguguguffugugagugu
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-103a-1//mir-107 Mature 3' sub 19 agcagcauuguacagggcu
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-7i Mature 5' 22 ugagguaguaguuugugcuguu
107 precursor mir-45 1a 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-451 a Mature 5' super 23 gaaaccguuaccauuacugagu u
112 isomiR mir-18a Mature 5' sub 21 uaaggugcaucuagugcagau
113 miRNA mir-126 Mature 3' 22 ucguaccsugaguaauaaugcg
Table 1-5
SEQ ID NO:ClassArchetypeTypeLength (nucleotides)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 cccccacugcuaaauuugacuggcuuuu
120 MiscRNA ENST00000364600.1//··· *17 Exact 31 gcugguccgaugguaguggguuaucagaacu
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 cccccacugcuaaauuugacuggcuu
131 miRNA mir-26a-1//mir-26a-2 Mature 5' 22 uucaaguaauccaggauaggcu
132 MiscRNA ENST00000364600.1//··· *17 Exact 32 ggcugguccgaugguaguggguuaucagaacu
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 ggcugguccgaugguaguggguuaucagaac
137 miRNA mir-92a-1//mir-92a-2 Mature 3' 22 uauugcacuugucccggccugu
138 isomiR let-7b Mature 5' sub 20 ugagguaguagguugugugg
139 isomiR mir-451 a Mature 5' sub 21 aaaccguuaccauuacugagu
140 isomiR mir-30e Mature 5' sub/super 23 guaaacauccuugacuggaagcu
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 ID NO:ClassArchetypeTypeLength (nucleotides)Sequence
143 isomiR mir-16-11/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 ugagguaguagguuguauag
148 isomiR mir-25 Mature 3' sub 21 cauugcacuugucucggucug
149 isomiR mir-16-2 Mature 3' sub/super Mature 3' sub/super 21 accaauauuacugucugcuu
150 isomiR let-7f-1//let-7f-2 Mature 5' sub 20 ugagguaguagauuguauag
151 isomiR mir-25 Mature 3' sub 20 cauugcacuugucucggucu
152 isomiR mir-425 Mature 5' sub 21 aaugacacgucacucccguu
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 agagguagguugcauag
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//t RNA-Glu-CTC-2-1
*4:tRNA-Lys-TTT-3-1//tRNA-Lys-TT-3-2//tRNA-Lys-TTT-3-3//tRNA-Lys-TTT-3-4//tRNA-Lys-TTT3-5//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
Table 1-7
*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-2-41/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//t RNA-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//ENST00000 365571.2//ENST00000578877.2//ENST00000364908.1
*18:ENST00000363745.1//ENST00000364409.1//ENST00000516507.1 //ENST00000391107.1//ENST00000459254.1


[0011] 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.

[0012] 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).

[0013] 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.



[0022] 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.

[0023] 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.

[0024] 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



[0025] Plasma samples from 24 patients with head and neck cancer and from 10 healthy subjects were used.

(2) Extraction of RNA in Serum



[0026] Extraction of RNA in serum was performed using the miRNeasy Mini kit (QIAGEN).
  1. 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. 2) To a new 1.5-mL tube, 200 µL of the supernatant was transferred.
  3. 3) To the tube, 1000 µL of the QIAzol Lysis Reagent was added and mixed thoroughly to denature protein components.
  4. 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. 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. 6) The tube was centrifuged at 12000 x g for 15 minutes at 4°C and 650 µL of the upper aqueous layer was transferred to a new 2-mL tube.
  7. 7) For the separation of RNA, 975 µL of 100% ethanol was added to the tube and mixed by pipetting.
  8. 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 x 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. 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. 10) To remove impurities attached on the filter, 650 µL of Buffer RWT was added to the column and centrifuged at 8000 x g for 15 seconds at room temperature. The flow-through solution from the column was discarded.
  11. 11) To clean the RNA adsorbed on the filter, 500 µL of Buffer RPE was added to the column and centrifuged at 8000 x g for 15 seconds at room temperature. The flow-through solution from the column was discarded.
  12. 12) To clean the RNA adsorbed on the filter, 500 µL of Buffer RPE was added to the column and centrifuged at 8000 x g for 2 minutes at room temperature. The flow-through solution from the column was discarded.
  13. 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 x g for 1 minute at room temperature.
  14. 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. 15) Centrifugation was performed at 8000 x 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



[0027] Exosomes in serum were collected as follows.

[0028] 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



[0029] The quantification of miRNAs or the like was performed as follows.

[0030] 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



[0031] 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



[0032] The results are presented in Tables 2-1 to 2-10.
Table 2-1
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value (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-1-1// ··· *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
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value (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' sub/super 21 270 59 2.56 0.842 7.16 0.001
Example 21 21 precursor mir-223 precursor miRNA 15 293 67 2.14 0.821 5.68 0.005
Example 22 22 precursor mir-223 precursor miRNA 16 317 73 2.71 0.833 6.67 0.005
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' 22 303 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' sub/super 22 24932 7946 1.73 0.821 12.89 0.001
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-1-1// ... *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
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value (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-2-1/ /··· *13 Exact 33 131 50 1.38 0.733 4.10 0.047
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-1-1/ / ··· *14 Exact 22 310 140 1.14 0.631 9.06 0.037
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-1-1/ /··· *15 Exact 25 415 191 1.12 0.617 9.15 0.053
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
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value (Log2)p-value
Example 65 65 precursor mir-16-1//mir-16-2 precursor miRNA 16 114 54 1.09 0.740 6.33 0.012
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//mir-29b-2 Mature 3' sub 21 187 93 1.01 0.650 7.06 0.023
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 0.73 1 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//mir-181 b-2 Mature 5' sub 19 20 203 -5.29 0.946 5.09 0.000
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//mir-181 b-2 Mature 5' sub 18 37 296 -5.43 0.942 5.40 0.000
Example 76 76 isomiR mir-181b-1//mir-181 b-2 Mature 5' sub 20 79 583 -5.95 0.917 5.40 0.000
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//mir-181 b-2 Mature 5' sub 21 33 223 -4.97 0.921 5.40 0.000
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//mir-181 b-2 precursor miRNA 17 17 100 -4.43 0.925 5.80 0.000
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//mir-181 b-2 precursor miRNA 16 12 71 -3.96 0.873 4.61 0.000


[0033] Table 2-5
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value (Log2)p-value
Example 83 83 isomiR mir-181b-1//mir-181 b-2 Mature 5' sub 22 64 343 -4.91 0.925 6.37 0.000
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 b-2 precursor miRNA 15 9 38 -3.16 0.844 4.63 0.000
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' sub/super 18 16 55 -2.15 0.850 5.48 0.009
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-7e Mature 5' 22 11 35 -2.24 0.821 3.18 0.002
Example 92 92 isomiR mir-16-2 Mature 3' sub/super 20 119 362 -1.87 0.967 7.97 0.000
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-103a -1//mir-107 Mature 3' sub 19 648 1544 -1.06 0.717 10.94 0.008
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-451 a 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
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value(L og2)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 miRNA 15 49 106 -1.11 0.783 6.13 0.036
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 -2//mir-550a-3 Mature 3' sub 21 63 133 -1.50 0.775 6.23 0.005
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. 1//··· *16 Exact 28 484 40 6.44 0.936 5.79 0.000
Example 123 120 MiscRNA ENST00000364600. 11/··· *17 Exact 31 1504 95 6.35 0.951 8.41 0.000
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 -2//mir-199b Mature 3' 22 2387 161 5.21 0.958 9.23 0.000
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. 1//··· *18 Exact 26 13226 2207 2.72 0.908 12.82 0.000
Table 2-7
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value (Log2)p-value
Example 134 131 miRNA mir-26a-1//mir-26a-2 Mature 5' 22 5509 853 2.66 0.931 11.03 0.000
Example 135 132 MiscRNA ENST00000364600. 1//··· *17 Exact 32 151813 17667 2.56 0.932 15.67 0.000
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. 1//··· *17 Exact 31 106642 25718 2.02 0.939 15.70 0.000
Example 140 137 miRNA mir-92a-1//mir-92a-2 Mature 3' 22 2418 8103 -2.07 0.941 11.90 0.000
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-451 a Mature 5' sub 21 13722 36210 -2.15 0.905 14.34 0.000
Example 143 140 isomiR mir-30e Mature 5' sub/super 23 414 1361 -2.21 0.972 9.67 0.000
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//mir-486-2 Mature 5' 22 2037 7408 -2.44 0.935 11.36 0.000
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//let-7a-3 Mature 5' sub 20 633 3159 -2.67 0.975 10.97 0.000
Example 150 147 isomiR mir-92a-1//mir-92a-2 Mature 3' sub 21 247 882 -2.73 0.904 8.30 0.000
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' sub/super 21 159 708 -2.87 0.921 8.60 0.000
Table 2-8
ExampleSEQ ID NO:ClassArchetypeTypeLength (nucleotides)Average in head and neck cancer patientsAverage in healthy subjectsLog2 FCAUCCut-off value (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
ExampleSEQ ID NOs:ClassArchetype and TypeFold Change
Example 117 115, 116 isomiRNA mir-142 Mature 3' sub Before surgery: -2.1
After surgery: -2.4
Table 2-10
ExamplesSEQ ID NOs:ClassArchetype and TypeCut-off valueAUC value
Example 118 11 and 30 miRNA mir-150-5p and mir-146b-5p 4.83 0.97628
Example 119 11 and 26 miRNA mir-150-5p and mir-29a-3p 5.05 0.96443
Example 120 11 and 117 miRNA mir-150-5p and mir-339-3p 4.82 0.94071
Example 121 30 and 118 miRNA mir-146b-5p and mir-17-3p 5.05 0.91406
Example 166 157 and 162 isomiR, LincRNA let-7i Mature 5' sub and ENST00000627566.1 3.03 0.967


[0034] 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 1 to 116, 122 to 165, and 167 to 168).

[0035] 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 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 claim 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 claim 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 claim 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 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 any one of claims 3 to 8, wherein the head and neck cancer is tongue cancer.
 





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Patent documents cited in the description