Genome-Wide Screening Identifies Prognostic Long Noncoding RNAs in Hepatocellular Carcinoma.
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ABSTRACT:
SUBMITTER: Feng Y
PROVIDER: S-EPMC8163536 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background & aimsLong noncoding RNAs (lncRNAs) are found to have profound impacts on diverse cellular processes. Although high-throughput sequencing studies have shown the differential lncRNA expression profiles between hepatocellular carcinoma (HCC) and nontumor livers, the functional impacts of lncRNAs on HCC development await further investigation. Herein, we sought to address the functional roles of lncRNAs in HCC pathogenesis by in vivo functional screening.MethodsWe performed genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/dead CRISPR-associated protein 9 (dCas9) lncRNA activation screening in HCC xenografts. We characterized the clinical relevance of positively selected lncRNAs using transcriptomic data sets. We used CRISPR-based gene activation and knockdown approaches to show the functional roles of positively selected lncRNAs including Cancer Susceptibility 11 (CASC11) in HCC. RNA sequencing and chromatin isolation by RNA purification sequencing were used to investigate the molecular mechanisms of CASC11 in HCC progression.ResultsThe in vivo functional screening identified 1603 positively selected lncRNAs, 538 of which were overexpressed in HCC patients. Systematic transcriptomic data analysis and clinical investigation showed that patients with high expression of these lncRNA candidates correlated with aggressive tumor behaviors. Overexpression of these lncRNAs aggravated HCC cell growth. Detailed characterization of a lncRNA candidate, CASC11, showed its pivotal role in cell proliferation and tumor growth. Mechanistically, chromatin isolation by RNA purification sequencing showed that CASC11 was bound to the CASC11/MYC proto-oncogene shared promoter region on chromosome 8q24. CASC11 modulated the transcriptional activity of MYC in a cis-regulatory manner, which affected the expression of MYC downstream target genes, consequently promoting G1/S progression.ConclusionsOur study showed the power of in vivo CRISPR screening, which comprehensively investigated the functionality of lncRNAs in HCC progression, providing a rationale for targeting these lncRNAs clinically.
| S-EPMC9490101 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a primary liver cancer associated with a growing incidence and extremely high mortality. However, the pathogenic mechanism is still not fully understood. In the present study, we identified 1,631 upregulated and 1,515 downregulated genes and found that cell cycle and metabolism-related pathways or biological processes highly dysregulated in HCC. To assess the biological importance of these DEGs, we carried out weighted gene coexpression network analysis (WGCNA) to identify the functional modules potentially involved in HCC pathogenesis or progression. The five modules were detected with Dynamic Tree Cut algorithm, and GO enrichment analysis revealed that these modules exhibited different biological processes or signaling pathways, such as metabolism-related pathways, cell proliferation-related pathways, and molecules in tumor microenvironment. Moreover, we also observed two immune cells, namely, cytotoxic cells and macrophage enriched in modules grey and brown, respectively, while T helper cell-2 (Th2) was enriched in module turquoise. Among the WGCNA network, four hub long noncoding RNAs (lncRNAs) were identified to be associated with HCC prognostic outcomes, suggesting that coexpression network analysis could uncover lncRNAs with functional importance, which may be associated with prognostic outcomes of HCC patients. In summary, this study demonstrated that network-based analysis could identify some functional modules and some hub-lncRNAs, which may be critical for HCC pathogenesis or progression.
| S-EPMC7559504 | biostudies-literature
Project description:Long noncoding RNAs (lncRNAs) can exert their function by interacting with the DNA via triplex structure formation. Even though this has been validated with a handful of experiments, a genome-wide analysis of lncRNA-DNA binding is needed. In this paper, we develop and interpret deep learning models that predict the genome-wide binding sites deciphered by ChIRP-Seq experiments of 12 different lncRNAs. Among the several deep learning architectures tested, a simple architecture consisting of two convolutional neural network layers performed the best suggesting local sequence patterns as determinants of the interaction. Further interpretation of the kernels in the model revealed that these local sequence patterns form triplex structures with the corresponding lncRNAs. We uncovered several novel triplexes forming domains (TFDs) of these 12 lncRNAs and previously experimentally verified TFDs of lncRNAs HOTAIR and MEG3. We experimentally verified such two novel TFDs of lncRNAs HOTAIR and TUG1 predicted by our method (but previously unreported) using Electrophoretic mobility shift assays. In conclusion, we show that simple deep learning architecture can accurately predict genome-wide binding sites of lncRNAs and interpretation of the models suggest RNA:DNA:DNA triplex formation as a viable mechanism underlying lncRNA-DNA interactions at genome-wide level.
| S-EPMC6333433 | biostudies-literature
Project description:ContextLong noncoding RNAs (lncRNAs) regulate pathological processes, yet their potential roles in papillary thyroid carcinoma (PTC) are poorly understood.ObjectiveTo profile transcriptionally dysregulated lncRNAs in PTC and identify lncRNAs associated with clinicopathological characteristics.DesignWe performed RNA sequencing of 12 paired PTC tumors and matched noncancerous tissues and correlated the expression of lncRNAs with clinical parameters. The 2 most significantly dysregulated lncRNAs were studied in an Ohio PTC cohort (n = 109) and in PTC data (n = 497) from The Cancer Genome Atlas.SettingA combination of laboratory-based studies and computational analysis using clinical data and samples and a publically available database.Main outcome measuresCorrelation between expression values and clinical parameters.ResultsWe identified 218 lncRNAs showing differential expression in PTC (fold change ≥ 2.0, P < .01). Significant correlation was observed between the expression of 2 lncRNAs (XLOC_051122 and XLOC_006074) and 1) lymph node metastasis (N stage) and 2) BRAF(V600E) mutation. Among patients with wild-type BRAF, the expression of these 2 lncRNAs showed significantly higher levels in the patients with lymph node metastasis. In silico analysis of these lncRNAs pinpointed cell movement and cellular growth and proliferation as targeted functions.ConclusionsComprehensive expression screening identified 2 novel lncRNAs associated with risk factors of adverse prognosis in PTC patients. These lncRNAs may be novel players in PTC carcinogenesis.
| S-EPMC5095253 | biostudies-literature
Project description:Cuproptosis is an unusual form of cell death caused by copper accumulation in mitochondria. Cuproptosis is associated with hepatocellular carcinoma (HCC). Long noncoding RNAs (LncRNAs) have been shown to be effective prognostic biomarkers, yet the link between lncRNAs and cuproptosis remains unclear. We aimed to build a prognostic model of lncRNA risk and explore potential biomarkers of cuproptosis in HCC. Pearson correlations were used to derive lncRNAs co-expressed in cuproptosis. The model was constructed using Cox, Lasso, and multivariate Cox regressions. Kaplan–Meier survival analysis, principal components analysis, receiver operating characteristic curve, and nomogram analyses were carried out for validation. Seven lncRNAs were identified as prognostic factors. A risk model was an independent prognostic predictor. Among these seven lncRNAs, prostate cancer associated transcript 6 (PCAT6) is highly expressed in different types of cancer, activating Wnt, PI3K/Akt/mTOR, and other pathways; therefore, we performed further functional validation of PCAT6 in HCC. Reverse transcription–polymerase chain reaction results showed that PCAT6 was aberrantly highly expressed in HCC cell lines (HepG2 and Hep3B) compared to LO2 (normal hepatocytes). When its expression was knocked down, cells proliferated and migrated less. PCAT6 might be a potential biomarker for predicting prognosis in HCC.
| S-EPMC10298112 | biostudies-literature
Project description:AbstractReliable biomarkers are of great significance for the treatment and diagnosis of hepatocellular carcinoma (HCC). This study identified potential prognostic epithelial-mesenchymal transition related lncRNAs (ERLs) by the cancer genome atlas (TCGA) database and bioinformatics.The differential expression of long noncoding RNA (lncRNA) was obtained by analyzing the lncRNA data of 370 HCC samples in TCGA. Then, Pearson correlation analysis was carried out with EMT related genes (ERGs) from molecular signatures database. Combined with the univariate Cox expression analysis of the total survival rate of hepatocellular carcinoma (HCC) patients, the prognostic ERLs were obtained. Then use "step" function to select the optimal combination of constructing multivariate Cox expression model. The expression levels of ERLs in HCC samples were verified by real-time quantitative polymerase chain reaction.Finally, we identified 5 prognostic ERLs (AC023157.3, AC099850.3, AL031985.3, AL365203.2, CYTOR). The model showed that these prognostic markers were reliable independent predictors of risk factors (P value <.0001, hazard ratio [HR] = 2.400, 95% confidence interval [CI] = 1.667-3.454 for OS). In the time-dependent receiver operating characteristic analysis, this prognostic marker is a good predictor of HCC survival (area under the curve of 1 year, 2 years, 3 years, and 5 years are 0.754, 0.720, 0.704, and 0.662 respectively). We analyzed the correlation of clinical characteristics of these prognostic markers, and the results show that this prognostic marker is an independent factor that can predict the prognosis of HCC more accurately. In addition, by matching with the Molecular Signatures Database, we obtained 18 ERLs, and then constructed the HCC prognosis model and clinical feature correlation analysis using 5 prognostic ERLs. The results show that these prognostic markers have reliable independent predictive value. Bioinformatics analysis showed that these prognostic markers were involved in the regulation of EMT and related functions of tumor occurrence and migration.Five prognostic types of ERLs identified in this study can be used as potential biomarkers to predict the prognosis of HCC.
| S-EPMC8322489 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is the most frequent subtype of primary liver cancer and one of the leading causes of cancer-related death worldwide. However, the molecular mechanisms underlying HCC pathogenesis have not been fully understood. Emerging evidences have recently suggested the crucial role of long noncoding RNAs (lncRNAs) in the tumorigenesis and progression of HCC. Various HCC-related lncRNAs have been shown to possess aberrant expression and participate in cancerous phenotypes (e.g. persistent proliferation, evading apoptosis, accelerated vessel formation and gain of invasive capability) through their binding with DNA, RNA or proteins, or encoding small peptides. Thus, a deeper understanding of lncRNA dysregulation would provide new insights into HCC pathogenesis and novel tools for the early diagnosis and treatment of HCC. In this review, we summarize the dysregulation of lncRNAs expression in HCC and their tumor suppressive or oncogenic roles during HCC tumorigenesis. Moreover, we discuss the diagnostic and therapeutic potentials of lncRNAs in HCC.
| S-EPMC7161154 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and confers a poor prognosis. Novel diagnostic or prognostic biomarkers and effective therapeutic targets for HCCs are urgently needed. Currently, dozens of long noncoding RNAs (lncRNAs) have been identified as playing critical roles in cancer development and progression. Advanced studies have shown that several well-known lncRNAs are dysregulated in HCC tissue as compared to adjacent noncancerous tissue. Furthermore, highly stable cell-free circulating nucleic acids (cfCNAs), including lncRNAs, aberrantly expressed in the plasma of HCC patients, have been detected. In this review, we focus on the most extensively investigated lncRNAs in HCC and discuss the potential of HCC-related lncRNAs as novel biomarkers for early diagnosis and prognosis.
| S-EPMC5557260 | biostudies-other
Project description:Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, especially in East Asia and China. Long noncoding RNAs (lncRNAs) are emerging as critical regulators that may be involved in the development and progression of cancers in humans. However, the contributions of lncRNAs to HCC development, metastasis, and recurrence remain largely unknown. In this study, we comprehensively investigated lncRNA expression profile in HCC and normal tissues using TCGA RNA sequencing data, one RNA sequencing dataset, and two microarray datasets from GEO. By analyzing these four datasets, we identified hundreds of expression-dysregulated lncRNAs in HCC tissues compared with normal tissues. Genomic copy number variation analysis showed that many of those lncRNAs disorder are related to the copy number amplification or deletion. Moreover, several lncRNAs expression levels are associated with HCC patients' overall and recurrence-free survival, such as RP1-228H13.5, TMCC1-AS1, LINC00205, and RP11-307C12.11. Furthermore, we identified two lncRNAs termed PVT1 and SNHG7 that may be involved in HCC cells metastasis by comparing lncRNAs expression profiles between early recurrence HCC tissues with metastasis and late recurrence HCC tissues without metastasis. Finally, loss-of-function assays confirmed that knockdown of SNHG7 and PVT1 impaired HCC cells invasion. Taken together, these findings may provide a valuable resource for further identification of novel biomarkers and therapeutic targets for HCC patients.
| S-EPMC5727245 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is the predominant subject of liver malignancies which arouse global concern. Advanced studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in HCC and implicate they may play distinct roles in the pathogenesis and metastasis of HCC. However, the underlying mechanisms remain largely unclear. In this review, we summarized the functions and mechanisms of those known aberrantly expressed lncRNAs identified in human HCC tissues. We hope to enlighten more comprehensive researches on the detailed mechanisms of lncRNAs and their application in clinic, such as being used as diagnostic and prognostic biomarkers and the targets for potential therapy. Although studies on lncRNAs in HCC are still deficient, an improved understanding of the roles played by lncRNAs in HCC will lead to a much more effective utilization of those lncRNAs as novel candidates in early detection, diagnosis, prevention and treatment of HCC.
| S-EPMC4670950 | biostudies-literature