Dataset Information

Identification of heterogeneous nuclear ribonucleoprotein as a candidate biomarker for diagnosis and prognosis of hepatocellular carcinoma

ABSTRACT:

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.

SUBMITTER: Du Y

PROVIDER: S-EPMC8576204 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors occurring mainly in patients with chronic liver disease. Detection of early HCC is critically important for treatment of these patients. METHODS: We employed a proteomic profiling approach to identify potential biomarker for early HCC detection. Based on Barcelona Clinic Liver Cancer (BCLC) staging classification, 15 early HCC and 25 late HCC tissue samples from post-operative HCC patients and their clinicopathological data were used for the discovery of biomarkers specific for the detection of early HCC. Differential proteins among cirrhotic, early, and late tissue samples were separated by two-dimensional gel electrophoresis (2-DE) and subsequently identified by mass spectrometry (MS). Receiver operating characteristic (ROC) curves analysis were performed to find potential biomarkers associated with early HCC. Diagnosis performance of the biomarker was obtained from diagnosis test. RESULTS: Protein spot SSP2215 was found to be significantly overexpressed in HCC, particularly in early HCC, and identified as heterogeneous nuclear ribonucleoprotein K (hnRNP K) by tandem mass spectrometry (MALDI TOF/TOF). The overexpression in HCC was subsequently validated by western blot and immunohistochemistry. ROC curve analysis showed that hnRNP K intensity could detect early HCC at 66.67 % sensitivity and 84 % specificity, which was superior to serum ?-fetoprotein (AFP) in detection of early HCC. Furthermore, the diagnosis test demonstrated, when combined with hnRNP K and serum AFP as biomarker panel to detect early HCC at different cut-off value, the sensitivity and specificity could be enhanced to 93.33 % and 96 %, respectively. CONCLUSIONS: hnRNP K is a potential tissue biomarker, either alone or in combination with serum AFP, for detection of early HCC. High expression of hnRNP K could be helpful to discriminate early HCC from a nonmalignant nodule, especially for patients with liver cirrhosis.

Project description:Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction. Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan-Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression. Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC. Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

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