Genome-scale profiling of gene expression in hepatocellular carcinoma: Classification, survival prediction, and identification of therapeutic targets

被引:141
|
作者
Lee, JS [1 ]
Thorgeirsson, SS [1 ]
机构
[1] NCI, Expt Carcinogenesis Lab, Canc Res Ctr, NIH, Bethesda, MD 20892 USA
关键词
D O I
10.1053/j.gastro.2004.09.015
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
The heterogeneous nature of human hepatocellular carcinoma (HCC) has hampered both treatment and prognostic predictions. Gene expression profiles of human HCC were analyzed to define the molecular characteristics of the tumors and to test the prognostic value of the expression profiles. By applying global gene expression analyses, including unsupervised and supervised methods, 2 distinctive subclasses of HCC that were highly homogeneous for both the underlying biology and the clinical outcome were discovered. Tumors from the low survival subclass had strong cell proliferation and antiapoptosis gene expression signatures. In addition, the low survival subclass displayed higher expression of genes involved in ubiquitination and sumoylation, suggesting an etiologic involvement of these processes in accelerating the progression of HCC. Genes most strongly associated with survival were identified by using the Cox proportional hazards survival analysis. This approach identified a limited number of genes that accurately predicted the length of survival and provided new molecular insights into the pathogenesis of HCC. Future studies will evaluate potential diagnostic markers and therapeutic targets identified during the global gene expression studies. Furthermore, cross-species similarity of gene expression patterns will also allow prioritization of a long list of genes obtained from human gene expression profiling studies and focus on genes whose expression is altered during tumorigenesis in both species.
引用
收藏
页码:S51 / S55
页数:5
相关论文
共 50 条
  • [41] Gene Expression Profiling and the Use of Genome-Scale In Silico Models of Escherichia coli for Analysis: Providing Context for Content
    Lewis, Nathan E.
    Cho, Byung-Kwan
    Knight, Eric M.
    Palsson, Bernhard O.
    JOURNAL OF BACTERIOLOGY, 2009, 191 (11) : 3437 - 3444
  • [42] Gene expression profiling of clear cell renal cell carcinoma: Gene identification and prognostic classification
    Takahashi, M
    Rhodes, DR
    Furge, KA
    Kanayamat, H
    Kagawa, S
    Haab, BB
    Teh, BT
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2001, 98 (17) : 9754 - 9759
  • [43] SHARP: genome-scale identification of gene–protein–reaction associations in cyanobacteria
    S. Krishnakumar
    Dilip A. Durai
    Pramod P. Wangikar
    Ganesh A. Viswanathan
    Photosynthesis Research, 2013, 118 : 181 - 190
  • [44] Use of gene expression profiling to determine prognosis and therapeutic targets for patients with appendiceal carcinoma.
    Kim, M. K.
    Blazer, D. G., III
    Stewart, J. H.
    Guy, C.
    Shen, P.
    Levine, E.
    Hsu, S. D.
    JOURNAL OF CLINICAL ONCOLOGY, 2011, 29 (04)
  • [45] Gene expression profiling for prediction of distant metastasis and survival in primary nasopharyngeal carcinoma.
    Kao, K. J.
    Cheng, S. H.
    Huang, A. T.
    JOURNAL OF CLINICAL ONCOLOGY, 2006, 24 (18) : 280S - 280S
  • [46] A genome-scale analysis for identification of genes required for growth or survival of Haemophilus influenzae
    Akerley, BJ
    Rubin, EJ
    Novick, VL
    Amaya, K
    Judson, N
    Mekalanos, JJ
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2002, 99 (02) : 966 - 971
  • [47] Identification of therapeutic miRNAs from the arsenic induced gene expression profile of hepatocellular carcinoma
    Mukherjee, Arnab
    Acharya, Prethi Bhaskar
    Singh, Akshita
    Selvam, Mukunthan Kuppusamy
    CHEMICAL BIOLOGY & DRUG DESIGN, 2023, 101 (05) : 1027 - 1041
  • [48] COBRAme: A computational framework for genome-scale models of metabolism and gene expression
    Lloyd, Colton J.
    Ebrahim, Ali
    Yang, Laurence
    King, Zachary A.
    Catoiu, Edward
    O'Brien, Edward J.
    Liu, Joanne K.
    Polsson, Bernhard O.
    PLOS COMPUTATIONAL BIOLOGY, 2018, 14 (07)
  • [49] Towards the Reconstruction of Integrated Genome-Scale Models of Metabolism and Gene Expression
    Cruz, Fernando
    Lima, Diogo
    Faria, Jose P.
    Rocha, Miguel
    Dias, Oscar
    PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2020, 1005 : 173 - 181
  • [50] Genome-scale metabolic models as platforms for identification of novel genes as antimicrobial drug targets
    Mienda, Bashir Sajo
    Salihu, Rabiu
    Adamu, Aliyu
    Idris, Shehu
    FUTURE MICROBIOLOGY, 2018, 13 (04) : 455 - 467