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
. 2011 Jun 22:5:96.
doi: 10.1186/1752-0509-5-96.

Proteomic patterns of cervical cancer cell lines, a network perspective

Affiliations

Proteomic patterns of cervical cancer cell lines, a network perspective

Juan Carlos Higareda-Almaraz et al. BMC Syst Biol. .

Abstract

Background: Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis.

Results: With this study, we sought to achieve a systemic perspective of the common proteomic profile of six cervical cancer cell lines, both positive and negative for HPV, and which differ from the profile corresponding to the non-tumourgenic cell line, HaCaT. Our objectives were to identify common cellular events participating in cancer maintenance, as well as the establishment of a pipeline to work with proteomic-derived results. We analyzed by means of 2D SDS-PAGE and MALDI-TOF mass spectrometry the protein extracts of six cervical cancer cell lines, from which we identified a consensus of 66 proteins. We call this group of proteins, the "central core of cervical cancer". Starting from this core set of proteins, we acquired a PPI network that pointed, through topological analysis, to some proteins that may well be playing a central role in the neoplastic process, such as 14-3-3ζ. In silico overrepresentation analysis of transcription factors pointed to the overexpression of c-Myc, Max and E2F1 as key transcription factors involved in orchestrating the neoplastic phenotype.

Conclusions: Our findings show that there is a "central core of cervical cancer" protein expression pattern, and suggest that 14-3-3ζ is key to determine if the cell proliferates or dies. In addition, our bioinformatics analysis suggests that the neoplastic phenotype is governed by a non-canonical regulatory pathway.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Pipeline. We compared the protein profiles of six cervical cancer cell lines by 2D SDS-PAGE, and established a set of proteins common to these cell lines, and which did not feature in our control. This set of proteins was termed "central core of cervical cancer", (a). From the central core, we reconstructed a PPI network (b), this network expanded and used to obtain the overrepresented GO's and pathways. Finally, we conducted an analysis of the transcription factors contained in the extended network using ChIP-Seq analysis of the ENCODE project (c).
Figure 2
Figure 2
Network Reconstruction. Network of differentially expressed proteins in cervical cancer cell-lines compared to a non-cancerous control. The connections between proteins represent experimentally verified protein-protein interactions between 33 out of the 66 proteins identified in this study. The proteins not included in this network do not have experimentally proven interactions. The network was constructed using Cytoscape and the plugin Bisogenet.
Figure 3
Figure 3
Overexpression of 14-3-3ζ. The expression levels of 14-3-3ζ in the six cervical cancer cell lines were assessed by western blot analysis. Whole brain extract was used a positive control, because 14-3-3ζ has a strong expression in this tissue. α-Tubulin was used as a loading control (Data not shown).
Figure 4
Figure 4
Network Extension. Expanded network taking identified proteins as bait. Proteins belonging to our core of differentially expressed proteins were used as bait to fish protein-protein experimental interactions by adding neighbors of input nodes to a distance of up to one protein. This resulted in a network of 1,321 nodes and 9,666 edges. The network was expanded using Cytoscape and the plugin Bisogenet. Pink nodes represent the bait proteins, while the blue nodes correspond to the proteins that were included as a result of the network expansion.
Figure 5
Figure 5
Model of Action of c-Myc, E2F1 and 14-3-3ζ. Model of the downstream events product of the overexpression and/or amplification of c-Myc, and its collaboration with the transcription factor E2F1. c-Myc promotes the expression of proteins that lead to survival and proliferation through processes such as metabolism, protein biosynthesis and transcription factors. Likewise, it enables the expression of proteins involved in epithelial mesenchymal transition. E2F1 and c-Myc work together to promote the expression of Cyclins and E2F factors that boost the transition between G1 and S phases of mitosis, as well as the expression of 14-3-3ζ and Bcl-2. Bcl-2 is an antiapoptotic protein that prevents the release of cytochrome c. The overexpression of 14-3-3ζ results in instability and degradation of p53, increased cell proliferation, and cytoplasmic sequestration of BAD with what brings drastic decrease of apoptosis.

Similar articles

Cited by

References

    1. Kreeger PK, Lauffenburger DA. Cancer systems biology: a network modeling perspective. Carcinogenesis. 2010;31:2–8. doi: 10.1093/carcin/bgp261. - DOI - PMC - PubMed
    1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100:57–70. doi: 10.1016/S0092-8674(00)81683-9. - DOI - PubMed
    1. Hanahan D, Weinberg RA. Hallmarks of cancer: The Next Generation. Cell. 2011;144:646–674. doi: 10.1016/j.cell.2011.02.013. - DOI - PubMed
    1. Muñoz N, Bosch FX, de Sanjosé S, Herrero R, Castellsagué X, Shah KV, Snijders PJ, Meijer CJ. Epidemiologic classification of human papillomavirus types associated with cervical cancer. N Engl J Med. 2003;348:518–527. doi: 10.1056/NEJMoa021641. - DOI - PubMed
    1. von Knebel Doeberitz M, Rittmüller C, zur Hausen H, Dürst M. Inhibition of tumorigenicity of cervical cancer cells in nude mice by HPV E6-E7 anti-sense RNA. Int J Cancer. 1992;51:831–834. doi: 10.1002/ijc.2910510527. - DOI - PubMed

Publication types

Substances