BackgroundCervical 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.ResultsWith 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.ConclusionsOur 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.
BackgroundOvarian cancer is the most lethal gynecologic disease due to delayed diagnosis, and ascites production is a characteristic of patients in advanced stages. The aim of this study was to perform the proteomic analysis of ascitic fluids of Mexican patients with ovarian carcinoma, in order to detect proteins with a differential expression pattern in the continuing search to identify biomarkers for this disease.MethodsSamples were collected from 50 patients from the Instituto Nacional de Cancerología of México under informed consent and with approval of the bioethics and scientific committees. After elimination of abundant proteins (Albumin/IgGs) samples were processed for 2D electrophoresis and further protein identification by Mass Spectrometry (MALDI-TOF). Molecules of interest were followed by western blot and lectin binding assays, and their tissue location by histo-immunofluorescence and confocal analysis.Results and discussionAn area with a differential expression pattern among samples was located in the 2D gels. Identified proteins were 6 alpha 1 isoforms and 1 alpha 2 isoform of Haptoglobin, and 2 isoforms of Transthyretin. While Transthyretin isoforms were constitutively expressed in all samples, clear differences in the expression pattern of Haptoglobin alpha isoforms were found. Moreover, increased levels of fucosylation of Haptoglobin alpha isoforms analyzed in 40 samples by Aleuria aurantia lectin binding by 1D overlay assay showed a positive correlation with advanced stages of the disease. Tissue detection of Haptoglobin and its fucosylated form, by histo-immunofluorescence in biopsies of ovarian cancer, also showed a correlation with ovarian cancer progression. Moreover, results show that fucosylated Haptoglobin is produced by tumor cells.ConclusionsIncreased numbers of highly fucosylated Haptoglobin alpha isoforms in ascitic fluids and the presence of fucosylated Haptoglobin in tumor tissues of ovarian cancer Mexican patients associated with advanced stages of the disease, reinforce the potential of fucosylated Haptoglobin alpha isoforms to be characterized as biomarkers for disease progression.
Type 2 diabetes mellitus is characterized by hyperglycemia and insulin-resistance. Diabetes results from pancreatic inability to secrete the insulin needed to overcome this resistance. We analyzed the protein profile from the pancreas of ten-week old diabetic db/db and wild type mice through proteomics. Pancreatic proteins were separated in two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and significant changes in db/db mice respect to wild type mice were observed in 27 proteins. Twenty five proteins were identified by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) and their interactions were analyzed using search tool for the retrieval of interacting genes/proteins (STRING) and database for annotation, visualization and integrated discovery (DAVID). Some of these proteins were Pancreatic α-amylase, Cytochrome b5, Lithostathine-1, Lithostathine-2, Chymotrypsinogen B, Peroxiredoxin-4, Aspartyl aminopeptidase, Endoplasmin, and others, which are involved in the metabolism of carbohydrates and proteins, as well as in oxidative stress, and inflammation. Remarkably, these are mostly endoplasmic reticulum proteins related to peptidase activity, i.e., they are involved in proteolysis, glucose catabolism and in the tumor necrosis factor-mediated signaling pathway. These results suggest mechanisms for insulin resistance, and the chronic inflammatory state observed in diabetes.
It has recently begun to be considered that cancer is a systemic disease and that it must be studied at every level of complexity using many of the currently available approaches, including high-throughput technologies and bioinformatics. To achieve such understanding in cervical cancer, we collected information on gene, protein and phosphoprotein expression of the HeLa cell line and performed a comprehensive analysis of the different signaling pathways, transcription networks and metabolic events in which they participate. A total expression analysis by RNA-Seq of the HeLa cell line showed that 19,974 genes were transcribed. Of these, 3,360 were over-expressed, and 2,129 under-expressed when compared to the NHEK cell line. A protein-protein interaction network was derived from the over-expressed genes and used to identify central elements and, together with the analysis of over-represented transcription factor motifs, to predict active signaling and regulatory pathways. This was further validated by Metal-Oxide Affinity Chromatography (MOAC) and Tandem Mass Spectrometry (MS/MS) assays which retrieved phosphorylated proteins. The 14-3-3 family members emerge as important regulators in carcinogenesis and as possible clinical targets. We observed that the different over- and under-regulated pathways in cervical cancer could be interrelated through elements that participate in crosstalks, therefore belong to what we term “meta-pathways”. Additionally, we highlighted the relations of each one of the differentially represented pathways to one or more of the ten hallmarks of cancer. These features could be maintained in many other types of cancer, regardless of mutations or genomic rearrangements, and favor their robustness, adaptations and the evasion of tissue control. Probably, this could explain why cancer cells are not eliminated by selective pressure and why therapy trials directed against molecular targets are not as effective as expected.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.