Transcriptomic and genomic analysis of human hepatocellular carcinomas and hepatoblastomas - PubMed (original) (raw)
Comparative Study
. 2006 Oct;44(4):1012-24.
doi: 10.1002/hep.21328.
Baoguo Ren, Sergei Keryanov, George C Tseng, Uma N M Rao, Satdarshan P Monga, Steven Strom, Anthony J Demetris, Michael Nalesnik, Yan P Yu, Sarangarajan Ranganathan, George K Michalopoulos
Affiliations
- PMID: 17006932
- PMCID: PMC1769554
- DOI: 10.1002/hep.21328
Comparative Study
Transcriptomic and genomic analysis of human hepatocellular carcinomas and hepatoblastomas
Jian-Hua Luo et al. Hepatology. 2006 Oct.
Abstract
This study analyzed gene expression patterns and global genomic alterations in hepatocellular carcinomas (HCC), hepatoblastomas (HPBL), tissue adjacent to HCC and normal liver tissue derived from normal livers and hepatic resections. We found that HCC and adjacent non-neoplastic cirrhotic tissue have considerable overlap in gene expression patterns compared to normal liver. Several genes including Glypican 3, spondin-2, PEG10, EDIL3 and Osteopontin are over-expressed in HCC vs. adjacent tissue whereas Ficolin 3 is the most consistently under-expressed gene. HCC can be subdivided into three clusters based on gene expression patterns. HCC and HPBL have clearly different patterns of gene expression, with genes IGF2, Fibronectin, DLK1, TGFb1, MALAT1 and MIG6 being over-expressed in HPBL versus HCC. In addition, specific areas of the genome appear unstable in HCC, with the same regions undergoing either deletion or increased gene dosage in all HCC. In conclusion, a set of specific genes and areas of genomic instability are found across the board in liver neoplasia.
Conflict of interest statement
Potential conflict of interest: Nothing to report.
Figures
Fig. 1
Classification model for NL, AT and HCC using gene expression profiles. For each tissue sample on x-axis, two cross-validated posterior probabilities (on y-axis) of the sample being predicted to either the red or the green group are shown. The sample is then predicted to the group with higher posterior probability (0.5). The presented classification models for each comparison are chosen with the minimum number of genes that allow good posterior probability separation. The title of each plot shows the minimal number of genes needed in each classification model that allows separation. (A) NL vs. HCC. (B) NL vs. AT. (C) AT vs. HCC. The data show that at least 222 genes are required for separation of AT vs. HCC, whereas 41 genes and 73 genes respectively are sufficient for HCC vs. normal liver and AT vs. normal liver.
Fig. 2
Sub classification of HCC using gene expression clustering. (A) Heat map of hierarchical clustering of all tissue samples (NL, AT, HCC and HPBL) using 419 tight clustered genes, derived by algorithms specified in Materials and Methods. The color bar under the hierarchical tree shows the sample information. Dark blue: normal samples. Light blue: tumor samples. White: tumor-adjacent samples. (B) Heat map of hierarchical clustering of all tumor samples (HCC and HPBL) using 419 tight clustered genes. Hepatoblastomas cluster between the two yellow lines. (C) Heat map of hierarchical clustering of tumor samples excluding 7 HPBL samples from children. HCC cases positive for hepatitis C are marked with red arrowheads. The two cases of fibrolamellar carcinomas are marked with black arrowheads.
Fig. 2
Sub classification of HCC using gene expression clustering. (A) Heat map of hierarchical clustering of all tissue samples (NL, AT, HCC and HPBL) using 419 tight clustered genes, derived by algorithms specified in Materials and Methods. The color bar under the hierarchical tree shows the sample information. Dark blue: normal samples. Light blue: tumor samples. White: tumor-adjacent samples. (B) Heat map of hierarchical clustering of all tumor samples (HCC and HPBL) using 419 tight clustered genes. Hepatoblastomas cluster between the two yellow lines. (C) Heat map of hierarchical clustering of tumor samples excluding 7 HPBL samples from children. HCC cases positive for hepatitis C are marked with red arrowheads. The two cases of fibrolamellar carcinomas are marked with black arrowheads.
Fig. 3
Genomic alterations in HCC. (A) Heat map display of genome copy alteration in HCC samples belonging to Clusters A and B. Red color indicates increase in gene copies, black indicates decreased copies and white indicates no changes. The chromosome areas with marked changes are shown on the right y-axis. Several loci show alterations in copy numbers (increase or decrease) across all HCC. (B) Genome-wide concordance analysis of gene copy number and expression changes. Average copy number of each locus in HCC genome (blue) was plotted along with the physical location in each of the chromosomes. The corresponding average mRNA levels of each of the genes in the HCC samples (red) were expressed as a ratio with that of NL samples and plotted as log2 in the graph. GPC3 and TIEG are indicated by arrows. (C) Concordance analysis GPC3 gene copy and mRNA expression in the 27 HCC samples. Red: Ratio of gene expression for HCC/NL; Blue: Gene copy number.
Fig. 3
Genomic alterations in HCC. (A) Heat map display of genome copy alteration in HCC samples belonging to Clusters A and B. Red color indicates increase in gene copies, black indicates decreased copies and white indicates no changes. The chromosome areas with marked changes are shown on the right y-axis. Several loci show alterations in copy numbers (increase or decrease) across all HCC. (B) Genome-wide concordance analysis of gene copy number and expression changes. Average copy number of each locus in HCC genome (blue) was plotted along with the physical location in each of the chromosomes. The corresponding average mRNA levels of each of the genes in the HCC samples (red) were expressed as a ratio with that of NL samples and plotted as log2 in the graph. GPC3 and TIEG are indicated by arrows. (C) Concordance analysis GPC3 gene copy and mRNA expression in the 27 HCC samples. Red: Ratio of gene expression for HCC/NL; Blue: Gene copy number.
Fig. 4
Over-expression of GPC3 and TIEG in hepatocellular carcinoma. (A) Representative photomicrographs of GPC3 and TIEG protein expression in benign (normal liver and tumor adjacent) liver tissues and HCC samples. (B) Expression scores of GPC3 and TIEG in NL, AT, HCC non-relapse and HCC relapsed samples (see Results).
Fig. 5
Comparative patterns of expression of different genes in AT, HCC, NL and HPBL. Genes were chosen from among the most over-expressed genes in different tissues, to illustrate the different patterns of gene expression in the various tissue categories. Each tissue category is separated from the others in the graph. The categories (AT, HCC, NL and HPBL) are aligned and labeled for each column of graphs at the top of the figure. Each bar illustrates the expression intensity (Affymetrix) in one specific tissue sample. Each of the different tissue categories included the following number of tissue samples: AT: 32, HCC: 37, NL: 21, HPBL: 7. Some of the genes (e.g. Glypican 3, PEG10, osteonectin and EDIL3) are over-expressed in both HCC and HPBL, but not in NL and AT. Others (e.g. osteopontin, MALAT1) are elevated in HCC but also, to a lesser degree, in AT. Ficolin 3 is under-expressed specifically in HCC and Serum Amyloid 2 (and, to a lesser extent, Metallothionein 1G) is under-expressed in both HCC and HPBL. DLK1 and IGF2 (and, to a lesser degree, TGF-beta 1) are uniquely over-expressed in HPBL. Graphs were derived from the individual points used for the statistical analysis of samples described in Supplemental Tables 1–8.
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