Reconstruction of expression profiles at genome level

We reconstructed tissue expression profiles by a computational approach, starting from unbiased cDNA libraries data. In particular, the expression profile of normal adult skeletal muscle (SM) was reconstructed by using information from 26,964 ESTs, corresponding to 8,517 genes expressed in SM, such as from data obtained by merging four cDNA libraries. Human alveolar rhabdomyosarcoma (ARMS) expression profile was reconstructed from 24,175 ESTs, derived from one library, accounting for 4,549 genes expressed in ARMS. Full data are accessible online [11].

The number of ESTs reported in each UniGene cluster recorded in the expression profile was used to estimate the level of activity of the corresponding gene, as previously described [12], under the assumption that the number of detected ESTs per gene is a function of its transcript frequency in the original population of messenger RNA. Thus, each expression profile is a catalogue of genes expressed in a given tissue. Each gene in the catalogue is identified by UniGene cluster ID and RefSeq accession number and it is associated to the encoded product description and linked to LocusLink and to GenBank databases.

Estimation of density of expressed genes along chromosomes

Genomic map positions, established at one-nucleotide level of resolution, were obtained by querying UCSC BLAT facility with representative DNA sequences of each of 8,517 genes expressed in skeletal muscle and of 5,520 genes expressed in ARMS, according to RefSeq or to UniGene database. Location of each gene in the human genome sequence is given by starting and ending position of the transcribed region, expressed as bp distances from the short arm telomere.

After stringent alignment quality check (see Methods) 98% of genes considered by the study were unambiguously located to their chromosomal positions. In particular, the precise chromosomal location was determined for 8,308 genes expressed in SM and for 5,414 genes expressed in ARMS.

Chromosomal base-pair coordinates of such genes were used to establish "gene density" along each chromosome. Gene density was expressed as a fraction of the genomic sequence covered by gene-associated sequences, in chromosomal regions spanned by a sliding window. Gene density of skeletal muscle genes and of ARMS genes was calculated using 1 Mb-wide windows with 10 Kb overlap, thus obtaining 304,691 different values of gene density along the whole human genome. Window width was set to 1 Mb, in order to scan the genome with a window wider than the average length of most human genes. Taking into account that the average size of human genes is about 14 Kb and the median value is 27 Kb [13], that gene density is highly variable in the human genome, ranging from zero to about 100 genes per Mb (e.g. 6p21.33), and that only a fraction of all human genes is expressed in a given tissue, the selected dimension of one megabase for the window used in the present study should be adequate for measuring gene density in the human genome. The shift between adjacent windows was set to 10 Kb, in order to obtain sufficiently numerous data points, for a precise calculation of gene density along human chromosomes.

Comparison between density of genes expressed in normal skeletal muscle and ARMS

Gene density values, normalized to the total number of expressed genes, were plotted against the base-pair scale length of different human autosomes and of the X chromosome.

Observed gene density values ranged from zero to 1.993 for SM and from zero to 1.197 for ARMS. Average gene density were 0.129 and 0.069 for SM and ARMS, respectively. The difference between normalised ARMS and normal skeletal muscle gene densities was measured. For 27.6% of the genome ARMS gene density was higher than SM. The absolute value of gene density difference was higher than 0.3 for 10,653 windows (7.6%). A total of 2,384 windows showing over 0.6 absolute difference were observed (0.7%), corresponding to 33 chromosomal regions harbouring genes overexpressed in ARMS if compared with SM and to 48 chromosomal segments, which appeared strongly downregulated in ARMS (Table 1 and Figure 2). The 0.6 threshold for the observed difference of gene density has been selected in order to pick up genomic regions in which the difference was either moderate or high, without producing an unmanageable increase of genomic regions included and, in particular, to avoid their fragmentation.

Chr.	Chromosomal regions			Diff. expr. in RMS	Chr.	Chromosomal regions			Diff. expr. in RMS
	Start(Mb)	End(Mb)	Cyt. band			Start(Mb)	End(Mb)	Cyt. band
1	14.09	14.75	p36.21*	+
	50.16	50.79	p32.3	+
	63.86	64.11	p31.3	+	13	59.57	59.69	q21.2	+
	91.99	92.09	p22.1	-		94.91	95.14	q32.1	-
	96.84	97.50	p21.3	-
	173.14	173.59	q25.2	-
2	29.21	30.22	p23.2 – p23.1*	+
	50.05	51.22	p16.3	-
	79.57	81.03	p12	-
	99.66	100.17	q11.2	-	14	65.07	65.58	q23.3	-
	137.73	138.53	q22.1	+		71.51	71.57	q24.2	-
	143.91	144.65	q22.2-q22.3	+
	205.48	206.28	q33.3	-
	212.32	213.26	q34	-
3	2.03	3.16	p26.3-p26.2	+
	29.28	29.78	p24.1	-
	37.17	37.19	p22.3	-
	37.24	37.61	p22.3	-
	53.92	55.06	p21.1-p14.3*	+
	61.51	62.03	p14.2	-	15	22.97	23.04	q11.2	-
	76.79	77.61	p12.3*	+
	101.86	102.32	q12.2-q12.3	-
	116.71	117.55	q13.31	+
	160.38	160.91	q25.33	-
	176.08	176.82	q26.31	-
4	53.52	54.15	q12	+
	87.50	88.06	q21.3	-
	114.54	114.90	q25-q26	-	16	82.50	83.51	q23.3	-
	151.75	152.30	q31.23-q31.3	-		88.74	88.88	q24.3*	+
	162.88	163.45	q32.2	-
	168.14	168.84	q32.3	+
5	19.37	20.08	p14.3	+
	89.80	90.59	q14.3	+	17	44.45	44.72	q21.31	-
	92.87	93.54	q15	+		45.08	45.13	q21.31	-
	168.12	168.58	q34-q35.1	-
6	1.51	2.39	p25.3-p25.2	+
	20.70	21.19	p22.3	-
	37.94	38.43	p21.2	-
	44.67	45.37	21.1*	+	18	7.66	8.29	p11.23	-
	69.40	70.07	q12-q13	-		49.86	50.84	q21.1-q21.2	-
	128.69	129.74	q22.33	-
	152.69	153.01	q25.2	+
7	36.54	37.32	p14.2-p14.1	+
	68.36	69.76	q11.22*	+
	100.90	10.1	q22.1*	+	19	38.48	38.54	q13.11*	+
	109.95	110.64	q31.1	-
	132.56	133.21	q33	-
8	98.97	99.41	q22.2	-	20	8.18	8.71	p12.3	-
9	8.41	8.90	p24.1	-
	109.93	109.99	q32*	+	21	16.32	16.61	q21.1	-
	112.29	113.53	q32-q33.1*	+
	121.63	122.47	q33.3*	+
10	0.50	0.75	p15.3*	+
	52.83	53.84	q21.1	-
	75.66	76.54	q22.2*	+	22	31.87	32.74	q12.3*	+
	78.64	79.18	q22.3	-
	94.41	94.48	q23.33	-
	108.33	109.16	q25.1	+
11	10.20	10.28	p15.4	-
	10.34	10.34	p15.4	-	X	8.74	9.47	p22.31-p22.22*	+
	20.60	21.90	q22.3	+		83.54	84.02	q21.2	-
	83.50	84.83	q14.1	-		94.08	94.79	q21.33	-
12	26.85	26.92	p11.23	-	Y	-	-	-

Local density of genes expressed in skeletal muscle and in RMS was calculated on the basis of 304,691 sliding windows, 1 Mb-wide and with 10 Kb overlap. Chromosomal regions in which the absolute difference between RMS and skeletal muscle gene density was over 0.6 were selected. Thirty-three chromosomal regions harbouring mostly genes expressed in RMS and 48 chromosomal segments, possibly related to function of differentiated skeletal muscle but silent in RMS are reported. Regions including chromosomal segments in which absolute difference between ARMS and skeletal muscle gene density exceeded 1.0 are in bold, whereas asterisks indicate regions in which overexpression of tumour genes has been reported for different tissues by Zhou and colleagues [10].

Complete plots of gene densities along different human chromosomes are available as supplementary material at our website [14]. As an example, details of the human X chromosome are shown in Figure 1.

Identification of genes differentially expressed in normal and tumour tissues may provide a valuable help in understanding tumour development, in discovering diagnostic and prognostic markers and in identifying novel targets for drug therapy. Digital expression profiling of six different solid tumour types has been used for discovering novel cancer genes [15], but the data set included both normalized and subtracted cDNA libraries and Fisher's exact test was inappropriately used. Artificial neural networks have been used to classify different cancer samples or cell lines on the basis of their expression signature [16]. As far as ARMS is concerned, a cDNA microarray study showed consistent patterns of gene expression in different ARMS cell lines [17].

In the present study, by associating transcripts to the corresponding gene sequence on the human genome, we mapped at genomic level genes expressed in SM and in ARMS. Moreover, we calculated density along each human chromosome of such genes, by using a sliding window approach. Because of the relatively low gene density per window, a statistical approach for comparing gene density was considered to be less convenient than a "qualitative", plot-based approach.

In human genome, possible clustering of genes highly or specifically expressed in specific tissues has been suggested by different studies (see for instance [18–20]) which considered both normal and cancer tissues. Fujii and colleagues [21] found that numerous groups of genes expressed in specific tumours, such as squamous cell carcinoma or adenocarcinoma, cluster in given regions of the human genome. On the other hand, recurrent non random imbalances pertaining specific chromosomal regions are frequently reported in tumours. Several experimental or computational methods are currently used for identification of such regions.

We identified specific chromosomal regions hosting more genes expressed in ARMS than expressed in SM. This provides a starting point for identifying novel candidate cancer genes. On the other hand, comparison of chromosomal maps of genes expressed respectively in SM or in ARMS revealed the existence of genome regions possibly involved in muscle differentiation. Further analysis of these regions could help in understanding why ARMS cells fail to differentiate.

In regions harboring more genes expressed in ARMS than in normal muscle a number of novel genes are present, such as genes with uncharacterized products, hypothetical proteins or transcribed sequences. Among known genes, some are particularly interesting, such as genes encoding for transcription factors or for proteins involved in cell cycle control, in signal transduction or in cell adhesion. For some of them, an involvement in tumors has been already reported. Nine genes encoding transcriptional factors and/or regulators of cell cycle lie in chromosomal regions harboring mostly genes expressed in RMS (DMRT-like family A2, 1p32.3; Transcription factor SMIF, 3p21.1-p14.3; Zinc finger protein 288, 3q13.31; Suppressor of Ty 3 homolog, 6p21.1; Zinc finger, HIT domain containing 1, 7q22.1; Tripartite motif-containing 32, 9q32.-q33.1; Pre-B-cell leukemia transcription factor 3, 9q33.3; PPP3CB: Protein phosphatase 3, catalytic subunit, beta isoform, 10q22.2; Adenovirus 5 E1A binding protein, 10p15.3 and NEL-like 1, 11q22.3). Four genes encoding for adhesion molecules, are also located in regions overexpressed in RMS (Contactin 4; Cadherin 18, Monogenic, audiogenic seizure susceptibility 1 homolog and Vinculin, found in 3p26.3-p26.2; 5p14.3, 5q14.3 and 10q22.2, respectively). Moreover, two oncogenes (Anaplastic lymphoma kinase and Nucleophosmin) map in 2p23.2-p23.1, whereas the "upregulated in colorectal cancer gene 1" maps in 7p14.2-p14.1 and the gene for the inhibitor of metalloproteinase 3, whose expression is induced in response to mitogenic stimulation, maps in 22q12.3.

An effort was made to compare our results with CESH data [9], but the two approaches are different in terms of sensitivity. At its highest definition CESH measures hybridization intensities on a 450-bands resolution metaphase, whereas our procedure localizes genes on the human genome sequence, at single nucleotide level.

However, it may be noticed that in the present study we identified with high confidence one chromosomal region (2p23.2-p23.1) containing a segment showing considerable differential gene density between RMS and SM, within a chromosomal region in which overexpression of RMS genes was detected by CESH [9]. In addition, in the long arm of chromosome 6, reportedly underexpressed in all ARMS lines analyzed by CESH, we found four different regions harboring genes expressed in SM but silenced in ARMS.

From a technical point of view, comparison with the work reporting detection of RITEs [10] was more feasible and meaningful even if they did not take in consideration any SM or ARMS library. We compared chromosomal regions harboring genes more expressed in ARMS than in SM with the list of regions showing increased gene expression in tumour of brain, breast, liver, and lung, identified by Zhou and colleagues [10]. We found that many regions showing higher gene densities in ARMS than in SM were included in this list of regions of increased expression in tumours of several tissues (these regions are indicated with asterisks in Table 1). Therefore, some of these regions could be associated to a general neoplastic phenotype, consequent to genomic rearrangements or due to deregulation of expression of adjacent genes.

Chromosomal regions where differential expression of genes was noticed in ARMS in this study, but were never reported previously, appear as candidate regions for harboring genes possibly involved in origin of ARMS or possible targets for chemotherapy.

Several recurring cytogenetical abnormalities have been observed by Comparative Genomic Hybridization (CGH) studies in RMS cell lines. They involved the gain or loss of complete chromosomes or of specific chromosomal regions. In spite of the different nature of these data, we attempted to find possible overlap between the location of chromosomal segments apparently rich or poor in genes expressed in RMS, identified by the present study, with those of chromosomal regions reportedly involved in prominent imbalances observed in rhabdomyosarcoma by several CGH studies. Several correspondences between ESTs based and CGH results were observed, such as overexpression of RMS genes in chromosomal regions reportedly amplified or translocated in RMS, and underexpression of RMS genes in genomic regions deleted in RMS. In particular, overexpression of genes in ARMS, in regions 1p31.3, 3p12.3, 22q12.3 and 7p14.2-14.1, is consistent respectively with 1p31-p21, 3p12 and 22q amplification and gain of 7p reported by Gordon and colleagues [22]. Underexpression of ARMS genes in regions, 3p24.1, 3p22.3, 3p14.2, 5q34-q32.1, 10q23.33 and 13q32.1 is consistent with loss of 3p, 5q32-qter, 10q23 and chromosome 13 [22]. Furthermore, gain of 2q, reported in 40% of 45 different samples of alveolar and embryonal RMS, 7q (31%), 11q (31%) and 16q (27%) [23] is consistent with our finding of overexpression in RMS of regions 2q22.1 and 2q22.2-q22.3, 7q11.22 and 7q22.1, 11q22.3 and 16q23.3. Moreover, Pandita and colleagues [24] reported gain of regions of chromosomes 2, 5, 7, 8, 11 and 12 in the majority of considered RMS, which is consistent with our findings of eleven distinct regions in these chromosomes harbouring genes expressed in ARMS but silent in SM.