scholarly journals A prognostic model composed of four long noncoding RNAs predicts the overall survival of Asian patients with hepatocellular carcinoma

2020 ◽  
Vol 9 (16) ◽  
pp. 5719-5730 ◽  
Author(s):  
Xuefeng Gu ◽  
Hongbo Li ◽  
Ling Sha ◽  
Wei Zhao
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Prognostic Model ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Asian Patients

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

Four long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

2018 ◽  
Vol 234 (6) ◽  
pp. 8709-8716 ◽  
Author(s):  
Jianchu Wang ◽  
Jian Pu ◽  
Tianwei Yao ◽  
Xiaojie Lu ◽  
Yibin Deng
Keyword(s):  
Hepatocellular Carcinoma ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Prognostic Biomarkers

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals The emerging role of microRNAs and long noncoding RNAs in drug resistance of hepatocellular carcinoma

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Ling Wei ◽  
Xingwu Wang ◽  
Liyan Lv ◽  
Jibing Liu ◽  
Huaixin Xing ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Drug Resistance ◽  
Human Cancer ◽  
Noncoding Rnas ◽  
Circular Rna ◽  
Long Noncoding Rnas ◽  
Protein Coding ◽  
Multiple Tumor ◽  
Nucleolar Rnas

Abstract Hepatocellular carcinoma (HCC) is the fifth most common malignancy worldwide and the second most lethal human cancer. A portion of patients with advanced HCC can significantly benefit from treatments with sorafenib, adriamycin, 5-fluorouracil and platinum drugs. However, most HCC patients eventually develop drug resistance, resulting in a poor prognosis. The mechanisms involved in HCC drug resistance are complex and inconclusive. Human transcripts without protein-coding potential are known as noncoding RNAs (ncRNAs), including microRNAs (miRNAs), small nucleolar RNAs (snoRNAs), long noncoding RNAs (lncRNAs) and circular RNA (circRNA). Accumulated evidences demonstrate that several deregulated miRNAs and lncRNAs are important regulators in the development of HCC drug resistance which elucidates their potential clinical implications. In this review, we summarized the detailed mechanisms by which miRNAs and lncRNAs affect HCC drug resistance. Multiple tumor-specific miRNAs and lncRNAs may serve as novel therapeutic targets and prognostic biomarkers for HCC.

scholarly journals Current Research Progress on Long Noncoding RNAs Associated with Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Haihong Shi ◽  
Yuxin Xu ◽  
Xin Yi ◽  
Dandan Fang ◽  
Xia Hou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Liver Cancer ◽  
Biological Activities ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Research Progress ◽  
Invasion And Metastasis ◽  
Complex Processes ◽  
And Function

Hepatocellular carcinoma (HCC) is the second leading cause of mortality among cancers. It has been found that long noncoding RNAs (lncRNAs) are involved in many human cancers, including liver cancer. It has been identified that carcinogenic and tumor-suppressing lncRNAs are associated with complex processes in liver cancer. These lncRNAs may participate in a variety of pathological and biological activities, such as cell proliferation, apoptosis, invasion, and metastasis. Here, we review the regulation and function of lncRNA in liver cancer and evaluate the potential of lncRNA as a new goal for liver cancer.

scholarly journals Epigenetic Regulation in Hepatocellular Carcinoma Requires Long Noncoding RNAs

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Laura Amicone ◽  
Franca Citarella ◽  
Carla Cicchini
Keyword(s):  
Hepatocellular Carcinoma ◽  
Epigenetic Regulation ◽  
Tumor Suppressors ◽  
Biological Significance ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Therapeutic Approaches ◽  
Adult Liver ◽  
Liver Malignancy ◽  
Epigenetic Modifiers

Recent evidence has proven the relevance of epigenetic changes in the development of hepatocellular carcinoma (HCC), the major adult liver malignancy. Moreover, HCC onset and progression correlate with the deregulation of several long noncoding RNAs (lncRNAs), exhibiting great biological significance. As discussed in this review, many of these transcripts are able to specifically act as tumor suppressors or oncogenes by means of their role as molecular platforms. Indeed, these lncRNAs are able to bind and recruit epigenetic modifiers on specific genomic loci, ultimately resulting in regulation of the gene expression relevant in cancer development. The evidence presented in this review highlights that lncRNAs-mediated epigenetic regulation should be taken into account for potential targeted therapeutic approaches.

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