Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Apr 13;17(4):348-61.
doi: 10.1016/j.ccr.2010.01.022.

A transcriptional signature and common gene networks link cancer with lipid metabolism and diverse human diseases

Heather A Hirsch  1 , Dimitrios Iliopoulos, Amita Joshi, Yong Zhang, Savina A Jaeger, Martha Bulyk, Philip N Tsichlis, X Shirley Liu, Kevin Struhl
Affiliations

A transcriptional signature and common gene networks link cancer with lipid metabolism and diverse human diseases

Heather A Hirsch et al. Cancer Cell. .

Abstract

Transcriptional profiling of two isogenic models of transformation identifies a gene signature linking cancer with inflammatory and metabolic diseases. In accord with this common transcriptional program, many drugs used for treatment of diabetes and cardiovascular diseases inhibit transformation and tumor growth. Unexpectedly, lipid metabolism genes are important for transformation and are upregulated in cancer tissues. As in atherosclerosis, oxidized LDL and its receptor OLR1 activate the inflammatory pathway through NF-kappaB, leading to transformation. OLR1 is important for maintaining the transformed state in developmentally diverse cancer cell lines and for tumor growth, suggesting a molecular connection between cancer and atherosclerosis. We suggest that the interplay between this common transcriptional program and cell-type-specific factors gives rise to phenotypically disparate human diseases.

PubMed Disclaimer

Figures

Figure 1
Figure 1. A 343-gene signature of cellular transformation
(A) Phase contrast images (scale bars: 10µm) of morphology of non-transformed and transformed cells. MCF10A cells are transformed after Src induction by tamoxifen (TAM) treatment. BJ fibroblasts have stable integration of one (hTERT) or two (hTERT, SV40E) or three (hTERT, SV40E, HRAS-V12) genetic elements. (B) Differentially expressed genes during MCF10A cell transformation at the indicated time points after TAM treatment using different (0.5, 1, 2 fold) log cut-offs. (C) Differentially expressed genes in BJ fibroblasts were analyzed as two way comparisons in all combinations for the three cell types using different (0.5, 1, 2, 4-fold) log cut-offs. (D) The 343-gene signature of cellular transformation defined by the overlap of differentially expressed genes in the two isogenic models. (E) Transcription factor families whose DNA-binding motifs are enriched in the 2 kb or 10 kb regions flanking the transcriptional start site of up- or down-regulated genes within the indicated biofunctions and diseases. (F) Relationship between the 343 gene set and the indicated diseases. The overlap counts and p-values are indicated.
Figure 2
Figure 2. Linkage of the cancer gene signature to inflammatory and metabolic diseases
(A) Heat-map representation of the 54 common genes between cancer and sub-categories of metabolic syndrome gene set. (B) Common central nodes between gene networks derived from differentially expressed genes from MCF10A, BJ fibroblast and Metabolic Syndrome gene sets. Each sphere represents a central node of a gene network. The 24 common nodes are listed. (C) Comparison of genes and central nodes between the cancer gene signature and gene sets of 32 diseases derived from literature (number of references indicated). For each disease, the number of genes and nodes are indicated along with the overlap with the cancer gene signature and the p-value for the significance of the overlap. (D) Heat map representation of the relationship between common nodes of cellular transformation and the indicated diseases, with significant relationships indicated in red..
Figure 3
Figure 3. Many drugs used to treat non-cancer diseases block cellular transformation
(A) Percentage of transformed cells (morphology assay) observed by treating TAM-induced MCF10 ER-Src cells with the indicated drugs. (B) Soft agar colony assay of the effect of the indicated drugs on transformation. (C) Tumor growth (mean ± SD) of ER-Src cells after 4 cycles of i.p treatments with the indicated drugs.
Figure 4
Figure 4. Metabolic genes affect the tumorigenicity of transformed cells
(A) Number of colonies in soft agar (mean ± SD) of untreated and TAM-treated MCF10A ER-Src cells 24h post transfection with siRNAs against the indicated genes (NC indicates negative control siRNA). Number of colonies are presented as the mean ± SD of three experiments. (B) Soft agar colony or foci assay in non-transformed (EH) and transformed (ELR) BJ fibroblasts 24h post transfection with siRNAs against the indicated genes (mean ± SD).
Figure 5
Figure 5. OLR1 regulates transformation, cell growth and motility
(A) OLR1 mRNA expression levels (mean ± SD) in untreated and TAM-treated MCF10A ER-Src cells and the EH, EL and ELR BJ fibroblasts. (B) Representative phase-contrast images (scale bars: 25µm) of MCF10A ER-Src cells that were or were not treated with TAM together with two different siRNAs against OLR1. (C) Migration and invasion assays in untreated and TAM-treated MCF10A ER-Src cells in the presence or absence of control or OLR1 siRNAs. For all panels, the data are presented as mean ± SD. (D) Cell growth of MCF10A ER-Src TAM-treated cells (mean ± SD) after treatment with control or OLR1 siRNAs or with an OLR1 antibody and an IgG isotype control relative to untreated cells.
Figure 6
Figure 6. OLR1 regulates cell growth and tumorigenicity of cancer cells
(A) Cell growth of normal (MCF10A, HME1, PWR-1E) and cancer (MCF7, HepG2, HeLa) cells after treatment with control or OLR1 siRNAs. Data are presented as mean ± SD. (B) Soft agar colony assays for the cancer cell lines. The data are presented as mean ± SD.
Figure 7
Figure 7. OLR1 regulates transformation through NF-κB pathway
(A) TNFα levels (mean ± SD) at the indicated time points during transformation. (B) VEGF, HIF1A and CA9 mRNA levels (mean ±SD) assessed in non-treated (NT) and TAM-treated (36h) MCF10A ER-Src cells in the presence or absence of two different siRNAs against OLR1. (C) NF-κB activity (ELISA assay; mean ± SD) in untreated and TAM-treated MCF10A ER-Src cells in the presence of the indicated siRNAs or 10 µM simvastatin. (D) IκBα phosphorylation levels (ELISA assay; mean ± SD) in untreated and TAM-treated MCF10A ER-Src cells in the presence or absence of control or OLR1 siRNAs. (E) VEGF, HIF1A and CA9 mRNA levels (mean ± SD) assessed in Ab-IgG or Ab-TNFα treated ER-Src cells. (F) Representative phase contrast images (scale bars: 10µm) of untreated and TAM-treated MCF10A ER-Src cells in the presence or absence of simvastatin. (G) Representative phase-contrast images (scale bars: 10µm) and (H) number of colonies of MCF10A cells treated with oxidized LDL (oxLDL) in the presence or absence of 5uM NF-κB inhibitor (BAY-117082). For all relevant panels, the data are presented as mean ± SD.
Figure 8
Figure 8. OLR1 is important for tumor growth and overexpressed along with GLRX and SNAP23 in cancer tissues
(A) Tumor volume (mean ± SD) of mice injected at time 0 with transformed MCF10A-ER-Src cells that were untreated, or treated by intraperitoneal injections every 5 days (4 cycles starting at day 15; arrows indicate the day or injections) with 100 nM siRNA against OLR11 or an siRNA control. (B) OLR1 expression levels (mean ± SD) from tumors derived from the above experiment. (C) Expression of OLR1, GLRX, and SNAP23 in breast cancer tissues separated by clinicopathological stage. (D) Expression of OLR1, GLRX, and SNAP23 in prostate cancer tissues separated by clinicopathological stage.
See this image and copyright information in PMC

Comment in

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Badis G, Berger MF, Philippakis AA, Talukder S, Gehrke AR, Jaeger SA, Chan ET, Metzler G, Vedenko A, Chen X, et al. Diversity and complexity in DNA recognition by transcription factors. Science. 2009;324:1720–1723. - PMC - PubMed
    1. Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357:539–545. - PubMed
    1. Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, Weinberg RA. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet. 2008;40:499–507. - PMC - PubMed
    1. Berger MF, Badis G, Gehrke AR, Talukder S, Philippakis AA, Pena-Castillo L, Alleyne TM, Mnaimneh S, Botvinnik OB, Chan ET, et al. Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences. Cell. 2008;133:1266–1276. - PMC - PubMed
    1. Berger MF, Philippakis AA, Qureshi AM, He FS, Estep PW, 3rd, Bulyk ML. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat.Biotechnol. 2006;24:1429–1435. - PMC - PubMed

Publication types

Substances

Associated data