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Defining the gene expression signature of rhabdomyosarcoma by meta-analysis.

Romualdi C, De Pittà C, Tombolan L, Bortoluzzi S, Sartori F, Rosolen A, Lanfranchi G - BMC Genomics (2006)

Bottom Line: Our results point to a general down regulation of the energy production pathways, suggesting a hypoxic physiology for RMS cells.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: CRIBI Biotechnology Centre and Biology Department, University of Padova, Padova, Italy. chiara.romualdi@unipd.it <chiara.romualdi@unipd.it>

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.

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Related in: MedlinePlus

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.
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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.

Mentions: The results of each individual expression study have been evaluated under the testing assumption of no relationship between expression values of a gene and the distinction between RMS and healthy tissue. P-values resulting from statistical tests have been assigned to all genes and then, after sorting them through p-values, q-values (false discovery rate, FDR) [16] were calculated and associated to genes. The FDR threshold for the identification of differentially expressed genes has been set at 0.01. Figure 1 shows the q-values plots of the six datasets analyzed. The X-axis is the rank index for genes sorted by p-values. Given a specific value of q, the intersection with the curves shows the number of genes identified as differentially under- (panel A) or overexpressed (panel B) in each dataset. Table 2 lists the total numbers and percentages of differentially expressed genes resulting from the meta-analysis of each single RMS dataset. From Fig. 1A,B and Table 2 it is evident that RMS results in a general down regulation of the transcriptome: 4 out of 5 studies (Khan, Baer, Wachtel, Schaaf datasets) detect a higher number of underexpressed genes and the percentage they represent in the different platforms is comparable with the exception of Khan dataset, which results in a higher difference (23% underexpressed and 4% overexpressed). This discrepancy could results from differences in the platforms. As suggested by Rhodes et al. [11] the number of differentially expressed genes at a given q value is highly influenced by the total number of probes and samples as well as by more general issues like the nature of probe sequences, array quality, etc.


Defining the gene expression signature of rhabdomyosarcoma by meta-analysis.

Romualdi C, De Pittà C, Tombolan L, Bortoluzzi S, Sartori F, Rosolen A, Lanfranchi G - BMC Genomics (2006)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1636648&req=5

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.
Mentions: The results of each individual expression study have been evaluated under the testing assumption of no relationship between expression values of a gene and the distinction between RMS and healthy tissue. P-values resulting from statistical tests have been assigned to all genes and then, after sorting them through p-values, q-values (false discovery rate, FDR) [16] were calculated and associated to genes. The FDR threshold for the identification of differentially expressed genes has been set at 0.01. Figure 1 shows the q-values plots of the six datasets analyzed. The X-axis is the rank index for genes sorted by p-values. Given a specific value of q, the intersection with the curves shows the number of genes identified as differentially under- (panel A) or overexpressed (panel B) in each dataset. Table 2 lists the total numbers and percentages of differentially expressed genes resulting from the meta-analysis of each single RMS dataset. From Fig. 1A,B and Table 2 it is evident that RMS results in a general down regulation of the transcriptome: 4 out of 5 studies (Khan, Baer, Wachtel, Schaaf datasets) detect a higher number of underexpressed genes and the percentage they represent in the different platforms is comparable with the exception of Khan dataset, which results in a higher difference (23% underexpressed and 4% overexpressed). This discrepancy could results from differences in the platforms. As suggested by Rhodes et al. [11] the number of differentially expressed genes at a given q value is highly influenced by the total number of probes and samples as well as by more general issues like the nature of probe sequences, array quality, etc.

Bottom Line: Our results point to a general down regulation of the energy production pathways, suggesting a hypoxic physiology for RMS cells.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: CRIBI Biotechnology Centre and Biology Department, University of Padova, Padova, Italy. chiara.romualdi@unipd.it <chiara.romualdi@unipd.it>

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.

Show MeSH
Related in: MedlinePlus