Fanconi anemia genes are highly expressed in primitive CD34(+ )hematopoietic cells

BACKGROUND: Fanconi anemia (FA) is a complex recessive genetic disease characterized by progressive bone marrow failure (BM) and a predisposition to cancer. We have previously shown using the Fancc mouse model that the progressive BM failure results from a hematopoietic stem cell defect suggesting that function of the FA genes may reside in primitive hematopoietic stem cells. METHODS: Since genes involved in stem cell differentiation and/or maintenance are usually regulated at the transcription level, we used a semiquantitative RT-PCR method to evaluate FA gene transcript levels in purified hematopoietic stem cells. RESULTS: We show that most FA genes are highly expressed in primitive CD34-positive and negative cells compared to lower levels in more differentiated cells. However, in CD34(- )stem cells the Fancc gene was found to be expressed at low levels while Fancg was undetectable in this population. Furthermore, Fancg expression is significantly decreased in Fancc -/- stem cells as compared to wild-type cells while the cancer susceptibility genes Brca1 and Fancd1/Brac2 are upregulated in Fancc-/- hematopoietic cells. CONCLUSIONS: These results suggest that FA genes are regulated at the mRNA level, that increased Fancc expression in LTS-CD34(+ )cells correlates with a role at the CD34(+ )differentiation stage and that lack of Fancc affects the expression of other FA gene, more specifically Fancg and Fancd1/Brca2, through an unknown mechanism

Combination of meta-analysis and graph clustering to identify prognostic markers of ESCC

Esophageal squamous cell carcinoma (ESCC) is one of the most malignant gastrointestinal cancers and occurs at a high frequency rate in China and other Asian countries. Recently, several molecular markers were identified for predicting ESCC. Notwithstanding, additional prognostic markers, with a clear understanding of their underlying roles, are still required. Through bioinformatics, a graph-clustering method by DPClus was used to detect co-expressed modules. The aim was to identify a set of discriminating genes that could be used for predicting ESCC through graph-clustering and GO-term analysis. The results showed that CXCL12, CYP2C9, TGM3, MAL, S100A9, EMP-1 and SPRR3 were highly associated with ESCC development. In our study, all their predicted roles were in line with previous reports, whereby the assumption that a combination of meta-analysis, graph-clustering and GO-term analysis is effective for both identifying differentially expressed genes, and reflecting on their functions in ESCC