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    ๋ฆฌ ์ดˆ๋Œ€์ˆ˜์— ๊ด€ํ•œ ๊ณ ์ „ W-๋Œ€์ˆ˜

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€, 2021.8. ์„œ์˜๋ฆฐ.In this thesis, we review definitions of classical affine W-algebras and classical finite W-algebras associated to Lie superalgebras and the relation between them. The main purpose of this paper is to provide explicit formulas of free generators of classical finite W-algebras associated to Lie superalgebras. Using these formulas, we are able to calculate Poisson brackets between generators. Also, using free generators of classical affine W-algebras introduced by Suh (2020), we are able to find explicit forms of energy momentum states of classical affine W-algebras.๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š”, ๋จผ์ € ๋ฆฌ ์ดˆ๋Œ€์ˆ˜์— ๊ด€ํ•œ ๊ณ ์ „ ์•„ํ•€ W-๋Œ€์ˆ˜์™€ ๊ณ ์ „ ์œ ํ•œ W-๋Œ€์ˆ˜์˜ ์ •์˜๋ฅผ ์ƒ๊ธฐํ•˜๊ณ  ์ด ๋‘˜ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ์„ค๋ช…ํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์˜ ์ฃผ ๋ชฉ์ ์€ ๋ฆฌ ์ดˆ๋Œ€์ˆ˜์— ๊ด€ํ•œ ๊ณ ์ „ ์œ ํ•œ W-๋Œ€์ˆ˜์˜ ์ƒ์„ฑ์›๋“ค์˜ ๊ตฌ์ฒด์ ์ธ ๊ณต์‹์„ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด ์ƒ์„ฑ์›๋“ค์˜ ๊ณต์‹์„ ์ด์šฉํ•˜์—ฌ, ๊ณ ์ „ ์œ ํ•œ W-๋Œ€์ˆ˜์˜ ๊ด„ํ˜ธ ์—ฐ์‚ฐ์„ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋˜ํ•œ, ์ตœ๊ทผ์— ๋ฐœ๊ฒฌ๋œ ๊ณ ์ „ ์•„ํ•€ W-๋Œ€์ˆ˜์˜ ๊ตฌ์ฒด์ ์ธ ์ƒ์„ฑ์›๋“ค์„ ์‚ฌ์šฉํ•˜์—ฌ, ๊ณ ์ „ ์•„ํ•€ W-๋Œ€์ˆ˜๋ฅผ ์ผ์ฐจ ๋ถ„ํ•ดํ•˜๊ฒŒ ํ•ด์ฃผ๋Š” ์—๋„ˆ์ง€ ์šด๋™๋Ÿ‰ ์ƒํƒœ๋ฅผ ์ฐพ์•„ ์†Œ๊ฐœํ•  ๊ฒƒ์ด๋‹ค.1. Introduction 1 2 Poisson vertex algebras 6 2.1 Poisson vertex algebras 6 2.2 Relation between Poisson vertex algebras and Poisson algebras 11 3 Classical affine W-algebras associated to Lie superalgebras 15 3.1 De finition via Drinfeld-Sokolov reduction 15 3.2 De finition via BRST complex 21 3.3 Equivalence between two de finitions 29 4 Structure of classical affine W-algebras 36 4.1 Generators of classical affine W-algebras 36 4.2 Energy momentum states of classical affine W-algebras 39 5 Classical finite W-algebras associated to Lie superalgebras 45 5.1 Relation between classical affine and finite W-algebras 46 5.2 Generators of classical finite W-algebras 48 6 Conclusion 54 Appendix A Vertex algebras 55 A.1 De finition of vertex algebras 55 A.2 Zhu algebra of vertex algebras 58 Appendix B Quantum W-algebras 60 B.1 Quantum affine W-algebras 60 B.2 Quantum fi nite W-algebras 64 Bibliography 68 Abstract (in Korean) 71์„

    ๊ณต๊ณต๊ธฐ๊ด€ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ์˜ PCSI 2.0 ๋„์ž…์— ๋”ฐ๋ฅธ ํšจ๊ณผ ๋ถ„์„ - ๊ณต๊ธฐ์—…ใƒป์ค€์ •๋ถ€๊ธฐ๊ด€ ์ค‘์‹ฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ํ–‰์ •ํ•™๊ณผ, 2018. 2. ๊น€๋ด‰ํ™˜.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณต๊ณต๊ธฐ๊ด€์˜ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ ๊ธฐ๋ฒ•์ธ PCSI(Public-service Customer Satisfaction Index) 2.0์˜ ๋„์ž…์œผ๋กœ ๊ณ ๊ฐ๋งŒ์กฑ๋„์˜ ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ณ ์ž ์‹ค์ฆ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. PCSI๋Š” ๊ณต๊ณต๊ธฐ๊ด€์˜ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•œ ๊ณต๊ณต๊ธฐ๊ด€ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ๋ชจ๋ธ์ด๋‹ค. 2007๋…„์— ์ตœ์ดˆ ๋„์ž…๋˜์–ด ๊ณต๊ธฐ์—…, ์ค€์ •๋ถ€๊ธฐ๊ด€, ๊ธฐํƒ€ ๊ณต๊ณต๊ธฐ๊ด€์„ ๋Œ€์ƒ์œผ๋กœ ๋งค๋…„ 1ํšŒ ๊ธฐํš์žฌ์ •๋ถ€ ์ฃผ๊ด€์œผ๋กœ ์‹ค์‹œ๋œ๋‹ค. ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ๋Š” ๋Œ€๊ตญ๋ฏผ ์„œ๋น„์Šค ๊ฐœ์„ ์„ ํ†ตํ•ด ๊ณต๊ณต๊ธฐ๊ด€์˜ ๊ณ ๊ฐ์ง€ํ–ฅ์  ๋งˆ์ธ๋“œ ํ˜•์„ฑ์— ๊ธฐ์—ฌํ•˜์˜€์œผ๋ฉฐ ๊ด€๋ฃŒ์ฃผ์˜๋ฅผ ๊ฐœ์„ ํ•˜๋Š” ์—ญํ• ์„ ํ–ˆ๋‹ค๊ณ  ํ‰๊ฐ€๋œ๋‹ค. ํ•˜์ง€๋งŒ ์ง€์†์ ์ธ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์ ์ˆ˜์˜ ์ ˆ๋Œ€์ ์ˆ˜ ์ƒํ–ฅํ™”๋กœ ์‹ ๋ขฐ๋„ ์ €ํ•˜, ๊ธฐ๊ด€๋ณ„ ํŠน์ˆ˜์„ฑ์˜ ๋ฏธ๋ฐ˜์˜ ๋“ฑ ๋ฌธ์ œ์ ์ด ์ œ๊ธฐ๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ 2015๋…„์— ๊ธฐ์กด ์กฐ์‚ฌ๋ชจ๋ธ์„ ๋ณด์™„ํ•œ PCSI 2.0 ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ์ ์šฉํ•˜์˜€๋‹ค. PCSI 2.0์—์„œ๋Š” ์ธก์ • ํ•ญ๋ชฉ์˜ ์ •๊ตํ™”, ์กฐ์‚ฌ๋ฐฉ๋ฒ• ๋ณ€ํ™”, ๊ฐœ์ธ๊ณผ ๋ฒ•์ธ๊ณ ๊ฐ์˜ ๊ตฌ๋ณ„ํ•œ ๋งž์ถคํ˜• ์„ค๋ฌธ, ๊ณ ๊ฐ ์ •์˜ ํ™•๋Œ€ ๋ฐ ๋ชจ์ง‘๋‹จ ๊ฒ€์ฆ ํ™•๋Œ€๋กœ ์กฐ์‚ฌ ๋ฐฉ์‹์„ ๊ฐœ์„ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด ์‹ ๊ทœ๋ชจ๋ธ์— ๋Œ€ํ•œ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜๊ณ ์ž 77๊ฐœ ๊ธฐ๊ด€(๊ณต๊ธฐ์—…, ์ค€์ •๋ถ€๊ธฐ๊ด€)์˜ 4๊ฐœ๋…„(2013~2016๋…„) ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๊ณ , ์ž๋ฃŒ๋ฅผ ํ† ๋Œ€๋กœPCSI 2.0์— ๋Œ€ํ•œ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์กฐ์‚ฌ๋ฐฉ์‹์˜ ๊ณ ๋„ํ™” ๋ฐ ๋ณ€๊ฒฝ์œผ๋กœ ํ‰๊ท  ์ ์ˆ˜๊ฐ€ ํ•˜๋ฝ๋˜์—ˆ๊ณ , ๊ธฐ๊ด€๋ณ„ ์ ์ˆ˜์˜ ๋ถ„ํฌ๊ฐ€ ๋„“์–ด์กŒ๋‹ค. PCSI 2.0 ๋„์ž… ํ›„ ๊ณ ๊ฐ์„ ๊ฐœ์ธ๊ณผ ๋ฒ•์ธ์œผ๋กœ ๋ถ„๋ฆฌํ•˜์—ฌ ๊ณ ๊ฐ ์œ ํ˜•์— ๋”ฐ๋ผ ๋ฒ•์ธ ๊ณ ๊ฐ์ด ๋งŽ์„์ˆ˜๋ก ์ ์ˆ˜๊ฐ€ ๋†’์•„์ง€๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๋ชจ์ง‘๋‹จ์˜ ๊ฒ€์ฆ ๊ฐ•ํ™” ๋“ฑ์œผ๋กœ ๋ชจ์ง‘๋‹จ์„ ํ™•๋Œ€ ํ•˜์˜€์œผ๋‚˜, ๋ชจ์ง‘๋‹จ์€ ์ ์ˆ˜์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ถ„์„๋˜์—ˆ๋‹ค. ๋ฐ˜๋ฉด ํ‘œ๋ณธ์‚ฐ์ถœ ๋ฐฉ์‹์˜ ๋ณ€๊ฒฝ์œผ๋กœ ํ‘œ๋ณธ ํฌ๊ธฐ์— ๋”ฐ๋ฅธ ์ ์ˆ˜์— ๋Œ€ํ•œ ์˜ํ–ฅ์€ ์ ์–ด์กŒ๋‹ค. ๋˜ํ•œ ๊ธฐ๊ด€์˜ ํฌ๊ธฐ(์ •์› ์ˆ˜)๋Š” ํด์ˆ˜๋ก ์ ์ˆ˜๊ฐ€ ๋†’์•„์ง€๊ณ , ๊ณต๊ธฐ์—…์ด ์ค€์ •๋ถ€๊ธฐ๊ด€๋ณด๋‹ค ์ ์ˆ˜๊ฐ€ ๋‚ฎ๋‹ค๊ณ  ๋ถ„์„๋˜์—ˆ๋‹ค. ๋ฐ˜๋ฉด ์กฐ์‚ฌ๋ฐฉ์‹์€ ์ ์ˆ˜์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ถ„์„๋œ๋‹ค. ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ๊ฐ€ ๊ณต๊ณต๊ธฐ๊ด€์˜ ์„ฑ๊ณผํ‰๊ฐ€๋กœ์„œ ์ ์šฉ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ณต์ •ํ•˜๊ณ  ๋ชจ๋“  ๊ธฐ๊ด€์ด ์ˆ˜์šฉ๊ฐ€๋Šฅํ•œ ์กฐ์‚ฌ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฑฐ๋“ญ๋‚˜์•ผํ•  ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ํ–ฅํ›„ ๊ณต๊ณต๊ธฐ๊ด€ ๊ณ ๊ฐ๋งŒ์กฑ๋„๊ฐ€ ๊ณ ๊ฐ์ด ์•„๋‹Œ ์ œ๋„์  ํŠน์„ฑ ๋“ฑ์— ์˜ํ•œ ์˜ํ–ฅ์„ ์ตœ์†Œํ™”ํ•˜์—ฌ ์ „์ฒด ๊ธฐ๊ด€์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์ˆ˜ ๋ฐœ์ „์„ ์œ„ํ•ด ๋ฐœ์ „์‹œ์ผœ์•ผ ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ณต๊ณต๊ธฐ๊ด€๊ณผ ๊ตญ๋ฏผ์ด ๋ชจ๋‘ ์ˆ˜์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋‚˜์•„๊ฐˆ ์ˆ˜ ์žˆ๋Š” ๊ณ„๊ธฐ๊ฐ€ ๋˜๊ธธ ๋ฐ”๋ž€๋‹ค.์ œ 1 ์žฅ ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ 1 ์ œ 2 ์žฅ ์„ ํ–‰์—ฐ๊ตฌ ๋ฐ ์—ฐ๊ตฌ๊ฐ€์„ค 5 ์ œ 1 ์ ˆ ๊ณ ๊ฐ๋งŒ์กฑ์˜ ์„ ํ–‰์—ฐ๊ตฌ 5 1. ๊ณ ๊ฐ ๋งŒ์กฑ์˜ ์ •์˜ 5 2. ๊ณต๊ณต ์„œ๋น„์Šค์˜ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ ๊ฐœ๋…๊ณผ ์˜์˜ 7 3. ํ•ด์™ธ์˜ ๊ณต๊ณต์„œ๋น„์Šค ๋ถ€๋ฌธ ๋งŒ์กฑ๋„ ์กฐ์‚ฌ ์‚ฌ๋ก€ ๋ฐ ๋น„๊ต 8 ์ œ 2 ์ ˆ PCSI์™€ PCIS 2.0 ๋ชจ๋ธ์˜ ์ดํ•ด 12 1. PCSI 1.0 ๋ชจ๋ธ์˜ ์ดํ•ด 12 2. PCSI 2.0 ๋ชจ๋ธ์˜ ์ดํ•ด 14 ์ œ 3 ์ ˆ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ์˜ ์ ์ˆ˜ ์ƒ์Šน์š”์ธ 20 ์ œ 4 ์ ˆ ์—ฐ๊ตฌ๊ฐ€์„ค 22 ์ œ 3 ์žฅ ์‹ค์ฆ๋ถ„์„ 31 ์ œ 1 ์ ˆ ์ž๋ฃŒ ๋ฐ ๋ชจํ˜• 31 1. ์กฐ์‚ฌ ๋Œ€์ƒ ์ž๋ฃŒ 31 2. ์กฐ์‚ฌ ๋ชจํ˜• 33 3. ๊ธฐ์ดˆํ†ต๊ณ„๋Ÿ‰ 34 ์ œ 2 ์ ˆ ์‹ค์ฆ๋ถ„์„ 35 1. ๊ณต๊ณต๊ธฐ๊ด€ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์ ์ˆ˜ t-test 35 2. ๊ณต๊ณต๊ธฐ๊ด€ ๊ณ ๊ฐ๋งŒ์กฑ๋„ ์กฐ์‚ฌ ํšŒ๊ท€๋ถ„์„ 38 ์ œ 4 ์žฅ ๊ฒฐ๋ก  ๋ฐ ์ •์ฑ…์  ์‹œ์‚ฌ์  43 ์ฐธ๊ณ ๋ฌธํ—Œ 47 ๋ถ€ ๋ก 49 Abstract 51Maste

    Identification of tumorigenesis related signaling pathways in the tumors with nuclear ฮฒ-catenin overexpression

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    ์˜๊ณผ๋Œ€ํ•™/๋ฐ•์‚ฌMutations of ฮฒ-catenin, CTNNB1, have been reported in various cancers, including colorectal cancer, breast cancer and lung cancer. Most ฮฒ-catenin mutations are located in exon 3 of ฮฒ-catenin genes, which lead to stabilization of ฮฒ-catenin by blocking the destruction complex of ฮฒ-catenin. In colorectal cancers, APC mutations are detected in 70-80% of colon cancers and ฮฒ-catenin mutations are mutually exclusive and detected in about 5% of the colorectal cancers. Although the involvement of APC mutations in the altered Wnt/ฮฒ-catenin pathway activity is well documented, the functional relevance between the deregulated Wnt/ฮฒ-catenin signaling pathway and ฮฒ-catenin mutations has not yet been fully understood in colon cancers. Also, the molecular significances of ฮฒ-catenin mutations in the other cancers are not fully understood. Unlike colorectal carcinomas, nearly all of solid-pseudopapillary neoplasm (SPN) exhibit somatic mutation in exon3 of ฮฒ-catenin, which results in abnormal nuclear accumulation (overexpression) of ฮฒ-catenin. Although ฮฒ-catenin mutation and activation of the Wnt/ฮฒ-catenin signaling pathway have been implicated in the pathogenesis of SPN, the molecular regulatory networks remain poorly understood. To identify the altered pathways by nuclear overexpression of ฮฒ-catenin in tumors, colon cancer and SPN were selected as study models. ฮฒ-catenin mutation is responsible for the development of a small subset of colon cancers, while development of SPN is solely driven by ฮฒ-catenin mutation. The molecular significances of nuclear ฮฒ-catenin overexpression were compared in colon cancer as a model of nuclear ฮฒ-catenin overexpression by altered genes involved in ฮฒ-catenin degradation, and SPN as a model of nuclear ฮฒ-catenin overexpression by ฮฒ-catenin mutation. Since nuclear ฮฒ-catenin shows heterogeneous expressions in colon cancers, the colorectal tumors were classified by percentage of nuclear ฮฒ-catenin and identified a subset of colon cancers by comparing gene expression profiles in the colon cancers showing heterogeneous expression of nuclear ฮฒ-catenin. As a result, it was found that overexpressed nuclear ฮฒ-catenin activates genes involved in Wnt/ฮฒ-catenin signaling, Notch signaling, Hedgehog signaling and ECM-receptor interaction in colon cancer. To identify gene subsets associated with nuclear ฮฒ-catenin overexpression by ฮฒ-catenin mutation, mRNA expression profiles of SPN and other pancreatic tumors (pancreatic adenocarcinomas and neuroendocrine tumors) were performed. All SPNs harbor ฮฒ-catenin mutation, while the other pancreatic tumors harbor no ฮฒ-catenin mutation. Unsupervised clustering analysis of mRNA expression distinguished SPNs as a distinct type of pancreatic tumor. Analysis of differentially expressed genes in SPN demonstrated that genes involved in Wnt/ฮฒ-catenin, Hedgehog and androgen receptor signaling pathways as well as epithelial-mesenchymal transition were activated in solid-pseudopapillary neoplasms. Finally, the altered expression level of genes involved in three activated signaling pathways was confirmed by using cells transfected with ฮฒ-catenin constructs. Transfection of mutant ฮฒ-catenin constructs resulted in the translocation of androgen receptor into the nucleus and up-regulated several molecules involved in Wnt/ฮฒ-catenin, Notch and Hedgehog signaling. In conclusion, overexpression of nuclear ฮฒ-catenin commonly or selectively affects the signaling pathways of the Wnt/ฮฒ-catenin, Notch, Hedgehog and androgen receptor, and contributes to the tumorigenesis of colon cancers and SPN.ope

    LEF1; TFE3; and AR are putative diagnostic markers of solid pseudopapillary neoplasms

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    The diagnosis of solid pseudopapillary neoplasms (SPNs) is challenging because some SPNs share many similar morphological and immunohistochemical features with other pancreatic neoplasms. In this study, we investigated potential diagnostic markers of SPN. Based on the SPN-specific upregulated genes from a previous DNA microarray and proteome study, we selected six immunohistochemical markers [beta-catenin, androgen receptor (AR), lymphoid enhancer-binding factor 1 (LEF1), transcription factor for immunoglobulin heavy-chain enhancer 3 (TFE3), fused in sarcoma (FUS), and WNT inhibitory factor 1 (WIF-1)]. We also evaluated the Ki-67 proliferative index to investigate its associations with prognosis. To validate these markers, we studied 91 SPNs as well as 51 pancreatic ductal carcinomas (PDC) and 48 neuroendocrine tumors (NET) as controls. We found frequent and diffuse nuclear expressions of ฮฒ-catenin (98.9%), AR (81.3%), LEF1 (93.4%), TFE3 (74.7%), FUS (84.6%), and cytoplasmic expression of WIF-1 (96.7%) in SPNs. In contrast, PDCs and NETs showed no expression. (P < 0.001). When beta-catenin, LEF1, and TFE3 staining were combined, the sensitivity and specificity were 100% and 91.9%, respectively. Four (4.4%) SPNs showed distant metastasis and these tumors were associated with a relatively high Ki-67 proliferative index (โ‰ฅ 5%; P = 0.013). We identified LEF1, TFE3, and AR as putative diagnostic markers of SPN, auxiliary to ฮฒ-catenin. Incorporated into an immunohistochemical panel, these markers could be beneficial to distinguish SPN from PDC and NET. In addition, we suggest that the Ki-67 proliferative index can be a predictive marker of metastasis in SPNs.ope

    Identification of specifically activated angiogenic molecules in HMGB-1-induced angiogenesis

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    High-mobility group box-1 (HMGB-1) is expressed in almost all cells, and its dysregulated expression correlates with inflammatory diseases, ischemia, and cancer. Some of these conditions accompany HMGB-1-mediated abnormal angiogenesis. Thus far, the mechanism of HMGB-1-induced angiogenesis remains largely unknown. In this study, we performed time-dependent DNA microarray analysis of endothelial cells (ECs) after HMGB-1 or VEGF treatment. The pathway analysis of each gene set upregulated by HMGB-1 or VEGF showed that most HMGB-1-induced angiogenic pathways were also activated by VEGF, although the activation time and gene sets belonging to the pathways differed. In addition, HMGB-1 upregulated some VEGFR signaling-related angiogenic factors including EGR1 and, importantly, novel angiogenic factors, such as ABL2, CEACAM1, KIT, and VIPR1, which are reported to independently promote angiogenesis under physiological and pathological conditions. Our findings suggest that HMGB-1 independently induces angiogenesis by activating HMGB-1-specific angiogenic factors and also functions as an accelerator for VEGF-mediated conventional angiogenesis.ope

    Metabolic characteristics of solid pseudopapillary neoplasms of the pancreas: their relationships with high intensity (18)F-FDG PET images

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    Objective: We aimed to investigate the metabolic characteristics of Solid pseudopapillary neoplasms (SPNs) in relation signal intensities on (18)F-FDG PET scans. Summary Background Data: SPNs of the pancreas commonly show high uptake of 18F-FDG. However, the metabolic characteristics underlying the high (18)F-FDG uptake in SPNs are not well characterized. Materials and Methods: mRNA expressions for glucose metabolism were analyzed in five SPNs, five pancreatic ductal adenocarcinomas (PCAs), and paired normal pancreatic tissues. Among the proteins involved in glucose metabolism, the expressions of five proteins (GLUT1, HK1, PFKM, ENO2, and PKM2) were evaluated in 36 SPNs by immunohistochemistry. Clinical patterns of SPN on PET scans were classified according to the proportion of (18)F-FDG uptake within the whole tumor volume (hot: >/= 70%, mixed: 30 /= 3%) was associated with high SUVmax in pancreatic SPNs (p = 0.002). Conclusions: SPN cells harbor an active molecular capacity for increased glucose metabolism. Especially, defective type SPNs were associated with low metabolic activity and related to low Ki-67 index.ope

    Clinicopathologic Features and Molecular Characteristics of Glucose Metabolism Contributing to ยนโธF-fluorodeoxyglucose Uptake in Gastrointestinal Stromal Tumors

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    Fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET/CT) is useful in the preoperative diagnosis of gastrointestinal stromal tumors (GISTs); however, the molecular characteristics of glucose metabolism of GIST are unknown. We evaluated 18F-FDG uptake on preoperative PET/CT of 40 patients and analyzed the expression of glycolytic enzymes in resected GIST tissues by qRT-PCR, western blotting, and immunohistochemistry. Results of receiver operating characteristic curve analysis showed that the maximum standardized uptake value (SUVmax) cut-off value of 4.99 had a sensitivity of 89.5%, specificity was 76.2%, and accuracy of 82.5% for identifying tumors with a high risk of malignancy. We found that 18F-FDG uptake correlated positively with tumor size, risk grade, and expression levels of glucose transporter 1 (GLUT1), hexokinase 1 (HK1), and lactate dehydrogenase A (LDHA). Elevated HK and LDH activity was found in high-risk tumors. Among the isoforms of GLUT and HK, GLUT1 and HK1 expression increased with higher tumor risk grade. In addition, overexpression of glycolytic enzymes M2 isoform of pyruvate kinase (PKM2) and LDHA was observed in GISTs, especially in high-risk tumors. These results suggest that upregulation of GLUT1, HK1, PKM2, and LDHA may play an important role in GIST tumorigenesis and may be useful in the preoperative prediction of malignant potential.ope

    ฮฒ-catenin activation down-regulates cell-cell junction-related genes and induces epithelial-to-mesenchymal transition in colorectal cancers

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    WNT signaling activation in colorectal cancers (CRCs) occurs through APC inactivation or ฮฒ-catenin mutations. Both processes promote ฮฒ-catenin nuclear accumulation, which up-regulates epithelial-to-mesenchymal transition (EMT). We investigated ฮฒ-catenin localization, transcriptome, and phenotypic differences of HCT116 cells containing a wild-type (HCT116-WT) or mutant ฮฒ-catenin allele (HCT116-MT), or parental cells with both WT and mutant alleles (HCT116-P). We then analyzed ฮฒ-catenin expression and associated phenotypes in CRC tissues. Wild-type ฮฒ-catenin showed membranous localization, whereas mutant showed nuclear localization; both nuclear and non-nuclear localization were observed in HCT116-P. Microarray analysis revealed down-regulation of Claudin-7 and E-cadherin in HCT116-MT vs. HCT116-WT. Claudin-7 was also down-regulated in HCT116-P vs. HCT116-WT without E-cadherin dysregulation. We found that ZEB1 is a critical EMT factor for mutant ฮฒ-catenin-mediated loss of E-cadherin and Claudin-7 in HCT116-P and HCT116-MT cells. We also demonstrated that E-cadherin binds to both WT and mutant ฮฒ-catenin, and loss of E-cadherin releases ฮฒ-catenin from the cell membrane and leads to its degradation. Alteration of Claudin-7, as well as both Claudin-7 and E-cadherin respectively caused tight junction (TJ) impairment in HCT116-P, and dual loss of TJs and adherens junctions (AJs) in HCT116-MT. TJ loss increased cell motility, and subsequent AJ loss further up-regulated that. Immunohistochemistry analysis of 101 CRCs revealed high (14.9%), low (52.5%), and undetectable (32.6%) ฮฒ-catenin nuclear expression, and high ฮฒ-catenin nuclear expression was significantly correlated with overall survival of CRC patients (Pโ€‰=โ€‰0.009). Our findings suggest that ฮฒ-catenin activation induces EMT progression by modifying cell-cell junctions, and thereby contributes to CRC aggressiveness.ope

    Prognosis of stage III colorectal carcinomas with FOLFOX adjuvant chemotherapy can be predicted by molecular subtype

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    Individualizing adjuvant chemotherapy is important in patients with advanced colorectal cancers (CRCs), and the ability to identify molecular subtypes predictive of good prognosis for stage III CRCs after adjuvant chemotherapy could be highly beneficial. We performed microarray-based gene expression analysis on 101 fresh-frozen primary samples from patients with stage III CRCs treated with FOLFOX adjuvant chemotherapy and 35 matched non-neoplastic mucosal tissues. CRC samples were classified into four molecular subtypes using nonnegative matrix factorization, and for comparison, we also grouped CRC samples using the proposed consensus molecular subtypes (CMSs). Of the 101 cases, 80 were classified into a CMS group, which shows a 79% correlation between the CMS classification and our four molecular subtypes. We found that two of our subtypes showed significantly higher disease-free survival and overall survival than the others. Group 2, in particular, which showed no disease recurrence or death, was characterized by high microsatellite instability (MSI-H, 6/21), abundant mucin production (12/21), and right-sided location (12/21), this group strongly correlated with CMS1 (microsatellite instability immune type). We further identified the molecular characteristics of each group and selected 10 potential biomarker genes from each. When these were compared to the previously reported molecular classifier genes; we found that 31 out of 40 selected genes were matched with those previously reported. Our findings indicate that molecular classification can reveal specific molecular subtypes correlating with clinicopathologic features of CRCs and can have predictive value for the prognosis for stage III CRCs with FOLFOX adjuvant chemotherapy.ope

    ํ•™์Šต์กฐ์ง ๊ตฌ์ถ•์š”์ธ์ด ์ง๋ฌด๋งŒ์กฑ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ : ๋ณด๊ฑด์˜๋ฃŒ๊ณ„ ๊ณต๊ณต์กฐ์ง์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› :๋ณด๊ฑดํ•™๊ณผ, ๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต,2006.Maste
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