Dataset Information

Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients.

ABSTRACT: Long non-coding RNAs (lncRNAs) are often aberrantly expressed in Hepatocellular Carcinoma (HCC). We hypothesize that lncRNAs modulate HCC prognoses through differential deregulation of key lncRNAs affecting important gene network in key cancer pathways associated with pertinent clinical phenotype. Here, we present a novel approach integrating lncRNA-mRNA expression profiles with clinical characteristics to identify lncRNA signatures in clinically-relevant co-expression lncRNA-mRNA networks residing in pertinent cancer pathways. Notably one network, associated with poorer prognosis, comprises five up-regulated lncRNAs significantly correlated (|Pearson Correlation Coefficient|??0.9) with 91 up-regulated genes in the cell-cycle and Rho-GTPase pathways. All 5 lncRNAs and 85/91 (93.4%) of the correlated genes were significantly associated with higher tumor-grade while 3/5 lncRNAs were also associated with no tumor capsule. Interestingly, 2/5 lncRNAs that are correlated with numerous genes in this oncogenic network were experimentally shown to up-regulate genes involved in cell-cycle and transcriptional regulation. Another network comprising 4 down-regulated lncRNAs and 8 down-regulated metallothionein-family genes are significantly associated with tumor invasion. The identification of these key lncRNAs signatures that deregulate important network of genes in key cancer pathways associated with pertinent clinical phenotype may facilitate the design of novel therapeutic strategies targeting these 'master' regulators for better patient outcome.

SUBMITTER: Lim LJ

PROVIDER: S-EPMC7341759 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients.

Lim Lee Jin LJ Jin Yu Y Yang Henry H Chung Alexander Y F AYF Goh Brian K P BKP Chow Pierce K H PKH Chan Chung Yip CY Blanks William K WK Cheow Peng Chung PC Lee Ser Yee SY Lim Tony K H TKH Chong Samuel S SS Ooi London L P J LLPJ Lee Caroline G CG

Scientific reports 20200707 1

Long non-coding RNAs (lncRNAs) are often aberrantly expressed in Hepatocellular Carcinoma (HCC). We hypothesize that lncRNAs modulate HCC prognoses through differential deregulation of key lncRNAs affecting important gene network in key cancer pathways associated with pertinent clinical phenotype. Here, we present a novel approach integrating lncRNA-mRNA expression profiles with clinical characteristics to identify lncRNA signatures in clinically-relevant co-expression lncRNA-mRNA networks resid ...[more]

PMID: 32636408

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Project description:BackgroundFerroptosis is a novel form of regulated cell death involved in tumor progression. The role of ferroptosis-related lncRNAs in hepatocellular carcinoma (HCC) remains unclear.MethodsRNA-seq and clinical data for HCC patients were downloaded from The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal. Bioinformatics methods, including weighted gene coexpression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) analysis, were used to identify signature markers for diagnosis/prognosis. The tumor microenvironment, immune infiltration and functional enrichment were compared between the low-risk and high-risk groups. Subsequently, small molecule drugs targeting ferroptosis-related signature components were predicted via the L1000FWD and PubChem databases.ResultsThe prognostic model consisted of 2 ferroptosis-related mRNAs (SLC1A5 and SLC7A11) and 8 ferroptosis-related lncRNAs (AC245297.3, MYLK-AS1, NRAV, SREBF2-AS1, AL031985.3, ZFPM2-AS1, AC015908.3, MSC-AS1). The areas under the curves (AUCs) were 0.830 and 0.806 in the training and test groups, respectively. Decision curve analysis (DCA) revealed that the ferroptosis-related signature performed better than all pathological characteristics. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor. The survival probability of low- and high-risk patients could be clearly distinguished by the principal component analysis (PCA) plot. The risk score divided HCC patients into two distinct groups in terms of immune status, especially checkpoint gene expression, which was further supported by the Gene Ontology (GO) biological process, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, several small molecule drugs (SIB-1893, geldanamycin and PD-184352, etc) targeting ferroptosis-related signature components were identified for future reference.ConclusionWe constructed a new ferroptosis-related mRNA/lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

Project description:Simple Summary Hepatocellular carcinoma (HCC) is known for its poor prognosis, but the markers for prognosis still remain unclear to date. This study focused on the long non-coding RNAs (lncRNAs) that linked with cuproptosis and N6-methyladenosine (m6A) in HCC, using data from the The Cancer Genetic Atlas (TCGA) database. The identified lncRNAs were used to develop a risk assessment model through specific types of statistical analysis. This model helped explore how different risk profiles affect the tumor’s genetic mutation load and the surrounding immune environment. Five particular lncRNAs were found to be significant in survival rates, and certain genes (TP53 and CTNNB1) were frequently mutated in high-risk patients. Interestingly, high-risk patients with a low genetic mutation load had the poorest survival rates, while low-risk patients with a high mutation load had the best survival rates. Furthermore, high-risk cases showed increased activity in certain cellular pathways, such as the cell cycle and glucose production. This study resulted in a promising lncRNA model that could potentially help in assessing and managing HCC. However, further studies are needed to confirm these findings. Abstract Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis and m6A were obtained through correlation analysis. To construct a risk assessment model, univariate Cox regression analysis and LASSO Cox regression were employed. Analyze the regulatory effect of relevant risk assessment models on tumor mutation load (TMB) and immune microenvironment. A total of five lncRNAs (AC007405.3, AL031985.3, TMCC1-AS1, MIR210HG, TMEM220-AS1) with independent overall survival-related risk models were obtained by LASSO survival regression. TP53 and CTNNB1 were the three genes found to have the most mutations in high-risk group patients. The high-risk group with low TMB had the worst survival, whereas the low-risk group with high TMB had the best survival. KEGG pathway analysis revealed that the high-risk group was enriched with cell cycle, oocyte meiosis, cell senescence, and glycolysis/glucose production pathways. We constructed a reliable cuproptosis- and m6A-related lncRNA model for the prognosis of HCC. The model may provide new insights into managing HCC patients, but further research is needed to validate it.

Dataset Information

Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients.

Publications

Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients.

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