Comparative analysis of the human urinary proteome by 1D SDS-PAGE and chip-HPLC-MS/MS identification of the AACT putative urinary biomarker

Zhang, Yuan; Li, Yanyan; Qiu, Feng; Qiu, Zhijun

doi:10.1016/j.jchromb.2010.10.026

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…Based on the evidence that urinary proteome can be highly informative also in non-urogenital diseases, Zhang et al [ 80 ] have compared the urinary proteomic profiles of normal individuals and NSCLC patients with the purpose of identifying biomarkers for NSCLC diagnosis. From among the unique proteins identified by 1DE and nano-LC-MS/MS, clusterin, kallikrein, gelsolin, leucine-rich α-2-glycoprotein and α-1-antichymotrypsin were related to lung cancer.…”

Section: Proteomics Of Lung Cancermentioning

confidence: 99%

Respiratory Proteomics Today: Are Technological Advances for the Identification of Biomarker Signatures Catching up with Their Promise? A Critical Review of the Literature in the Decade 2004–2013

et al. 2014

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To improve the knowledge on a variety of severe disorders, research has moved from the analysis of individual proteins to the investigation of all proteins expressed by a tissue/organism. This global proteomic approach could prove very useful: (i) for investigating the biochemical pathways involved in disease; (ii) for generating hypotheses; or (iii) as a tool for the identification of proteins differentially expressed in response to the disease state. Proteomics has not been used yet in the field of respiratory research as extensively as in other fields, only a few reproducible and clinically applicable molecular markers, which can assist in diagnosis, having been currently identified. The continuous advances in both instrumentation and methodology, which enable sensitive and quantitative proteomic analyses in much smaller amounts of biological material than before, will hopefully promote the identification of new candidate biomarkers in this area. The aim of this report is to critically review the application over the decade 2004–2013 of very sophisticated technologies to the study of respiratory disorders. The observed changes in protein expression profiles from tissues/fluids of patients affected by pulmonary disorders opens the route for the identification of novel pathological mediators of these disorders.

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Section: Proteomics Of Lung Cancermentioning

confidence: 99%

Respiratory Proteomics Today: Are Technological Advances for the Identification of Biomarker Signatures Catching up with Their Promise? A Critical Review of the Literature in the Decade 2004–2013

et al. 2014

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“…It is crucial to use an effective protocol to eliminate interferences and to concentrate urinary proteins [89][90][91][92]. There are several protocols that can be employed to concentrate urinary proteins, such as centrifugal filtration, lyophilization, precipitation, and ultracentrifugation [89][90][91][92][93][94][95][96]. With current advanced proteomic techniques, these initial studies of urinary proteome may provide clinically relevant information regarding lung cancer diagnosis.…”

Section: Urinementioning

confidence: 99%

Glycoproteomics using fluid‐based specimens in the discovery of lung cancer protein biomarkers: Promise and challenge

Gabrielson

Askin

et al. 2013

Proteomics Clinical Apps

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Lung cancer is the number one cancer in the US and worldwide. In spite of the rapid progression in personalized treatments, the overall survival rate of lung cancer patients is still suboptimal. Over the past decade, tremendous efforts have been focused on the discovery of protein biomarkers to facilitate the early detection and monitoring lung cancer progression during treatment. In addition to tumor tissues and cancer cell lines, a variety of biological material has been studied. Particularly in recent years, studies using fluid-based specimen or so-called “fluid-biopsy” specimen have progressed rapidly. Fluid specimens are relatively easier to collect than tumor tissue, and they can be repeatedly sampled during the disease progression. Glycoproteins have long been recognized to play fundamental roles in many physiological and pathological processes. In this review, we focus the discussion on recent advances of glycoproteomics, particularly in the identification of potential protein biomarkers using so-called fluid-based specimens in lung cancer. The purpose of this review is to summarize current strategies, achievements and perspectives in the field. This insight will highlight the discovery of tumor-associated glycoprotein biomarkers in lung cancer and their potential clinical applications.

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“…Urine is a non-invasively collected biospecimen that has been relatively under-represented as a source of potential molecular biomarkers of lung cancer. Discovery level studies have identified differences in metal elements, [ 1 ] specific proteins, [ 2 ] proteomic signatures, [ 3 ] ratios of fluorescent peaks, [ 4 ] non-volatile metabolites, [ 5 ] exosomal proteins, [ 6 ] and tobacco metabolites, [ 7 ] in the urine of people with lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer

et al. 2015

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BackgroundThe mixture of volatile organic compounds in the headspace gas of urine may be able to distinguish lung cancer patients from relevant control populations.MethodsSubjects with biopsy confirmed untreated lung cancer, and others at risk for developing lung cancer, provided a urine sample. A colorimetric sensor array was exposed to the headspace gas of neat and pre-treated urine samples. Random forest models were trained from the sensor output of 70 % of the study subjects and were tested against the remaining 30 %. Models were developed to separate cancer and cancer subgroups from control, and to characterize the cancer. An additional model was developed on the largest clinical subgroup.Results90 subjects with lung cancer and 55 control subjects participated. The accuracies, reported as C-statistics, for models of cancer or cancer subgroups vs. control ranged from 0.795 – 0.917. A model of lung cancer vs. control built using only subjects from the largest available clinical subgroup (30 subjects) had a C-statistic of 0.970. Models developed and tested to characterize cancer histology, and to compare early to late stage cancer, had C-statistics of 0.849 and 0.922 respectively.ConclusionsThe colorimetric sensor array signature of volatile organic compounds in the urine headspace may be capable of distinguishing lung cancer patients from clinically relevant controls. The incorporation of clinical phenotypes into the development of this biomarker may optimize its accuracy.

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Comparative analysis of the human urinary proteome by 1D SDS-PAGE and chip-HPLC-MS/MS identification of the AACT putative urinary biomarker

Cited by 19 publications

References 23 publications

Respiratory Proteomics Today: Are Technological Advances for the Identification of Biomarker Signatures Catching up with Their Promise? A Critical Review of the Literature in the Decade 2004–2013

Respiratory Proteomics Today: Are Technological Advances for the Identification of Biomarker Signatures Catching up with Their Promise? A Critical Review of the Literature in the Decade 2004–2013

Glycoproteomics using fluid‐based specimens in the discovery of lung cancer protein biomarkers: Promise and challenge

Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer

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