The emergence of long non-coding RNAs in hepatocellular carcinoma: an update.
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ABSTRACT:
SUBMITTER: Peng L
PROVIDER: S-EPMC6036883 | biostudies-other | 2018
REPOSITORIES: biostudies-other
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Project description:Recent advances in next-generation sequencing technology in transcriptome analysis have helped identify numerous non-coding RNAs. The long non-coding RNA (lncRNA) is commonly defined as an RNA molecule with a length of 200 bp-100 kbp that lacks protein-coding potential. LncRNAs play a critical role in the regulation of gene expression, including chromatin modification, transcription and post-transcriptional processing. It has been confirmed that dysregulation of lncRNAs is associated with a number of human diseases, particularly tumors. In this study, we focused on the most extensively investigated lncRNAs in hepatocellular carcinoma (HCC). The biological functions and molecular mechanisms of the majority of lncRNAs have yet to be investigated. The improved knowledge on lncRNAs in HCC may help identify lncRNAs that may be used as novel prognostic markers and therapeutic targets.
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Project description:Hepatocellular carcinoma (HCC) is among the utmost deadly human malignancies. This type of cancer has been associated with several environmental, viral, and lifestyle risk factors. Among the epigenetic factors which contribute in the pathogenesis of HCC is dysregulation of long non-coding RNAs (lncRNAs). These transcripts modulate expression of several tumor suppressor genes and oncogenes and alter the activity of cancer-related signaling axes. Several lncRNAs such as NEAT1, MALAT1, ANRIL, and SNHG1 have been up-regulated in HCC samples. On the other hand, a number of so-called tumor suppressor lncRNAs namely CASS2 and MEG3 are down-regulated in HCC. The interaction between lncRNAs and miRNAs regulate expression of a number of mRNA coding genes which are involved in the pathogenesis of HCC. H19/miR-15b/CDC42, H19/miR-326/TWIST1, NEAT1/miR-485/STAT3, MALAT1/miR-124-3p/Slug, MALAT1/miR-195/EGFR, MALAT1/miR-22/SNAI1, and ANRIL/miR-144/PBX3 axes are among functional axes in the pathobiology of HCC. Some genetic polymorphisms within non-coding regions of the genome have been associated with risk of HCC in certain populations. In the current paper, we describe the recent finding about the impact of lncRNAs in HCC.
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Project description:Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Increasing studies showed that long non-coding RNAs (lncRNAs), a novel class of RNAs that are greater than 200 nucleotides in length but lack the ability to encode proteins, exert crucial roles in the occurrence and progression of HCC. LncRNAs promote the proliferation, migration, invasion, autophagy, and apoptosis of tumor cells by regulating downstream target gene expression and cancer-related signaling pathways. Meanwhile, lncRNA can be used as biomarkers to predict the efficacy of HCC treatment strategies, such as surgery, radiotherapy, chemotherapy, and immunotherapy, and as a potential individualized tool for HCC diagnosis and treatment. In this review, we overview up-to-date findings on lncRNAs as potential biomarkers for HCC surgery, radiotherapy, chemotherapy resistance, target therapy, and immunotherapy, and discuss the potential clinical application of lncRNA as tools for HCC diagnosis and treatment.
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Project description:Functional characterization of long non-coding RNAs (lncRNAs) and their pathological relevance is still a challenging task. Abnormal expression of a few long non-coding RNAs have been found associated with hepatocellular carcinoma, with potential implications to both improve our understanding of molecular mechanism of liver carcinogenesis and to discover biomarkers for early diagnosis or therapy. However, the understanding of the global role of lncRNAs during HCC development is still in its infancy. In this study, we produced RNA-Seq data from 23 liver tissues (controls, cirrhotic and HCCs) and applied statistical and gene network analysis approaches to identify and characterize expressed lncRNAs. We detected 5,525 lncRNAs across different tissue types and identified 57 differentially expressed lncRNAs in HCC compared with adjacent non-tumour tissues using stringent criteria (FDR<0.05, Fold Change>2). Using weighted gene co-expression network analysis (WGCNA), we found that differentially expressed lncRNAs are co-expressed with genes involved in cell cycle regulation, TGF-β signalling and liver metabolism. Furthermore, we found that more than 20% of differentially expressed lncRNAs are associated to actively transcribed enhancers and that the co-expression patterns with their closest genes change dramatically during HCC development. Our study provides the most comprehensive compendium of lncRNAs expressed in HCC, as well as in control or cirrhotic livers. Our results identified both known oncogenic lncRNAs (such as H19 and CRNDE) and novel lncRNAs involved in cell cycle deregulation and liver metabolism deficits occurring during HCC development.
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Project description:Long non-coding RNAs (lncRNAs) play multifaceted roles in modulating gene expression under both physiological and pathological processes. The dysregulation of lncRNAs has been increasingly linked with many human diseases, including a plethora of cancers. Mounting evidence indicates that lncRNAs are aberrantly expressed in hepatocellular carcinoma (HCC) and can regulate HCC progression, as well as metastasis. In this review, we summarize the recent findings on the expanding roles of lncRNAs in modulating various functions of HCC, and elaborate on how can lncRNAs impact HCC metastasis and progression via interacting with chromatin, RNA, and proteins at the epigenetic, transcriptional, and post-transcriptional levels. This mini-review also highlights the current advances regarding the signaling pathways of lncRNAs in HCC metastasis and sheds light on the possible application of lncRNAs for the prevention and treatment of HCC.
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Project description:Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignancies worldwide. Studies seeking to advance the overall understanding of lncRNA profiling in HCC remain rare.The transcriptomic profiling of 12 HCC tissues and paired adjacent normal tissues was determined using high-throughput RNA sequencing. Fifty differentially expressed mRNAs (DEGs) and lncRNAs (DELs) were validated in 21 paired HCC tissues via quantitative real-time PCR. The correlation between the expression of DELs and various clinicopathological characteristics was analyzed using Student's t-test or linear regression. Co-expression networks between DEGs and DELs were constructed through Pearson correlation co-efficient and enrichment analysis. Validation of DELs' functions including proliferation and migration was performed via loss-of-function RNAi assays.In this study, we identified 439 DEGs and 214 DELs, respectively, in HCC. Furthermore, we revealed that multiple DELs, including NONHSAT003823, NONHSAT056213, NONHSAT015386 and especially NONHSAT122051, were remarkably correlated with tumor cell differentiation, portal vein tumor thrombosis, and serum or tissue alpha fetoprotein levels. In addition, the co-expression network analysis between DEGs and DELs showed that DELs were involved with metabolic, cell cycle, chemical carcinogenesis, and complement and coagulation cascade-related pathways. The silencing of the endogenous level of NONHSAT122051 or NONHSAT003826 could significantly attenuate the mobility of both SK-HEP-1 and SMMC-7721 HCC cells.These findings not only add knowledge to the understanding of genome-wide transcriptional evaluation of HCC but also provide promising targets for the future diagnosis and treatment of HCC.
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Project description:BackgroundLong non-coding RNA (LncRNA) plays an important role in the occurrence and development of hepatocellular carcinoma (HCC). This study aims to establish an immune-related LncRNA model for risk assessment and prognosis prediction in HCC patients.MethodsHepatocellular carcinoma patient samples with complete clinical data and corresponding whole transcriptome expression were obtained from the Cancer Genome Atlas (TCGA). Immune-related genes were acquired from the Gene Set Enrichment Analysis (GSEA) website and matched with LncRNA in the TCGA to get immune-related LncRNA. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for screening the candidate LncRNAs and calculating the risk coefficient to establish the prognosis model. Patients were divided into a high-risk group and a low-risk group depending on the median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort. GSEA and principal component analysis were used for function evaluation.ResultsA total of 319 samples met the screening criteria and were randomly distributed across the training cohort and the validation cohort. After comparison with the IMMUNE_RESPONSE gene set and the IMMUNE_SYSTEM_PROCESS gene set, a total of 3094 immune-related LncRNAs were screened. Ultimately, four immune-related LncRNAs were used to construct a formula using LASSO regression. According to the formula, the low-risk group showed a higher survival rate than the high-risk group in the validation cohort and the whole cohort. The receiver operating characteristic curves data demonstrated that the risk score was more specific than other traditional clinical characteristics in predicting the 5-year survival rate for HCC.ConclusionThe four-immune-related-LncRNA model can be used for survival prediction in HCC and guide clinical therapy.
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Project description:AbstractIncreasing evidence suggests that long non-coding RNAs (lncRNAs) are of vital importance for various biological processes, and dysregulation of lncRNAs is frequently associated with various diseases such as psoriasis. LncRNAs modulate gene expression at the transcriptional, post-transcriptional, and translational levels; however, the specific regulatory mechanisms of lncRNAs in psoriasis remain largely unexplored. This review provides an overview of recent studies investigating mechanisms and functions of lncRNAs in psoriasis, especially focusing on the role of lncRNAs in keratinocytes, T cells, and dendritic cells.
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Project description:Alpha Fetal Protein (AFP) was one of the traditional biomarker for diagnosis of Hepatocellular carcinoma (HCC) clinically, however, with the low specificity of AFP, the early diagnosis or the metastasis prediction of HCC is inferior. A new, minimally invasive and more specificity biomarker for the diagnosis or metastasis prediction of HCC are necessary.In this study, we applied an lncRNA microarray to screen the potential biomarker for HCC. The multi-stage validation and risk score formula detection was used for validation.We discovered three lncRNA, RP11-160H22.5, XLOC_014172 and LOC149086, which were up-regulated in HCC comparing with the cancer-free controls with the merged area under curve (AUC) in training set and validation set of 0.999 and 0.896. Furthermore, XLOC_014172 and LOC149086 was confirmed highly increased in metastasis HCC patients with the merged AUC in training set and validation set of 0.900 and 0.934. Besides, most patients presented a decreased level of the three lncRNAs after operation, while the patients with secondary increased level might be associated with tumor hematogenous metastasis.RP11-160H22.5, XLOC_014172 and LOC149086 might be the potential biomarker for the tumorigenesis prediction and XLOC_014172 and LOC149086 for metastasis prediction in the future.
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Project description:Autophagy plays a vital role in hepatocellular carcinoma (HCC) pathogenesis. Long non-coding RNAs (lncRNAs) are considered regulators of autophagy, and the aim of the present study was to investigate the prognostic value of autophagy-related lncRNA (ARlncRNA) and develop a new prognostic signature to predict the 1-year and 3-year overall survival (OS) of HCC patients. Transcriptome and clinical survival information of HCC patients was obtained from The Cancer Genome Atlas database. A set of ARlncRNAs was identified by co-expression analysis, from which seven ARlncRNAs (AC005229.4, AL365203.2, AL117336.3, AC099850.3, ELFN1-AS1, LUCAT1, and AL031985.3) were selected for use as a predictive signature. Risk scores were derived for each patient, who were then divided into high-risk and low-risk groups according to the median risk value. The OS of high-risk patients was significantly lower than that of low-risk patients (P < 0.0001). The 1- and 3-year time-dependent ROC curves were used to evaluate the predictive ability of the risk score (AUC = 0.785 of 1 year, 0.710 of 3 years), and its predictive ability was found to be better than TNM stage. Moreover, the risk score was significantly, linearly related to pathological grade and TNM stage (P < 0.05). Overall, a novel nomogram to predict the 1-year and 3-year OS of HCC patients was developed, which shows good reliability and accuracy, for use in improved treatment decision-making.
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