Dataset Information

Kidney-type glutaminase (GLS1) is a biomarker for pathologic diagnosis and prognosis of hepatocellular carcinoma.

ABSTRACT: The lack of sensitive and specific biomarkers hinders pathological diagnosis and prognosis for hepatocellular carcinoma (HCC). Since glutaminolysis plays a crucial role in carcinogenesis and progression, we sought to determine if the expression of kidney-type and liver-type glutaminases (GLS1 and GLS2) were informative for pathological diagnosis and prognosis of HCC. We compared the expression of GLS1 and GLS2 in a large set of clinical samples including HCC, normal liver, and other liver diseases. We found that GLS1 was highly expressed in HCC; whereas, expression of GLS2 was mainly confined to non-tumor hepatocytes. The sensitivity and specificity of GLS1 for HCC were 96.51% and 75.21%, respectively. A metabolic switch from GLS2 to GLS1 was observed in a series of tissues representing progressive pathologic states mimicking HCC oncogenic transformation, including normal liver, fibrotic liver, dysplasia nodule, and HCC. We found that high expression of GLS1 and low expression of GLS2 in HCC correlated with survival time of HCC patients. Expression of GLS1 and GLS2 were independent indexes for survival time; however, prognosis was predominantly determined by the level of GLS1 expression. These findings indicate that GLS1 expression is a sensitive and specific biomarker for pathological diagnosis and prognosis of HCC.

SUBMITTER: Yu D

PROVIDER: S-EPMC4480704 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Kidney-type glutaminase (GLS1) is a biomarker for pathologic diagnosis and prognosis of hepatocellular carcinoma.

Yu Decai D Shi Xianbiao X Meng Gang G Chen Jun J Yan Chen C Jiang Yong Y Wei Jiwu J Ding Yitao Y

Oncotarget 20150401 10

The lack of sensitive and specific biomarkers hinders pathological diagnosis and prognosis for hepatocellular carcinoma (HCC). Since glutaminolysis plays a crucial role in carcinogenesis and progression, we sought to determine if the expression of kidney-type and liver-type glutaminases (GLS1 and GLS2) were informative for pathological diagnosis and prognosis of HCC. We compared the expression of GLS1 and GLS2 in a large set of clinical samples including HCC, normal liver, and other liver diseas ...[more]

PMID: 25844758

Similar Datasets

Project description:Liver cancer, classified as a malignant hepatic tumor, can be divided into two categories: primary, originating within the liver, and secondary, resulting from metastasis to the liver from other organs. Hepatocellular carcinoma (HCC) is the main form of primary liver cancer and the third leading cause of cancer-related deaths. The diagnosis and prognosis of HCC using current methods still face numerous challenges. This study aims to develop novel diagnostic and prognostic models while identifying new biomarkers for improved HCC treatment. Diagnostic and prognostic models for HCC were constructed using traditional binary classification methods and machine learning algorithms based on the TCGA database (Downloaded in August 2023). The mechanisms by which APLN (Apelin) affects HCC were investigated using single-cell sequencing data sourced from the GEO database (GSE149614). The diagnostic models yielded by various algorithms could effectively distinguished HCC samples from normal ones. The prognostic model, composed of four genes, was constructed using LASSO and Cox regression algorithms, demonstrating good performance in predicting the three-year survival rate of HCC patients. The HCC biomarker Apelin (APLN) was identified in this study. APLN in liver cancer tissues mainly comes from endothelial cells and is associated with the carcinogenesis of these cells. APLN expression is significantly upregulated in liver cancer tissues, marking it as a viable indicator of endothelial cell malignancy in HCC. Furthermore, APLN expression was determined to be an independent predictor of tumor endothelial cell carcinogenesis, unaffected by its modifications such as single nucleotide variation, copy number variation, and methylation. Additionally, liver cancers characterized by high APLN expression are likely to progress rapidly after T2 stage. Our study presents diagnostic and prognostic models for HCC with appreciably improved accuracy and reliability compared to previous reports. APLN is a reliable HCC biomarker and contributes to the establishment of our models.

Project description:Background:Osteosarcoma (OS) is a malignant bone tumor that often develops during the period of rapid growth associated with adolescence. Despite successful primary tumor control accompanied by adjuvant chemotherapy, death from pulmonary metastases occurs in approximately 30% of patients within 5?years. As overall survival in patients remains unchanged over the last 30?years, urgent needs for novel therapeutic strategies exist. Cancer metastasis is characterized by complex molecular events which result from alterations in gene and protein expression/function. Recent studies suggest that metabolic adaptations, or "metabolic reprogramming," may similarly contribute to cancer metastasis. The goal of this study was to specifically interrogate the metabolic vulnerabilities of highly metastatic OS cell lines in a series of in vitro and in vivo experiments, in order to identify a tractable metabolically targeted therapeutic strategy for patients. Methods:Nutrient deprivation and drug treatment experiments were performed in MG63.3, 143B, and K7M2 OS cell lines to identify the impact of glutaminase-1 (GLS1) inhibition and metformin treatment on cell proliferation. We functionally validated the impact of drug treatment with extracellular flux analysis, nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry. 13C-glucose and 13C-glutamine tracing was employed to identify specific contributions of these nutrients to the global metabolic profiles generated with GLS1 inhibition and metformin treatment in vivo. Results:Highly metastatic OS cell lines require glutamine for proliferation, and exposure to CB-839, in combination with metformin, induces both primary tumor growth inhibition and a distinct reduction in metastatic outgrowth in vivo. Further, combination-treated OS cells showed a reduction in cellular mitochondrial respiration, while NMR confirmed the pharmacodynamic effects of glutaminase inhibition in tumor tissues. We observed global decreases in glycolysis and tricarboxylic acid (TCA) cycle functionality, alongside an increase in fatty acid oxidation and pyrimidine catabolism. Conclusions:This data suggests combination-treated cells cannot compensate for metformin-induced electron transport chain inhibition by upregulating glutaminolysis to generate TCA cycle intermediates required for cell proliferation, translating into significant reductions in tumor growth and metastatic progression. This therapeutic approach could be considered for future clinical development for OS patients presenting with or at high risk of developing metastasis.

Project description:Background Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high mortality rate. However, spliceosomal genes are still lacking in the diagnosis and prognosis of HCC. Methods Identification of differentially expressed genes (DEGs) was performed using the limma package in R software. Modules highly related to HCC were obtained by weighted gene co-expression network analysis (WGCNA), and the module genes were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The biomarker for diagnosing HCC was determined by receiver operating characteristic (ROC) curve analysis, and the effect of the biomarker in the diagnosis of HCC was evaluated by performing five-fold cross-validation with logistic regression. HCC specimens from preoperatively treated patients were tested for biomarker by real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-Meier analysis was used to assess the relationship between biomarker and patient survival. The role of biomarker was evaluated using ESTIMATE analysis in the tumor microenvironment. Results In this study, 389 DEGs were screened out from three Gene Expression Omnibus (GEO) datasets. We also found that the turquoise module of 123 genes from The Cancer Genome Atlas (TCGA) data was the key module with the highest correlation with HCC traits. Then, 123 genes were analyzed using the KEGG enrichment pathway, and eight genes were found to be most significantly related to the spliceosome pathway. We selected 8 genes and 389 DEGs shared genes, and finally got the only gene, heterogeneous nuclear ribonucleoprotein (hnRNPU). The high expression of hnRNPU was associated with poor prognosis of HCC, and hnRNPU was a biomarker for diagnosing HCC. In the tissues of patients with excellent HCC treatment hnRNPU messenger RNA (mRNA) was lower than in the tissues of patients with poor HCC treatment. High expression of hnRNPU was significantly increased in HCC patients with low stromal (P<0.05), low immune (P<0.05), and low estimation scores (P<0.05), and with high tumor purity (P<0.05) and high malignant progression (P<0.05) of the HCC. Conclusions The hnRNPU gene identified in this study may become a new biomarker for the diagnosis and prognosis of HCC.

Dataset Information

Kidney-type glutaminase (GLS1) is a biomarker for pathologic diagnosis and prognosis of hepatocellular carcinoma.

Publications

Kidney-type glutaminase (GLS1) is a biomarker for pathologic diagnosis and prognosis of hepatocellular carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets