Defining the gene expression signature of rhabdomyosarcoma by meta-analysis

Abstract

Background: Rhabdomyosarcoma is a highly malignant soft tissue sarcoma in childhood and arises as a consequence of regulatory disruption of the growth and differentiation pathways of myogenic precursor cells. The pathogenic pathways involved in this tumor are mostly unknown and therefore a better characterization of RMS gene expression profile would represent a considerable advance. The availability of publicly available gene expression datasets have opened up new challenges especially for the integration of data generated by different research groups and different array platforms with the purpose of obtaining new insights on the biological process investigated.

Results: In this work we performed a meta-analysis on four microarray and two SAGE datasets of gene expression data on RMS in order to evaluate the degree of agreement of the biological results obtained by these different studies and to identify common regulatory pathways that could be responsible of tumor growth. Regulatory pathways and biological processes significantly enriched has been investigated and a list of differentially meta-profiles have been identified as possible candidate of aggressiveness of RMS.

Conclusion: Our results point to a general down regulation of the energy production pathways, suggesting a hypoxic physiology for RMS cells. This result agrees with the high malignancy of RMS and with its resistance to most of the therapeutic treatments. In this context, different isoforms of the ANT gene have been consistently identified for the first time as differentially expressed in RMS. This gene is involved in anti-apoptotic processes when cells grow in low oxygen conditions. These new insights in the biological processes responsible of RMS growth and development demonstrate the effective advantage of the use of integrated analysis of gene expression studies.

Figure 1

False discovery rate (FDR) trend in each dataset. On the x-axis genes are ranked according to the p-values obtained by statistical test, while on the y-axis the Q-value (FDR) is reported. Panel A: underexpressed genes; panel B: overexpressed genes.

Figure 2

Integrated metabolic map (from KEGG database) of Kreb cycle and Oxidative phosphorialtion. Boxes in red represent gene products that are differentially underexpressed in at least one dataset. Colored bands inside red boxes correspond to the dataset in which the product gene is differentially expressed (according to the legend).

Figure 3

Frequency distribution of the mean pairwise Spearman correlation coefficient obtained from the comparison (with a jackknives procedure) of the expression profiles of probes belonging to the same Entrez Gene.

Figure 4

Genes differentially expressed in at least five datasets using meta-profiles.