Combining clinical information and gene expression patterns for cancer prognosis = 임상정보와 유전자 발현패턴의 연계분석을 통한 암 예후진단

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 305
  • Download : 0
Knowing accurate prognosis of cancer is one of the most important problems in therapeutic studies and applications. Well proven prognostication is essentially needed to achieve higher degrees of patients’ survival and welfare with providing appropriate options for therapies and medication. As genome-wide data sets are being sufficiently generated in various omics fields, it is possible to analyze conventional clinical features such as pathological stages (TNM stage or stage grouping), pathological grade and histological subtypes with related molecular level data. In this thesis, we propose computational methods to correlate gene expression profiles with clinical features and to provide improved approaches for cancer prognosis. First, we proposed a new method for extracting cancer metastasis related genes with gene expression data and pathological information (pathological M stage and histological subtypes). We analyzed differently expressed genes in primary colon tumors and their metastases in liver. In this process, we tried to reduce metastasis independent noise features which might come from the difference of organs and differently activated organ specific viability. Using appropriately defined set operations to a large scale data set, we could show that our result is biologically related to the metastasis processes and free from noise effects especially from tissue specificity. Second, we proposed a monotonically expressed gene analysis (MEGA) for extracting breast cancer lymph node invasion and tumor size related gene sets by utilizing expression patterns over a two dimensional $N\timesT$ space with providing appropriate meta-analysis test results of various cancer analyses. The test has been conducted on completely independent data sets. We showed that gene sets selected from the suggested functions were strongly correlated with cancer prognoses including metastasis, relapse and survival, and showed significantly better results than conventional appro...
Advisors
Lee, Do-Heonresearcher이도헌researcher
Description
한국과학기술원 : 바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2010
Identifier
418646/325007  / 020055015
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2010.2, [ vii, 93 p. ]

Keywords

Prognosis; Metastasis; Cancer; Bioinformatics; Clinical Information; 임상정보; 예후; 전이; 암; 생물정보학

URI
http://hdl.handle.net/10203/27079
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=418646&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0