[A clinical and genetic analysis of a pedigree with two 46,XY patients suffering from 17alpha-hydroxylase deficiency].

OBJECTIVE: To investigate the molecular defects of CYP17A1 gene in a pedigree with two 46,XY patients suffering from 17alpha-hydroxylase deficiency (17-OHD) and explore the steroid biosynthetic difference in carriers of 17-OHD before and after adrenocorticotrophic hormone (ACTH) test. METHODS: Clinical data and hormone profiles were collected from the members of the pedigree. CYP17A1 genotyping was performed in the patients and family members with PCR-direct sequencing. A short ACTH test was evaluated in some cases. RESULTS: The CYP17 genes of the patients were proved to hold a homozygous mutation with a base deletion and a base transversion (TAC/AA) in exon 6, which produced a missense mutation of Tyr-->Lys at codon 329 and changed the open reading frame following this codon. The hormone response of the carriers after ACTH stimulation was abnormal between the patients and normal controls. CONCLUSION: 17-OHD in this family was caused by CYP17A1 mutation (TAC329AA); some hormonal response to ACTH stimulation was abnormal in carriers.

Microarray data classification based on ensemble independent component selection.

Independent component analysis (ICA) has been widely deployed to the analysis of microarray datasets. Although it was pointed out that after ICA transformation, different independent components (ICs) are of different biological significance, the IC selection problem is still far from fully explored. In this paper, we propose a genetic algorithm (GA) based ensemble independent component selection (EICS) system. In this system, GA is applied to select a set of optimal IC subsets, which are then used to build diverse and accurate base classifiers. Finally, all base classifiers are combined with majority vote rule. To show the validity of the proposed method, we apply it to classify three DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that our ensemble method obtains stable and satisfying classification results when compared with several existing methods.