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Enolase-1 serves as a biomarker of diagnosis and prognosis in hepatocellular carcinoma patients.

ABSTRACT: Background:Hepatocellular carcinoma (HCC) is an aggressive malignancy with high incidence rate and poor prognosis. Enolase-1 (ENO1), a key glycolytic enzyme, has been implicated in the tumorigenesis of various cancers. However, its diagnostic value and clinical significance in HCC are unclear. Methods:Data of 374 HCC tissues and 50 nontumor tissues were retrieved from The Cancer Genome Atlas database, and the expression level of ENO1 mRNA in HCC was evaluated. In addition, a meta-analysis of 12 HCC cohorts deposited in the Gene Expression Omnibus database was conducted to determine ENO1 expression levels. The diagnostic power of ENO1 in distinguishing HCC tissues from non-HCC tissues was confirmed by receiver operating characteristic (ROC) curve analysis. A tissue microarray comprising 93 HCC specimens and 87 adjacent normal specimens was used to validate ENO1 expression, and its prognostic value in HCC was ascertained by Kaplan-Meier analysis and Cox regression models. In addition, the gene set enrichment analysis was performed to predict the molecular mechanism of ENO1 action in HCC. Results:ENO1 was overexpressed in HCC tissues and associated with worse outcomes in terms of overall survival (OS) (P<0.01) and disease-free survival (P<0.01). ENO1 expression (P<0.01) was an independent prognostic variable for the OS of HCC patients. Moreover, as per the ROC curve analysis, it had good diagnostic power as well. In addition, elevated expression of ENO1 was significantly correlated with the cell cycle and DNA replication pathway, consistent with its association with pro-proliferative genes such as MKI67, PCNA, CDK4, CDK2, and MELK. Conclusion:ENO1 was markedly upregulated and was an oncogene-associated protein in HCC. It is a promising prognostic and diagnostic biomarker for HCC.

SUBMITTER: Zhu W

PROVIDER: S-EPMC6245378 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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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 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.

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Enolase-1 serves as a biomarker of diagnosis and prognosis in hepatocellular carcinoma patients.

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