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Integrated glycoproteomics demonstrates fucosylated serum paraoxonase 1 alterations in small cell lung cancer.

ABSTRACT: Small cell lung cancer (SCLC) is an aggressive type of lung cancer, and the detection of SCLCs at an early stage is necessary for successful therapy and for improving cancer survival rates. Fucosylation is one of the most common glycosylation-based modifications. Increased levels of fucosylation have been reported in a number of pathological conditions, including cancers. In this study, we aimed to identify and validate the aberrant and selective fucosylated glycoproteins in the sera of patients with SCLC. Fucosylated glycoproteins were enriched by the Aleuria aurantia lectin column after serum albumin and IgG depletion. In a narrowed down and comparative data analysis of both label-free proteomics and isobaric peptide-tagging chemistry iTRAQ approaches, the fucosylated glycoproteins were identified as up- or down-regulated in the sera of limited disease and extensive disease stage patients with SCLC. Verification was performed by multiple reaction monitoring-mass spectrometry to select reliable markers. Four fucosylated proteins, APCS, C9, SERPINA4, and PON1, were selected and subsequently validated by hybrid A. aurantia lectin ELISA (HLE) and Western blotting. Compared with Western blotting, the HLE analysis of these four proteins produced more optimal diagnostic values for SCLC. The PON1 protein levels were significantly reduced in the sera of patients with SCLC, whereas the fucosylation levels of PON1 were significantly increased. Fucosylated PON1 exhibited an area under curve of 0.91 for the extensive disease stage by HLE, whereas the PON1 protein levels produced an area under curve of 0.82 by Western blot. The glycan structural analysis of PON1 by MS/MS identified a biantennary fucosylated glycan modification consisting of a core + 2HexNAc + 1Fuc at increased levels in the sera of patients with SCLC. In addition, the PON1 levels were decreased in the sera of the Lewis lung carcinoma lung cancer mouse model that we examined. Our data suggest that fucosylated protein biomarkers, such as PON1, and their fucosylation levels and patterns can serve as diagnostic and prognostic serological markers for SCLC.

SUBMITTER: Ahn JM

PROVIDER: S-EPMC3879622 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Integrated glycoproteomics demonstrates fucosylated serum paraoxonase 1 alterations in small cell lung cancer.

Ahn Jung-Mo JM Sung Hye-Jin HJ Yoon Yeon-Hee YH Kim Byung-Gyu BG Yang Won Suk WS Lee Cheolju C Park Hae-Min HM Kim Bum-Jin BJ Kim Byung-Gee BG Lee Soo-Youn SY An Hyun-Joo HJ Cho Je-Yoel JY

Molecular & cellular proteomics : MCP 20131001 1

Small cell lung cancer (SCLC) is an aggressive type of lung cancer, and the detection of SCLCs at an early stage is necessary for successful therapy and for improving cancer survival rates. Fucosylation is one of the most common glycosylation-based modifications. Increased levels of fucosylation have been reported in a number of pathological conditions, including cancers. In this study, we aimed to identify and validate the aberrant and selective fucosylated glycoproteins in the sera of patients ...[more]

PMID: 24085812

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Project description:BackgroundNon-small cell lung cancer (NSCLC), a leading cause of cancer deaths, represents a heterogeneous group of neoplasms, mostly comprising squamous cell carcinoma (SCC), adenocarcinoma (AC) and large-cell carcinoma (LCC). The objectives of this study were to utilize integrated genomic data including copy-number alteration, mRNA, microRNA expression and candidate-gene full sequencing data to characterize the molecular distinctions between AC and SCC.MethodsComparative genomic hybridization followed by mutational analysis, gene expression and miRNA microarray profiling were performed on 123 paired tumor and non-tumor tissue samples from patients with NSCLC.ResultsAt DNA, mRNA and miRNA levels we could identify molecular markers that discriminated significantly between the various histopathological entities of NSCLC. We identified 34 genomic clusters using aCGH data; several genes exhibited a different profile of aberrations between AC and SCC, including PIK3CA, SOX2, THPO, TP63, PDGFB genes. Gene expression profiling analysis identified SPP1, CTHRC1 and GREM1 as potential biomarkers for early diagnosis of the cancer, and SPINK1 and BMP7 to distinguish between AC and SCC in small biopsies or in blood samples. Using integrated genomics approach we found in recurrently altered regions a list of three potential driver genes, MRPS22, NDRG1 and RNF7, which were consistently over-expressed in amplified regions, had wide-spread correlation with an average of ~800 genes throughout the genome and highly associated with histological types. Using a network enrichment analysis, the targets of these potential drivers were seen to be involved in DNA replication, cell cycle, mismatch repair, p53 signalling pathway and other lung cancer related signalling pathways, and many immunological pathways. Furthermore, we also identified one potential driver miRNA hsa-miR-944.ConclusionsIntegrated molecular characterization of AC and SCC helped identify clinically relevant markers and potential drivers, which are recurrent and stable changes at DNA level that have functional implications at RNA level and have strong association with histological subtypes.

Project description:BackgroundNon-small cell lung carcinoma (NSCLC) remains the leading cause of cancer deaths in the United States. More than half of NSCLC patients have clinical presentations with locally advanced or metastatic disease at the time of diagnosis. The large-scale genomic analysis of NSCLC has demonstrated that molecular alterations are substantially different between adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). However, a comprehensive analysis of proteins and glycoproteins in different subtypes of NSCLC using advanced proteomic approaches has not yet been conducted.MethodsWe applied mass spectrometry (MS) technology featuring proteomics and glycoproteomics to analyze six primary lung SqCCs and eleven ADCs, and we compared the expression level of proteins and glycoproteins in tumors using quantitative proteomics. Glycoproteins were analyzed by enrichment using a chemoenzymatic method, solid-phase extraction of glycopeptides, and quantified by iTRAQ-LC-MS/MS. Protein quantitation was further annotated via Ingenuity Pathway Analysis.ResultsOver 6000 global proteins and 480 glycoproteins were quantitatively identified in both SqCC and ADC. ADC proteins (8337) consisted of enzymes (22.11%), kinases (5.11%), transcription factors (6.85%), transporters (6.79%), and peptidases (3.30%). SqCC proteins (6967) had a very similar distribution. The identified glycoproteins, in order of relative abundance, included membrane (42%) and extracellular matrix (>33%) glycoproteins. Oncogene-coded proteins (82) increased 1.5-fold among 1047 oncogenes identified in ADC, while 124 proteins from SqCC were up-regulated in tumor tissues among a total of 827 proteins. We identified 680 and 563 tumor suppressor genes from ADC and SqCC, respectively.ConclusionOur systematic analysis of proteins and glycoproteins demonstrates changes of protein and glycoprotein relative abundance in SqCC (TP53, U2AF1, and RXR) and in ADC (SMARCA4, NOTCH1, PTEN, and MST1). Among them, eleven glycoproteins were upregulated in both ADC and SqCC. Two glycoproteins (ELANE and IGFBP3) were only increased in SqCC, and six glycoproteins (ACAN, LAMC2, THBS1, LTBP1, PSAP and COL1A2) were increased in ADC. Ingenuity Pathway Analysis (IPA) showed that several crucial pathways were activated in SqCC and ADC tumor tissues.

Project description:BackgroundSmall cell lung cancer (SCLC) is a malignant disease with poor prognosis. At the time of diagnosis most patients are already in a metastatic stage. Current diagnosis is based on imaging, histopathology, and immunohistochemistry, but no blood-based biomarkers have yet proven to be clinically successful for diagnosis and screening. The precise mechanisms of SCLC are not fully understood, however, several genetic mutations, protein and metabolic aberrations have been described. We aim at identifying metabolite alterations related to SCLC and to expand our knowledge relating to this aggressive cancer.MethodsA total of 30 serum samples of patients with SCLC, collected at the time of diagnosis, and 25 samples of healthy controls were included in this study. The samples were analyzed with nuclear magnetic resonance spectroscopy. Multivariate, univariate and pathways analyses were performed.ResultsSeveral metabolites were identified to be altered in the pre-treatment serum samples of small-cell lung cancer patients compared to healthy individuals. Metabolites involved in tricarboxylic acid cycle (succinate: fold change (FC) = 2.4, p = 0.068), lipid metabolism (LDL triglyceride: FC = 1.3, p = 0.001; LDL-1 triglyceride: FC = 1.3, p = 0.012; LDL-2 triglyceride: FC = 1.4, p = 0.009; LDL-6 triglyceride: FC = 1.5, p < 0.001; LDL-4 cholesterol: FC = 0.5, p = 0.007; HDL-3 free cholesterol: FC = 0.7, p = 0.002; HDL-4 cholesterol FC = 0.8, p < 0.001; HDL-4 apolipoprotein-A1: FC = 0.8, p = 0.005; HDL-4 apolipoprotein-A2: FC ≥ 0.7, p ≤ 0.001), amino acids (glutamic acid: FC = 1.7, p < 0.001; glutamine: FC = 0.9, p = 0.007, leucine: FC = 0.8, p < 0.001; isoleucine: FC = 0.8, p = 0.016; valine: FC = 0.9, p = 0.032; lysine: FC = 0.8, p = 0.004; methionine: FC = 0.8, p < 0.001; tyrosine: FC = 0.7, p = 0.002; creatine: FC = 0.9, p = 0.030), and ketone body metabolism (3-hydroxybutyric acid FC = 2.5, p < 0.001; acetone FC = 1.6, p < 0.001), among other, were found deranged in SCLC.ConclusionsThis study provides novel insight into the metabolic disturbances in pre-treatment SCLC patients, expanding our molecular understanding of this malignant disease.

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Integrated glycoproteomics demonstrates fucosylated serum paraoxonase 1 alterations in small cell lung cancer.

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Integrated glycoproteomics demonstrates fucosylated serum paraoxonase 1 alterations in small cell lung cancer.

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