Determination of the protein expression profiles of breast cancer cell lines by quantitative proteomics using iTRAQ labelling and tandem mass spectrometry

Calderón-González, Karla Grisel; Rustarazo, Ma Luz Valero; Labra-Barrios, María Luisa; Bazán-Méndez, César Isaac; Tavera-Tapia, Alejandra; Herrera-Aguirre, María Esther; Pino, Manuel M. Sánchez del; Gallegos-Pérez, José Luis; González‐Márquez, Humberto; Hernández-Hernández, José Manuel; León-Ávila, Gloria; Rodrı́guez-Cuevas, Sergio; Guisa-Hohenstein, Fernando; Luna-Arias, Juan Pedro

doi:10.1016/j.jprot.2015.04.018

Cited by 32 publications

(16 citation statements)

References 83 publications

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“…In addition, we selected the specific overexpressed and subexpressed proteins of the different molecular classes of breast cancer cell lines, and classified them according to protein class and biological process. Data in this article is related to the research article “Determination of the protein expression profiles of breast cancer cell lines by Quantitative Proteomics using iTRAQ Labelling and Tandem Mass Spectrometry” (Calderón-González et al [1] in press).…”

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confidence: 99%

Data set of the protein expression profiles of Luminal A, Claudin-low and overexpressing HER2 + breast cancer cell lines by iTRAQ labelling and tandem mass spectrometry

et al. 2015

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Breast cancer is the most common and the leading cause of mortality in women worldwide. There is a dire necessity of the identification of novel molecules useful in diagnosis and prognosis. In this work we determined the differentially expression profiles of four breast cancer cell lines compared to a control cell line. We identified 1020 polypeptides labelled with iTRAQ with more than 95% in confidence. We analysed the common proteins in all breast cancer cell lines through IPA software (IPA core and Biomarkers). In addition, we selected the specific overexpressed and subexpressed proteins of the different molecular classes of breast cancer cell lines, and classified them according to protein class and biological process. Data in this article is related to the research article “Determination of the protein expression profiles of breast cancer cell lines by Quantitative Proteomics using iTRAQ Labelling and Tandem Mass Spectrometry” (Calderón-González et al. [1] in press).

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confidence: 99%

Data set of the protein expression profiles of Luminal A, Claudin-low and overexpressing HER2 + breast cancer cell lines by iTRAQ labelling and tandem mass spectrometry

et al. 2015

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“…Subsequently, the dried peptides were labeled following the manufacturer's recommendations of the isobaric tags for relative and absolute quantitation (iTRAQ) 4-plex kits (SCIEX, Framingham, MA, USA) with iTRAQ tags, as follows: iTRAQ 114 for CON, iTRAQ 115 for SCH, iTRAQ 116 for CON replicate and iTRAQ 117 for SCH replicate. The eluted peptides were analyzed by LC-MS/MS, according to a previously described protocol (13).…”

Section: Protein Preparation and Liquid Chromatography-tandem Mass Spmentioning

confidence: 99%

Integrated microRNA and proteome analysis reveal a regulatory module in hepatic lipid metabolism disorders in mice with subclinical hypothyroidism

et al. 2019

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Subclinical hypothyroidism (SCH) is becoming a global health problem due to its increasing prevalence and potential adverse effects, including cardiovascular diseases and nonalcoholic fatty liver disease (NAFLD). However, the association between SCH and NAFLD remains controversial. MicroRNAs (miRNAs/miRs) have been reported to be implicated in lipid metabolism disorders; however, how miRNAs regulate hepatic lipid metabolism in SCH mice remains unknown. The present study investigated miRNA alterations and proteome profiles in an SCH mouse model, which was generated by methimazole administration in mice for 16 weeks. Next, the profiles of 17 miRNAs that are critical to hepatic lipid metabolism and the proteome were investigated using reverse transcription-quantitative polymerase chain reaction and iTRAQ labeling in the liver specimens of SCH (n=9) and control (n=7) mice. Putative target prediction of miRNAs was also conducted using TargetScan and miRanda. Compared with the control mice, SCH mice had 8 miRNAs and 36 proteins with significantly different expression in the liver tissues. Furthermore, a regulatory module containing 3 miRNAs (miR-34a-5p, miR-24-3p and miR-130a-3p) and 4 proteins (thioredoxin, selenium-binding protein 2, elongation factor 1β and prosaposin) was identified. Overall, integrated analysis of miRNAs and the proteome highlighted a regulatory module between miRNAs and proteins, which, to a certain extent, may contribute to a better understanding of hepatic lipid metabolism disorders in SCH mice.

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“…Fibrinogen A fragment [van den Broek et al, 2010]; BAG6, DDX39, ANXA8, COX4 [Calderon-Gonzalez et al, 2015]; GCDFP-15 [Darb-Esfahani et al, 2014]; Versican [Du et al, 2013]; AGR2 [Li et al, 2015c]; ubiquitin ligase [Goka and Lippman, 2015]; ferritin light chain [Jezequel et al, 2012] Gastric cancer a1-antitrypsin precursor [Hsu et al, 2007]; pepsinogen C [Terasawa et al, 2014]; Cathepsin B [Ebert et al, 2005]; galectin-1/-3 [Thijssen et al, 2015]; miR-214 [Zhang et al, 2015b]; angiopoietinlike protein 2 [Yoshinaga et al, 2015]; MOR1 [Yao et al, 2015]; circular RNA [Li et al, 2015a]…”

Section: Breast Cancermentioning

confidence: 99%

Translational Research and Plasma Proteomic in Cancer

Santini

Giovane

Auletta

et al. 2015

J of Cellular Biochemistry

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Proteomics is a recent field of research in molecular biology that can help in the fight against cancer through the search for biomarkers that can detect this disease in the early stages of its development. Proteomic is a speedily growing technology, also thanks to the development of even more sensitive and fast mass spectrometry analysis. Although this technique is the most widespread for the discovery of new cancer biomarkers, it still suffers of a poor sensitivity and insufficient reproducibility, essentially due to the tumor heterogeneity. Common technical shortcomings include limitations in the sensitivity of detecting low abundant biomarkers and possible systematic biases in the observed data. Current research attempts are trying to develop high-resolution proteomic instrumentation for high-throughput monitoring of protein changes that occur in cancer. In this review, we describe the basic features of the proteomic tools which have proven to be useful in cancer research, showing their advantages and disadvantages. The application of these proteomic tools could provide early biomarkers detection in various cancer types and could improve the understanding the mechanisms of tumor growth and dissemination.

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Determination of the protein expression profiles of breast cancer cell lines by quantitative proteomics using iTRAQ labelling and tandem mass spectrometry

Cited by 32 publications

References 83 publications

Data set of the protein expression profiles of Luminal A, Claudin-low and overexpressing HER2 + breast cancer cell lines by iTRAQ labelling and tandem mass spectrometry

Data set of the protein expression profiles of Luminal A, Claudin-low and overexpressing HER2 + breast cancer cell lines by iTRAQ labelling and tandem mass spectrometry

Integrated microRNA and proteome analysis reveal a regulatory module in hepatic lipid metabolism disorders in mice with subclinical hypothyroidism

Translational Research and Plasma Proteomic in Cancer

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