筋萎縮性側索硬化症のためのマイクロRNAバイオマーカーの探索

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(1)Vol.2018-BIO-56 No.2 2018/12/14. ØCrg¶qZ€C IPSJ SIG Technical Report. Ú. Ò¤VQ g×=±whŠw « é RNA Ì ¦ Ú ”§ ”wsg >±³Â1,a). &2ï2,b). “A• Ò¤VQ g×=±¢ ALS£ xz ¿X b;Dós ®sÏ•OUsMOSw &!Qìñw 1 mp K” { ´>U æb ” tm•o z ñ x×C$sÒÑwMš› æO { Çá ”é ï w!QU\ wìñwj¼ p K” t ‹ TT˜’ cz ;óÆ¶wÝ § Ç¶Ü x°iÆÌp K” { ALS › ‰•S ‘ | æ^ d” ¨; ;Ï› Þb hŠtz \ wìñq wÚ « é RNA¢ miRNA£ Cqw ÈQUß€^ •h{ H× z }CQ ALS¢ sALS£z H Q ALS¢ fALS£ S‘ | ALS !Ÿ©ß æ ž T’ wBZ miRNA › Ð‚h{ \ •’ wBZ miRNA Óé Ñ • çtxz Rüüs› ;Mh-£Á` ¶6t‘ ” !:¬R› &;` h{ f wALz f‘ xz ô^SpH× q ñ › à b ” \ q UpV ” miRNA › ' ` h{ ` hUl o z \ •’ w miRNA xz ALS wœO$s'™w miRNA Ì ¦ Ú ”§ ”pK“ ˜” {. ¨;. 1. xa Št ALS(Amyotrophic lateral sclerosis, Ò¤VQ ±) [1] xÏ whŠw. g×=. ®sÏ•OU¬q^ •o MsM. &!ŸQìñpK” { Ï•OU¬q` sMj¼x. 0tI. ” Uz GV sðJq ` o 6\` sMáˆÇá ”é ï U´! æ•pK” hŠz ´!æ•wÊëªŠ› q ” \ q UÉ` X z f ‹ f ‹ z îg$t´>wj¼› ¬ b” wUÉ` Mq M ˜U[wC2t‹. ½¦ Û ¿ « µÃ”» w. æ s<› ;Mh¢ j¶æ \ú¶$s ¯t. rU. ˜UDósz ñ B÷s. q sl o M” Uz f •p‹ z r w‘ O sRü› üs. b •yf ‹ f ‹ Ì. ¦ Ú”§ ”q ` o. ®swT^ Q z ¯. ï ·ï ±µUsMwUqÝpK” { ŠZ€pxz aˆt. :T’. [2] px² iZ› ;Mo ©¶~. tUÂ› æl hU\ \ px¡ wµ. l o º0› ••!Ë` h£{. ¶.$sÃ”» rsÑ é ”› $ 1 tÔb{. 2.1 miRNA CqÓé Ñ •. ˜^ •hH. ALS, }C. ALS,. ¬ç”ÓwB¤. microRNA wÃ”» [3] › ;Mo Ì ¦ Ú ”§ ”› ÏRb ” \ q › è¦b{ Ï•Ow¬q^ •o MsM‰ìñt0` ˜U0›sB¤ miRNA › ;MhÌ. o ‹ Cq”w!=b” miRNA U› pV •yz f wª$. GEO w GSE52917 T’ ¼ ¢ ï é ” Å ` h { é.$t x GSE52917 series matrix.txt.gz q M O Ê²w Ñ •. çU. “Series matrix” t. çt. O` z \ •› ;Mh{ \ wÑ •. xwBZ miRNA Óé Ñ •. 2 a) b). ⓒ 2018 Information Processing Society of Japan. çU. ‡ •o M” { f. wº xz H Q ALS U | ¨; !Ÿ› -Ë` o M” U °C±w‹ w. z }CQ ALS U. sl o M” { ‡ h\ wÃ”» t x 3391. | H×. U. q. ¨w miRNA U. ‡ •o M” {. 2.2 § Â°æ s<t‘ ” !:¬R xij ∈ RN ×M › i jèw miRNA w j jèw±ï Óçt SZ” miRNA CqÓé Ñ •. ¤ G¶g»¶æúg¶J sq¦èG w- Z€t [email protected] [email protected] ŠZ€xj¶æ q ` o Z[AˆpK” { [2]. ç Ã ” » [3] x. ¦ Ú”§ ”U. ÏRpV •yz î;$s™¯xK” ` z ‡ hz B¤pKl. 1. çÃ”». r s 0 Å w miRNA C q Ó é Ñ •. x. ¨; !Ÿ-Ë ¢ °C±£z H× z w. oz. R. ˜’ cz ´!æ•wÚç. ˜^ Q ‡ ‡ s’ sMq MO wUq. ˜xz ÊëªŠw. Š` o M” ®. 2. Ã”» q MO. Ýq sl o M” { \ w‘ O sÄØwhŠz Úç½¦ Û ¿ « µ Ã”» w. pV ” DóQ‹ K” {. üüs› ;Mh-£s` ¶6t‘ ” !:¬RO¯ q § Â°. O ðJUK” { \ whŠz ÙåwGFÛsÚç½¦ Û ¿ « µ Ã”» w. wrssr T’ ´¼› ‰. rs Oq ` o x¶ UTvo T’. çq b” { § Â°æ s<p. x\ •›. xij = ai +. X. bik δkj. k. 1.

(2) Vol.2018-BIO-56 No.2 2018/12/14. ØCrg¶qZ€C IPSJ SIG Technical Report. t P ‹ Pi › ". mi RNAp r o fil e s. Pi = Pχ 2 >. . uℓi σℓ. 2 #. (1). q MO Üp Ç)b ” { \ \ p σℓ xªj. Ca t e g o r i c a l r e g r e s s i o n. PCAb a s e d u n s u pe r v i s e dFE. ¾:U x ‘ “ GV MÌw. )z Pχ2 [> x] x. ËÐüÍw§u¬ppK” {. Oz±4Y` h P ‹U 0.01 Ž<w miRNA › ¬Rb ” {. 2.4 PCA › ;Mh¢ Q ¬R^ •h miRNA iZ › ;Mo 6S PCA › æMz. mi RNAs e l e c t i o n. RüÛY” vℓj › 6-‰b ” { Ít. RüÛY”t0b ”. § Â°æ s<. vℓj = aℓ +. PCA. X. bℓk δkj. k. › 6Sz îæb” { -‰` h P ‹›. Oz±4Y` z 4Y. P ‹U 0.05 Ž<w ℓ › ¬•{ ¬y•h vℓ › ;Mo z R t. PCAl o a d i n g. î÷^ •h lda. :› ;Mo ¢ Q › îæb” { prior =. rep(1/4,4) q CV=T w¦ Ó ³ ã ï › ¦. `z. wOˆ. U‰a p K” \ q q z leave one out cross validation › A. LDA. e` z Q. ó›. b” {. 3. AL 3.1 § Â°æ s<t‘ ” !:¬R $ 1. ŠZ€wÃ”» rsÑ é ”{ LDA• ¢ Q z PCA• üs. ‚” { § Â°æ s<t‘ l o ¬R^ •h miRNA xx¶æ. Fig. 1 Data analysis flow in this study. PCA: principal component analysis, LDA: liner discriminat analysis.. q MO Üp s<b ” { ai , bik x i jèw miRNA { z f •ŽŽx. ^ •o M” s<-‰w. ts”. miRNA p K l h¢ ¯ 1£{ \ w miRNA UÌ x` TsMhŠz \ w‡ ‡ pxžc. ws. :pK” { R tî÷. :p K ” lm › ;Mo ¤ miRNA. ] q t P ‹› -‰z {‡ l h P ‹› BH ,j [4] p. p. ¦. Ú”§ ”tsl o M” Tr O T› ¬Ý` hMU±ï ÓçU. < :pK“ δjk x j ±ï ÓçU k jèw§ Â°æ tÖl hq V iZ. § Â ° æ s<› ;M h !:¬Rw-‰ALt m M o \. Rü. Oz. ±4Y` o z 4Y P ‹U 0.01 Ž<p K” miRNA › ¬R. tüZ” \ q. UpV o ` ‡ Mz ™¯UsM{ f \ p× S› ÿnb” h Šz ¬R^ •h 90miRNA wˆ› ;Mo 6S Rüüs› æMz. RüÛY” vℓ › -‰` o § Â °æ s<› po x. Šhq \ – z ℓ = 1, 2, 5, 8 U 0.05 Ž<w4Y P ‹› Ël o M” \ q Url h ($ 2){ \ w. mw. Rü˜:› ;Mo. ±ï Óç› › tQ üs` hALU¯ 2 pK” { }. ` h{. CQ ALS ñ Rüüs› ;Mh-£s` ¶6t‘ ” !:¬R P P ‡ c xij › i xij = 0, i x2ij = N ts” ‘ O tªj= P b ” { Ít Sii′ = j xij xi′ j p [^ •” æ»› 0¯=. ŽŽx‘ X Q. ^ •o M” {. 2.3. Rüüs› ;Mh-£s` ¶6t‘ ” !:¬R. 3.2. Rüüs› æl hALz HË Rü˜:U§Â°æ s<. Rü˜:. p 0.05 Ž<w4Y P ‹ (P = 2.45 × 10−5 ) › Ël o M” \. › -‰` z Rüüs› îæb” { RüÛY” vℓ ∈ RM P x vℓj = i uℓi xij p -‰b ” { Ít Sx RüÛY”. q Url h ($ 3){ HË Rü˜:› ;Mo ¤ miRNA t. `z {. Õ« Ä ç uℓ ∈ RN › -‰b ” \ q p. t0b ” § Â°æ s<. vℓj = aℓ +. X. ËÐüÍ, (1) Ü, p-‰` z 4Y P ‹. U 0.01 Ž<w miRNA › :Q hq \ – z ¶æp 67miRNA U\ wÚE› ¬h` o Mh (¯ 1, $ 4){ \ w miRNA U Ì. bℓk δkj. k. ¦ Ú”§ ”tsl o M” Tr O T› ¬Ý` hMU±ï. ÓçU. x` TsMhŠz \ w‡ ‡ pxžc. tüZ. :p K ” {. ” \ q UpV o ` ‡ Mz ™¯UsM{ f \ p× Sÿnw. Oz±4Y` z 4Y P ‹U 0.05 Ž<w. hŠz ¬R^ •h 67miRNA wˆ› ;Mo 6S Rüüs. › îæb ” { aℓ , bℓk xH ℓ -‰` h P ‹›. Ç)^ •” P ‹›. Rü{. ws<. ℓ › ¬•{ \ w ℓ › ;Mo z uℓi U¨¢ µüÍpK” q MO. › æMz. <Á>†w‹ q t z. xŠhq \ – z ℓ = 1, 2, 3, 8 U 0.05 Ž<w4Y P ‹› Ël. ËÐüÍ› –l o i jèw miRNA. ⓒ 2018 Information Processing Society of Japan. RüÛY” vℓ › -‰` o § Â °æ s<› po. 2.

(3) Vol.2018-BIO-56 No.2 2018/12/14. ØCrg¶qZ€C IPSJ SIG Technical Report Èx Liguori ’ [5] U‰. PC1 P=1.98e−05 PC2 P=3.56e−09 PC5 P=4.56e−02 PC8 P=1.47e−02 ●. ●. ●. ●. ●. ● ●. ●. ●. ● ● ●. ● ●. 0.1. ● ●. ● ● ● ●. ●. ● ●. 0.0. ● ● ● ●. ● ● ●. ●. ●. ● ● ● ●. ● ●. ● ●. ●. ●. ●. ●●. ● ●. ●. ● ●. 629 miR-642b miR-661 miR-760 miR-769-3p miR-99b-star. ●. −0.3. −0.2. ● ●. ● ● ●. ●. ●. ●. healthy control sALS patient ALS mutation carrier fALS patient. miR-4783-3p miR-4793-3p miR-574-5p miR-584 miR-610 miR-. ●. ● ●. ●. healthy control sALS patient ALS mutation carrier fALS patient. healthy control sALS patient ALS mutation carrier fALS patient. miR-4701-3p miR-4710 miR-4728-5p miR-4746-3p miR-4769-5p. ●. ● ●. ● ●. ● ● ● ●. ●. miR-4462 miR-4493 miR-4521 miR-4530 miR-4647 miR-4667-5p. ● ● ●. ●. ● ●. ●. ●. ● ● ● ● ●. ● ●. ●. ● ●. miR-3663-5p miR-3907 miR-3960 miR-4271 miR-4429 miR-4440. ●. ●. ●. ●. ●. miR-320a miR-320b miR-320c miR-320d miR-338-5p miR-3655. ● ● ●. ●. ●. ● ● ● ●. star miR-2278 miR-2392 miR-296-3p miR-3120-5p miR-32-star. ● ● ● ● ● ● ●. ● ●. −0.2. −0.1. ●. ●. ●. ● ● ●. ● ● ●. ●. ●. ●. ●. ● ●. ●. ●. ●. ●. ● ● ●. ●. ● ●. ● ● ● ●. ●. ●. 0.130. miR-1538 miR-188-5p miR-1909-star miR-194-star miR-195-. ● ● ●. ●. ●. ●. 1268b miR-1273d miR-1285 miR-1290 miR-1296 miR-1322. ● ●. 0.0. ● ●. 3689f mir-3937 mir-3960 mir-423 mir-4525 mir-4539 mir-4669 mir-466 mir-550a-1 mir-550a-2 mir-550a-3 miR-106b miR-. ●. ● ● ●. 0.0. 0.135. ●. ● ●. ●. mir-1271 mir-185 mir-1910 mir-297 mir-3611 mir-3689b mir-. ●. ●. ●. −0.1. (68 miRNA). ● ● ●. 0.1. ● ●. ● ●. ● ● ●. ● ● ●. ● ● ● ●. ● ● ●. ●. ●. 0.2. ●. ●. § Â°æ s<› ;Mh!:¬R{. ●. ●. ● ● ●. ● ● ● ● ● ●. with 38 miRNAs downrefulated in sALS [5].. ● ● ●. −0.2. ●. −0.4. Table 1 List of selected miRNAs. Bold letters indicate ovelaps. 0.140. ●. ●. 0.2. å ¿ Ó{. 0.4. xw miRNA q w¦ ”Ì” 0.3. ` h}CQ ALS p ÿ<b ”. healthy control sALS patient ALS mutation carrier fALS patient. ¬R^ •h miRNA wæ µ Ä {. 0.2. ¯ 1. †Otžè (22 miRNA). miR-1180 miR-1275 miR-1306 miR-130b miR-134 miR-1469. $ 2. § Â °æ s<p ¬y•h 90miRNA(¯ 1) wˆ› ;Mo -‰. miR-185 miR-1915 miR-2861 miR-297 miR-3064-5p miR-3665. ` hALp˜’ •”. miR-4306 miR-4455 miR-4497 miR-4656 miR-4745-5p miR-. ` h4Y P ‹Uz 0.05 Ž<pKl hH |. RüÛY”wO j z § Â°æ s<p-‰. 4787-5p miR-483-5p miR-595 miR-638 miR-665. ÛY”w6{[${. |. |. Rü. (45 miRNA). Fig. 2 Boxplots using four PC loading computed by PCA ap-. let-7a let-7b let-7c let-7d miR-103a miR-106a miR-107 miR-. plied to 90 miRNAs selected by categorical regression. 122 miR-1246 miR-1280 miR-1281 miR-140-3p miR-146a miR-. (Table 1).. Rüüs› ;Mh-£s` ¶6t‘ ” !:¬R {. 151-3p miR-151-5p miR-16 miR-1825 miR-191 miR-19b miRmiR-3135b miR-3175 miR-3185 miR-320e miR-3613-5p miR-. 0.2. 2110 miR-221 miR-22 miR-23a miR-24 miR-25 miR-30d ● ● ● ● ●. 425 miR-4454 miR-4466 miR-4485 miR-4488 miR-4532 miR93 miR-940. ● ●. ●● ● ●. ● ●●. 0.1. 455-3p miR-4707-5p miR-4734 miR-652 miR-663 miR-92a miR-. ● ●. ●. ● ●. ● ● ● ●. $ 2 t Ô^ •h æ»{ fALS:H. mw. RüÛY”› ;Mh¢. Q. w. ‰. Q ALS, sALS:}CQ ALS. Table 2 Confusion matrix of liner regression analysis using. ● ●. ● ●. −0.1. ¯ 2. 0.0. ● ● ●. ●. ● ● ●. ●. ●. ●. ●. ●● ● ● ●. four PC loading shown in Fig. 2.. ●. −0.2. ●. sALS ñ. !Ÿ-Ë. 8. 1. 0. fALS ñ. 2. 5. 0. 0. H×. 1. 0. 16. 8. sALS ñ. 1. 0. 1. 5. o M” \ q Url h ($ 5){ \ w o. ±ï Óç› ›. tQ. Rü˜:› ;M. üs` hALU¯ 3 pK” {. ¯ 2 t z‚” q }CQ ALS ñ M” { ¯ 2 p xz. mw. 5. wQ. UGV X ²Í` o. w}CQ ALS ñ. ` T}CQ ALS ñ. q ` o'. ● ● ●. wO j z. p V ÁTl h{. $ 3. Rüüs› ;Mo -‰` h. fALS patient. H×. ALS mutation carrier. fALS ñ. ●. sALS patient. !Ÿ-Ë. ● ●. ●. healthy control. '. ●. −0.3. Yr. RüÛY”wO j z § Â°æ s. <p -‰` h4Y P ‹Uz 0.05 Ž< (P = 2.45 × 10−5 ) p Kl hH. RüÛY”w6{[${. Fig. 3 Boxplot using the second PC loading computed by PCA. wQ. applied to miRNA profiles (P = 2.45 × 10−5 ).. pK” \ q › ßQ ” q z \ •x„…å ï ¼Üq !˜’ s M{ ` T` z ¯ 3 px ñ. pK” q '. ¤. q ÿR:U}CQ ALS. pV o M” \ q U˜T” {. 4. ^æ ŠZ€px. ⓒ 2018 Information Processing Society of Japan. xŽÍw. :w miRNA w¤T’ —. 3.

(4) Vol.2018-BIO-56 No.2 2018/12/14. ØCrg¶qZ€C IPSJ SIG Technical Report ¯ 3. $ 5 t Ô^ •h. mw. 10. æ»{ fALS:H. RüÛY”› ;Mh¢. Q. w. ‰. Q ALS, sALS:}CQ ALS. Table 3 Confusion matrix of liner regression analysis using. 5. four PC loading shown in Fig. 5. Yr. −15 −10. PC2 −5 0. '. $ 4. H°z HË. !Ÿ-Ë. fALS ñ. H×. sALS ñ. !Ÿ-Ë. 8. 1. 0. fALS ñ. 2. 5. 0. 1. H×. 0. 0. 14. 7. sALS ñ. 2. 0. 3. 8. ¯ 4. $ 2 q $ 5 tÔ^ •h. mw. 2. RüÛY”w. wÐ ž ¹ ï ì. :w‹{ æ• $ 2z »• $ 5{. 0 10. 30 PC1. Rü˜:„Í${ z. Table 4 Person’s correlation coefficients between PC loading. 50. show in Fig. 2 (rows) and Fig. 5 (columnes).. U¬R^ •h 67miRNA. (¯ 1){ Fig. 4 Scatter plot between the first and second PC scores.. PC1. PC2. PC3. PC1. 0.65. -0.24. 0.41. PC8 0.10. PC2. -0.50. 0.58. -0.79. -0.08. PC5. 0.32. -0.04. 0.04. -0.39. PC8. 0.26. -0.05. -0.06. 0.53. Red open circles correponds to selected 67 miRNAs (Ta-. ¦` h{ miRNA w¬Rt x§ Â °æ s<q. Rüüs›. ble 1). ;Mh-£s` ¶6t ‘ ” !:¬R› ;Mh{ †Oq ‹ z 4Y P ‹U 0.01 Ž<q MO ,jpz miRNA › ¬R` h{. 0.3. PC1 P=2.28e−03 PC2 P=5.77e−03 PC3 P=5.77e−03 PC8 P=3.60e−02. ● ● ● ● ●. ● ● ●. ●. ● ● ●. ● ●. ●. ●. ●. ●. ● ● ● ●. ●. ● ●. ● ●. ●. ●. 0.12. ●. ●. ● ● ● ●. ● ● ●. ● ● ●. ●. ● ● ●. ●. ●. ●. ●. ●. ●. ● ●. ●. ● ●. ●. ● ● ● ●● ●. −0.1. ●. 0.0. ● ●. ● ● ● ● ●. −0.1. ● ●. ●. ● ●. ●. ● ● ●. ● ●. ● ●. ●. ●. ●. ●. ● ● ●. ● ● ● ● ●. −0.2. −0.1. ●. 0.10. ●. ● ●. ● ●. ●. −0.3. −0.3. ● ● ●. ●. ●. iZw-$t. ●. ●. ™SwôMz žèQwK” ¬RUs^ •”. healthy control sALS patient ALS mutation carrier fALS patient. ×t§. $iq MO \ q › Ô` o M” i– O {. ‡ hz ¬R^ •h. PCA ` hÔùw. S‘ |. RüÛY”wO j z § Â°æ s<p-‰`. h4Y P ‹Uz 0.05 Ž<pKl hH |. |. |. RüÛ. RüÛY”wO j z ›. plied to 67 miRNAs selected by PCA (Table 1).. t. ™t ì. mw. RüÛY”Uz® § Â °æ s. <p -‰` h4Y P ‹U 0.05¯ q MO ‰a ,jp ¬y• o S“ z ¬y•h Rüz. Y”w6{[${. Fig. 5 Boxplots using four PC loading computed by PCA ap-. miRNA iZ› ;Mo. ` o M” ÛY”‰œw¨ÅQ‹ GV M¢ $ 2, $ 5£{ f ‹ f ‹ †Oq ‹ ‰:w. Rüüsp ¬y•h 67miRNA(¯ 1) wˆ› ;Mo -‰` hALp˜’ •”. ˜’ cz \ •. q MO \ q xz $ 3 pÔ^ •” ‘ O s› •w‘ QU‡. ●. healthy control sALS patient ALS mutation carrier fALS patient. ●. healthy control sALS patient ALS mutation carrier fALS patient. −0.2. Žxß€^ •sMq MO \ q pK” { t‹ ●. ●. $ 5. Rüüs› ;Mh-£s` ¶6. t‘ ” !:¬RwÔùtx$ 3 tÔ^ •h‘ O s‘ QŽ. ●. ●. healthy control sALS patient ALS mutation carrier fALS patient. ●. ●. ● ●. ●. ●. −0.2. ●. ●. ,jts” wt0` o z. ●. ●. ● ● ●. Qp Kl o ‹ z miRNA w¬R. ● ●. ● ● ●. ●. M” \ q U•˜•” { GV s§Mxz § Â°æ s<px t 0b ” r w‘ O s‘. ● ●●. ●. £{ \ w\ q. Sžè` h,jp miRNA › ¬R` o. ●. ●. 0.0. ●. ●. 0.0. ●. ●. ●● ●. ●. pz P = 1.5 × 10−19 z ¦ ¿ ¶zp. T’ z †OxK”. ●. ●. ● ●. U. ● ●. ● ●. ●. 0.1. 0.13. ●●. 0.11. ●. ●. ●. ●. U†Opžèt¬R^ •h¢ ¯ 1, Ñ Ÿ ¿ ³ ß ”wY¬¬p. ● ●. ●. ●. 0.09. ●. Rüüsx 67 miRNA q „. ●. ●. 0.1. ● ● ● ●. ● ●. ● ●. ● ●. ●. ●. ●. ●. ●. ●. ●. ●. ●. 0.2. ● ● ●● ● ●. ●. ●. ●. 0.1. ●. 0.2. ● ● ● ●. ●. 0.2. 0.14. 0.15. ● ● ● ●●. § Â°æ s<x 90 miRNAz. …‰a X ’ Mw:› ¬R` z ‡ hz Ts“ w:w miRNA. Rw„O UH |. Rü‹ z § Â°æ s<UH |. |. |. Rüüs› ;Mh-£s` ¶6t‘ ” !:¬. Rüw. |. |. |. Rüp. Rü¤z H. |. mUžè` o ¬y•o M” { îMz ot. H? RüTq MO \ q iZpxsX z žèt¬y•o M :x¢ ¦xŽ<£ w miRNA › ¬R` z ¬R` h miRNA. ”H |. T’. H |. Rüüs› ;Mo Ët—:xwùR!:› \R~ ¬. Rb ” \ q p ALS wÌ. ¦ Ú ”§ ”› ÏRb ” \ q › è. ⓒ 2018 Information Processing Society of Japan. | |. RütmMo xì. :‹ GV M (¯ 4 w. jèw0¯RüU˜pb” { P ‹xf •g•z. P = 1.11 × 10−7 , 6.14 × 10−6 , 5.41 × 10−5 ){ îMtžèt 4.

(5) Vol.2018-BIO-56 No.2 2018/12/14. ØCrg¶qZ€C IPSJ SIG Technical Report. ¬y•o M” miRNA w:x§ Â°æ s<› ;Mh¬Rw. wË. Ôùx. x› £ UK” Ôùwz Öž ë. miRNA ¤. miRNAz. Rüüs› ;Mh. -£s` ¶6t‘ ” !:¬RwÔùxz. `tw-$s. miRNA ¤. miRNA taW sM¢ ¯ 1£ \ q › ßQ ” q z \ w°•. ›. üÛY”U˜’ •” wx‡×tµ¯. R. ¢. ¶sz±w. swÔù “ &` x°. Oq ` o x¡“ q ^ •o S“ z. sw7t. wÃ”» T’ •ŠhÔùz }CQ ALS q H×. wË. z±iZ› › tæO \ q › Yp=b” txsœT’. xè®‡ ` M{ !:¬R› æl hKq z f •› Ët‹ O ° s RüüstTZ” \ q pz ‘ “ é ÌµÄ QwK”. z±› æO ` TsM{ ` T` z. w\ú¶$z K” Mxz ©¶$s^æUžAi– O { f • x>` o z J¶$sqÔq ` o xz qM\ q p xsMUz. M{. f •px°•` o MsM$ 2 wH~ Rü˜:q $ 5 w. Rüüs› ;Mh-£s` ¶6t‘ ” !:¬Rpxf w. H@ Rü˜:w)x?› Sé` o M” i– O T{ f •x. 7s^æs` t z Ã”» æˆ$t z }CQ ALS ›Ÿ$t. ¯ 2 q ¯ 3 w§Mz b s˜j z }CQ ALS ñ. ó. !ˆ` o M” miRNA › V j l q ¬RpV o M” { r j ’. ` o M” q ¥˜•” { îMz H× z H Q ALS ñ. › –O ‚V Tx^æ› 4hsMwpxsMi– O T{ f `. t. z !Ÿ-Ë w. wQ. wQ Qóxz ¯ 2 q ¯ 3 q px„…. ‰a iUz }CQ ALS ñ. wQ. óiZ x¯ 3 p GV X. o C\ÃST’ tQ yH Q ALS x ALS ¶.w. Â S. › ŽŠ” tbWcz Gæüw ALS x}CQ ALS t. b”. wiT’ sS^ ’ pK” {. ²Í` h{ \ w\ q xz !:¬Rt-£s` ¶6› ;M” \ q wO. \ w‘ O s^æt0` o x\ w‘ O sÑ Ÿ ç» ”. w!. AQ› ¯q` o M” q ¥˜•” { -£K“ ¶6pK” § Â. :¬RpxsX z å ï ¼ÜÑ ¥ è µÄ [6] • LASSO [7] w. °æ s<px§ Â°æ ºwü„U—sX z § Â°æ. 7så ¿ Í”. wü. w!:¬R› æQ y\ w‘ O s¡l h!:. „UGV M!:› ¬Rb” q MO ,jU&;^ •” { \ w. ¬R¢ ‘ “ ‘MÍÑ ¥ ”Ú ï µ › aRp V ” wt _€b £. \ q x°_z MM‘ O t_Q ” Uz ðJxÖÆçÂŸ q ^. › æ˜sMp A‰wp xsMTq MO SæUK” i– O {. •” § Â°æ ºü„w{MpK” { K” § Â°æ ºwü„. iUz f wÔùz ¶±ï Óç› ¶6·¿ Ä q ÂµÄ ·¿ Ä. UGV M!:x°_z ‘ X sM¢ § Â°æ. tüZo ¶6·¿ Ä p!:¬R› æMz QóxÂµÄ ·¿. wQ tx–. Q sM£ !:w7t¥Q ” { iUz °Mpz. txÝÝ. Ä p¬Ýb” q MO MO›. ’ sMq z Ñ Ÿ ç» ”. w!. pV o MsMó:w±Ò § Â°æ T’ sl o M” hŠtG. :¬RŽÍta¶6› b” DóQUÍUl o ` ‡ O { ` T. V sü„› ‹ l o M ” DóQ‹ K ” { T“ t }CQ ALS. `z. UîMxMX mTw±Ò § Â°æ t. X xsX z °j:U—sMH. •o M” U. tx. sz ±ï Óç:x. p. ±ï Óçq žc` ‹. ALS ñ x˜cT. ±. ÝÝpV sMq ` ‘ O { f O s” q z § Â°æ ºü„U–. ï Óç` TsM{ \ wÝ6pz ¶.› ¶6·¿ Ä q ÂµÄ. ^ M!:x±Ò § Â°æ UÝÝpV sMz ‘ “ ¼l h!:. ·¿ Ä tüZo !:¬R› æO \ q xqî$pK” TGM. q MO \ q tsl o ` ‡ Mz ±Ò § Â°æ › Y` X Sé`. tYðpK” {. o M” !:xz \ú¶$txY` M\ q › ` o M” t‹ ˜’ cz Æ&ps!:q ` o. †^ •o ` ‡ O \ q ts” {. ‹ ` z ±Ò § Â°æ › ¬ç”Óq ` o SépV ” !:Us Z •yz ¬y•” wx® •’ W¯ ph‡ h‡ ±Ò § Â°æ w)U–^ Tl h!:tsl o ` ‡ Mz \ w‘ O s!:. 5. Aæ ;•¶6$s. Ow¤px°`t-£K“ ¶6wMU. ®pK” q ^ •z ›t!:¬RwÔùt-£s` ¶6U– ˜•” \ q xCpK” { ` T` z \ w«p_” ‘ O tz. › ;MhQ ÞÃçxa¶6› I\ ` z QóUÿ<b” i. U‰ t>Šh,j¢ \ wÔùx›. – O { ¯ 2 › _” v“ pxz ‡ ^ t\ •UIV o ` ‡ l o. ºwü„x–^ X K ” ‚V iz q MO ,j£ › ® -£¯. M” 7t_Q ” { îMz $ 3 › _” v“ pxz }CQ ALS. q ˆs` o z f •tùO ‘ O tÃ”» › C»b” \ q x). w§ Â ° æ ºü„U. e› PO { Ã”» æˆJ¶w,Štíl o ‘ “ f‘ x`•. w§ Â ° æ t z‚o Ì’ Tt GV. M{ \ •x{• l q b” q }CQ ALS t. tx°Œw±Ò § Â°æ U. Ù ŠZ€wj¶æ. xF÷J¶U[²w•Rjø. 105-2118-M-009-001-MY2 w¬ å ï Ä w•Rz t|JZ…. Liguori ’ [5] xŠZ€wj¶æ. [2] Z[® ™¯ t. xwB¤ miRNA U}CQ ALS wñ æü¢. pK” ‚V iq ¥O wx²iZ i– O T{. ‡ •o M” \ q › ™¯` o M” wT‹ `. •sM{. q ›C. w)ŸUGV X z. ` h{ O j x¤. xU¯ 1 t ‹. x£ x. p ÿ<` o M” \ ‡ •o M ” UG. Rüüs› ;Mh-£s` ¶. 6pwˆ¬R^ •o M” { \ •px§ Â°æ s<› ;Mh !:¬Rt ,nMo z }CQ ALS wQ. UO ‡ X æX x. c‹ sTl hi– O { § Â °æ s<wv„› ýQ ” t xz. Liguori ’ [5] U•l h‘ O t z }CQ ALS q H× w ⓒ 2018 Information Processing Society of Japan. ,kZ€ (C) 17K00417 w•R› !Z o æ˜•h{ €ß [1]. Y Zarei, S., Carr, K., Reiley, L., Diaz, K., Guerra, O., Altamirano, P., Pagani, W., Lodin, D., Orozco, G. and Chinea, A.: A comprehensive review of amyotrophic lateral sclerosis, Surgical Neurology International, Vol. 6, No. 1, p. 171 (online), DOI: 10.4103/2152-7806.169561 (2015).. 5.

(6) ØCrg¶qZ€C IPSJ SIG Technical Report. [2]. [3]. [4]. [5]. [6]. [7]. Vol.2018-BIO-56 No.2 2018/12/14. Taguchi, Y.-H. and Wang, H.: Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis, International Journal of Molecular Sciences, Vol. 19, No. 5, p. 1318 (online), DOI: 10.3390/ijms19051318 (2018). Freischmidt, A., Mller, K., Zondler, L., Weydt, P., Volk, A. E., Boˇziˇc, A. L., Walter, M., Bonin, M., Mayer, B., von Arnim, C. A. F., Otto, M., Dieterich, C., Holzmann, K., Andersen, P. M., Ludolph, A. C., Danzer, K. M. and Weishaupt, J. H.: Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers, Brain, Vol. 137, No. 11, pp. 2938–2950 (online), DOI: 10.1093/brain/awu249 (2014). Benjamini, Y. and Hochberg, Y.: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 57, No. 1, pp. 289–300 (online), available from hhttp://www.jstor.org/stable/2346101i (1995). Liguori, M.| Nuzziello, N.| Introna, A.| Consiglio, A.| Licciulli, F.| EustachioDŸ Errico| Scarafino, A.| Distaso, E.| Simone, I. L.• Dysregulation of MicroRNAs and Target Genes Networks in Peripheral Blood of Patients With Sporadic Amyotrophic Lateral Sclerosis, Frontiers in Molecular Neuroscience, Vol. 11, p. 288 (online), DOI: 10.3389/fnmol.2018.00288 (2018). Breiman, L.: Random Forests, Machine Learning, Vol. 45, No. 1, pp. 5–32 (online), DOI: 10.1023/A:1010933404324 (2001). Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective, Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 73, No. 3, pp. 273–282 (online), DOI: 10.1111/j.14679868.2011.00771.x (2011).. ⓒ 2018 Information Processing Society of Japan. 6.

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