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Onco-proteogenomics identifies urinary S100A9 and GRN as potential combinatorial biomarkers for early diagnosis of hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC), the major type of liver cancer, is among the most lethal cancers owing to its aggressive nature and frequently late detection. Therefore, the possibility to identify early diagnostic markers could be of significant benefit. Urine has especially become one of the most attractive body fluids in biomarker discovery as it can be obtained non-invasively in large quantities and is stable as compared with other body fluids. To identify potential protein biomarker for early diagnosis of HCC, we explored protein expression profiles in urine from HCC patients and normal controls (n = 44) by shotgun proteomics using nano-liquid chromatography coupled tandem mass spectrometry (nanoLC-MS/MS) and stable isotope dimethyl labeling. We have systematically mapped 91 proteins with differential expressions (p < 0.05), which included 8 down-regulated microtubule proteins and 83 up-regulated proteins involved in signal and inflammation response. Further integrated proteogenomic approach composed of proteomic, genomic and transcriptomic analysis identified that S100A9 and GRN were co-amplified (p < 0.001) and co-expressed (p < 0.01) in HCC tumors and urine samples. In addition, the amplifications of S100A9 or GRN were found to be associated with poor survival in HCC patients, and their co-amplification was also prognosed worse overall survival than individual ones. Our results suggest that urinary S100A9 and GRN as potential combinatorial biomarkers can be applied to early diagnosis of hepatocellular carcinoma and highlight the utility of onco-proteogenomics for identifying protein markers that can be applied to disease-oriented translational medicine.

SUBMITTER: Huang CH

PROVIDER: S-EPMC4669941 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Onco-proteogenomics identifies urinary S100A9 and GRN as potential combinatorial biomarkers for early diagnosis of hepatocellular carcinoma.

Huang Chun-Hao CH Kuo Chao-Jen CJ Liang Shih-Shin SS Chi Shu-Wen SW Hsi Edward E Chen Chi-Chao CC Lee King-Teh KT Chiou Shyh-Horng SH

BBA clinical 20150303

Hepatocellular carcinoma (HCC), the major type of liver cancer, is among the most lethal cancers owing to its aggressive nature and frequently late detection. Therefore, the possibility to identify early diagnostic markers could be of significant benefit. Urine has especially become one of the most attractive body fluids in biomarker discovery as it can be obtained non-invasively in large quantities and is stable as compared with other body fluids. To identify potential protein biomarker for ear ...[more]

PMID: 26675302

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Project description:Hepatocellular carcinoma (HCC) has a high mortality rate owing to its complexity. Identification of abnormally expressed genes in HCC tissues compared to those in normal liver tissues is a viable strategy for investigating the mechanisms of HCC tumorigenesis and progression as a means of developing novel treatments. A significant advantage of the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) is that the data therein were collected from different independent researchers and may be integrated, allowing for a more robust data analysis. Accordingly, in the present study, the gene expression profiles for HCC and control samples were downloaded from the GEO and TCGA. Functional enrichment analysis was performed using a Metascape dataset, and a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins (STRING) online database. The prognostic value of mRNA for HCC was assessed using the Kaplan-Meier Plotter, a public online tool. A gene mRNA heatmap and DNA amplification numbers were obtained from cBioPortal. A total of 2,553 upregulated genes were identified. Functional enrichment analysis revealed that these differentially expressed genes (DEGs) were mainly accumulated in metabolism of RNA and the cell cycle. Considering the complexity and heterogeneity of the molecular alterations in HCC, multiple genes for the prognostication of patients with HCC are more reliable than a single gene. Thus, the PPI network and univariate Cox regression analysis were applied to screen candidate genes (small nuclear ribonucleoprotein polypeptide B and B1, nucleoporin 37, Rac GTPase activating protein 1, kinesin family member 20A, minichromosome maintenance 10 replication initiation factor, ubiquitin conjugating enzyme E2 C and hyaluronan mediated motility receptor) that are associated with the overall survival and progression-free survival of patients with HCC. In conclusion, the present study identified a set of genes that are associated with overall survival and progression-free survival of patients with HCC, providing valuable information for the prognosis of HCC.

Dataset Information

Onco-proteogenomics identifies urinary S100A9 and GRN as potential combinatorial biomarkers for early diagnosis of hepatocellular carcinoma.

Publications

Onco-proteogenomics identifies urinary S100A9 and GRN as potential combinatorial biomarkers for early diagnosis of hepatocellular carcinoma.

Similar Datasets

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