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Cancers as Wounds that Do Not Heal: Differences and Similarities between Renal Regeneration/Repair and Renal Cell Carcinoma
Riss J, Khanna C, Koo S, Chandramouli GV, Yang HH, Hu Y, Kleiner DE, Rosenwald A, Schaefer CF, Ben-Sasson SA, Yang L, Powell J, Kane DW, Star RA, Aprelikova O, Bauer K, Vasselli JR, Maranchie JK, Kohn KW, Buetow KH, Linehan WM, Weinstein JN, Lee MP, Klausner RD, Barrett JC.
Cancer Res. 2006 Jul 15;66(14):7216-24.
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Abstract: Cancers have been described as wounds that do not heal, suggesting that the two share common features. By comparing microarray data from a model of renal regeneration and repair (RRR) with reported gene expression in renal cell carcinoma (RCC), we asked whether those two processes do, in fact, share molecular features and regulatory mechanisms. The majority (77%) of the genes expressed in RRR and RCC were concordantly regulated, whereas only 23% were discordant (i.e., changed in opposite directions). The orchestrated processes of regeneration, involving cell proliferation and immune response, were reflected in the concordant genes. The discordant gene signature revealed processes (e.g., morphogenesis and glycolysis) and pathways (e.g., hypoxia-inducible factor and insulin-like growth factor-I) that reflect the intrinsic pathologic nature of RCC. This is the first study that compares gene expression patterns in RCC and RRR. It does so, in particular, with relation to the hypothesis that RCC resembles the wound healing processes seen in RRR. However, careful attention to the genes that are regulated in the discordant direction provides new insights into the critical differences between renal carcinogenesis and wound healing. The observations reported here provide a conceptual framework for further efforts to understand the biology and to develop more effective diagnostic biomarkers and therapeutic strategies for renal tumors and renal ischemia.
Supplemental Data:
- Supplemental Figure 4. Differentially expressed genes were validated by QPCR
The gene expression of IGFBP1, IGFBP 3, CTGF, AKT, FRAP, MYC, NF-kB, HK1, SIRT7, PHD1, was validated by QPCR. The gene expression of PHD2 and PHD3 was quantified as well.
- Supplemental Table 4. The RRR 1,325 genes expression data and specific functional gene-clusters
1,325 unique genes were identified in the current microarray dataset. The gene expression is presented as up or down from normal-ischemic kidneys. Two separate groups of microarray experiments were conducted, and the results were subsequently normalized to eliminate systematic bias. The first group consisted of normal and ischemic tissues, as well as and 1 and 2 days post-injury. The
second group consisted of normal kidneys and 5 and 14 days post-injury. The data from days 1 and 2 were normalized by the mean of the normal-ischemic group, and the data from days 5 and 14 by the mean of the corresponding normal kidney. The genes were further clustered according to RCC vs. normal kidney; renal cell culture hypoxia responsive genes vs. normoxia; HIF regulated genes; VHL, IGF1, MYC, NF-kB pathway genes; purine pathway genes; gene expression following renal ischemia reperfusion and/or acute renal failure (ARF) vs. normal tissue; and gene expression in response to serum (1, 2).
- Supplemental Table 5. An ontology analysis in timely dependent fashion: distinct and common ontologies
Table 5A. The differentially expressed genes were clustered according to their pattern of
expression as early, late or continually RRR. Functional ontology analysis was performed (p<0.05). The presented ontologies are the ontology core and are hyperlinked to EMBL-EBI. The average RRR expression (log2) of each ontology
is presented in a green to red scale; green down-regulated, red up-regulated. The numbers and average RRR expression of up- and down- regulated genes, the category p-value and enrichment are shown as well.
Table 5B. The genes in the three phases of renal regeneration and the concordant and discordant genes are analyzed for GO (summary sheets). These genes were crossed with the data from supplemental Table 4 (cross sheets); green downregulated and red up-regulated in RRR.
- Supplemental Table 6. The differently expressed genes in both RRR and RCC exhibited distinct ontologies for the concordance vs. discordance genes
The differentially expressed genes in both RRR and RCC were clustered according to their concordance vs. discordant change. Functional ontology was analysis performed (p<0.05). The ontologies are hyperlinked to EMBL-EBI. The average RRR expression of each ontology is presented in a green to red scale; green down-regulated, red up-regulated. The number and average RRR
expression of genes up- / down- regulated in both RRR and RCC, the category p-value and enrichment are also given (the expression direction and values are as in RRR, relative to the normal kidney).
- Supplemental Table 7. The significance of gene in the various expression patterns of early, late, continues, pathways and the concordant or discordant groups was analyzed by using the chi square test. See methods for further explanation.
- Supplemental Table 8. The RRR genes in non-probabilistic GO ontologies
The comprehensive probabilistic analysis may fail to capture many key aspects of the concordant and discordant gene functions. Therefore, we also categorized the genes into gene-by-gene, non-probabilistic GO.
- Supplemental Table 9. An ontology analysis of the concordant and discordant genes in pathway dependent fashion: distinct and common ontologies
The concordantly and discordantly differentially expressed genes were clustered according to their regulation by the pathways of VHL, hypoxia, HIF, IGF1, MYC, p53 and NF-kB. Functional ontology was analysis performed (p<0.05).
- Supplemental text
| Contact information: | |
| Dr. Joseph Riss |
| Wound healing and oncogenesis |
| NCI/ NIH Bldg 37/ Rm. 5046 |
| Mail Stop 4264 |
| 37 Convent MSC 4264 |
| Bethesda, MD 20892 |
| Tel No.: 1-301-402-7203 |
| Fax: 1-301-480-2772 |
| E-Mail: rissjo@mail.nih.gov |
| Link to WHO/NCI | |
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