Author:
Musumarra G.,Barresi V.,Condorelli D.F.,Scirè S.
Abstract
Abstract
A multivariate analysis of the National Cancer Institute
gene expression database is reported here. The
soft independent modelling of a class analogy approach
achieved cell line classification according to
histological origin. With the PCA method, based on
the expression of 9605 genes and ESTs, classification
of colon, leukaemia, renal, melanoma and CNS cells
could be performed, but not of lung, breast and ovarian
cells. Another multivariate procedure, called partial
least squares discriminant analysis (PLS-DA),
provides bioinformatic clues for the selection of a limited
number of gene transcripts most effective in discriminating
different tumoral histotypes. Among
them it is possible to identify candidates in the development
of new diagnostic tests for cancer detection
and unknown genes deserving high priority in further
studies. In particular, melan-A, acid phosphatase 5,
dopachrome tautomerase, S100-β and acid ceramidase
were found to be among the most important
genes for melanoma. The potential of the present
bioinformatic approach is exemplified by its ability to
identify differentiation and diagnostic markers already
in use in clinical settings, such as protein S-100,
a prognostic parameter in patients with metastatic
melanoma and a screening marker for melanoma
metastasis.
Subject
Clinical Biochemistry,Molecular Biology,Biochemistry
Cited by
72 articles.
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